diff --git a/2025-10-09-invdes-seminar/00_setup_guide.ipynb b/2025-10-09-invdes-seminar/00_setup_guide.ipynb
new file mode 100644
index 00000000..0db39bc6
--- /dev/null
+++ b/2025-10-09-invdes-seminar/00_setup_guide.ipynb
@@ -0,0 +1,344 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6d5d7470",
+   "metadata": {},
+   "source": [
+    "# Grating Coupler: A Tidy3D Simulation Setup\n",
+    "\n",
+    "> In this notebook, we will set up a baseline simulation for a dual-layer grating coupler. This device is designed to efficiently couple light from an optical fiber to a photonic integrated circuit. We will define the geometry, materials, and all the necessary components for a Tidy3D simulation. This initial setup will serve as the starting point for our optimization in the subsequent notebooks.\n",
+    "\n",
+    "## Initial Grating Design\n",
+    "\n",
+    "We start by selecting a symmetric set of widths and gaps for both the silicon and silicon nitride layers. These values satisfy the minimum feature rules and provide a sensible baseline that couples light into the waveguide."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e8fd7d9",
+   "metadata": {},
+   "source": [
+    "## Defining Our Initial Guess: A Uniform Grating\n",
+    "\n",
+    "Before we can optimize, we need a starting point. A common first approach is to create a periodic, uniform grating where every tooth and gap is identical. We choose a 50% duty cycle and set the period based on effective index estimates around the 1.55 µm band. This baseline is physically reasonable but, as we will see, will be very far from optimal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e634cede",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import tidy3d as td\n",
+    "from setup import get_mode_monitor_power, make_simulation, num_elements\n",
+    "from tidy3d import web"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7f6fef32",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "initial_width_si = 0.45\n",
+    "initial_gap_si = 0.55\n",
+    "initial_width_sin = 0.35\n",
+    "initial_gap_sin = 0.65\n",
+    "\n",
+    "widths_si = np.full(num_elements, initial_width_si)\n",
+    "gaps_si = np.full(num_elements, initial_gap_si)\n",
+    "widths_sin = np.full(num_elements, initial_width_sin)\n",
+    "gaps_sin = np.full(num_elements, initial_gap_sin)\n",
+    "sim = make_simulation(\n",
+    "    widths_si,\n",
+    "    gaps_si,\n",
+    "    widths_sin,\n",
+    "    gaps_sin,\n",
+    "    include_field_monitor=True,\n",
+    ")\n",
+    "\n",
+    "ax = sim.plot(y=0)\n",
+    "ax.set_title(\"Cross-section of the initial grating geometry (y=0)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79d476d7",
+   "metadata": {},
+   "source": [
+    "## Running the Simulation\n",
+    "\n",
+    "We'll use `web.run` to submit the job to Tidy3D, and get some initial results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a13094fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:19:25 CEST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'gc_setup'</span> with task_id                              \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-eab86622-9b02-4925-970d-bf57348b3ca4'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:19:25 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'gc_setup'\u001b[0m with task_id                              \n",
+       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-eab86622-9b02-4925-970d-bf57348b3ca4'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>View task using web UI at                                         \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-eab86622-9b02-4925-970d-bf57348b3ca4\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-eab86622-9b</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-eab86622-9b02-4925-970d-bf57348b3ca4\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">02-4925-970d-bf57348b3ca4'</span></a>.                                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                         \n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=861366;https://tidy3d.simulation.cloud/workbench?taskId=fdve-eab86622-9b02-4925-970d-bf57348b3ca4\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=363908;https://tidy3d.simulation.cloud/workbench?taskId=fdve-eab86622-9b02-4925-970d-bf57348b3ca4\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=861366;https://tidy3d.simulation.cloud/workbench?taskId=fdve-eab86622-9b02-4925-970d-bf57348b3ca4\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=293143;https://tidy3d.simulation.cloud/workbench?taskId=fdve-eab86622-9b02-4925-970d-bf57348b3ca4\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=861366;https://tidy3d.simulation.cloud/workbench?taskId=fdve-eab86622-9b02-4925-970d-bf57348b3ca4\u001b\\\u001b[32m-eab86622-9b\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=861366;https://tidy3d.simulation.cloud/workbench?taskId=fdve-eab86622-9b02-4925-970d-bf57348b3ca4\u001b\\\u001b[32m02-4925-970d-bf57348b3ca4'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=935049;https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91fe7e07323740d1be69a4bbd1f183e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:19:29 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task      \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:19:29 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task      \n",
+       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
+       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:19:30 CEST </span>status = success                                                  \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:19:30 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81aac7fdc067407fbf992620dc05cc34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:19:32 CEST </span>loading simulation from simulation_data.hdf5                      \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:19:32 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                      \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sim_data = web.run(sim, task_name=\"gc_setup\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0bcd72b",
+   "metadata": {},
+   "source": [
+    "## Visualizing the Results\n",
+    "\n",
+    "With the simulation complete, we analyze the mode monitor spectrum and inspect the field distribution to understand how light couples into the device."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "107ca2a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "power_da = get_mode_monitor_power(sim_data)\n",
+    "freqs = power_da.coords[\"f\"].values\n",
+    "wavelengths = td.C_0 / freqs\n",
+    "power = np.squeeze(power_da.data)\n",
+    "power_db = 10 * np.log10(power)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f57eed0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(wavelengths, power_db)\n",
+    "ax.set_xlabel(\"Wavelength (µm)\")\n",
+    "ax.set_ylabel(\"Transmission (dB)\")\n",
+    "ax.set_title(\"Mode monitor power spectrum\")\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77512366",
+   "metadata": {},
+   "source": [
+    "## Baseline Performance: The Need for Optimization\n",
+    "\n",
+    "The transmission spectrum shows large coupling loss (below -30 dB) near 1.55 µm, confirming that our initial guess is insufficient. We therefore turn to optimization to explore the broader design space efficiently."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d5151192",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = sim_data.plot_field(\"field_monitor\", \"Ey\", \"abs^2\")\n",
+    "ax.set_title(\"Field intensity |Ey|^2 in the symmetry plane\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025-10-09-invdes-seminar/01_bayes.ipynb b/2025-10-09-invdes-seminar/01_bayes.ipynb
new file mode 100644
index 00000000..1a61035a
--- /dev/null
+++ b/2025-10-09-invdes-seminar/01_bayes.ipynb
@@ -0,0 +1,825 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "411eea3f",
+   "metadata": {},
+   "source": [
+    "# Grating Coupler: Bayesian Optimization for Initial Design\n",
+    "\n",
+    "> With our simulation setup in place, we now turn to optimization. Our goal is to find a set of grating parameters that maximizes the coupling efficiency. Since each simulation is computationally expensive, we will use Bayesian optimization. This technique is ideal for optimizing \"black-box\" functions that are costly to evaluate.\n",
+    "\n",
+    "## Why Bayesian Optimization?\n",
+    "\n",
+    "Exhaustive searches would require thousands of simulations. Bayesian optimization instead builds a probabilistic surrogate of the objective, balancing exploration of uncertain regions with exploitation of promising designs to converge in far fewer solver calls. It intelligently explores the parameter space to find the optimal design with a minimal number of simulations. Bayesian optimization works best when the design space has only a handful of effective degrees of freedom; beyond roughly five independent variables the surrogate becomes harder to learn, so we reserve higher-dimensional searches for gradient-based methods discussed later in the series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4a776b66",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import tidy3d as td\n",
+    "from bayes_opt import BayesianOptimization\n",
+    "from setup import (\n",
+    "    center_wavelength,\n",
+    "    get_mode_monitor_power,\n",
+    "    make_simulation,\n",
+    "    max_gap_si,\n",
+    "    max_gap_sin,\n",
+    "    max_width_si,\n",
+    "    max_width_sin,\n",
+    "    min_gap_si,\n",
+    "    min_gap_sin,\n",
+    "    min_width_si,\n",
+    "    min_width_sin,\n",
+    "    num_elements,\n",
+    ")\n",
+    "from setup import (\n",
+    "    first_gap_si as default_first_gap_si,\n",
+    ")\n",
+    "from tidy3d import web"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cdec7978",
+   "metadata": {},
+   "source": [
+    "## The Evaluation Function\n",
+    "\n",
+    "The optimizer queries this function with a candidate set of grating parameters. We construct the simulation, run it in the cloud, and return the coupling efficiency from the mode monitor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d9a2952f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluate(\n",
+    "    width_si: float,\n",
+    "    gap_si: float,\n",
+    "    width_sin: float,\n",
+    "    gap_sin: float,\n",
+    "    first_gap_si: float,\n",
+    ") -> float:\n",
+    "    \"\"\"Return the coupling efficiency for a uniform grating parameterized array.\"\"\"\n",
+    "    widths_si = np.full(num_elements, width_si)\n",
+    "    gaps_si = np.full(num_elements, gap_si)\n",
+    "    widths_sin = np.full(num_elements, width_sin)\n",
+    "    gaps_sin = np.full(num_elements, gap_sin)\n",
+    "\n",
+    "    sim = make_simulation(\n",
+    "        widths_si,\n",
+    "        gaps_si,\n",
+    "        widths_sin,\n",
+    "        gaps_sin,\n",
+    "        first_gap_si=first_gap_si,\n",
+    "    )\n",
+    "    sim_data = web.run(sim, task_name=\"gc_bopt_eval\", verbose=False)\n",
+    "\n",
+    "    power_da = get_mode_monitor_power(sim_data)\n",
+    "    target_power = power_da.sel(f=td.C_0 / center_wavelength, method=\"nearest\").item()\n",
+    "\n",
+    "    return target_power"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1f09563",
+   "metadata": {},
+   "source": [
+    "## Setting Up the Bayesian Optimizer\n",
+    "\n",
+    "We configure the optimizer with sensible defaults and practical bounds:\n",
+    "- `parameter_bounds` (the `pbounds` argument) defines the design window we explore.\n",
+    "- `init_points` sets how many random samples to collect before modeling.\n",
+    "- `n_iter` controls the number of guided optimization iterations.\n",
+    "\n",
+    "## Framing the Problem: A 5-Parameter Global Search\n",
+    "\n",
+    "Rather than tune every tooth individually (30 variables per layer), we search a five-dimensional space of uniform widths, gaps, and inter-layer offset. This captures the dominant physics, keeps simulations fast, and yields a design that later gradient-based passes can refine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1e3c0d2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "seed = 1234\n",
+    "\n",
+    "init_points = 15\n",
+    "n_iter = 60\n",
+    "\n",
+    "parameter_bounds = {\n",
+    "    \"width_si\": (min_width_si, max_width_si),\n",
+    "    \"gap_si\": (min_gap_si, max_gap_si),\n",
+    "    \"width_sin\": (min_width_sin, max_width_sin),\n",
+    "    \"gap_sin\": (min_gap_sin, max_gap_sin),\n",
+    "    \"first_gap_si\": (\n",
+    "        default_first_gap_si - 0.2,\n",
+    "        default_first_gap_si + 0.2,\n",
+    "    ),\n",
+    "}\n",
+    "\n",
+    "default_design = {\n",
+    "    \"width_si\": 0.45,\n",
+    "    \"gap_si\": 0.55,\n",
+    "    \"width_sin\": 0.35,\n",
+    "    \"gap_sin\": 0.65,\n",
+    "    \"first_gap_si\": default_first_gap_si,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8a6cf940",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = BayesianOptimization(\n",
+    "    f=evaluate,\n",
+    "    pbounds=parameter_bounds,\n",
+    "    random_state=seed,\n",
+    "    verbose=2,\n",
+    ")\n",
+    "\n",
+    "optimizer.probe(params=default_design, lazy=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f958972",
+   "metadata": {},
+   "source": [
+    "## Running the Optimization\n",
+    "\n",
+    "Calling `optimizer.maximize(...)` alternates between exploration and exploitation to efficiently discover improved grating designs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "44b0a4cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|   iter    |  target   | width_si  |  gap_si   | width_sin |  gap_sin  | first_... |\n",
+      "-------------------------------------------------------------------------------------\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m0.0073733\u001b[39m | \u001b[39m0.45     \u001b[39m | \u001b[39m0.55     \u001b[39m | \u001b[39m0.35     \u001b[39m | \u001b[39m0.65     \u001b[39m | \u001b[39m-0.7     \u001b[39m |\n",
+      "| \u001b[39m2        \u001b[39m | \u001b[39m0.0008788\u001b[39m | \u001b[39m0.2723675\u001b[39m | \u001b[39m0.6976870\u001b[39m | \u001b[39m0.5501821\u001b[39m | \u001b[39m0.8497510\u001b[39m | \u001b[39m-0.588009\u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m0.0054719\u001b[39m | \u001b[39m0.3453333\u001b[39m | \u001b[39m0.4211714\u001b[39m | \u001b[39m0.8414977\u001b[39m | \u001b[39m0.9706975\u001b[39m | \u001b[39m-0.549626\u001b[39m |\n",
+      "| \u001b[35m4        \u001b[39m | \u001b[35m0.0182109\u001b[39m | \u001b[35m0.4220355\u001b[39m | \u001b[35m0.6007961\u001b[39m | \u001b[35m0.7467703\u001b[39m | \u001b[35m0.7988914\u001b[39m | \u001b[35m-0.751899\u001b[39m |\n",
+      "| \u001b[35m5        \u001b[39m | \u001b[35m0.0463257\u001b[39m | \u001b[35m0.6050765\u001b[39m | \u001b[35m0.6024665\u001b[39m | \u001b[35m0.2110147\u001b[39m | \u001b[35m0.8409786\u001b[39m | \u001b[35m-0.546943\u001b[39m |\n",
+      "| \u001b[35m6        \u001b[39m | \u001b[35m0.1310502\u001b[39m | \u001b[35m0.4283973\u001b[39m | \u001b[35m0.6923169\u001b[39m | \u001b[35m0.2603049\u001b[39m | \u001b[35m0.5581768\u001b[39m | \u001b[35m-0.526743\u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m0.0719833\u001b[39m | \u001b[39m0.6862403\u001b[39m | \u001b[39m0.5177620\u001b[39m | \u001b[39m0.8309841\u001b[39m | \u001b[39m0.5217852\u001b[39m | \u001b[39m-0.672760\u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m0.0075415\u001b[39m | \u001b[39m0.8822146\u001b[39m | \u001b[39m0.5489387\u001b[39m | \u001b[39m0.8417181\u001b[39m | \u001b[39m0.4006367\u001b[39m | \u001b[39m-0.618295\u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m0.0182013\u001b[39m | \u001b[39m0.7341231\u001b[39m | \u001b[39m0.3750336\u001b[39m | \u001b[39m0.9398941\u001b[39m | \u001b[39m0.6094985\u001b[39m | \u001b[39m-0.536273\u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m0.0004387\u001b[39m | \u001b[39m0.1538283\u001b[39m | \u001b[39m0.3474296\u001b[39m | \u001b[39m0.2378842\u001b[39m | \u001b[39m0.7724166\u001b[39m | \u001b[39m-0.662150\u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m0.0311192\u001b[39m | \u001b[39m0.5799791\u001b[39m | \u001b[39m0.2346592\u001b[39m | \u001b[39m0.6491464\u001b[39m | \u001b[39m0.5307679\u001b[39m | \u001b[39m-0.698813\u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m0.0100697\u001b[39m | \u001b[39m0.2007048\u001b[39m | \u001b[39m0.6857549\u001b[39m | \u001b[39m0.6527557\u001b[39m | \u001b[39m0.3047348\u001b[39m | \u001b[39m-0.653023\u001b[39m |\n",
+      "| \u001b[39m13       \u001b[39m | \u001b[39m0.0003374\u001b[39m | \u001b[39m0.9209105\u001b[39m | \u001b[39m0.8324193\u001b[39m | \u001b[39m0.9936651\u001b[39m | \u001b[39m0.9711612\u001b[39m | \u001b[39m-0.583214\u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m0.0454211\u001b[39m | \u001b[39m0.3567258\u001b[39m | \u001b[39m0.6999333\u001b[39m | \u001b[39m0.5824750\u001b[39m | \u001b[39m0.4369726\u001b[39m | \u001b[39m-0.747073\u001b[39m |\n",
+      "| \u001b[39m15       \u001b[39m | \u001b[39m0.0001013\u001b[39m | \u001b[39m0.1484863\u001b[39m | \u001b[39m0.5613187\u001b[39m | \u001b[39m0.9856037\u001b[39m | \u001b[39m0.3867598\u001b[39m | \u001b[39m-0.852247\u001b[39m |\n",
+      "| \u001b[39m16       \u001b[39m | \u001b[39m0.0001163\u001b[39m | \u001b[39m0.7646707\u001b[39m | \u001b[39m0.6698429\u001b[39m | \u001b[39m0.5773060\u001b[39m | \u001b[39m0.3749887\u001b[39m | \u001b[39m-0.808312\u001b[39m |\n",
+      "| \u001b[35m17       \u001b[39m | \u001b[35m0.1979598\u001b[39m | \u001b[35m0.4932038\u001b[39m | \u001b[35m0.7623939\u001b[39m | \u001b[35m0.9322595\u001b[39m | \u001b[35m0.6526389\u001b[39m | \u001b[35m-0.703451\u001b[39m |\n",
+      "| \u001b[39m18       \u001b[39m | \u001b[39m0.0420824\u001b[39m | \u001b[39m0.4342475\u001b[39m | \u001b[39m0.9086304\u001b[39m | \u001b[39m0.6175712\u001b[39m | \u001b[39m0.8364651\u001b[39m | \u001b[39m-0.835367\u001b[39m |\n",
+      "| \u001b[39m19       \u001b[39m | \u001b[39m0.1550398\u001b[39m | \u001b[39m0.5717839\u001b[39m | \u001b[39m0.6450617\u001b[39m | \u001b[39m0.7648533\u001b[39m | \u001b[39m0.8786783\u001b[39m | \u001b[39m-0.624585\u001b[39m |\n",
+      "| \u001b[39m20       \u001b[39m | \u001b[39m0.0173762\u001b[39m | \u001b[39m0.7657129\u001b[39m | \u001b[39m0.6924573\u001b[39m | \u001b[39m0.4554630\u001b[39m | \u001b[39m0.8476774\u001b[39m | \u001b[39m-0.548759\u001b[39m |\n",
+      "| \u001b[39m21       \u001b[39m | \u001b[39m0.0103088\u001b[39m | \u001b[39m0.3609571\u001b[39m | \u001b[39m0.4353946\u001b[39m | \u001b[39m0.6382913\u001b[39m | \u001b[39m0.6644991\u001b[39m | \u001b[39m-0.779752\u001b[39m |\n",
+      "| \u001b[39m22       \u001b[39m | \u001b[39m-.252e-05\u001b[39m | \u001b[39m0.5518924\u001b[39m | \u001b[39m0.3739278\u001b[39m | \u001b[39m0.3270467\u001b[39m | \u001b[39m0.4845818\u001b[39m | \u001b[39m-0.678366\u001b[39m |\n",
+      "| \u001b[39m23       \u001b[39m | \u001b[39m0.0478891\u001b[39m | \u001b[39m0.3677692\u001b[39m | \u001b[39m0.9577216\u001b[39m | \u001b[39m0.8502971\u001b[39m | \u001b[39m0.8410685\u001b[39m | \u001b[39m-0.587448\u001b[39m |\n",
+      "| \u001b[39m24       \u001b[39m | \u001b[39m0.0117099\u001b[39m | \u001b[39m0.3129293\u001b[39m | \u001b[39m0.3103665\u001b[39m | \u001b[39m0.8075439\u001b[39m | \u001b[39m0.6267554\u001b[39m | \u001b[39m-0.832730\u001b[39m |\n",
+      "| \u001b[39m25       \u001b[39m | \u001b[39m-.078e-05\u001b[39m | \u001b[39m0.9634377\u001b[39m | \u001b[39m0.6998712\u001b[39m | \u001b[39m0.2540615\u001b[39m | \u001b[39m0.3467823\u001b[39m | \u001b[39m-0.704897\u001b[39m |\n",
+      "| \u001b[39m26       \u001b[39m | \u001b[39m0.0237110\u001b[39m | \u001b[39m0.1532478\u001b[39m | \u001b[39m0.9574945\u001b[39m | \u001b[39m0.8714769\u001b[39m | \u001b[39m0.7460283\u001b[39m | \u001b[39m-0.599979\u001b[39m |\n",
+      "| \u001b[39m27       \u001b[39m | \u001b[39m0.0056280\u001b[39m | \u001b[39m0.2923166\u001b[39m | \u001b[39m0.3690183\u001b[39m | \u001b[39m0.8161264\u001b[39m | \u001b[39m0.5788663\u001b[39m | \u001b[39m-0.758092\u001b[39m |\n",
+      "| \u001b[39m28       \u001b[39m | \u001b[39m0.0038580\u001b[39m | \u001b[39m0.8658399\u001b[39m | \u001b[39m0.5028183\u001b[39m | \u001b[39m0.2870910\u001b[39m | \u001b[39m0.4292652\u001b[39m | \u001b[39m-0.830763\u001b[39m |\n",
+      "| \u001b[39m29       \u001b[39m | \u001b[39m0.0078036\u001b[39m | \u001b[39m0.7499116\u001b[39m | \u001b[39m0.3966842\u001b[39m | \u001b[39m0.9658977\u001b[39m | \u001b[39m0.8530094\u001b[39m | \u001b[39m-0.755338\u001b[39m |\n",
+      "| \u001b[39m30       \u001b[39m | \u001b[39m0.1157439\u001b[39m | \u001b[39m0.5443851\u001b[39m | \u001b[39m0.7401320\u001b[39m | \u001b[39m0.8935677\u001b[39m | \u001b[39m0.7118383\u001b[39m | \u001b[39m-0.662840\u001b[39m |\n",
+      "| \u001b[35m31       \u001b[39m | \u001b[35m0.2105787\u001b[39m | \u001b[35m0.5109902\u001b[39m | \u001b[35m0.7369656\u001b[39m | \u001b[35m0.9325311\u001b[39m | \u001b[35m0.6044277\u001b[39m | \u001b[35m-0.710801\u001b[39m |\n",
+      "| \u001b[39m32       \u001b[39m | \u001b[39m0.1015856\u001b[39m | \u001b[39m0.4985134\u001b[39m | \u001b[39m0.7971441\u001b[39m | \u001b[39m0.9840112\u001b[39m | \u001b[39m0.5859352\u001b[39m | \u001b[39m-0.731262\u001b[39m |\n",
+      "| \u001b[35m33       \u001b[39m | \u001b[35m0.2929319\u001b[39m | \u001b[35m0.4668610\u001b[39m | \u001b[35m0.7081652\u001b[39m | \u001b[35m0.9045646\u001b[39m | \u001b[35m0.6235182\u001b[39m | \u001b[35m-0.700799\u001b[39m |\n",
+      "| \u001b[39m34       \u001b[39m | \u001b[39m0.2547242\u001b[39m | \u001b[39m0.4411075\u001b[39m | \u001b[39m0.6864361\u001b[39m | \u001b[39m0.9257583\u001b[39m | \u001b[39m0.6128403\u001b[39m | \u001b[39m-0.659792\u001b[39m |\n",
+      "| \u001b[39m35       \u001b[39m | \u001b[39m0.2869863\u001b[39m | \u001b[39m0.4362650\u001b[39m | \u001b[39m0.7225410\u001b[39m | \u001b[39m0.8522442\u001b[39m | \u001b[39m0.5907469\u001b[39m | \u001b[39m-0.694859\u001b[39m |\n",
+      "| \u001b[39m36       \u001b[39m | \u001b[39m0.1508852\u001b[39m | \u001b[39m0.5678676\u001b[39m | \u001b[39m0.6554857\u001b[39m | \u001b[39m0.7862522\u001b[39m | \u001b[39m0.9035742\u001b[39m | \u001b[39m-0.596729\u001b[39m |\n",
+      "| \u001b[39m37       \u001b[39m | \u001b[39m0.0001828\u001b[39m | \u001b[39m0.8116160\u001b[39m | \u001b[39m0.7498178\u001b[39m | \u001b[39m0.7773058\u001b[39m | \u001b[39m0.8474984\u001b[39m | \u001b[39m-0.565359\u001b[39m |\n",
+      "| \u001b[39m38       \u001b[39m | \u001b[39m0.1558042\u001b[39m | \u001b[39m0.4190181\u001b[39m | \u001b[39m0.6762714\u001b[39m | \u001b[39m0.8902169\u001b[39m | \u001b[39m0.6044185\u001b[39m | \u001b[39m-0.766724\u001b[39m |\n",
+      "| \u001b[39m39       \u001b[39m | \u001b[39m0.0615939\u001b[39m | \u001b[39m0.4824886\u001b[39m | \u001b[39m0.8316575\u001b[39m | \u001b[39m0.9776277\u001b[39m | \u001b[39m0.5939431\u001b[39m | \u001b[39m-0.725002\u001b[39m |\n",
+      "| \u001b[39m40       \u001b[39m | \u001b[39m0.0009466\u001b[39m | \u001b[39m0.4260180\u001b[39m | \u001b[39m0.9574253\u001b[39m | \u001b[39m0.5652613\u001b[39m | \u001b[39m0.4124581\u001b[39m | \u001b[39m-0.603899\u001b[39m |\n",
+      "| \u001b[35m41       \u001b[39m | \u001b[35m0.3052871\u001b[39m | \u001b[35m0.4829541\u001b[39m | \u001b[35m0.6837362\u001b[39m | \u001b[35m0.8423627\u001b[39m | \u001b[35m0.6068568\u001b[39m | \u001b[35m-0.658536\u001b[39m |\n",
+      "| \u001b[39m42       \u001b[39m | \u001b[39m0.2853187\u001b[39m | \u001b[39m0.4325592\u001b[39m | \u001b[39m0.7198579\u001b[39m | \u001b[39m0.8356172\u001b[39m | \u001b[39m0.6495425\u001b[39m | \u001b[39m-0.639055\u001b[39m |\n",
+      "| \u001b[39m43       \u001b[39m | \u001b[39m0.0057628\u001b[39m | \u001b[39m0.3705161\u001b[39m | \u001b[39m0.2609075\u001b[39m | \u001b[39m0.7641069\u001b[39m | \u001b[39m0.6423157\u001b[39m | \u001b[39m-0.659451\u001b[39m |\n",
+      "| \u001b[35m44       \u001b[39m | \u001b[35m0.3306672\u001b[39m | \u001b[35m0.4659282\u001b[39m | \u001b[35m0.7322124\u001b[39m | \u001b[35m0.8186520\u001b[39m | \u001b[35m0.5603383\u001b[39m | \u001b[35m-0.589711\u001b[39m |\n",
+      "| \u001b[39m45       \u001b[39m | \u001b[39m0.0637769\u001b[39m | \u001b[39m0.7237675\u001b[39m | \u001b[39m0.4719243\u001b[39m | \u001b[39m0.9012700\u001b[39m | \u001b[39m0.8521591\u001b[39m | \u001b[39m-0.683192\u001b[39m |\n",
+      "| \u001b[39m46       \u001b[39m | \u001b[39m0.2798818\u001b[39m | \u001b[39m0.4530196\u001b[39m | \u001b[39m0.6695373\u001b[39m | \u001b[39m0.7778687\u001b[39m | \u001b[39m0.5610986\u001b[39m | \u001b[39m-0.517369\u001b[39m |\n",
+      "| \u001b[39m47       \u001b[39m | \u001b[39m0.0881508\u001b[39m | \u001b[39m0.5057719\u001b[39m | \u001b[39m0.7380572\u001b[39m | \u001b[39m0.7278540\u001b[39m | \u001b[39m0.5740273\u001b[39m | \u001b[39m-0.587645\u001b[39m |\n",
+      "| \u001b[39m48       \u001b[39m | \u001b[39m0.0035109\u001b[39m | \u001b[39m0.5964185\u001b[39m | \u001b[39m0.8220468\u001b[39m | \u001b[39m0.6801599\u001b[39m | \u001b[39m0.4002636\u001b[39m | \u001b[39m-0.824338\u001b[39m |\n",
+      "| \u001b[39m49       \u001b[39m | \u001b[39m0.2747169\u001b[39m | \u001b[39m0.4181071\u001b[39m | \u001b[39m0.7099867\u001b[39m | \u001b[39m0.8705469\u001b[39m | \u001b[39m0.5255766\u001b[39m | \u001b[39m-0.529208\u001b[39m |\n",
+      "| \u001b[39m50       \u001b[39m | \u001b[39m0.3080333\u001b[39m | \u001b[39m0.4922586\u001b[39m | \u001b[39m0.6941605\u001b[39m | \u001b[39m0.8643236\u001b[39m | \u001b[39m0.5927388\u001b[39m | \u001b[39m-0.535345\u001b[39m |\n",
+      "| \u001b[35m51       \u001b[39m | \u001b[35m0.3401091\u001b[39m | \u001b[35m0.4928017\u001b[39m | \u001b[35m0.6612841\u001b[39m | \u001b[35m0.8494354\u001b[39m | \u001b[35m0.5017494\u001b[39m | \u001b[35m-0.580241\u001b[39m |\n",
+      "| \u001b[39m52       \u001b[39m | \u001b[39m0.0610332\u001b[39m | \u001b[39m0.5403722\u001b[39m | \u001b[39m0.7064469\u001b[39m | \u001b[39m0.8611785\u001b[39m | \u001b[39m0.4557262\u001b[39m | \u001b[39m-0.499999\u001b[39m |\n",
+      "| \u001b[39m53       \u001b[39m | \u001b[39m0.0004800\u001b[39m | \u001b[39m0.2965229\u001b[39m | \u001b[39m0.5130573\u001b[39m | \u001b[39m0.9113122\u001b[39m | \u001b[39m0.8969618\u001b[39m | \u001b[39m-0.655383\u001b[39m |\n",
+      "| \u001b[39m54       \u001b[39m | \u001b[39m0.0004491\u001b[39m | \u001b[39m0.6320543\u001b[39m | \u001b[39m0.9286664\u001b[39m | \u001b[39m0.3262753\u001b[39m | \u001b[39m0.4691168\u001b[39m | \u001b[39m-0.792151\u001b[39m |\n",
+      "| \u001b[39m55       \u001b[39m | \u001b[39m-.059e-06\u001b[39m | \u001b[39m0.7225987\u001b[39m | \u001b[39m0.7814960\u001b[39m | \u001b[39m0.9490208\u001b[39m | \u001b[39m0.7021836\u001b[39m | \u001b[39m-0.631338\u001b[39m |\n",
+      "| \u001b[39m56       \u001b[39m | \u001b[39m0.0984273\u001b[39m | \u001b[39m0.4318422\u001b[39m | \u001b[39m0.6338376\u001b[39m | \u001b[39m0.8339571\u001b[39m | \u001b[39m0.5472310\u001b[39m | \u001b[39m-0.588418\u001b[39m |\n",
+      "| \u001b[39m57       \u001b[39m | \u001b[39m0.2522390\u001b[39m | \u001b[39m0.4993704\u001b[39m | \u001b[39m0.7178712\u001b[39m | \u001b[39m0.8838245\u001b[39m | \u001b[39m0.5343174\u001b[39m | \u001b[39m-0.611596\u001b[39m |\n",
+      "| \u001b[39m58       \u001b[39m | \u001b[39m0.0013628\u001b[39m | \u001b[39m0.5086085\u001b[39m | \u001b[39m0.4484080\u001b[39m | \u001b[39m0.6478783\u001b[39m | \u001b[39m0.8399235\u001b[39m | \u001b[39m-0.656199\u001b[39m |\n",
+      "| \u001b[39m59       \u001b[39m | \u001b[39m0.3249743\u001b[39m | \u001b[39m0.4336563\u001b[39m | \u001b[39m0.7500968\u001b[39m | \u001b[39m0.8294141\u001b[39m | \u001b[39m0.6004466\u001b[39m | \u001b[39m-0.521322\u001b[39m |\n",
+      "| \u001b[39m60       \u001b[39m | \u001b[39m0.0045961\u001b[39m | \u001b[39m0.2058637\u001b[39m | \u001b[39m0.4205156\u001b[39m | \u001b[39m0.5770930\u001b[39m | \u001b[39m0.6793391\u001b[39m | \u001b[39m-0.749740\u001b[39m |\n",
+      "| \u001b[39m61       \u001b[39m | \u001b[39m0.2894116\u001b[39m | \u001b[39m0.4351076\u001b[39m | \u001b[39m0.7693285\u001b[39m | \u001b[39m0.8755958\u001b[39m | \u001b[39m0.6059072\u001b[39m | \u001b[39m-0.587952\u001b[39m |\n",
+      "| \u001b[39m62       \u001b[39m | \u001b[39m0.2460004\u001b[39m | \u001b[39m0.5474913\u001b[39m | \u001b[39m0.6475864\u001b[39m | \u001b[39m0.8261593\u001b[39m | \u001b[39m0.5361610\u001b[39m | \u001b[39m-0.584619\u001b[39m |\n",
+      "| \u001b[35m63       \u001b[39m | \u001b[35m0.4105454\u001b[39m | \u001b[35m0.4851866\u001b[39m | \u001b[35m0.6830568\u001b[39m | \u001b[35m0.8179798\u001b[39m | \u001b[35m0.4487647\u001b[39m | \u001b[35m-0.620102\u001b[39m |\n",
+      "| \u001b[35m64       \u001b[39m | \u001b[35m0.4206707\u001b[39m | \u001b[35m0.5037197\u001b[39m | \u001b[35m0.6401855\u001b[39m | \u001b[35m0.8492939\u001b[39m | \u001b[35m0.4103559\u001b[39m | \u001b[35m-0.649940\u001b[39m |\n",
+      "| \u001b[39m65       \u001b[39m | \u001b[39m0.0001989\u001b[39m | \u001b[39m0.2346840\u001b[39m | \u001b[39m0.5770102\u001b[39m | \u001b[39m0.9265339\u001b[39m | \u001b[39m0.9321881\u001b[39m | \u001b[39m-0.559223\u001b[39m |\n",
+      "| \u001b[39m66       \u001b[39m | \u001b[39m0.0094011\u001b[39m | \u001b[39m0.8772178\u001b[39m | \u001b[39m0.3845868\u001b[39m | \u001b[39m0.4147531\u001b[39m | \u001b[39m0.4624283\u001b[39m | \u001b[39m-0.888762\u001b[39m |\n",
+      "| \u001b[35m67       \u001b[39m | \u001b[35m0.4515458\u001b[39m | \u001b[35m0.4488560\u001b[39m | \u001b[35m0.6740889\u001b[39m | \u001b[35m0.8434351\u001b[39m | \u001b[35m0.3677091\u001b[39m | \u001b[35m-0.632994\u001b[39m |\n",
+      "| \u001b[39m68       \u001b[39m | \u001b[39m0.0036803\u001b[39m | \u001b[39m0.5670710\u001b[39m | \u001b[39m0.7408829\u001b[39m | \u001b[39m0.7190808\u001b[39m | \u001b[39m0.8326031\u001b[39m | \u001b[39m-0.514584\u001b[39m |\n",
+      "| \u001b[39m69       \u001b[39m | \u001b[39m0.3368213\u001b[39m | \u001b[39m0.4775856\u001b[39m | \u001b[39m0.6289333\u001b[39m | \u001b[39m0.7901015\u001b[39m | \u001b[39m0.3531571\u001b[39m | \u001b[39m-0.614244\u001b[39m |\n",
+      "| \u001b[39m70       \u001b[39m | \u001b[39m0.3057500\u001b[39m | \u001b[39m0.4726135\u001b[39m | \u001b[39m0.6997438\u001b[39m | \u001b[39m0.8437632\u001b[39m | \u001b[39m0.3618967\u001b[39m | \u001b[39m-0.705129\u001b[39m |\n",
+      "| \u001b[39m71       \u001b[39m | \u001b[39m0.0002174\u001b[39m | \u001b[39m0.1533260\u001b[39m | \u001b[39m0.3689217\u001b[39m | \u001b[39m0.8258886\u001b[39m | \u001b[39m0.9693316\u001b[39m | \u001b[39m-0.854644\u001b[39m |\n",
+      "| \u001b[39m72       \u001b[39m | \u001b[39m0.2047453\u001b[39m | \u001b[39m0.4482659\u001b[39m | \u001b[39m0.6326013\u001b[39m | \u001b[39m0.9147124\u001b[39m | \u001b[39m0.3559934\u001b[39m | \u001b[39m-0.616587\u001b[39m |\n",
+      "| \u001b[39m73       \u001b[39m | \u001b[39m0.3427054\u001b[39m | \u001b[39m0.4640541\u001b[39m | \u001b[39m0.6318369\u001b[39m | \u001b[39m0.7694482\u001b[39m | \u001b[39m0.3810457\u001b[39m | \u001b[39m-0.607110\u001b[39m |\n",
+      "| \u001b[39m74       \u001b[39m | \u001b[39m0.3078646\u001b[39m | \u001b[39m0.4084491\u001b[39m | \u001b[39m0.7297597\u001b[39m | \u001b[39m0.8116489\u001b[39m | \u001b[39m0.3825055\u001b[39m | \u001b[39m-0.603807\u001b[39m |\n",
+      "| \u001b[39m75       \u001b[39m | \u001b[39m0.1999793\u001b[39m | \u001b[39m0.4353690\u001b[39m | \u001b[39m0.6496413\u001b[39m | \u001b[39m0.8248983\u001b[39m | \u001b[39m0.4054286\u001b[39m | \u001b[39m-0.661702\u001b[39m |\n",
+      "| \u001b[39m76       \u001b[39m | \u001b[39m0.0004930\u001b[39m | \u001b[39m0.9368767\u001b[39m | \u001b[39m0.8231586\u001b[39m | \u001b[39m0.4244510\u001b[39m | \u001b[39m0.5212154\u001b[39m | \u001b[39m-0.680232\u001b[39m |\n",
+      "=====================================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "optimizer.maximize(init_points=init_points, n_iter=n_iter)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b89b286f",
+   "metadata": {},
+   "source": [
+    "## Analyzing the Results\n",
+    "\n",
+    "We extract the optimizer history, track the best observed loss, and visualize how the search converges toward high-efficiency gratings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2d05ffdf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization complete.\n",
+      "Best parameters: {'width_si': np.float64(0.4488560097489734), 'gap_si': np.float64(0.674088964498069), 'width_sin': np.float64(0.8434351040463873), 'gap_sin': np.float64(0.3677091398546308), 'first_gap_si': np.float64(-0.6329943025756396)}\n",
+      "Best objective (power): 0.4515458912419618\n",
+      "Best objective (dB): 3.45\n"
+     ]
+    }
+   ],
+   "source": [
+    "best = optimizer.max\n",
+    "\n",
+    "results = optimizer.res\n",
+    "iterations = np.arange(1, len(results) + 1)\n",
+    "targets = np.asarray([res[\"target\"] for res in results], dtype=float)\n",
+    "targets = np.maximum(targets, 1e-12)\n",
+    "coupling_loss_db = -10 * np.log10(targets)\n",
+    "best_loss = np.minimum.accumulate(coupling_loss_db)\n",
+    "\n",
+    "best_loss_db = -10 * np.log10(max(best[\"target\"], 1e-12))\n",
+    "\n",
+    "print(\"Optimization complete.\")\n",
+    "print(f\"Best parameters: {best['params']}\")\n",
+    "print(f\"Best objective (power): {best['target']}\")\n",
+    "print(f\"Best objective (dB): {best_loss_db:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4606275e",
+   "metadata": {},
+   "source": [
+    "## Interpreting the Optimization Progress\n",
+    "\n",
+    "The scatter points show every simulation the optimizer evaluated, while the red curve tracks the best coupling loss found so far. Early iterations explore widely; later ones cluster near promising regions as the surrogate model focuses on exploitation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1d91747f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "ax.scatter(iterations, coupling_loss_db, label=\"Samples\")\n",
+    "ax.plot(iterations, best_loss, color=\"red\", label=\"Best so far\")\n",
+    "ax.set_xlabel(\"Iteration\")\n",
+    "ax.set_ylabel(\"Coupling loss (dB)\")\n",
+    "ax.set_title(\"Bayesian optimization progress\")\n",
+    "ax.legend()\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "135ed68e",
+   "metadata": {},
+   "source": [
+    "## Visualizing the Optimized Design\n",
+    "\n",
+    "We reconstruct the best-performing structure, inspect its geometry, and analyze the spectral response to confirm the optimizer's progress."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d8151e0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_params = {name: float(value) for name, value in best[\"params\"].items()}\n",
+    "best_widths_si = np.full(num_elements, best_params[\"width_si\"])\n",
+    "best_gaps_si = np.full(num_elements, best_params[\"gap_si\"])\n",
+    "best_widths_sin = np.full(num_elements, best_params[\"width_sin\"])\n",
+    "best_gaps_sin = np.full(num_elements, best_params[\"gap_sin\"])\n",
+    "best_first_gap_si = best_params[\"first_gap_si\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f3191db5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "best_sim = make_simulation(\n",
+    "    best_widths_si,\n",
+    "    best_gaps_si,\n",
+    "    best_widths_sin,\n",
+    "    best_gaps_sin,\n",
+    "    first_gap_si=best_first_gap_si,\n",
+    "    include_field_monitor=True,\n",
+    ")\n",
+    "ax = best_sim.plot(y=0)\n",
+    "ax.set_title(\"Cross-section of the optimized grating (y=0)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ef0bcf39",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:42:48 CEST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'gc_bopt_final'</span> with task_id                         \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:42:48 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'gc_bopt_final'\u001b[0m with task_id                         \n",
+       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>View task using web UI at                                         \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">0e-4606-b55d-8870fb9ee68a'</span></a>.                                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                         \n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=457138;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=426305;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=457138;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=951133;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=457138;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a\u001b\\\u001b[32m-ad498e7b-cf\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=457138;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a\u001b\\\u001b[32m0e-4606-b55d-8870fb9ee68a'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=169149;https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68b9eff2d2214a8a840d3467487edde7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:42:50 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task      \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:42:50 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task      \n",
+       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
+       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:42:55 CEST </span>status = queued                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:42:55 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or             \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.     \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Terminating the Python script will not stop the job running on the\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>cloud.                                                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or             \n",
+       "\u001b[2;36m              \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.     \n",
+       "\u001b[2;36m              \u001b[0mTerminating the Python script will not stop the job running on the\n",
+       "\u001b[2;36m              \u001b[0mcloud.                                                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:43:02 CEST </span>starting up solver                                                \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:43:02 CEST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>running solver                                                    \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d603207f26374a9db101addf16203cce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:43:08 CEST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">36</span>%, exiting.                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:43:08 CEST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m36\u001b[0m%, exiting.                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>status = success                                                  \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>View simulation result at                                         \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">0e-4606-b55d-8870fb9ee68a'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                         \n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=277631;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=270507;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=277631;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=124060;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=277631;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a\u001b\\\u001b[4;34m-ad498e7b-cf\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=277631;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ad498e7b-cf0e-4606-b55d-8870fb9ee68a\u001b\\\u001b[4;34m0e-4606-b55d-8870fb9ee68a'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "57e4c0a17b1240b8bca782f344fcd95f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:43:11 CEST </span>loading simulation from simulation_data.hdf5                      \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:43:11 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                      \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "best_data = web.run(best_sim, task_name=\"gc_bopt_final\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3bd4eae3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "power_da = get_mode_monitor_power(best_data)\n",
+    "freqs = power_da.coords[\"f\"].values\n",
+    "wavelengths = td.C_0 / freqs\n",
+    "power = np.squeeze(power_da.data)\n",
+    "power_db = 10 * np.log10(power)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0ad87dab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "ax.plot(wavelengths, power_db)\n",
+    "ax.set_xlabel(\"Wavelength (µm)\")\n",
+    "ax.set_ylabel(\"Transmission (dB)\")\n",
+    "ax.set_title(\"Mode monitor spectrum (optimized design)\")\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1984bbfe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = best_data.plot_field(\"field_monitor\", \"Ey\", \"abs^2\")\n",
+    "ax.set_title(\"Field intensity |Ey|^2 for the optimized design\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2c434de",
+   "metadata": {},
+   "source": [
+    "The optimized geometry increases overlap between the free-space beam and the guided mode, yielding a stronger steady-state field inside the silicon nitride layer. In the next notebook we leverage this design as the starting point for gradient-based refinement."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "303207c7",
+   "metadata": {},
+   "source": [
+    "## Exporting the Best Design\n",
+    "\n",
+    "We serialize the best uniform grating parameters so the adjoint notebook can continue from this design without rerunning the Bayesian search."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "00274144",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved best design to /Users/yannick/flexcompute/projects/seminar/polish/notebooks/results/gc_bayes_opt_best.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "from pathlib import Path\n",
+    "\n",
+    "export_path = Path(\"./results/gc_bayes_opt_best.json\")\n",
+    "export_path.parent.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "export_payload = {\n",
+    "    \"width_si\": best_params[\"width_si\"],\n",
+    "    \"gap_si\": best_params[\"gap_si\"],\n",
+    "    \"width_sin\": best_params[\"width_sin\"],\n",
+    "    \"gap_sin\": best_params[\"gap_sin\"],\n",
+    "    \"first_gap_si\": best_params[\"first_gap_si\"],\n",
+    "    \"target_power\": float(best[\"target\"]),\n",
+    "    \"coupling_loss_db\": float(best_loss_db),\n",
+    "}\n",
+    "\n",
+    "with export_path.open(\"w\", encoding=\"utf-8\") as f:\n",
+    "    json.dump(export_payload, f, indent=2)\n",
+    "\n",
+    "print(f\"Saved best design to {export_path.resolve()}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025-10-09-invdes-seminar/02_adjoint.ipynb b/2025-10-09-invdes-seminar/02_adjoint.ipynb
new file mode 100644
index 00000000..65427097
--- /dev/null
+++ b/2025-10-09-invdes-seminar/02_adjoint.ipynb
@@ -0,0 +1,898 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "17e57ad4",
+   "metadata": {},
+   "source": [
+    "# Adjoint Optimization: High-Dimensional Gradient-Based Refinement\n",
+    "\n",
+    "> In the previous notebook, we used Bayesian Optimization to find a good starting design. The strength of that global optimization approach was its ability to efficiently search a low-dimensional parameter space. However, it was limited: we assumed the grating was uniform, with every tooth and gap being identical.\n",
+    "\n",
+    "> To push the performance further, we need to apodize the grating, which means varying the dimensions of each tooth individually to better match the profile of the incoming Gaussian beam. This drastically increases the number of design parameters. For our 15-element dual-layer grating, the design space just expanded from 5 global parameters to over 60 individual feature dimensions!\n",
+    "\n",
+    "> For such a high-dimensional problem, a global search is no longer efficient. In this notebook, we switch to a powerful local, gradient-based optimization technique, enabled by the adjoint method, to refine our design."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85439f3a",
+   "metadata": {},
+   "source": [
+    "## The Power of the Adjoint Method\n",
+    "\n",
+    "The key challenge in gradient-based optimization is computing the gradient itself. A naive approach like finite differences would require N+1 simulations to find the gradient with respect to N parameters. For our ~60 parameters, this is far too slow.\n",
+    "\n",
+    "This is where the adjoint method comes in. Tidy3D's automatic differentiation capability uses this method under the hood. It allows us to compute the gradient of our objective function (the coupling efficiency) with respect to all design parameters simultaneously in just two simulations per iteration, regardless of how many parameters there are. This efficiency is what makes it possible to locally optimize structures with thousands of free parameters. We start from the global design found earlier and use these gradients to walk toward a nearby, higher-performance solution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a9e277fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "from copy import deepcopy\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import autograd.numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tidy3d as td\n",
+    "from autograd import value_and_grad\n",
+    "from optim import adam_update, apply_updates, clip_params, init_adam\n",
+    "from setup import (\n",
+    "    center_wavelength,\n",
+    "    first_gap_sin,\n",
+    "    get_mode_monitor_power,\n",
+    "    make_simulation,\n",
+    "    max_gap_si,\n",
+    "    max_gap_sin,\n",
+    "    max_width_si,\n",
+    "    max_width_sin,\n",
+    "    min_gap_si,\n",
+    "    min_gap_sin,\n",
+    "    min_width_si,\n",
+    "    min_width_sin,\n",
+    "    num_elements,\n",
+    ")\n",
+    "from tidy3d import web"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6e59e2e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def objective(params):\n",
+    "    \"\"\"Objective function for adjoint optimization.\n",
+    "\n",
+    "    Takes a dictionary of geometry parameters and returns a scalar loss.\n",
+    "    The function is differentiable via autograd so the adjoint method can\n",
+    "    supply gradients for every parameter in one shot.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    params:\n",
+    "        Dictionary holding the current grating geometry arrays.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    float\n",
+    "        Negative of the coupling efficiency so gradient descent maximizes power.\n",
+    "    \"\"\"\n",
+    "    # Build the tidy3d simulation with the current parameters. Autograd traces\n",
+    "    # everything through the power extraction so the adjoint gradient can be\n",
+    "    # computed efficiently.\n",
+    "    sim = make_simulation(\n",
+    "        params[\"widths_si\"],\n",
+    "        params[\"gaps_si\"],\n",
+    "        params[\"widths_sin\"],\n",
+    "        params[\"gaps_sin\"],\n",
+    "        first_gap_si=params[\"first_gap_si\"],\n",
+    "        first_gap_sin=params[\"first_gap_sin\"],\n",
+    "    )\n",
+    "\n",
+    "    sim_data = web.run(sim, task_name=\"gc_adjoint\", verbose=False)\n",
+    "\n",
+    "    # Convert the mode monitor result into a scalar objective (negative power)\n",
+    "    # so minimization increases the coupled power at the target wavelength.\n",
+    "    power_da = get_mode_monitor_power(sim_data)\n",
+    "    freq0 = td.C_0 / center_wavelength\n",
+    "    target_power = power_da.sel(f=freq0, method=\"nearest\")\n",
+    "    return -target_power.item()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53f5b7e5",
+   "metadata": {},
+   "source": [
+    "## High-Dimensional Parameterization\n",
+    "\n",
+    "We load the best uniform design from the Bayesian search and expand those scalars into per-tooth arrays. Each layer now has individual widths and gaps, and `first_gap_si` remains a crucial phase-matching variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e668aef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scalars = {}\n",
+    "with Path(\"./results/gc_bayes_opt_best.json\").open(\"r\", encoding=\"utf-8\") as f:\n",
+    "    data = json.load(f)\n",
+    "    for key in data:\n",
+    "        scalars[key] = float(data[key])\n",
+    "\n",
+    "num_iters = 50\n",
+    "\n",
+    "params0 = {\n",
+    "    \"widths_si\": np.full(num_elements, scalars[\"width_si\"]),\n",
+    "    \"gaps_si\": np.full(num_elements, scalars[\"gap_si\"]),\n",
+    "    \"widths_sin\": np.full(num_elements, scalars[\"width_sin\"]),\n",
+    "    \"gaps_sin\": np.full(num_elements, scalars[\"gap_sin\"]),\n",
+    "    \"first_gap_si\": scalars[\"first_gap_si\"],\n",
+    "    \"first_gap_sin\": first_gap_sin,\n",
+    "}\n",
+    "\n",
+    "bounds = {\n",
+    "    \"widths_si\": (min_width_si, max_width_si),\n",
+    "    \"gaps_si\": (min_gap_si, max_gap_si),\n",
+    "    \"widths_sin\": (min_width_sin, max_width_sin),\n",
+    "    \"gaps_sin\": (min_gap_sin, max_gap_sin),\n",
+    "    \"first_gap_si\": (None, None),\n",
+    "    \"first_gap_sin\": (min_gap_sin, None),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9057369b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vg_fun = value_and_grad(objective)\n",
+    "params = deepcopy(params0)\n",
+    "opt_state = init_adam(params, lr=1e-2)\n",
+    "target_powers = []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58c4a412",
+   "metadata": {},
+   "source": [
+    "## Running the Gradient Descent\n",
+    "\n",
+    "Each iteration proceeds as follows:\n",
+    "1. Evaluate both the loss and gradient with `value_and_grad`.\n",
+    "2. Use the Adam optimizer to compute a parameter update with momentum.\n",
+    "3. Apply the update to the parameters.\n",
+    "4. Clip the result to obey fabrication bounds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d4fbe647",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iter 0: target_power=0.4515\n",
+      "iter 1: target_power=0.4687\n",
+      "iter 2: target_power=0.4864\n",
+      "iter 3: target_power=0.4909\n",
+      "iter 4: target_power=0.5035\n",
+      "iter 5: target_power=0.5053\n",
+      "iter 6: target_power=0.5152\n",
+      "iter 7: target_power=0.5217\n",
+      "iter 8: target_power=0.5242\n",
+      "iter 9: target_power=0.5307\n",
+      "iter 10: target_power=0.5322\n",
+      "iter 11: target_power=0.5378\n",
+      "iter 12: target_power=0.5413\n",
+      "iter 13: target_power=0.5435\n",
+      "iter 14: target_power=0.5484\n",
+      "iter 15: target_power=0.5513\n",
+      "iter 16: target_power=0.5544\n",
+      "iter 17: target_power=0.5544\n",
+      "iter 18: target_power=0.5564\n",
+      "iter 19: target_power=0.5599\n",
+      "iter 20: target_power=0.5617\n",
+      "iter 21: target_power=0.5642\n",
+      "iter 22: target_power=0.5654\n",
+      "iter 23: target_power=0.5677\n",
+      "iter 24: target_power=0.5679\n",
+      "iter 25: target_power=0.5698\n",
+      "iter 26: target_power=0.5720\n",
+      "iter 27: target_power=0.5739\n",
+      "iter 28: target_power=0.5753\n",
+      "iter 29: target_power=0.5769\n",
+      "iter 30: target_power=0.5777\n",
+      "iter 31: target_power=0.5781\n",
+      "iter 32: target_power=0.5793\n",
+      "iter 33: target_power=0.5808\n",
+      "iter 34: target_power=0.5815\n",
+      "iter 35: target_power=0.5823\n",
+      "iter 36: target_power=0.5843\n",
+      "iter 37: target_power=0.5844\n",
+      "iter 38: target_power=0.5857\n",
+      "iter 39: target_power=0.5869\n",
+      "iter 40: target_power=0.5882\n",
+      "iter 41: target_power=0.5888\n",
+      "iter 42: target_power=0.5891\n",
+      "iter 43: target_power=0.5903\n",
+      "iter 44: target_power=0.5910\n",
+      "iter 45: target_power=0.5917\n",
+      "iter 46: target_power=0.5928\n",
+      "iter 47: target_power=0.5934\n",
+      "iter 48: target_power=0.5934\n",
+      "iter 49: target_power=0.5921\n"
+     ]
+    }
+   ],
+   "source": [
+    "for n in range(num_iters):\n",
+    "    value, grad = vg_fun(params)\n",
+    "    target_power = -value\n",
+    "\n",
+    "    target_powers.append(target_power)\n",
+    "    print(f\"iter {n}: target_power={target_power:.4f}\")\n",
+    "\n",
+    "    updates, opt_state = adam_update(grad, opt_state)\n",
+    "    params = apply_updates(params, updates)\n",
+    "    params = clip_params(params, bounds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e6abb0a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "ax.plot(np.arange(len(target_powers)), target_powers, marker=\"o\")\n",
+    "ax.set_xlabel(\"Iteration\")\n",
+    "ax.set_ylabel(\"Target Power\")\n",
+    "ax.set_title(\"Adjoint Optimization History\")\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80a3947a",
+   "metadata": {},
+   "source": [
+    "## Visualizing the Results\n",
+    "\n",
+    "The steadily rising power confirms the adjoint-driven search is homing in on a better design."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5f269667",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_spectrum(param_set, task_name):\n",
+    "    sim = make_simulation(\n",
+    "        param_set[\"widths_si\"],\n",
+    "        param_set[\"gaps_si\"],\n",
+    "        param_set[\"widths_sin\"],\n",
+    "        param_set[\"gaps_sin\"],\n",
+    "        first_gap_si=param_set[\"first_gap_si\"],\n",
+    "        first_gap_sin=param_set[\"first_gap_sin\"],\n",
+    "    )\n",
+    "    sim_data = web.run(sim, task_name=task_name)\n",
+    "    power_da = get_mode_monitor_power(sim_data)\n",
+    "    freqs = power_da.coords[\"f\"].values\n",
+    "    wavelengths = td.C_0 / freqs\n",
+    "    power = np.squeeze(power_da.data)\n",
+    "    sort_idx = np.argsort(wavelengths)\n",
+    "    wavelengths = wavelengths[sort_idx]\n",
+    "    power = np.array(power)[sort_idx]\n",
+    "    return wavelengths, power"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ef925dc",
+   "metadata": {},
+   "source": [
+    "## Performance Payoff\n",
+    "\n",
+    "Comparing the spectra shows the apodized design significantly boosts coupling near 1.55 µm relative to the uniform baseline from Bayesian optimization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "8e460c41",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:32:31 CEST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'gc_adjoint_before'</span> with task_id                     \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-26bc3505-71ef-4e8b-89e5-1daa528ac773'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:32:31 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'gc_adjoint_before'\u001b[0m with task_id                     \n",
+       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-26bc3505-71ef-4e8b-89e5-1daa528ac773'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>View task using web UI at                                         \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-26bc3505-71ef-4e8b-89e5-1daa528ac773\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-26bc3505-71</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-26bc3505-71ef-4e8b-89e5-1daa528ac773\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">ef-4e8b-89e5-1daa528ac773'</span></a>.                                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                         \n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=813;https://tidy3d.simulation.cloud/workbench?taskId=fdve-26bc3505-71ef-4e8b-89e5-1daa528ac773\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=80050;https://tidy3d.simulation.cloud/workbench?taskId=fdve-26bc3505-71ef-4e8b-89e5-1daa528ac773\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=813;https://tidy3d.simulation.cloud/workbench?taskId=fdve-26bc3505-71ef-4e8b-89e5-1daa528ac773\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=744898;https://tidy3d.simulation.cloud/workbench?taskId=fdve-26bc3505-71ef-4e8b-89e5-1daa528ac773\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=813;https://tidy3d.simulation.cloud/workbench?taskId=fdve-26bc3505-71ef-4e8b-89e5-1daa528ac773\u001b\\\u001b[32m-26bc3505-71\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=813;https://tidy3d.simulation.cloud/workbench?taskId=fdve-26bc3505-71ef-4e8b-89e5-1daa528ac773\u001b\\\u001b[32mef-4e8b-89e5-1daa528ac773'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=225935;https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fc188939e38469cb51ef125b07d97e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:32:33 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task      \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:32:33 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task      \n",
+       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
+       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:32:34 CEST </span>status = success                                                  \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:32:34 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "626ebe5c82a24b34958cab433dc9f036",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:32:35 CEST </span>loading simulation from simulation_data.hdf5                      \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:32:35 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                      \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'gc_adjoint_after'</span> with task_id                      \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'gc_adjoint_after'\u001b[0m with task_id                      \n",
+       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>View task using web UI at                                         \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-27</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">37-4e1f-ac96-ea8f4570f030'</span></a>.                                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                         \n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=969941;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=939687;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=969941;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=507152;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=969941;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030\u001b\\\u001b[32m-7ed8820b-27\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=969941;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030\u001b\\\u001b[32m37-4e1f-ac96-ea8f4570f030'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=716680;https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cba9261bca10400882e8bc237305e819",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:32:37 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task      \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:32:37 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task      \n",
+       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
+       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:32:44 CEST </span>status = queued                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:32:44 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or             \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.     \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Terminating the Python script will not stop the job running on the\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>cloud.                                                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or             \n",
+       "\u001b[2;36m              \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.     \n",
+       "\u001b[2;36m              \u001b[0mTerminating the Python script will not stop the job running on the\n",
+       "\u001b[2;36m              \u001b[0mcloud.                                                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:32:51 CEST </span>starting up solver                                                \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:32:51 CEST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>running solver                                                    \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fae036b62269483086fc994cb871941f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:32:55 CEST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">40</span>%, exiting.                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:32:55 CEST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m40\u001b[0m%, exiting.                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>status = success                                                  \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>View simulation result at                                         \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-27</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">37-4e1f-ac96-ea8f4570f030'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                         \n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=399114;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=668314;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=399114;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=256736;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=399114;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030\u001b\\\u001b[4;34m-7ed8820b-27\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=399114;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7ed8820b-2737-4e1f-ac96-ea8f4570f030\u001b\\\u001b[4;34m37-4e1f-ac96-ea8f4570f030'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21580f7bced64f6eb6f004e87f0144c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:32:57 CEST </span>loading simulation from simulation_data.hdf5                      \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:32:57 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                      \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "w_before, p_before = compute_spectrum(params0, \"gc_adjoint_before\")\n",
+    "w_after, p_after = compute_spectrum(params, \"gc_adjoint_after\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "23ee67d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "before_db = 10 * np.log10(p_before)\n",
+    "after_db = 10 * np.log10(p_after)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "ax.plot(w_before, before_db, label=\"Before adjoint\")\n",
+    "ax.plot(w_after, after_db, label=\"After adjoint\")\n",
+    "ax.set_xlabel(\"Wavelength (µm)\")\n",
+    "ax.set_ylabel(\"Transmission (dB)\")\n",
+    "ax.set_title(\"Mode Monitor Spectrum (Before vs After)\")\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fa2f7e0",
+   "metadata": {},
+   "source": [
+    "## Final Apodized Geometry\n",
+    "\n",
+    "Visual inspection highlights the non-uniform duty cycle discovered by the optimizer to better mode-match the incident beam."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "bd465801",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "final_sim = make_simulation(\n",
+    "    params[\"widths_si\"],\n",
+    "    params[\"gaps_si\"],\n",
+    "    params[\"widths_sin\"],\n",
+    "    params[\"gaps_sin\"],\n",
+    "    first_gap_si=params[\"first_gap_si\"],\n",
+    "    first_gap_sin=params[\"first_gap_sin\"],\n",
+    ")\n",
+    "ax = final_sim.plot(y=0)\n",
+    "ax.set_title(\"Apodized grating geometry after adjoint optimization (y=0)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06007895",
+   "metadata": {},
+   "source": [
+    "Lastly, we need to export the optimized grating geometry for further analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "2d79fae8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved adjoint design to /Users/yannick/flexcompute/projects/seminar/polish/notebooks/results/gc_adjoint_best.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "def serialize_params(param_dict):\n",
+    "    \"\"\"Detach autograd containers into JSON-serializable Python objects.\"\"\"\n",
+    "    return {\n",
+    "        \"widths_si\": [float(value) for value in param_dict[\"widths_si\"]],\n",
+    "        \"gaps_si\": [float(value) for value in param_dict[\"gaps_si\"]],\n",
+    "        \"widths_sin\": [float(value) for value in param_dict[\"widths_sin\"]],\n",
+    "        \"gaps_sin\": [float(value) for value in param_dict[\"gaps_sin\"]],\n",
+    "        \"first_gap_si\": float(param_dict[\"first_gap_si\"]),\n",
+    "        \"first_gap_sin\": float(param_dict[\"first_gap_sin\"]),\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "export_path = Path(\"./results/gc_adjoint_best.json\")\n",
+    "export_path.parent.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "payload = serialize_params(params)\n",
+    "payload[\"target_power\"] = float(target_powers[-1]) if target_powers else None\n",
+    "\n",
+    "with export_path.open(\"w\", encoding=\"utf-8\") as f:\n",
+    "    json.dump(payload, f, indent=2)\n",
+    "\n",
+    "print(f\"Saved adjoint design to {export_path.resolve()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62c6a39d",
+   "metadata": {},
+   "source": [
+    "## Conclusion and Next Steps\n",
+    "\n",
+    "Switching to a gradient-based approach unlocked high-dimensional refinements and reduced the coupling loss by more than a decibel. The resulting design is finely tuned for nominal fabrication, so the next notebook introduces robust optimization to preserve performance under realistic manufacturing variations."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025-10-09-invdes-seminar/03_sensitivity.ipynb b/2025-10-09-invdes-seminar/03_sensitivity.ipynb
new file mode 100644
index 00000000..2b6affef
--- /dev/null
+++ b/2025-10-09-invdes-seminar/03_sensitivity.ipynb
@@ -0,0 +1,1694 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "495ae8bd",
+   "metadata": {},
+   "source": [
+    "# Fabrication Sensitivity Analysis: Is Our Design Robust?\n",
+    "\n",
+    "> The adjoint-optimized grating from the previous notebook delivers excellent nominal performance. In practice, however, fabrication variability means the manufactured device rarely matches the design exactly. Here we quantify how the current design responds to some assumed process deviations to see whether it is robust or brittle.\n",
+    "\n",
+    "> In the adjoint notebook we purposefully focused on maximizing performance at the nominal geometry. The natural follow-up question is: *how does that optimized design behave once it leaves the computer?* Photonic fabrication processes inevitably introduce small deviations in etched dimensions. Even a well-controlled foundry run can exhibit ±20 nm variations in tooth widths and gaps due to lithography or etch bias. A design that is overly sensitive to these changes might look great in simulation yet fail to meet targets on wafer, so our immediate goal is to measure that sensitivity before pursuing robustness improvements."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10594b5",
+   "metadata": {},
+   "source": [
+    "## Modeling Fabrication Errors with a Bias\n",
+    "\n",
+    "We begin by reloading the best adjoint design and defining a simple bias model. A ±20 nm shift in feature dimensions is a realistic foundry tolerance, so we will simulate three cases: the nominal geometry, an over-etched device (features narrower than intended), and an under-etched device (features wider than intended). This gives an intuitive first look at the design's sensitivity before launching a full Monte Carlo analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "04f09f74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import autograd.numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import tidy3d as td\n",
+    "from autograd import value_and_grad\n",
+    "from scipy.stats import norm\n",
+    "from setup import (\n",
+    "    center_wavelength,\n",
+    "    default_spacer_thickness,\n",
+    "    get_mode_monitor_power,\n",
+    "    make_simulation,\n",
+    ")\n",
+    "from tidy3d import web"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6657cb9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_nominal_parameters(path):\n",
+    "    \"\"\"Load a design JSON (Bayes or adjoint) into numpy-friendly fields.\"\"\"\n",
+    "    data = json.loads(Path(path).read_text(encoding=\"utf-8\"))\n",
+    "    return {\n",
+    "        \"widths_si\": np.array(data[\"widths_si\"]),\n",
+    "        \"gaps_si\": np.array(data[\"gaps_si\"]),\n",
+    "        \"widths_sin\": np.array(data[\"widths_sin\"]),\n",
+    "        \"gaps_sin\": np.array(data[\"gaps_sin\"]),\n",
+    "        \"first_gap_si\": data[\"first_gap_si\"],\n",
+    "        \"first_gap_sin\": data[\"first_gap_sin\"],\n",
+    "        \"spacer_thickness\": default_spacer_thickness,\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "29aec207",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_variation_builder(nominal):\n",
+    "    \"\"\"Return a closure that maps process deltas to a tidy3d Simulation.\"\"\"\n",
+    "    base_widths_si = np.array(nominal[\"widths_si\"])\n",
+    "    base_gaps_si = np.array(nominal[\"gaps_si\"])\n",
+    "\n",
+    "    def builder(overlay_delta=0.0, spacer_delta=0.0, etch_bias=0.0):\n",
+    "        # Etch bias widens features when positive and narrows them when\n",
+    "        # negative, so widths grow with the bias while gaps shrink, mirroring\n",
+    "        # the fabrication effect of over/under etching.\n",
+    "        pert_widths_si = base_widths_si + etch_bias\n",
+    "        pert_gaps_si = base_gaps_si - etch_bias\n",
+    "\n",
+    "        return make_simulation(\n",
+    "            pert_widths_si,\n",
+    "            pert_gaps_si,\n",
+    "            nominal[\"widths_sin\"],\n",
+    "            nominal[\"gaps_sin\"],\n",
+    "            first_gap_si=nominal[\"first_gap_si\"] + overlay_delta,\n",
+    "            first_gap_sin=nominal[\"first_gap_sin\"],\n",
+    "            spacer_thickness=nominal[\"spacer_thickness\"] + spacer_delta,\n",
+    "        )\n",
+    "\n",
+    "    return builder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0505451c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "design_path = Path(\"./results\") / \"gc_adjoint_best.json\"\n",
+    "\n",
+    "# Load the best apodized design from the previous notebook.\n",
+    "# This will be our nominal, or central, design point for the analysis.\n",
+    "nominal = load_nominal_parameters(design_path)\n",
+    "builder = make_variation_builder(nominal)\n",
+    "\n",
+    "# Define the fabrication bias in microns (20 nm).\n",
+    "bias = 0.02\n",
+    "\n",
+    "# Create simulations for each fabrication scenario: over-etched, nominal,\n",
+    "# and under-etched. Positive bias widens features, while a negative bias\n",
+    "# corresponds to over-etching that narrows them.\n",
+    "bias_cases = {\n",
+    "    \"Over-etched (-20 nm)\": builder(etch_bias=-bias),\n",
+    "    \"Nominal\": builder(),\n",
+    "    \"Under-etched (+20 nm)\": builder(etch_bias=bias),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "39d4febe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea1dbdb96aaa439180f914bf53ca5b8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:34:03 CEST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span> tasks.                      \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:34:03 CEST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m3\u001b[0m tasks.                      \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:34:06 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.075</span> for the whole batch.               \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:34:06 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.075\u001b[0m for the whole batch.               \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after   \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>the Batch has completed.                                          \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after   \n",
+       "\u001b[2;36m              \u001b[0mthe Batch has completed.                                          \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de1c81b1abf043fd839a403cb95f79d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:34:18 CEST </span>Batch complete.                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:34:18 CEST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83adb57949944de59cc884ddb6bee574",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bias_data = web.run_async(bias_cases)\n",
+    "\n",
+    "bias_wavelengths = None\n",
+    "bias_spectra = {}\n",
+    "\n",
+    "for label, sim_data in bias_data.items():\n",
+    "    power_da = get_mode_monitor_power(sim_data)\n",
+    "    freqs = power_da.coords[\"f\"].values\n",
+    "    wavelengths = td.C_0 / freqs\n",
+    "    power = np.asarray(power_da.data).squeeze()\n",
+    "    order = np.argsort(wavelengths)\n",
+    "    wavelengths = wavelengths[order]\n",
+    "    power = power[order]\n",
+    "\n",
+    "    if bias_wavelengths is None:\n",
+    "        bias_wavelengths = wavelengths\n",
+    "\n",
+    "    bias_spectra[label] = power"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50c73781",
+   "metadata": {},
+   "source": [
+    "## Interpreting the Sensitivity Plot\n",
+    "\n",
+    "The curves below compare the nominal spectrum to ±20 nm biased geometries. The separation between them conveys how quickly our high-efficiency design degrades under realistic fabrication shifts in tooth width and gap. Watch for both a drop in peak efficiency and a shift of the optimal wavelength."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6a994928",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "colors = {\n",
+    "    \"Over-etched (-20 nm)\": \"tab:orange\",\n",
+    "    \"Nominal\": \"tab:green\",\n",
+    "    \"Under-etched (+20 nm)\": \"tab:blue\",\n",
+    "}\n",
+    "\n",
+    "for label, spectrum in bias_spectra.items():\n",
+    "    ax.plot(\n",
+    "        bias_wavelengths,\n",
+    "        10 * np.log10(spectrum),\n",
+    "        label=label,\n",
+    "        color=colors.get(label, None),\n",
+    "        linewidth=2 if label == \"Nominal\" else 1.8,\n",
+    "        alpha=0.9,\n",
+    "    )\n",
+    "\n",
+    "ax.set_xlabel(\"Wavelength (µm)\")\n",
+    "ax.set_ylabel(\"Transmission (dB)\")\n",
+    "ax.set_title(\"Impact of ±20 nm Fabrication Bias\")\n",
+    "ax.legend()\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cb42ec87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sigma_spec = {\n",
+    "    \"overlay\": 0.025,\n",
+    "    \"spacer\": 0.02,\n",
+    "    \"widths_si\": 0.01,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8249bf45",
+   "metadata": {},
+   "source": [
+    "## Monte Carlo\n",
+    "\n",
+    "After inspecting the deterministic bias sweep, we broaden the analysis with a Monte Carlo study. We randomly sample overlay, spacer, and width variations according to foundry-provided sigma values to estimate the distribution of coupling efficiency across a wafer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8abfdc5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "seed = 42\n",
+    "num_mc_samples = 100\n",
+    "design_path = Path(\"./results\") / \"gc_adjoint_best.json\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f6e0b847",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nominal = load_nominal_parameters(design_path)\n",
+    "builder = make_variation_builder(nominal)\n",
+    "\n",
+    "sigma_vector = np.array([sigma_spec[\"overlay\"], sigma_spec[\"spacer\"], sigma_spec[\"widths_si\"]])\n",
+    "rng = np.random.default_rng(seed)\n",
+    "samples = rng.standard_normal(size=(num_mc_samples, len(sigma_vector))) * sigma_vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3c6c19b",
+   "metadata": {},
+   "source": [
+    "We draw overlay, spacer, and silicon-width perturbations from independent Gaussian models whose sigmas come straight from the (hypothetical) foundry tolerance table. Each row in the `samples` array represents one die that we will feed into the simulation pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1c93f5af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sims = {\"nominal\": builder()}\n",
+    "sims.update({f\"sample_{idx + 1}\": builder(*tuple(sample)) for idx, sample in enumerate(samples)})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75f5ca55",
+   "metadata": {},
+   "source": [
+    "The closure returned by `make_variation_builder` maps each sampled triplet into a full tidy3d `Simulation`. We keep the nominal design in the dictionary so the subsequent analysis can always reference the baseline spectrum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "817a9dd2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a82ba929a20b4a68a1abec9580caa437",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:34:52 CEST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">101</span> tasks.                    \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:34:52 CEST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m101\u001b[0m tasks.                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:36:28 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.525</span> for the whole batch.               \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:36:28 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m2.525\u001b[0m for the whole batch.               \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after   \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>the Batch has completed.                                          \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after   \n",
+       "\u001b[2;36m              \u001b[0mthe Batch has completed.                                          \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f29b971e86364b528bb71299fe72614a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:37:32 CEST </span>Batch complete.                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:37:32 CEST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a15724fc40f34baf93e84209bc83a695",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_data = web.run_async(sims)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38c474f3",
+   "metadata": {},
+   "source": [
+    "We submit the entire batch with `web.run_async` so Tidy3D executes the jobs in parallel since they are all independent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cae4cc32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ordered_names = list(sims.keys())\n",
+    "wavelengths = None\n",
+    "linear_spectra = []\n",
+    "\n",
+    "for name in ordered_names:\n",
+    "    sim_data = batch_data[name]\n",
+    "    power_da = get_mode_monitor_power(sim_data)\n",
+    "    freqs = power_da.coords[\"f\"].values\n",
+    "    wl = td.C_0 / freqs\n",
+    "    power = np.asarray(power_da.data).squeeze()\n",
+    "    order = np.argsort(wl)\n",
+    "    wl = wl[order]\n",
+    "    power = power[order]\n",
+    "\n",
+    "    if wavelengths is None:\n",
+    "        wavelengths = wl\n",
+    "\n",
+    "    linear_spectra.append(power)\n",
+    "\n",
+    "linear_array = np.vstack(linear_spectra)\n",
+    "nominal_index = ordered_names.index(\"nominal\")\n",
+    "nominal_spectrum = linear_array[nominal_index]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86096b73",
+   "metadata": {},
+   "source": [
+    "Once the solver responses return, we stack them into a 2D array and compute statistics such as the mean trace, percentile envelope, and nominal curve for direct comparison."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a5594acf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "\n",
+    "for name, spectrum in zip(ordered_names, linear_array):\n",
+    "    if name == \"nominal\":\n",
+    "        continue\n",
+    "    spectrum_db = 10 * np.log10(spectrum)\n",
+    "    ax.plot(\n",
+    "        wavelengths,\n",
+    "        spectrum_db,\n",
+    "        color=\"lightgray\",\n",
+    "        alpha=0.6,\n",
+    "        linewidth=1,\n",
+    "        zorder=1,\n",
+    "    )\n",
+    "\n",
+    "mean_spectrum = linear_array.mean(axis=0)\n",
+    "p10_spectrum = np.percentile(linear_array, 10, axis=0)\n",
+    "p90_spectrum = np.percentile(linear_array, 90, axis=0)\n",
+    "\n",
+    "ax.fill_between(\n",
+    "    wavelengths,\n",
+    "    10 * np.log10(p10_spectrum),\n",
+    "    10 * np.log10(p90_spectrum),\n",
+    "    color=\"tab:blue\",\n",
+    "    alpha=0.18,\n",
+    "    label=\"P10–P90\",\n",
+    "    zorder=2,\n",
+    ")\n",
+    "ax.plot(\n",
+    "    wavelengths,\n",
+    "    10 * np.log10(mean_spectrum),\n",
+    "    color=\"tab:blue\",\n",
+    "    linewidth=2,\n",
+    "    linestyle=\"--\",\n",
+    "    label=\"Mean\",\n",
+    "    zorder=3,\n",
+    ")\n",
+    "ax.plot(\n",
+    "    wavelengths,\n",
+    "    10 * np.log10(nominal_spectrum),\n",
+    "    color=\"black\",\n",
+    "    linewidth=2.5,\n",
+    "    label=\"Nominal\",\n",
+    "    zorder=4,\n",
+    ")\n",
+    "\n",
+    "ax.set_xlabel(\"Wavelength (µm)\")\n",
+    "ax.set_ylabel(\"Transmission (dB)\")\n",
+    "ax.set_title(\"Monte Carlo Ensemble of Spectra\")\n",
+    "ax.legend()\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1543f365",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>wavelength_um</th>\n",
+       "      <td>1.550000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_linear</th>\n",
+       "      <td>0.570517</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_db</th>\n",
+       "      <td>2.437318</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std_linear</th>\n",
+       "      <td>0.031333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std_db</th>\n",
+       "      <td>0.245317</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p10_linear</th>\n",
+       "      <td>0.524760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p10_db</th>\n",
+       "      <td>2.800390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p90_linear</th>\n",
+       "      <td>0.603760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p90_db</th>\n",
+       "      <td>2.191357</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sigma_overlay</th>\n",
+       "      <td>0.025000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sigma_spacer</th>\n",
+       "      <td>0.020000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sigma_widths_si</th>\n",
+       "      <td>0.010000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    value\n",
+       "wavelength_um    1.550000\n",
+       "mean_linear      0.570517\n",
+       "mean_db          2.437318\n",
+       "std_linear       0.031333\n",
+       "std_db           0.245317\n",
+       "p10_linear       0.524760\n",
+       "p10_db           2.800390\n",
+       "p90_linear       0.603760\n",
+       "p90_db           2.191357\n",
+       "sigma_overlay    0.025000\n",
+       "sigma_spacer     0.020000\n",
+       "sigma_widths_si  0.010000"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def linear_to_loss_db(x):\n",
+    "    return -10 * np.log10(x)\n",
+    "\n",
+    "\n",
+    "idx_center = np.argmin(np.abs(wavelengths - center_wavelength))\n",
+    "eta_center = linear_array[:, idx_center]\n",
+    "\n",
+    "mc_summary = {\n",
+    "    \"wavelength_um\": wavelengths[idx_center],\n",
+    "    \"mean_linear\": eta_center.mean(),\n",
+    "    \"mean_db\": linear_to_loss_db(eta_center.mean()),\n",
+    "    \"std_linear\": eta_center.std(ddof=1),\n",
+    "    \"std_db\": linear_to_loss_db(eta_center.mean() - eta_center.std(ddof=1))\n",
+    "    - linear_to_loss_db(eta_center.mean()),\n",
+    "    \"p10_linear\": np.percentile(eta_center, 10),\n",
+    "    \"p10_db\": linear_to_loss_db(np.percentile(eta_center, 10)),\n",
+    "    \"p90_linear\": np.percentile(eta_center, 90),\n",
+    "    \"p90_db\": linear_to_loss_db(np.percentile(eta_center, 90)),\n",
+    "    \"sigma_overlay\": sigma_spec[\"overlay\"],\n",
+    "    \"sigma_spacer\": sigma_spec[\"spacer\"],\n",
+    "    \"sigma_widths_si\": sigma_spec[\"widths_si\"],\n",
+    "}\n",
+    "\n",
+    "pd.Series(mc_summary).to_frame(\"value\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b23f290",
+   "metadata": {},
+   "source": [
+    "The helper converts the center-wavelength transmission into dB loss and aggregates mean, standard deviation, and percentile values. These single-number metrics offer a quick dashboard before moving on to more detailed adjoint sensitivities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c34e6a8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eta_center_db = linear_to_loss_db(eta_center)\n",
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "ax.hist(eta_center_db[1:], bins=\"auto\", color=\"tab:blue\", alpha=0.7, label=\"Samples\")\n",
+    "ax.axvline(eta_center_db[0], color=\"black\", linewidth=2, label=\"Nominal\")\n",
+    "ax.set_xlabel(f\"Transmission at {wavelengths[idx_center]:.3f} µm (dB)\")\n",
+    "ax.set_ylabel(\"Count\")\n",
+    "ax.set_title(\"Monte Carlo Transmission at Center Wavelength\")\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "134ec056",
+   "metadata": {},
+   "source": [
+    "## Adjoint"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b74e025d",
+   "metadata": {},
+   "source": [
+    "### Linearized Sensitivity via Adjoint\n",
+    "\n",
+    "Before launching a full robust optimization we want directional information: which fabrication knobs most strongly impact coupling efficiency near the nominal point? The objective below evaluates a single perturbed simulation and, through `value_and_grad`, returns both the power and its gradient with respect to the overlay, spacer, and silicon-width errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "12db8468",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nominal = load_nominal_parameters(design_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "3ded3426",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def objective(params):\n",
+    "    overlay_delta, spacer_delta, etch_bias = params\n",
+    "    sim = make_simulation(\n",
+    "        nominal[\"widths_si\"] + etch_bias,\n",
+    "        nominal[\"gaps_si\"] - etch_bias,\n",
+    "        nominal[\"widths_sin\"],\n",
+    "        nominal[\"gaps_sin\"],\n",
+    "        first_gap_si=nominal[\"first_gap_si\"] + overlay_delta,\n",
+    "        first_gap_sin=nominal[\"first_gap_sin\"],\n",
+    "        spacer_thickness=nominal[\"spacer_thickness\"] + spacer_delta,\n",
+    "    )\n",
+    "    sim_data = web.run(sim, task_name=\"gc_sensitivity_adj\")\n",
+    "    power_da = get_mode_monitor_power(sim_data)\n",
+    "    target_power = power_da.sel(f=td.C_0 / center_wavelength, method=\"nearest\")\n",
+    "    return target_power.item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2ad8a7d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:40:59 CEST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'gc_sensitivity_adj'</span> with task_id                    \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-3c127236-e412-4e37-ac23-057963892cbf'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:40:59 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'gc_sensitivity_adj'\u001b[0m with task_id                    \n",
+       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-3c127236-e412-4e37-ac23-057963892cbf'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>View task using web UI at                                         \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e412-4e37-ac23-057963892cbf\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e4</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e412-4e37-ac23-057963892cbf\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">12-4e37-ac23-057963892cbf'</span></a>.                                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                         \n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=822320;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e412-4e37-ac23-057963892cbf\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=305669;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e412-4e37-ac23-057963892cbf\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=822320;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e412-4e37-ac23-057963892cbf\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=684228;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e412-4e37-ac23-057963892cbf\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=822320;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e412-4e37-ac23-057963892cbf\u001b\\\u001b[32m-3c127236-e4\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=822320;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e412-4e37-ac23-057963892cbf\u001b\\\u001b[32m12-4e37-ac23-057963892cbf'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=607928;https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a50fe0373c8446c19e2524d6036927e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:41:01 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task      \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:41:01 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task      \n",
+       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
+       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0ed47f84d854f129d8a6516932b3808",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:41:05 CEST </span>status = queued                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:41:05 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or             \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.     \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Terminating the Python script will not stop the job running on the\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>cloud.                                                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or             \n",
+       "\u001b[2;36m              \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.     \n",
+       "\u001b[2;36m              \u001b[0mTerminating the Python script will not stop the job running on the\n",
+       "\u001b[2;36m              \u001b[0mcloud.                                                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:41:12 CEST </span>starting up solver                                                \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:41:12 CEST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:41:13 CEST </span>running solver                                                    \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:41:13 CEST\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8500cef39845471dac1bfca531f0dc2d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:41:16 CEST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">40</span>%, exiting.                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:41:16 CEST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m40\u001b[0m%, exiting.                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>status = postprocess                                              \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                              \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:41:21 CEST </span>status = success                                                  \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:41:21 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:41:23 CEST </span>View simulation result at                                         \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e412-4e37-ac23-057963892cbf\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e4</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e412-4e37-ac23-057963892cbf\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">12-4e37-ac23-057963892cbf'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:41:23 CEST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                         \n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=158846;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e412-4e37-ac23-057963892cbf\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=226963;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e412-4e37-ac23-057963892cbf\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=158846;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e412-4e37-ac23-057963892cbf\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=339713;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e412-4e37-ac23-057963892cbf\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=158846;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e412-4e37-ac23-057963892cbf\u001b\\\u001b[4;34m-3c127236-e4\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=158846;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3c127236-e412-4e37-ac23-057963892cbf\u001b\\\u001b[4;34m12-4e37-ac23-057963892cbf'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e92c8eb2389467d98e238abfd3f7031",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:41:25 CEST </span>loading simulation from simulation_data.hdf5                      \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:41:25 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                      \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> tasks.                      \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m1\u001b[0m tasks.                      \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:41:32 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span> for the whole batch.               \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:41:32 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m for the whole batch.               \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after   \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>the Batch has completed.                                          \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after   \n",
+       "\u001b[2;36m              \u001b[0mthe Batch has completed.                                          \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95ce33a1b05249ad8ada33d24ed91bf5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:41:43 CEST </span>Batch complete.                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:41:43 CEST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45e78d44b26b497eac6661b6a1ce4c66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "params0 = np.zeros(3)\n",
+    "value, grad = value_and_grad(objective)(params0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "52b48b49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_4ff36\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_4ff36_level0_col0\" class=\"col_heading level0 col0\" >adjoint</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4ff36_level0_row0\" class=\"row_heading level0 row0\" >mean_linear</th>\n",
+       "      <td id=\"T_4ff36_row0_col0\" class=\"data row0 col0\" >0.5912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4ff36_level0_row1\" class=\"row_heading level0 row1\" >std_linear</th>\n",
+       "      <td id=\"T_4ff36_row1_col0\" class=\"data row1 col0\" >0.0385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4ff36_level0_row2\" class=\"row_heading level0 row2\" >p10_linear</th>\n",
+       "      <td id=\"T_4ff36_row2_col0\" class=\"data row2 col0\" >0.5418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4ff36_level0_row3\" class=\"row_heading level0 row3\" >p90_linear</th>\n",
+       "      <td id=\"T_4ff36_row3_col0\" class=\"data row3 col0\" >0.6405</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4ff36_level0_row4\" class=\"row_heading level0 row4\" >mean_db</th>\n",
+       "      <td id=\"T_4ff36_row4_col0\" class=\"data row4 col0\" >2.2830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4ff36_level0_row5\" class=\"row_heading level0 row5\" >p10_db</th>\n",
+       "      <td id=\"T_4ff36_row5_col0\" class=\"data row5 col0\" >2.6616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4ff36_level0_row6\" class=\"row_heading level0 row6\" >p90_db</th>\n",
+       "      <td id=\"T_4ff36_row6_col0\" class=\"data row6 col0\" >1.9348</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x11bdf1f00>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordered = (\"overlay\", \"spacer\", \"widths_si\")\n",
+    "\n",
+    "grads = dict(zip(ordered, grad))\n",
+    "sigmas = {k: float(sigma_spec[k]) for k in ordered}\n",
+    "\n",
+    "grad_vec = np.fromiter((grads[k] for k in ordered), dtype=float)\n",
+    "sigma_vec = np.fromiter((sigmas[k] for k in ordered), dtype=float)\n",
+    "\n",
+    "# Linearized error propagation\n",
+    "scaled = grad_vec * sigma_vec\n",
+    "variance = scaled @ scaled\n",
+    "std = float(np.sqrt(variance))\n",
+    "\n",
+    "# 10th/90th percentiles for Gaussian assumption\n",
+    "z = 1.28155\n",
+    "p10 = value - z * std\n",
+    "p90 = value + z * std\n",
+    "\n",
+    "# Normalized variance contribution per parameter\n",
+    "if variance == 0.0:\n",
+    "    importance_dict = dict.fromkeys(ordered, 0.0)\n",
+    "else:\n",
+    "    importance_dict = {k: (s**2) / variance for k, s in zip(ordered, scaled)}\n",
+    "\n",
+    "adj_summary = {\n",
+    "    \"mean_linear\": value,\n",
+    "    \"std_linear\": std,\n",
+    "    \"p10_linear\": p10,\n",
+    "    \"p90_linear\": p90,\n",
+    "    \"mean_db\": linear_to_loss_db(value),\n",
+    "    \"p10_db\": linear_to_loss_db(p10),\n",
+    "    \"p90_db\": linear_to_loss_db(p90),\n",
+    "    \"importance\": importance_dict,\n",
+    "}\n",
+    "\n",
+    "result = {\n",
+    "    \"center_wavelength_um\": center_wavelength,\n",
+    "    \"sigmas\": sigmas,\n",
+    "    \"gradients_linear\": grads,\n",
+    "    \"summary\": adj_summary,\n",
+    "}\n",
+    "\n",
+    "adjoint_stats = pd.Series(\n",
+    "    {\n",
+    "        k: adj_summary[k]\n",
+    "        for k in (\n",
+    "            \"mean_linear\",\n",
+    "            \"std_linear\",\n",
+    "            \"p10_linear\",\n",
+    "            \"p90_linear\",\n",
+    "            \"mean_db\",\n",
+    "            \"p10_db\",\n",
+    "            \"p90_db\",\n",
+    "        )\n",
+    "    },\n",
+    "    name=\"adjoint\",\n",
+    ")\n",
+    "adjoint_stats.to_frame().style.format(\"{:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "31b94606",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_d5143\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_d5143_level0_col0\" class=\"col_heading level0 col0\" >importance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d5143_level0_row0\" class=\"row_heading level0 row0\" >overlay</th>\n",
+       "      <td id=\"T_d5143_row0_col0\" class=\"data row0 col0\" >5.282%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d5143_level0_row1\" class=\"row_heading level0 row1\" >spacer</th>\n",
+       "      <td id=\"T_d5143_row1_col0\" class=\"data row1 col0\" >93.998%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d5143_level0_row2\" class=\"row_heading level0 row2\" >widths_si</th>\n",
+       "      <td id=\"T_d5143_row2_col0\" class=\"data row2 col0\" >0.720%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x11bdf3c10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "importance_ser = pd.Series(adj_summary[\"importance\"], name=\"importance\")\n",
+    "display(importance_ser.to_frame().style.format(\"{:.3%}\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdb03b8b",
+   "metadata": {},
+   "source": [
+    "### Interpreting Variance Contributions\n",
+    "\n",
+    "Normalizing the gradient-scaled sigmas reveals how much each parameter contributes to the linearized variance. Plotting the breakdown highlights the dominant sensitivities we should target when we redesign for robustness."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f05343ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7, 4))\n",
+    "ax.bar(importance_ser.index, importance_ser.values, alpha=0.75)\n",
+    "ax.set_ylim(0, 1)\n",
+    "ax.set_ylabel(\"Normalized variance contribution\")\n",
+    "ax.set_title(\"Adjoint Sensitivity Importance\")\n",
+    "ax.grid(axis=\"y\", alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9eb1445",
+   "metadata": {},
+   "source": [
+    "## Comparison"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dbc2987",
+   "metadata": {},
+   "source": [
+    "### Monte Carlo vs. Adjoint View\n",
+    "\n",
+    "Finally we line up the Monte Carlo results with the adjoint prediction. Agreement between the two lenses justifies replacing expensive sampling with cheaper gradient estimates in the next notebook, while any mismatch would signal nonlinearity that the linearized model misses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "c3dcafd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_4d8bf\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_4d8bf_level0_col0\" class=\"col_heading level0 col0\" >mean_linear</th>\n",
+       "      <th id=\"T_4d8bf_level0_col1\" class=\"col_heading level0 col1\" >std_linear</th>\n",
+       "      <th id=\"T_4d8bf_level0_col2\" class=\"col_heading level0 col2\" >p10_linear</th>\n",
+       "      <th id=\"T_4d8bf_level0_col3\" class=\"col_heading level0 col3\" >p90_linear</th>\n",
+       "      <th id=\"T_4d8bf_level0_col4\" class=\"col_heading level0 col4\" >mean_db</th>\n",
+       "      <th id=\"T_4d8bf_level0_col5\" class=\"col_heading level0 col5\" >p10_db</th>\n",
+       "      <th id=\"T_4d8bf_level0_col6\" class=\"col_heading level0 col6\" >p90_db</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4d8bf_level0_row0\" class=\"row_heading level0 row0\" >Monte Carlo</th>\n",
+       "      <td id=\"T_4d8bf_row0_col0\" class=\"data row0 col0\" >0.5705</td>\n",
+       "      <td id=\"T_4d8bf_row0_col1\" class=\"data row0 col1\" >0.0313</td>\n",
+       "      <td id=\"T_4d8bf_row0_col2\" class=\"data row0 col2\" >0.5248</td>\n",
+       "      <td id=\"T_4d8bf_row0_col3\" class=\"data row0 col3\" >0.6038</td>\n",
+       "      <td id=\"T_4d8bf_row0_col4\" class=\"data row0 col4\" >2.4373</td>\n",
+       "      <td id=\"T_4d8bf_row0_col5\" class=\"data row0 col5\" >2.8004</td>\n",
+       "      <td id=\"T_4d8bf_row0_col6\" class=\"data row0 col6\" >2.1914</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4d8bf_level0_row1\" class=\"row_heading level0 row1\" >Adjoint</th>\n",
+       "      <td id=\"T_4d8bf_row1_col0\" class=\"data row1 col0\" >0.5912</td>\n",
+       "      <td id=\"T_4d8bf_row1_col1\" class=\"data row1 col1\" >0.0385</td>\n",
+       "      <td id=\"T_4d8bf_row1_col2\" class=\"data row1 col2\" >0.5418</td>\n",
+       "      <td id=\"T_4d8bf_row1_col3\" class=\"data row1 col3\" >0.6405</td>\n",
+       "      <td id=\"T_4d8bf_row1_col4\" class=\"data row1 col4\" >2.2830</td>\n",
+       "      <td id=\"T_4d8bf_row1_col5\" class=\"data row1 col5\" >2.6616</td>\n",
+       "      <td id=\"T_4d8bf_row1_col6\" class=\"data row1 col6\" >1.9348</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x11b9b5bd0>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "comparison = pd.DataFrame(\n",
+    "    {\n",
+    "        \"Monte Carlo\": {\n",
+    "            \"mean_linear\": mc_summary[\"mean_linear\"],\n",
+    "            \"std_linear\": mc_summary[\"std_linear\"],\n",
+    "            \"p10_linear\": mc_summary[\"p10_linear\"],\n",
+    "            \"p90_linear\": mc_summary[\"p90_linear\"],\n",
+    "            \"mean_db\": mc_summary[\"mean_db\"],\n",
+    "            \"p10_db\": mc_summary[\"p10_db\"],\n",
+    "            \"p90_db\": mc_summary[\"p90_db\"],\n",
+    "        },\n",
+    "        \"Adjoint\": {\n",
+    "            \"mean_linear\": adj_summary[\"mean_linear\"],\n",
+    "            \"std_linear\": adj_summary[\"std_linear\"],\n",
+    "            \"p10_linear\": adj_summary[\"p10_linear\"],\n",
+    "            \"p90_linear\": adj_summary[\"p90_linear\"],\n",
+    "            \"mean_db\": adj_summary[\"mean_db\"],\n",
+    "            \"p10_db\": adj_summary[\"p10_db\"],\n",
+    "            \"p90_db\": adj_summary[\"p90_db\"],\n",
+    "        },\n",
+    "    }\n",
+    ").T\n",
+    "\n",
+    "comparison.style.format(\"{:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6d639979",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "ax.hist(\n",
+    "    eta_center,\n",
+    "    bins=\"auto\",\n",
+    "    color=\"lightgray\",\n",
+    "    edgecolor=\"white\",\n",
+    "    alpha=0.7,\n",
+    "    density=True,\n",
+    "    label=\"Monte Carlo\",\n",
+    ")\n",
+    "\n",
+    "x = np.linspace(eta_center.min() * 0.95, eta_center.max() * 1.05, 300)\n",
+    "pdf = norm.pdf(x, loc=adj_summary[\"mean_linear\"], scale=adj_summary[\"std_linear\"])\n",
+    "ax.plot(x, pdf, color=\"tab:red\", linewidth=2, linestyle=\"--\", label=\"Adjoint Gaussian\")\n",
+    "\n",
+    "ax.set_xlabel(f\"Transmission at {center_wavelength:.3f} µm (linear)\")\n",
+    "ax.set_ylabel(\"Density\")\n",
+    "ax.set_title(\"Monte Carlo vs Adjoint Sensitivity\")\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "025977d9",
+   "metadata": {},
+   "source": [
+    "## Analysis and Conclusion\n",
+    "\n",
+    "The ±20 nm sweep already hinted that the design is somewhat brittle: the peak efficiency drops by roughly a dB and the optimal wavelength shifts under bias. The Monte Carlo and adjoint statistics confirm that fabrication variability will erode performance across a wafer. To address this we need to optimize directly for robustness.\n",
+    "\n",
+    "## Next Step: Designing for Robustness\n",
+    "\n",
+    "In the next notebook we will incorporate the process variations into the objective function itself, searching for geometries that maintain high efficiency across the biased scenarios rather than just at the nominal point."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025-10-09-invdes-seminar/04_adjoint_robust.ipynb b/2025-10-09-invdes-seminar/04_adjoint_robust.ipynb
new file mode 100644
index 00000000..269726b0
--- /dev/null
+++ b/2025-10-09-invdes-seminar/04_adjoint_robust.ipynb
@@ -0,0 +1,458 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ec818b9b",
+   "metadata": {},
+   "source": [
+    "# Robust Adjoint Optimization for Manufacturability\n",
+    "\n",
+    "> In the previous notebook, we discovered that our apodized grating is somewhat brittle: realistic ±20 nm fabrication errors can reduce efficiency by almost a decibel. A design that only works on paper is not a practical solution.\n",
+    "\n",
+    "> In this final notebook we incorporate fabrication awareness directly into the adjoint optimization loop. Instead of optimizing a single, nominal simulation, we will maximize the performance across multiple fabrication corners so the resulting device maintains high efficiency even when etched dimensions shift on the wafer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1b47bfd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "from copy import deepcopy\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import autograd.numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tidy3d as td\n",
+    "from autograd import value_and_grad\n",
+    "from optim import adam_update, apply_updates, clip_params, init_adam\n",
+    "from setup import (\n",
+    "    center_wavelength,\n",
+    "    first_gap_sin,\n",
+    "    get_mode_monitor_power,\n",
+    "    make_simulation,\n",
+    "    max_gap_si,\n",
+    "    max_gap_sin,\n",
+    "    max_width_si,\n",
+    "    max_width_sin,\n",
+    "    min_gap_si,\n",
+    "    min_gap_sin,\n",
+    "    min_width_si,\n",
+    "    min_width_sin,\n",
+    ")\n",
+    "from tidy3d import web\n",
+    "\n",
+    "ETCH_BIAS = 0.02  # 20 nm fabrication bias expressed in microns.\n",
+    "\n",
+    "\n",
+    "def apply_bias(param_dict, etch_bias):\n",
+    "    \"\"\"Return a new parameter dictionary with widths widened by bias.\"\"\"\n",
+    "    return {\n",
+    "        \"widths_si\": param_dict[\"widths_si\"] + etch_bias,\n",
+    "        \"gaps_si\": param_dict[\"gaps_si\"] - etch_bias,\n",
+    "        \"widths_sin\": param_dict[\"widths_sin\"] + etch_bias,\n",
+    "        \"gaps_sin\": param_dict[\"gaps_sin\"] - etch_bias,\n",
+    "        \"first_gap_si\": param_dict[\"first_gap_si\"],\n",
+    "        \"first_gap_sin\": param_dict[\"first_gap_sin\"],\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5aab0e16",
+   "metadata": {},
+   "source": [
+    "## Defining a Robust Multi-Objective Function\n",
+    "\n",
+    "We evaluate the design under three fabrication scenarios: nominal, over-etched (−20 nm), and under-etched (+20 nm). We then maximize the mean transmission and simultaneously minimize the standard deviation in performance between these different scenarios, which should lead to a more robust design overall. The amount of weight we place on the standard deviation minimization determins the tradeoff between nominal performance and robustness."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f55644b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "STD_PENALTY = 2.0  # Penalty for standard deviation in power\n",
+    "\n",
+    "\n",
+    "def robust_objective(params):\n",
+    "    freq0 = td.C_0 / center_wavelength\n",
+    "    scenarios = {\n",
+    "        \"nominal\": params,\n",
+    "        \"over\": apply_bias(params, -ETCH_BIAS),\n",
+    "        \"under\": apply_bias(params, ETCH_BIAS),\n",
+    "    }\n",
+    "\n",
+    "    sims = {\n",
+    "        name: make_simulation(\n",
+    "            scenario[\"widths_si\"],\n",
+    "            scenario[\"gaps_si\"],\n",
+    "            scenario[\"widths_sin\"],\n",
+    "            scenario[\"gaps_sin\"],\n",
+    "            first_gap_si=scenario[\"first_gap_si\"],\n",
+    "            first_gap_sin=scenario[\"first_gap_sin\"],\n",
+    "        )\n",
+    "        for name, scenario in scenarios.items()\n",
+    "    }\n",
+    "\n",
+    "    batch_data = web.run_async(sims, verbose=False, local_gradient=True)\n",
+    "\n",
+    "    powers = []\n",
+    "    for name in (\"nominal\", \"over\", \"under\"):\n",
+    "        sim_data = batch_data[name]\n",
+    "        power_da = get_mode_monitor_power(sim_data)\n",
+    "        target_power = power_da.sel(f=freq0, method=\"nearest\")\n",
+    "        powers.append(target_power.item())\n",
+    "\n",
+    "    powers = np.array(powers)\n",
+    "    mean_power = np.mean(powers)\n",
+    "    variance = np.mean((powers - mean_power) ** 2)\n",
+    "    std_power = np.sqrt(variance)\n",
+    "    robust_metric = mean_power - STD_PENALTY * std_power\n",
+    "\n",
+    "    return -robust_metric"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f242f7e",
+   "metadata": {},
+   "source": [
+    "### Starting Point and Bounds\n",
+    "\n",
+    "We seed the optimizer with the fabrication-sensitive adjoint design and enforce the same foundry limits as before so the updates remain manufacturable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d0e4d00e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = json.loads(Path(\"./results/gc_adjoint_best.json\").read_text(encoding=\"utf-8\"))\n",
+    "\n",
+    "num_iters = 10\n",
+    "\n",
+    "params0 = {\n",
+    "    \"widths_si\": np.array(data[\"widths_si\"], dtype=float),\n",
+    "    \"gaps_si\": np.array(data[\"gaps_si\"], dtype=float),\n",
+    "    \"widths_sin\": np.array(data[\"widths_sin\"], dtype=float),\n",
+    "    \"gaps_sin\": np.array(data[\"gaps_sin\"], dtype=float),\n",
+    "    \"first_gap_si\": float(data[\"first_gap_si\"]),\n",
+    "    \"first_gap_sin\": float(data.get(\"first_gap_sin\", first_gap_sin)),\n",
+    "}\n",
+    "\n",
+    "bounds = {\n",
+    "    \"widths_si\": (min_width_si, max_width_si),\n",
+    "    \"gaps_si\": (min_gap_si, max_gap_si),\n",
+    "    \"widths_sin\": (min_width_sin, max_width_sin),\n",
+    "    \"gaps_sin\": (min_gap_sin, max_gap_sin),\n",
+    "    \"first_gap_si\": (None, None),\n",
+    "    \"first_gap_sin\": (min_gap_sin, None),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93e0619d",
+   "metadata": {},
+   "source": [
+    "## Running the Robust Optimization\n",
+    "\n",
+    "Starting from the adjoint-optimized design found earlier, we use Adam to minimize the robust objective."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "fd52c91f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iter 0: objective=0.5454\n",
+      "iter 1: objective=0.5533\n",
+      "iter 2: objective=0.5512\n",
+      "iter 3: objective=0.5519\n",
+      "iter 4: objective=0.5558\n",
+      "iter 5: objective=0.5582\n",
+      "iter 6: objective=0.5592\n",
+      "iter 7: objective=0.5587\n",
+      "iter 8: objective=0.5591\n",
+      "iter 9: objective=0.5587\n"
+     ]
+    }
+   ],
+   "source": [
+    "vg_fun = value_and_grad(robust_objective)\n",
+    "params = deepcopy(params0)\n",
+    "opt_state = init_adam(params, lr=1e-3)\n",
+    "\n",
+    "objective_history = []\n",
+    "\n",
+    "for n in range(num_iters):\n",
+    "    value, grad = vg_fun(params)\n",
+    "    objective_value = -value\n",
+    "\n",
+    "    objective_history.append(objective_value)\n",
+    "    print(f\"iter {n}: objective={objective_value:.4f}\")\n",
+    "\n",
+    "    updates, opt_state = adam_update(grad, opt_state)\n",
+    "    params = apply_updates(params, updates)\n",
+    "    params = clip_params(params, bounds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a509ddc7",
+   "metadata": {},
+   "source": [
+    "### Tracking Progress\n",
+    "\n",
+    "Plotting the objective over iterations lets us confirm that the robust objective steadily improves (higher is better)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3732b06c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "ax.plot(np.arange(len(objective_history)), objective_history, marker=\"o\")\n",
+    "ax.set_xlabel(\"Iteration\")\n",
+    "ax.set_ylabel(\"Objective\")\n",
+    "ax.set_title(\"Robust adjoint optimization history\")\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb0cbd5c",
+   "metadata": {},
+   "source": [
+    "### Pre- and Post-Optimization Bias Sweeps\n",
+    "\n",
+    "To visualize the payoff, we re-run the ±20 nm fabrication corners for the original and robust designs. This mirrors the analysis step from the sensitivity notebook so we can compare apples to apples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "90b134cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def to_numpy_params(param_dict):\n",
+    "    \"\"\"Detach autograd arrays into plain numpy arrays for analysis/export.\"\"\"\n",
+    "    return {\n",
+    "        \"widths_si\": np.array(param_dict[\"widths_si\"], dtype=float),\n",
+    "        \"gaps_si\": np.array(param_dict[\"gaps_si\"], dtype=float),\n",
+    "        \"widths_sin\": np.array(param_dict[\"widths_sin\"], dtype=float),\n",
+    "        \"gaps_sin\": np.array(param_dict[\"gaps_sin\"], dtype=float),\n",
+    "        \"first_gap_si\": float(param_dict[\"first_gap_si\"]),\n",
+    "        \"first_gap_sin\": float(param_dict[\"first_gap_sin\"]),\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def run_bias_sweep(param_dict, task_prefix, bias=ETCH_BIAS):\n",
+    "    \"\"\"Run nominal/over/under simulations and return spectra in linear scale.\"\"\"\n",
+    "    scenarios = [\n",
+    "        (\"Over-etched (-20 nm)\", apply_bias(param_dict, -bias)),\n",
+    "        (\"Nominal\", param_dict),\n",
+    "        (\"Under-etched (+20 nm)\", apply_bias(param_dict, bias)),\n",
+    "    ]\n",
+    "\n",
+    "    sims = {\n",
+    "        f\"{task_prefix}_{idx}\": make_simulation(\n",
+    "            scenario[\"widths_si\"],\n",
+    "            scenario[\"gaps_si\"],\n",
+    "            scenario[\"widths_sin\"],\n",
+    "            scenario[\"gaps_sin\"],\n",
+    "            first_gap_si=scenario[\"first_gap_si\"],\n",
+    "            first_gap_sin=scenario[\"first_gap_sin\"],\n",
+    "        )\n",
+    "        for idx, (_, scenario) in enumerate(scenarios)\n",
+    "    }\n",
+    "\n",
+    "    batch_data = web.run_async(sims, verbose=False)\n",
+    "\n",
+    "    wavelengths = None\n",
+    "    spectra = {}\n",
+    "    for idx, (label, _) in enumerate(scenarios):\n",
+    "        sim_data = batch_data[f\"{task_prefix}_{idx}\"]\n",
+    "        power_da = get_mode_monitor_power(sim_data)\n",
+    "        freqs = power_da.coords[\"f\"].values\n",
+    "        wl = td.C_0 / freqs\n",
+    "        power = np.asarray(power_da.data).squeeze()\n",
+    "        order = np.argsort(wl)\n",
+    "        wl = wl[order]\n",
+    "        power = power[order]\n",
+    "        if wavelengths is None:\n",
+    "            wavelengths = wl\n",
+    "        spectra[label] = power\n",
+    "    return wavelengths, spectra\n",
+    "\n",
+    "\n",
+    "params_initial = to_numpy_params(params0)\n",
+    "params_robust = to_numpy_params(params)\n",
+    "\n",
+    "w_before, spectra_before = run_bias_sweep(params_initial, \"gc_robust_bias_before\", bias=ETCH_BIAS)\n",
+    "w_after, spectra_after = run_bias_sweep(params_robust, \"gc_robust_bias_after\", bias=ETCH_BIAS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86af00bc",
+   "metadata": {},
+   "source": [
+    "## The Final Payoff: Visualizing Robustness\n",
+    "\n",
+    "The left panel shows how sensitive the previous design is to ±20 nm fabrication bias, while the right panel show the spectrum after robust optimization. We can see observe a slight shift in the spectra, but to make any quantitative statement, we'll not to run another sensitivity analysis, which we'll do in the next notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5d3bb559",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labels = [\"Over-etched (-20 nm)\", \"Nominal\", \"Under-etched (+20 nm)\"]\n",
+    "colors = {\n",
+    "    \"Over-etched (-20 nm)\": \"tab:orange\",\n",
+    "    \"Nominal\": \"tab:green\",\n",
+    "    \"Under-etched (+20 nm)\": \"tab:blue\",\n",
+    "}\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4), sharey=True)\n",
+    "\n",
+    "for label in labels:\n",
+    "    axes[0].plot(\n",
+    "        w_before,\n",
+    "        10 * np.log10(spectra_before[label]),\n",
+    "        label=label,\n",
+    "        color=colors[label],\n",
+    "        linewidth=2 if label == \"Nominal\" else 1.6,\n",
+    "        alpha=0.9,\n",
+    "    )\n",
+    "\n",
+    "axes[0].set_title(\"Before robust optimization\")\n",
+    "axes[0].set_xlabel(\"Wavelength (µm)\")\n",
+    "axes[0].set_ylabel(\"Transmission (dB)\")\n",
+    "axes[0].grid(True, alpha=0.3)\n",
+    "\n",
+    "for label in labels:\n",
+    "    axes[1].plot(\n",
+    "        w_after,\n",
+    "        10 * np.log10(spectra_after[label]),\n",
+    "        label=label,\n",
+    "        color=colors[label],\n",
+    "        linewidth=2 if label == \"Nominal\" else 1.6,\n",
+    "        alpha=0.9,\n",
+    "    )\n",
+    "\n",
+    "axes[1].set_title(\"After robust optimization\")\n",
+    "axes[1].set_xlabel(\"Wavelength (µm)\")\n",
+    "axes[1].grid(True, alpha=0.3)\n",
+    "axes[1].legend(loc=\"best\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a67bc8c6",
+   "metadata": {},
+   "source": [
+    "### Exporting the Robust Design\n",
+    "\n",
+    "Finally we save the fabrication-aware geometry so downstream notebooks - or a GDS handoff - can reuse it without re-running the optimization loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "64b37506",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved robust design to /Users/yannick/flexcompute/projects/seminar/polish/notebooks/results/gc_adjoint_robust_best.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "export_path = Path(\"./results/gc_adjoint_robust_best.json\")\n",
+    "export_payload = {\n",
+    "    \"widths_si\": params_robust[\"widths_si\"].tolist(),\n",
+    "    \"gaps_si\": params_robust[\"gaps_si\"].tolist(),\n",
+    "    \"widths_sin\": params_robust[\"widths_sin\"].tolist(),\n",
+    "    \"gaps_sin\": params_robust[\"gaps_sin\"].tolist(),\n",
+    "    \"first_gap_si\": params_robust[\"first_gap_si\"],\n",
+    "    \"first_gap_sin\": params_robust[\"first_gap_sin\"],\n",
+    "    \"etch_bias_modeled\": ETCH_BIAS,\n",
+    "}\n",
+    "\n",
+    "export_path.parent.mkdir(parents=True, exist_ok=True)\n",
+    "export_path.write_text(json.dumps(export_payload, indent=2), encoding=\"utf-8\")\n",
+    "print(f\"Saved robust design to {export_path.resolve()}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025-10-09-invdes-seminar/05_robust_comparison.ipynb b/2025-10-09-invdes-seminar/05_robust_comparison.ipynb
new file mode 100644
index 00000000..80ba8a2d
--- /dev/null
+++ b/2025-10-09-invdes-seminar/05_robust_comparison.ipynb
@@ -0,0 +1,426 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c382ea91",
+   "metadata": {},
+   "source": [
+    "# Monte Carlo View: Nominal vs Robust Grating\n",
+    "\n",
+    "> The robust adjoint design trades a sliver of peak efficiency for tighter fabrication yield. Building on the fabrication-aware optimizer from the previous notebook, we now quantify how much that robustness actually helps under process variation.\n",
+    "\n",
+    "> This notebook compares the nominal adjoint design against the robustness-optimized variant using a matched Monte Carlo experiment, highlighting the yield benefits of carrying fabrication awareness into the optimization loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "62860f03",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import autograd.numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import tidy3d as td\n",
+    "from setup import (\n",
+    "    center_wavelength,\n",
+    "    default_spacer_thickness,\n",
+    "    get_mode_monitor_power,\n",
+    "    make_simulation,\n",
+    ")\n",
+    "from tidy3d import web"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c5669f17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "design_paths = {\n",
+    "    \"nominal\": Path(\"./results\") / \"gc_adjoint_best.json\",\n",
+    "    \"robust\": Path(\"./results\") / \"gc_adjoint_robust_best.json\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3965c290",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_nominal_parameters(path):\n",
+    "    \"\"\"Load a design JSON (Bayes or adjoint) into numpy-friendly fields.\"\"\"\n",
+    "    data = json.loads(Path(path).read_text(encoding=\"utf-8\"))\n",
+    "    return {\n",
+    "        \"widths_si\": np.array(data[\"widths_si\"], dtype=float),\n",
+    "        \"gaps_si\": np.array(data[\"gaps_si\"], dtype=float),\n",
+    "        \"widths_sin\": np.array(data[\"widths_sin\"], dtype=float),\n",
+    "        \"gaps_sin\": np.array(data[\"gaps_sin\"], dtype=float),\n",
+    "        \"first_gap_si\": float(data[\"first_gap_si\"]),\n",
+    "        \"first_gap_sin\": float(data[\"first_gap_sin\"]),\n",
+    "        \"spacer_thickness\": default_spacer_thickness,\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def make_variation_builder(nominal):\n",
+    "    \"\"\"Return a closure that maps process deltas to a tidy3d Simulation.\"\"\"\n",
+    "    base_widths_si = np.array(nominal[\"widths_si\"])\n",
+    "    base_gaps_si = np.array(nominal[\"gaps_si\"])\n",
+    "\n",
+    "    def builder(*, overlay_delta=0.0, spacer_delta=0.0, etch_bias=0.0):\n",
+    "        # Etch bias widens features when positive and narrows them when\n",
+    "        # negative, so widths grow with the bias while gaps shrink, mirroring\n",
+    "        # the fabrication effect of over/under etching.\n",
+    "        pert_widths_si = base_widths_si + etch_bias\n",
+    "        pert_gaps_si = base_gaps_si - etch_bias\n",
+    "\n",
+    "        return make_simulation(\n",
+    "            pert_widths_si,\n",
+    "            pert_gaps_si,\n",
+    "            nominal[\"widths_sin\"],\n",
+    "            nominal[\"gaps_sin\"],\n",
+    "            first_gap_si=nominal[\"first_gap_si\"] + overlay_delta,\n",
+    "            first_gap_sin=nominal[\"first_gap_sin\"],\n",
+    "            spacer_thickness=nominal[\"spacer_thickness\"] + spacer_delta,\n",
+    "        )\n",
+    "\n",
+    "    return builder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "113c27f0",
+   "metadata": {},
+   "source": [
+    "## Shared Monte Carlo Draws\n",
+    "We reuse the exact same random perturbations for both designs so any differences in the statistics stem from the geometry rather than sampling noise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d773fdf8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sigma_overlay = 0.025  # microns\n",
+    "sigma_spacer = 0.02\n",
+    "sigma_widths_si = 0.01\n",
+    "\n",
+    "seed = 42\n",
+    "num_mc_samples = 100\n",
+    "\n",
+    "sigma_vector = np.array([sigma_overlay, sigma_spacer, sigma_widths_si], dtype=float)\n",
+    "rng = np.random.default_rng(seed)\n",
+    "perturbations = rng.standard_normal(size=(num_mc_samples, sigma_vector.size)) * sigma_vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a20de9b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_monte_carlo(nominal_params, *, label=\"design\"):\n",
+    "    \"\"\"Evaluate a design under shared Monte Carlo perturbations.\"\"\"\n",
+    "    builder = make_variation_builder(nominal_params)\n",
+    "\n",
+    "    sims = {\n",
+    "        f\"{label}_nominal\": builder(),\n",
+    "    }\n",
+    "    for idx, (overlay_delta, spacer_delta, etch_bias) in enumerate(perturbations):\n",
+    "        sims[f\"{label}_mc_{idx:03d}\"] = builder(\n",
+    "            overlay_delta=overlay_delta,\n",
+    "            spacer_delta=spacer_delta,\n",
+    "            etch_bias=etch_bias,\n",
+    "        )\n",
+    "\n",
+    "    batch = web.run_async(sims, verbose=False)\n",
+    "    freq0 = td.C_0 / center_wavelength\n",
+    "\n",
+    "    nominal_value = None\n",
+    "    sample_values = []\n",
+    "\n",
+    "    for key in sims:\n",
+    "        sim_data = batch[key]\n",
+    "        power_da = get_mode_monitor_power(sim_data)\n",
+    "        center_power = power_da.sel(f=freq0, method=\"nearest\").item()\n",
+    "        if key.endswith(\"nominal\"):\n",
+    "            nominal_value = float(center_power)\n",
+    "        else:\n",
+    "            sample_values.append(float(center_power))\n",
+    "\n",
+    "    return {\n",
+    "        \"nominal\": nominal_value,\n",
+    "        \"samples\": np.array(sample_values, dtype=float),\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a816af68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "design_results = {}\n",
+    "for label, path in design_paths.items():\n",
+    "    params = load_nominal_parameters(path)\n",
+    "    design_results[label] = run_monte_carlo(params, label=label)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7d575288",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def linear_to_loss_db(values):\n",
+    "    \"\"\"Convert linear transmission to loss in dB (positive = loss).\"\"\"\n",
+    "    linear = np.clip(np.array(values, dtype=float), 1e-12, None)\n",
+    "    return -10.0 * np.log10(linear)\n",
+    "\n",
+    "\n",
+    "def summarize(center_samples):\n",
+    "    \"\"\"Compute summary statistics in linear and dB scales.\"\"\"\n",
+    "    linear = np.array(center_samples, dtype=float)\n",
+    "    stats = {\n",
+    "        \"mean_linear\": np.mean(linear),\n",
+    "        \"std_linear\": np.std(linear, ddof=0),\n",
+    "        \"p10_linear\": np.percentile(linear, 10),\n",
+    "        \"p90_linear\": np.percentile(linear, 90),\n",
+    "    }\n",
+    "\n",
+    "    loss_db = linear_to_loss_db(linear)\n",
+    "    stats.update(\n",
+    "        {\n",
+    "            \"mean_db\": np.mean(loss_db),\n",
+    "            \"p10_db\": np.percentile(loss_db, 10),\n",
+    "            \"p90_db\": np.percentile(loss_db, 90),\n",
+    "        }\n",
+    "    )\n",
+    "    return stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "254e0dfb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_linear</th>\n",
+       "      <th>std_linear</th>\n",
+       "      <th>p10_linear</th>\n",
+       "      <th>p90_linear</th>\n",
+       "      <th>mean_db</th>\n",
+       "      <th>p10_db</th>\n",
+       "      <th>p90_db</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>nominal</th>\n",
+       "      <td>0.570310</td>\n",
+       "      <td>0.031264</td>\n",
+       "      <td>0.524390</td>\n",
+       "      <td>0.603797</td>\n",
+       "      <td>2.445684</td>\n",
+       "      <td>2.191094</td>\n",
+       "      <td>2.803462</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>robust</th>\n",
+       "      <td>0.573423</td>\n",
+       "      <td>0.026416</td>\n",
+       "      <td>0.530658</td>\n",
+       "      <td>0.600035</td>\n",
+       "      <td>2.420031</td>\n",
+       "      <td>2.218235</td>\n",
+       "      <td>2.751854</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         mean_linear  std_linear  p10_linear  p90_linear   mean_db    p10_db  \\\n",
+       "nominal     0.570310    0.031264    0.524390    0.603797  2.445684  2.191094   \n",
+       "robust      0.573423    0.026416    0.530658    0.600035  2.420031  2.218235   \n",
+       "\n",
+       "           p90_db  \n",
+       "nominal  2.803462  \n",
+       "robust   2.751854  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summary = {label: summarize(result[\"samples\"]) for label, result in design_results.items()}\n",
+    "summary_df = pd.DataFrame(summary).T\n",
+    "summary_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d4819ee",
+   "metadata": {},
+   "source": [
+    "## Distribution of Center-Wavelength Loss\n",
+    "Both designs now face identical process draws. The plot below overlays the center wavelength loss distributions in dB. Dashed vertical lines mark the nominal (unperturbed) efficiency for each design."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "828f2601",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "bins = \"auto\"\n",
+    "colors = {\n",
+    "    \"nominal\": \"tab:blue\",\n",
+    "    \"robust\": \"tab:green\",\n",
+    "}\n",
+    "\n",
+    "for label, result in design_results.items():\n",
+    "    losses_db = linear_to_loss_db(result[\"samples\"])\n",
+    "    ax.hist(\n",
+    "        losses_db,\n",
+    "        bins=bins,\n",
+    "        alpha=0.6,\n",
+    "        label=f\"{label.capitalize()} design\",\n",
+    "        color=colors.get(label, None),\n",
+    "        edgecolor=\"white\",\n",
+    "    )\n",
+    "    nominal_loss = linear_to_loss_db([result[\"nominal\"]])[0]\n",
+    "    ax.axvline(\n",
+    "        nominal_loss,\n",
+    "        color=colors.get(label, None),\n",
+    "        linestyle=\"--\",\n",
+    "        linewidth=2,\n",
+    "    )\n",
+    "\n",
+    "ax.set_xlabel(\"Center wavelength loss (dB)\")\n",
+    "ax.set_ylabel(\"Sample count\")\n",
+    "ax.set_title(\"Monte Carlo comparison at shared perturbations\")\n",
+    "ax.legend()\n",
+    "ax.grid(alpha=0.25)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff18ff1a",
+   "metadata": {},
+   "source": [
+    "## What the numbers say\n",
+    "\n",
+    "Both designs were tested under identical Monte Carlo perturbations (N = 100, sigma_overlay = 25 nm, sigma_spacer = 20 nm, sigma_width = 10 nm) and evaluated at the center wavelength.\n",
+    "\n",
+    "**Results:**\n",
+    "- **Average loss:** Robust 2.42 dB vs nominal 2.45 dB (delta = -0.03 dB). In linear scale, that’s 0.573 vs 0.570, or about +0.5% higher mean transmission.\n",
+    "- **Variability:** Standard deviation (linear) drops from 0.031 to 0.026 (-15%).\n",
+    "- **Spread (10th–90th percentile):** 0.61 to 0.53 dB (-13%).\n",
+    "- **Tails:**  \n",
+    "  90th-percentile loss improves (2.80 → 2.75 dB, better worst-case).  \n",
+    "  10th-percentile loss rises slightly (2.19 → 2.22 dB, marginally less best-case).\n",
+    "\n",
+    "**In short:**  \n",
+    "The robust design trades a sliver of peak efficiency for noticeably tighter performance under fabrication variability, which is exactly the trend we’d expect when carrying process awareness into the optimization."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45517bad",
+   "metadata": {},
+   "source": [
+    "At first glance, the numbers may not seem dramatic, but the difference is real and meaningful.  \n",
+    "Even a few hundredths of a decibel can translate to higher wafer-level yield when scaled to thousands of devices.  \n",
+    "It’s also worth remembering that the specific magnitude depends on many details of the experiment:\n",
+    "\n",
+    "- How and when robustness was introduced into the optimization (for example, from the start or as a final fine-tuning).  \n",
+    "- The starting point, optimizer settings, and number of iterations used.  \n",
+    "- The perturbation model and its assumed standard deviations or correlations.  \n",
+    "- The type of device. Grating couplers are quite resonant and inherently sensitive to fabrication noise, so they tend to show smaller relative gains.\n",
+    "\n",
+    "This notebook is meant as a conceptual demonstration rather than an exhaustive benchmark.  \n",
+    "There are many other ways to define and train for robustness, and exploring them is part of what makes photonic inverse design both challenging and exciting."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "encoding": "# coding: utf-8",
+   "executable": "/usr/bin/env python",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025-10-09-invdes-seminar/06_measurement_calibration.ipynb b/2025-10-09-invdes-seminar/06_measurement_calibration.ipynb
new file mode 100644
index 00000000..1507e57e
--- /dev/null
+++ b/2025-10-09-invdes-seminar/06_measurement_calibration.ipynb
@@ -0,0 +1,1195 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9bce848f",
+   "metadata": {},
+   "source": [
+    "# Measurement Calibration: Bridging Simulation and Fabrication\n",
+    "\n",
+    "> Our robust adjoint design is ready for fabrication, but once real devices come back from the foundry, their spectral responses rarely match the nominal simulation exactly. In this notebook we demonstrate a way to calibrate the simulation model to match measured data using adjoint optimization, recovering the as-built geometry so subsequent optimization or analysis stays grounded in reality."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0563acbd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "from copy import deepcopy\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import autograd.numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as npl\n",
+    "import tidy3d as td\n",
+    "from autograd import value_and_grad\n",
+    "from optim import adam_update, apply_updates, clip_params, init_adam\n",
+    "from setup import (\n",
+    "    get_mode_monitor_power,\n",
+    "    make_simulation,\n",
+    "    max_width_sin,\n",
+    "    min_width_sin,\n",
+    "    widths_gaps_to_centers,\n",
+    ")\n",
+    "from tidy3d import web"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "411c06f4",
+   "metadata": {},
+   "source": [
+    "## Calibration Workflow Overview\n",
+    "\n",
+    "We assume access to three ingredients:\n",
+    "1. The robust nominal design.\n",
+    "2. A measured spectrum from fabricated hardware (here we synthesize one by applying a known bias and measurement noise).\n",
+    "3. A differentiable simulation model we can tune so the simulated spectrum matches the measured data.\n",
+    "\n",
+    "The goal is to infer the effective SiN tooth widths that best reproduce the measurement, keeping the digital twin aligned with the hardware."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "93eda25c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def centers_widths_to_spacing(centers, widths, *, last_gap):\n",
+    "    \"\"\"Return (first_gap, gaps) that preserve the supplied centers.\"\"\"\n",
+    "    centers = np.array(centers)\n",
+    "    widths = np.array(widths)\n",
+    "    if widths.size == 0:\n",
+    "        return 0.0, np.zeros_like(widths)\n",
+    "    left_edges = centers - widths / 2\n",
+    "    right_edges = centers + widths / 2\n",
+    "    first_gap = left_edges[0]\n",
+    "    if widths.size == 1:\n",
+    "        gaps = np.array([last_gap], dtype=widths.dtype)\n",
+    "    else:\n",
+    "        interior = left_edges[1:] - right_edges[:-1]\n",
+    "        gaps = np.concatenate((interior, np.array([last_gap], dtype=widths.dtype)))\n",
+    "    return first_gap, gaps\n",
+    "\n",
+    "\n",
+    "def extract_spectrum(sim_data):\n",
+    "    \"\"\"Return (freqs, power) from the mode monitor of `sim_data`.\"\"\"\n",
+    "    power_da = get_mode_monitor_power(sim_data)\n",
+    "    freqs = power_da.coords[\"f\"].values\n",
+    "    power = np.array(power_da.data).squeeze()\n",
+    "    return freqs, power\n",
+    "\n",
+    "\n",
+    "def to_wavelength_db(freqs, power):\n",
+    "    \"\"\"Convert a spectrum to (wavelength, power_db) sorted by wavelength.\"\"\"\n",
+    "    wavelengths = td.C_0 / freqs\n",
+    "    order = np.argsort(wavelengths)\n",
+    "    wl = wavelengths[order]\n",
+    "    power_lin = np.array(power)[order]\n",
+    "    power_db = 10 * np.log10(np.clip(power_lin, 1e-12, None))\n",
+    "    return wl, power_db"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "560fa555",
+   "metadata": {},
+   "source": [
+    "## Generating Reference and Synthetic Measurement Data\n",
+    "\n",
+    "The baseline spectrum corresponds to the calibrated simulation before any fabrication shifts. To emulate a measured device we create a second spectrum with a uniform +20 nm SiN width bias and add multiplicative noise, representing typical measurement variability."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "99b48095",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rng = npl.random.default_rng(1234)\n",
+    "\n",
+    "robust_path = Path(\"./results\") / \"gc_adjoint_robust_best.json\"\n",
+    "\n",
+    "robust_data = json.loads(robust_path.read_text(encoding=\"utf-8\"))\n",
+    "widths_si_nominal = np.array(robust_data[\"widths_si\"], dtype=float)\n",
+    "gaps_si_nominal = np.array(robust_data[\"gaps_si\"], dtype=float)\n",
+    "widths_sin_nominal = np.array(robust_data[\"widths_sin\"], dtype=float)\n",
+    "gaps_sin_nominal = np.array(robust_data[\"gaps_sin\"], dtype=float)\n",
+    "first_gap_si_nominal = float(robust_data[\"first_gap_si\"])\n",
+    "first_gap_sin_nominal = float(robust_data[\"first_gap_sin\"])\n",
+    "\n",
+    "sin_centers, _ = widths_gaps_to_centers(\n",
+    "    widths_sin_nominal, gaps_sin_nominal, first_gap=first_gap_sin_nominal\n",
+    ")\n",
+    "sin_last_gap_template = gaps_sin_nominal[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "10ffbfde",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:35:36 CEST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'gc_measurement_baseline'</span> with task_id               \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-3617c025-51fc-4666-a8d3-53735e52ccb5'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:35:36 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'gc_measurement_baseline'\u001b[0m with task_id               \n",
+       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-3617c025-51fc-4666-a8d3-53735e52ccb5'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>View task using web UI at                                         \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-3617c025-51fc-4666-a8d3-53735e52ccb5\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-3617c025-51</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-3617c025-51fc-4666-a8d3-53735e52ccb5\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">fc-4666-a8d3-53735e52ccb5'</span></a>.                                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                         \n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=305522;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3617c025-51fc-4666-a8d3-53735e52ccb5\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=335588;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3617c025-51fc-4666-a8d3-53735e52ccb5\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=305522;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3617c025-51fc-4666-a8d3-53735e52ccb5\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=282906;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3617c025-51fc-4666-a8d3-53735e52ccb5\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=305522;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3617c025-51fc-4666-a8d3-53735e52ccb5\u001b\\\u001b[32m-3617c025-51\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=305522;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3617c025-51fc-4666-a8d3-53735e52ccb5\u001b\\\u001b[32mfc-4666-a8d3-53735e52ccb5'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=267489;https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5dbc1db53f7842e68d4466d115cd3d75",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:35:39 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task      \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:35:39 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task      \n",
+       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
+       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:35:40 CEST </span>status = success                                                  \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:35:40 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f4831516bf94614b0272c08b9a4f0ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:35:42 CEST </span>loading simulation from simulation_data.hdf5                      \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:35:42 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                      \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Baseline spectrum from the robust design provides the \"uncalibrated\" reference.\n",
+    "baseline_sim = make_simulation(\n",
+    "    widths_si_nominal,\n",
+    "    gaps_si_nominal,\n",
+    "    widths_sin_nominal,\n",
+    "    gaps_sin_nominal,\n",
+    "    first_gap_si=first_gap_si_nominal,\n",
+    "    first_gap_sin=first_gap_sin_nominal,\n",
+    ")\n",
+    "baseline_data = web.run(baseline_sim, task_name=\"gc_measurement_baseline\")\n",
+    "base_freqs, base_power = extract_spectrum(baseline_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fde907a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'gc_measurement_truth'</span> with task_id                  \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'gc_measurement_truth'\u001b[0m with task_id                  \n",
+       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>View task using web UI at                                         \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">93-45fa-8630-08c8d88953f2'</span></a>.                                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                         \n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=295206;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=64437;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=295206;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=300679;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=295206;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2\u001b\\\u001b[32m-ebfa8102-7a\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=295206;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2\u001b\\\u001b[32m93-45fa-8630-08c8d88953f2'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=77414;https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af55e77abd9b4fb3a0c53ee0f82b7176",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:35:44 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task      \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:35:44 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task      \n",
+       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
+       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:35:48 CEST </span>status = queued                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:35:48 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or             \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.     \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Terminating the Python script will not stop the job running on the\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>cloud.                                                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or             \n",
+       "\u001b[2;36m              \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.     \n",
+       "\u001b[2;36m              \u001b[0mTerminating the Python script will not stop the job running on the\n",
+       "\u001b[2;36m              \u001b[0mcloud.                                                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:35:55 CEST </span>starting up solver                                                \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:35:55 CEST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>running solver                                                    \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "507c6aac91364de4a811b9496ef41e4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:36:00 CEST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">40</span>%, exiting.                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:36:00 CEST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m40\u001b[0m%, exiting.                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:36:01 CEST </span>status = success                                                  \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:36:01 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>View simulation result at                                         \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">93-45fa-8630-08c8d88953f2'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                         \n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=277169;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=962510;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=277169;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=532958;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=277169;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2\u001b\\\u001b[4;34m-ebfa8102-7a\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=277169;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ebfa8102-7a93-45fa-8630-08c8d88953f2\u001b\\\u001b[4;34m93-45fa-8630-08c8d88953f2'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0cfdc05cdfd6435988e2598f457697a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:36:02 CEST </span>loading simulation from simulation_data.hdf5                      \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:36:02 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                      \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Synthetic measurement: apply a uniform width bias to the SiN layer.\n",
+    "global_bias = 0.02\n",
+    "biased_widths_sin = np.clip(\n",
+    "    widths_sin_nominal + global_bias,\n",
+    "    min_width_sin,\n",
+    "    max_width_sin,\n",
+    ")\n",
+    "biased_first_gap_sin, biased_gaps_sin = centers_widths_to_spacing(\n",
+    "    sin_centers, biased_widths_sin, last_gap=sin_last_gap_template\n",
+    ")\n",
+    "\n",
+    "measurement_sim = make_simulation(\n",
+    "    widths_si_nominal,\n",
+    "    gaps_si_nominal,\n",
+    "    biased_widths_sin,\n",
+    "    biased_gaps_sin,\n",
+    "    first_gap_si=first_gap_si_nominal,\n",
+    "    first_gap_sin=biased_first_gap_sin,\n",
+    ")\n",
+    "measurement_data = web.run(measurement_sim, task_name=\"gc_measurement_truth\")\n",
+    "meas_freqs, meas_power = extract_spectrum(measurement_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4578da12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tooth_indices = npl.arange(1, biased_widths_sin.size + 1)\n",
+    "\n",
+    "noise_sigma = 0.01\n",
+    "noise = rng.normal(scale=noise_sigma, size=meas_power.shape)\n",
+    "noisy_power = np.clip(meas_power * (1.0 + noise), 1e-12, None)\n",
+    "\n",
+    "target_power = np.array(noisy_power, dtype=float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7f44a851",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wl_base, base_db = to_wavelength_db(base_freqs, base_power)\n",
+    "wl_meas, meas_db = to_wavelength_db(meas_freqs, noisy_power)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "ax.plot(wl_base, base_db, label=\"Simulated (nominal)\", linewidth=2)\n",
+    "ax.plot(\n",
+    "    wl_meas,\n",
+    "    meas_db,\n",
+    "    label=\"Measured (synthetic)\",\n",
+    "    linewidth=2,\n",
+    "    linestyle=\"--\",\n",
+    ")\n",
+    "ax.set_xlabel(\"Wavelength (um)\")\n",
+    "ax.set_ylabel(\"Transmission (dB)\")\n",
+    "ax.set_title(\"Simulated vs Measured Spectrum\")\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3b8fde5",
+   "metadata": {},
+   "source": [
+    "## Calibration Objective\n",
+    "\n",
+    "We adjust the SiN tooth widths so the simulated spectrum matches the measured one. The loss is the mean-squared error between spectra sampled at the monitor frequencies, optimized with Adam while respecting fabrication bounds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d91af149",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def objective(params):\n",
+    "    widths_sin = params[\"widths_sin\"]\n",
+    "    first_gap_sin, gaps_sin = centers_widths_to_spacing(\n",
+    "        sin_centers, widths_sin, last_gap=sin_last_gap_template\n",
+    "    )\n",
+    "    sim = make_simulation(\n",
+    "        widths_si_nominal,\n",
+    "        gaps_si_nominal,\n",
+    "        widths_sin,\n",
+    "        gaps_sin,\n",
+    "        first_gap_si=first_gap_si_nominal,\n",
+    "        first_gap_sin=first_gap_sin,\n",
+    "    )\n",
+    "    sim_data = web.run(sim, task_name=\"gc_measurement_calibration_opt\", verbose=False)\n",
+    "    power_da = get_mode_monitor_power(sim_data)\n",
+    "    power = np.array(power_da.data).squeeze()\n",
+    "    diff = power - target_power\n",
+    "    return np.mean(diff**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a2b4a5fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iter 0: mse=2.998785e-04\n",
+      "iter 1: mse=2.653278e-04\n",
+      "iter 2: mse=2.277861e-04\n",
+      "iter 3: mse=1.898552e-04\n",
+      "iter 4: mse=1.532770e-04\n",
+      "iter 5: mse=1.238228e-04\n",
+      "iter 6: mse=8.893695e-05\n",
+      "iter 7: mse=6.581559e-05\n",
+      "iter 8: mse=5.027088e-05\n",
+      "iter 9: mse=3.819825e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "params0 = {\"widths_sin\": np.array(widths_sin_nominal, dtype=float)}\n",
+    "bounds = {\"widths_sin\": (min_width_sin, max_width_sin)}\n",
+    "\n",
+    "vg_fun = value_and_grad(objective)\n",
+    "params = deepcopy(params0)\n",
+    "opt_state = init_adam(params, lr=2e-3)\n",
+    "\n",
+    "mse_history = []\n",
+    "best_value = float(\"inf\")\n",
+    "best_params = deepcopy(params)\n",
+    "num_iters = 10\n",
+    "for n in range(num_iters):\n",
+    "    value, grad = vg_fun(params)\n",
+    "    value_float = float(value)\n",
+    "    mse_history.append(value_float)\n",
+    "    print(f\"iter {n}: mse={value_float:.6e}\")\n",
+    "\n",
+    "    if value_float < best_value:\n",
+    "        best_value = value_float\n",
+    "        best_params = deepcopy(params)\n",
+    "\n",
+    "    updates, opt_state = adam_update(grad, opt_state)\n",
+    "    params = apply_updates(params, updates)\n",
+    "    params = clip_params(params, bounds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "702147d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:43:36 CEST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'gc_measurement_calibration_final'</span> with task_id      \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:43:36 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'gc_measurement_calibration_final'\u001b[0m with task_id      \n",
+       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>View task using web UI at                                         \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">44-48e9-8aba-f2d4d07a85c2'</span></a>.                                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                         \n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=134535;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=53713;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=134535;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=381516;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=134535;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2\u001b\\\u001b[32m-d98330ac-1e\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=134535;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2\u001b\\\u001b[32m44-48e9-8aba-f2d4d07a85c2'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=643665;https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b9e7fe08b32409fad9beb460731d435",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:43:38 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task      \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:43:38 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task      \n",
+       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
+       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:43:39 CEST </span>status = queued                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:43:39 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or             \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.     \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Terminating the Python script will not stop the job running on the\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>cloud.                                                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or             \n",
+       "\u001b[2;36m              \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.     \n",
+       "\u001b[2;36m              \u001b[0mTerminating the Python script will not stop the job running on the\n",
+       "\u001b[2;36m              \u001b[0mcloud.                                                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:43:46 CEST </span>starting up solver                                                \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:43:46 CEST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:43:47 CEST </span>running solver                                                    \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:43:47 CEST\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe6fcbb2b4894ab292c6f847ce3de101",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:43:50 CEST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">40</span>%, exiting.                           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:43:50 CEST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m40\u001b[0m%, exiting.                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:43:51 CEST </span>status = postprocess                                              \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:43:51 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                              \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:43:53 CEST </span>status = success                                                  \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:43:53 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:43:55 CEST </span>View simulation result at                                         \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">44-48e9-8aba-f2d4d07a85c2'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:43:55 CEST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                         \n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=981539;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=846543;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=981539;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=677763;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=981539;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2\u001b\\\u001b[4;34m-d98330ac-1e\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m              \u001b[0m\u001b]8;id=981539;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d98330ac-1e44-48e9-8aba-f2d4d07a85c2\u001b\\\u001b[4;34m44-48e9-8aba-f2d4d07a85c2'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8bea8ca5877d461d9d250c4aa3fdd01e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:43:57 CEST </span>loading simulation from simulation_data.hdf5                      \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:43:57 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                      \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "calibrated_widths_sin = best_params[\"widths_sin\"]\n",
+    "calibrated_first_gap_sin, calibrated_gaps_sin = centers_widths_to_spacing(\n",
+    "    sin_centers, calibrated_widths_sin, last_gap=sin_last_gap_template\n",
+    ")\n",
+    "\n",
+    "calibrated_sim = make_simulation(\n",
+    "    widths_si_nominal,\n",
+    "    gaps_si_nominal,\n",
+    "    calibrated_widths_sin,\n",
+    "    calibrated_gaps_sin,\n",
+    "    first_gap_si=first_gap_si_nominal,\n",
+    "    first_gap_sin=calibrated_first_gap_sin,\n",
+    ")\n",
+    "calibrated_data = web.run(calibrated_sim, task_name=\"gc_measurement_calibration_final\")\n",
+    "calib_freqs, calib_power = extract_spectrum(calibrated_data)\n",
+    "wl_calib, calib_db = to_wavelength_db(calib_freqs, calib_power)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "70ab2c7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "ax.plot(npl.arange(len(mse_history)), mse_history, marker=\"o\")\n",
+    "ax.set_xlabel(\"Iteration\")\n",
+    "ax.set_ylabel(\"Mean squared error\")\n",
+    "ax.set_title(\"Calibration Objective History\")\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "75e5ecc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "deviation_indices = tooth_indices\n",
+    "nominal_deviation = np.zeros_like(widths_sin_nominal)\n",
+    "fabricated_deviation = biased_widths_sin - widths_sin_nominal\n",
+    "optimized_deviation = calibrated_widths_sin - widths_sin_nominal\n",
+    "fig, ax = plt.subplots(figsize=(8, 4))\n",
+    "ax.plot(\n",
+    "    deviation_indices,\n",
+    "    nominal_deviation,\n",
+    "    label=\"Simulated (nominal)\",\n",
+    "    linestyle=\"--\",\n",
+    "    color=\"tab:blue\",\n",
+    ")\n",
+    "ax.plot(\n",
+    "    deviation_indices,\n",
+    "    fabricated_deviation,\n",
+    "    label=\"Fabricated (biased)\",\n",
+    "    marker=\"o\",\n",
+    "    color=\"tab:orange\",\n",
+    ")\n",
+    "ax.plot(\n",
+    "    deviation_indices,\n",
+    "    optimized_deviation,\n",
+    "    label=\"Optimized (calibrated)\",\n",
+    "    marker=\"s\",\n",
+    "    color=\"tab:green\",\n",
+    ")\n",
+    "ax.set_xlabel(\"Tooth index\")\n",
+    "ax.set_ylabel(\"Width deviation (um)\")\n",
+    "ax.set_title(\"SiN Tooth Width Deviation from Nominal\")\n",
+    "ax.axhline(0.0, color=\"black\", linewidth=0.8, alpha=0.6)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "16fcbb46",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGJCAYAAABYRTOkAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAsFJJREFUeJzsnQV0FFcXx/8bd3eFBHd3h2JFSqHw4e7QFigOBQoUKN7iDkWKu7u7u0UICSHunux37lt22YQYYeP3d84mM7Mzs2/eyLtzVSKVSqVgGIZhGIbJJdRy64cZhmEYhmEIFkYYhmEYhslVWBhhGIZhGCZXYWGEYRiGYZhchYURhmEYhmFyFRZGGIZhGIbJVVgYYRiGYRgmV2FhhGEYhmGYXIWFEYZhGIZhchUWRhiG+YKIiAgMGDAANjY2kEgk+PXXX3O7SXmO27dvo06dOtDX1xd99ODBAxQULly4II6J/svp06cPihQpopj38PAQ6yxYsAC5DbVj+vTpud0M5htgYYTJMR4/foxOnTrB2dkZOjo6sLe3x3fffYd//vknt5uGqKgo8TBTfvjmB2iAoAex/KOhoQFHR0f873//w7Nnz7K83z///BObNm3C0KFD8e+//6Jnz54oTDx//lz0J12nISEhX3wfHx+Pn376CUFBQVi8eLHoI7quV6xYIfotp0lMTMTGjRvRqFEjmJmZQVtbWwgOffv2xZ07d1AQOHbsGAscBRiN3G4AUzi4du0aGjduDCcnJwwcOFC8cXt5eeHGjRtYunQpRo4cmevCyIwZM8Q0PdDzEzTwrFu3TkwnJCTg7du3WLVqFU6cOCEEEjs7u6/e57lz51CrVi1MmzYNhZGtW7eKazQ4OBh79uwRWiJlqI89PT2xdu3aZN+RMGJhYSGExJwiOjoaP/74ozjfDRo0wKRJk4RAQpqLXbt2YfPmzXj37h0cHBy+6XfoWJOSkpCbwsjy5ctTFUioD0gQZ/IvfPaYHGH27NkwNjYWqm0TE5Nk3/n5+SG/ERkZKdTzeQF6CPfo0SPZMhIk2rRpg6NHjwrh72uhc1KmTBmVtZGEJBrItLS0kNeh2qHbt29Ht27d4O7ujm3btn0hjMiv2ZTXcnaQUd+NHTtWCCKkoUlpTiNhkparAk1NTeTVe4g0WEw+h6r2Mkx2U7JkSWmjRo0ytS5dlsOHD5du3bpVWqJECam2tra0SpUq0osXL36x7vv376V9+/aVWllZSbW0tKRlypSRrl+//ov1oqOjpdOmTZMWL15c7M/GxkbaoUMH6Zs3b6Tu7u7iN1N+aH2id+/eUn19fbFuq1atpAYGBtL27duL75ydncX3KWnYsKH4yDl//rzY586dO6XTp0+X2tnZif107NhRGhISIo2JiZH+8ssvUktLS/Fbffr0EcsyQt62lNy5c0f83oYNG5ItDw4OFr/j4OAg+svV1VU6d+5caWJiYrJ2pvxQHxEfP36U9uvXT/Q39WOFChWkmzZtSvYb8v6cP3++dPHixVIXFxepmpqa9P79++L758+fi+M2NTUV+6hatar04MGD6R5nXFycWJ/6JSWhoaFiP2PGjFEs+/vvv8W1oKurKzUxMRG/sW3bNmlmuHz5smj/rVu3xPmitnt5eSXr85T9Q+earoXUlme27zPTdymhdmloaEi/++67TB2bh4eHdOjQoeK+0tHRkZqZmUk7deqkOL9y5NcB/Vc+bjrG1Nq6aNEiqZOTk9hngwYNpI8fP062v/TuoUuXLok2ODo6in6h/vn111+lUVFR6fa58vClfL/KuXfvnrRly5ZSQ0ND8dtNmjSRXr9+Pdk6GzduFNteuXJFOmrUKKmFhYVUT09P+sMPP0j9/Pwy1aeMamDNCJMjkD39+vXrePLkCcqVK5fh+hcvXsTOnTvx888/CzMEqb9btmyJW7duKbb/+PGj0ACQbX/EiBGwtLTE8ePH0b9/f4SFhSneEsmeTlqCs2fPCl+KX375BeHh4Th9+rRoT7NmzbBy5UrhH9GhQweh8iYqVKiQ7O20RYsWqFevnnDY09PTy1I/zJkzB7q6upgwYQLevHkj/GXojVNNTU2YBEgFTaYr8jsoWrQofv/990ztNyAgQHGsbm5uGD9+PMzNzcVxK5uiGjZsCG9vbwwePFiYzMh8NnHiRHz48AFLlixB6dKlhf/DqFGjhFp/zJgxYlvqW1KFkwmL2k39Te3bvXu3MEmQXwX1qzLkwxATE4NBgwaJc0img6dPn6Ju3brCX4j6gN6MyZTwww8/YO/evaL/U4P6iL7bt28fVq9enUxLcODAAcTGxopzKzcn0HVD/knUJmrDo0ePcPPmTaHtyAjShLi6uqJ69eriWqNzvWPHDqGBIKjvqP3kV0O/Q+tZW1uLN30yNxoYGGDy5MliXVqe2b7PqO9Sg653ujYz69NDmkn6XeorOr9kyqFrn84rmfSycl1v2bJF3E/Dhw8XbSaza5MmTYSPmPz407uH6Bqi/qH7j65Zusfpvnj//r34Tt7nPj4+4p6l6zMj6DqrX78+jIyMMG7cOHH90HVDx0nPlpo1ayZbn86bqamp0CRRn9D5oGucnkFMDqEioYZh0uXUqVNSdXV18aldu7Z03Lhx0pMnT4o33pTI33ro7V6Op6eneOsibYac/v37S21tbaUBAQHJtv/f//4nNTY2VrxZkXaA9kdvbylJSkoS//39/VN9u1J+K5swYcIX332tZqRcuXLJjrlr165SiUQi3haVoT5SfgtNi7TeGO3t7aV3795Ntu7MmTPFG+KrV6+SLafjovPy7t27ZMf1/fffJ1tvyZIlYt+ksZJDx0JtpTfdsLCwZG/MRkZGX7xdNm3aVFq+fPlkWh86B3Xq1BFaq/Sg64X2e/jw4WTLW7duLTQIcuiNu2zZstKsQMdjbm4unTx5smJZt27dpBUrVky2nvx87t69O9ly+l3l8/61fZ9e36UGvc3T+mlpTlKirG2QQ9oC2seWLVuypBkh7RNpKOXcvHlTLKe2ZeYeSq1Nc+bMEfcF3fdySFua1pCV8t4lzQZpWd6+fatY5uPjI7QkpLlJqRlp1qyZ4llAUNvpvJDWkskZOJqGyREoaoY0I+3atcPDhw/x119/ibckesM8dOjQF+vXrl0bVatWVczTm2T79u1x8uRJ8fZPzx96k27btq2YJs2A/EP7DQ0Nxb1798S2tB45FabmJEtalcxCb27fSq9evZLZ3ukNjdrfr1+/ZOvRcnLwpbfJzNjL6Y2RPtQ/9AZIb+etW7fGq1evFOvRWya9LdIboHJ/kWaI+vTSpUsZOhCSU2fXrl0Vy+hYSDtAocD0xqlMx44dhUZFDkWekGNs586dxZu0/PcDAwPFOXv9+rXQHKQFvW3TeVR+WyVtEh13ly5dFMvIj4PeqkkL8LWQpoHao3yMNE3XLL1tZ5Wv7fuUfZcWpAEkDA0NM9UO0sopRwTRsRYrVkz0mfx++VpIq0X3sZwaNWqI65eul8zcQ8ptIu0S9QuFTNN9cf/+/a9uD/XnqVOnRLtcXFwUy21tbYVm7MqVK4p+k0MaKOVnAZ0r2g85KTM5A5tpmByD1NmkZo+LixMP9/379wvnOlKnU44GZYfJ4sWLf7F9iRIlhDrX399fmDXINLBmzRrxSQ25kyFFPpQsWfKbvO1p22+NRpALVcqQUy9B4bgpl5PTIglVpLpOD3V1dTGoKUOCCPUhmQFIGCNosCdzRVqDXEaOxPRgpn1S3ytDph3598qQGUcZMu/QADN16lTxSasNygNbynNAgzQ5l5JZhswXdD3RoKosjJCJ6syZM2JQpIG2efPmYhAi81Bmomio3bRvai9BJhsyKZD5hkwzWeFr+z5l36UFmSEIEu4yA5nayFRIZiAS/GRKBRl0rWWFtO5VMr9l5h6iSB8yR9JLCQmXymSlTfR8oOcE3fMpoWuV7isS9MuWLZvmfUlCI5GyPUz2wcIIk+OQvZ8EE/rQQ4tyIdCb49eEkcpDDCmKpHfv3qmuo+zz8a3Q4JRyEE5Ps0JvVSQkpCS1ZektVx4svgZ66NPDWPmNm/qMNFRkQ08NOheqRPmNV/77xG+//SY0IalBwkN6kK8DaX5Ig0FvvjTglSpVChUrVkw24Lx8+RJHjhwRUSYkjJHPEQ148vDt1KC35cOHDwu/h9QGWBKCKCrsa7RpWe37lH2XFnTsBPlnVKpUKcP1STtIggj5U5H2kYReOh7q1+wO203tHqL7hPqFtGYkRNLxkB8RCUrki5RTocSqvv+Yr4eFESZXqVatmvhPTnwp3yRTQiYHekOVv12SapoeZim1AimhN1tyXqQ36LTCE7MywMjfoFJLikVaAmUVcW5AJh4ynyj3A81n1F/pOSHT2z0NEMqDyosXLxTfp4e8P+gcZLUNlEeD1O1kqiFHSDL7yJ1FlaEBjbQl9CFNHDklkyBBmqK0wkBJy0KCCDl0kjlIGRJupkyZgqtXr4rfTYu0rqNv7fu0aNWqlRhISaOTGSdWyplCwvvChQsVy+iYU7uGM0ta96pytta0ICGK1qVcKGTClEOmt6zeo/R8oOcEnbOU0LVK125KTSST+7DPCJMjnD9/PtW3DLldOaVKlfxLlG3YpFY9ePCgULnTw5c+pLKnt16KiElNVSuH1iM79LJly75YT94muWf/1z6UaZCh6Bca8OTQGzm1NzehBzw9jJU1BuSrQf1KfiUpoePOyD+FTD++vr7JfDZoG4p8IB8VihZJDysrKxHNQJqNlMJnynOWFjSQkFmPNBgUVUG/r2yiIcgPIqUmjkyAdK5JIE0LGtBJYBoyZIj4DeUPaXPoGMlUkx4kBKV2DX1r36cFDaqUR4Z8JFLLZEyCIwke5END0H2T8j6k7UiozyoUzaTs60PRMCT8k6CUWY2EcptomiJyUiLPSZLRPUr7pOcEPS8oMkYORd+RdouESbl5i8k7sGaEyRFIPUx2XArPJFUsDd4UYkgDmzxttTIUUkmqfOXQXkJZzT537lwh5JCzHD2QacAhdS8JMeQzQNMEvXFR+OHo0aPFg5Kc08hRjtYZNmyYcIwltThtT+0hlTmFUlIbMgpDpmRY9LZJYcc04JB/Cg1qJKTkFDSQ0W/KBx96AFMGVppWNn1RaCrZ5Sncl1Tg5CBM/UBvp3QMtF1KjUBKJz8SJGjbu3fvivNG25G2gEIhM+NESRk0aTAoX768OGc0+NMgQQM1DZjkS5QRJHzQAErHRvuR+6zIoYGIHG3JR4RCSym1Owmi33//fZptpLBRupboeksNugbpeiRz4t9//51m26hPSbMya9YsYXIiAYwcb7+179ODhA267qjtpN2h3yCNHfliUHtJGyAPe6bvSIgj8wxd79TvdB9k5JeUHnScdE7JOZV8eehaoP2lZZJShp4FdK+QsEcCDQkJ9IKRmq+G3KGdjpPOBQkd8uNKCfU/aVeoXXSPk78KXbvUPnKeZ/IgORS1wxRyjh8/LpJllSpVSoSBUthdsWLFpCNHjhSJtNJKeiZPUla5cuVkYYZyaFtalxImaWpqimRmFD66Zs2aL8IHKVyzaNGiivUo0ZJy6N+1a9dEcixqW2pJz9Ji4cKFIpSW2lm3bl0RkpxWaG/KUFB5aOHt27eTLaffpuUUcvy1ob0UFkp9cObMmS/WDw8Pl06cOFH0PR0nJXmisNoFCxYkCzlOLbRX3t+UZI62o+0pTJeOQRnlZFipQX3eq1cvcQ7oXFDftWnTRrpnzx5pZqAQTDrf9BuzZs364vvVq1eL8E0K0aVzQsnFxo4dK5KjpXcOaX9nz55Ncx1K7kbrUIK2tM6nr6+v6DcKIU2Z9CwzfZ9R36VFQkKCdN26ddL69euLsHbqVzqHdK6Uw34p8Zr8/NF92KJFC+mLFy++CFH/2qRn1H90Tqi/qQ0PHz5M1r707qFnz56J0FpqD7Vr4MCBYnvat/K1RcdIzwtKDEhhv5lJekbHR/ulRGaNGzcW93hm7r/Ujp/JXiT0J7cFIoZJaRumBEqpmVUYhmGYggf7jDAMwzAMk6uwMMIwDMMwTK7CwgjDMAzDMLlKvhBGyNOcip9RVkKKeiDva/KkVw6nTA2KnyffA/LsprA8CvEkz30mb0NuTOwvwjAMU3jIF8IIhaZRmCKFZlF9CEohTqGLkyZNSnc7qjxK+QgovI3qZlD4nrwiK8MwDMMweYN8G00zf/58Ec9P5dJTg2oaUCY+SnJDSYvkQg3lJKDYeio9zzAMwzBM7pNvk56RsEGJqdKCkjJRtkXl9MuUYIcKIqUnjFBSHPrIIY0MJc8iU09WU4YzDMMwTGFEKpWKQo52dnap1vfK18IIVdOkDIwLFixIcx1KW01poKk0tjKUkZG+SwuqaJleMS2GYRiGYb4OKpGRXuXzXBVGJkyYgHnz5qW7DqVyllemJChlMKXe/umnn0Q6aVVDhbQobbiyBoa0KVT4TFX1DEjbQrVSKP1zepIikzm4P1UP96nq4T5VLdyf+aNPqRo2FdHMqFxErgojY8aMEXUa0kO58ik5oDZu3Bh16tTBmjVr0t2OalNQtA0VVVLWjlA0DX2XFlSDgj4poX2oUhihttE++Sb6drg/VQ/3qerhPlUt3J/5o0/l+8nIzSFXhRFyMJWXg88I0oiQIELFkjZu3JhhR9F6VKr87NmzIqSXoCqmVDyqdu3aKmk/wzAMwzDfTr4QJ0kQodLjZC4hPxEqNU5+H8q+H7QOmXOoKitBVSkpNwmZXKgaJzm0UmVYEkQ4koZhGIZh8g75woGVSkGT0yp9UjrAyCOTKXKGNB9Upl4O5SMhDQppRihChspOy0vRMwzDMAyTN8i3eUZyCnK+IS0LObKq0mfEz88PVlZWbOtUAdyfqof7VPVwn6oW7s/80aeZHUP5DDIMwzAMk6uwMMIwDMMwTK7CwgjDMAzDMLkKCyMMwzAMw+QqLIwwDMMwDJOrsDDCMAzzlURERiM2Ni63m8EwBQYWRhiGYb6CqKgYXL12HzfvPFHkOWIY5ttgYYRhGOYr+ODrL/IxREZEITIyOrebwzAFAhZGGIZhvoKg4FDFdEBgSK62hWEKCiyMMAxTqAkKCoWb+3vEJyRkan1DQ33FNAsjDKMaWBhhGKbQEh0dizv3nuLlKw88evwqU9uUKlEUdetUFtPBIWFISmK/EYYpFIXyGIZhsoPnL92QmJgkpou7OmV6O0MDPVSpXAZmpkZQU5NkYwsZpnDAmhGGYQolfv5B+PgxEBI1idB0GBkZKL7z9PogwndTEhxMmpAkSCQSWFuZQVOT3+cYRhWwMMIwTKEjMTERz56/FdNFnO1gpOQHEhYWgecv3PDylXuybSKjonHj1iOcu3hLCCQMw6gOFkYYhil0vHV7L/xFdHS0UcwluXlGTV1d5A/x8wtCSGi4Yrm/f7D4b2igL8qr0zqv3nji+s2HiI2Lz/FjYJiCBOsYGYYpUMQnyqJiNNTUhTlFQUwoEPAaCHiDBK8wIFQdpTXfQGPnIiAuSrGagUQCO2kZ+Egc8Pr4XVS3jgX0zREQagXEa8PSXCa80L5JYAkPj0RgYAjsbC0V+3jz1gvv3n+ASxEHODvZJm8HwzBfwMIIwzD5Bv+wQLz84AEPPy94BHjD3e89PoT4ISwqAqHR4eJ/QmICmtnWgHb4I5RL9EA1zWiU0IiDheSz9qIMACd1U+gnyrQdKSmmfhcfzHsiIEyCkBe7YJgQgEDLwYBEHRb3RgAmJoB1GVgY1EN4vDUC/f0VwggJJq/feIppMvf4fPBHhfLFoaerk0O9xDD5DxZGGIbJkyQmJeKh53PcfvsY9zyewuO9Dx76PlN8ryFRR4I0UUxLIEUVjWi00gpHebMSiDTSh8SkNuxizFAq4jJi1YzwUK8qrGPfwiZW5itikIYgQugnhsIu+gW8dUvjtUEtFI26jySJOrSTImEY5wv40ecFLLTuwN2kPQI8z0F67SESXJvhUUIlQM0QZmYmwv+ENCcSsGaEYdKDhRGGYfIM4dGROP/sBk4/voqzT64hKEKWVMzJwB51barD1M4Yl3xuoIplBZhrmyDS7xTaagahqrE9nBPDYJgYhPjYu3gdpQVPvQrw0SmJj9quiJZoIVKqhtuwwfugCLxP1ESwVB1BSerif4RU5j4nFxk0IUUdHW1Ut3BCuHkpxOq3ACLiYJnoDolaaSDIDUiIhWmcN9SkiYiR6CHyw1u4hZkgRicSuohBVasQJJRqixDLGtDX11U4vXI9G4b5EhZGGIbJdR+Pc0+u4b9rR3HmyVWFz4ccAw091LCqDHU1NZibmGCUazs4x+hBLzYcVjoV4BT9CPeNG+ODNA51A3dATxqBMkZhsLNXx1P1CgiTmEBLUwdO1hawc7BErHQS3gV+wNuPnnjr+w5vPnriidcr+IUFJvvdI+/d4RdrDAcDW+hp6sLcwBThJerApdZE6GtqAX7PoO51G6avAhEYkQh/7SJIlGiKbSsEH4GG3wdoPNkNGy0DoPT3QLkOCFcrBjcPX1SpVAY6Olo52s8Mk5eRSFlMT5ewsDAYGxsjNDQURkZGKtknvSH5+fnByspKeOUz3wb3Z/7sU7eP7/DvlYPYc/M4/MOCvvheX1sPjcrUQC3zajDWNIS9gRpq+2+D2vPDCJGY4Zbpj0iUfHqfkqjDwTAR5cqXgaRUS+FwStDjzT8gWGgm9PV002wLrecd/BF33Z4Ik9D1V/fx6N0LaKlpIkmaBCMtQ9jqWeFVqBvU1dXRtFxt/FCtOZpXqAfv934ig6uloQaqar5C+KtrMHI/KnOYVf4NAJesBiDSrCz0bFxQvU6tdNvEpA/f9/mjTzM7hrIwkgEsjOR9uD/zV5/ec3+C5ae24tiDi1+YLKyMzNG2ahM0L18PtYpXhrubN9yfP4Fm0EvU9VgE3aQIxboBmg64Y98fUmMnWDgXQ9VqFVXa1oDwYFx6fgvnn93EuafXERj+pY+Jmb4xulRvg3KGpVHc2QkVypWQfUHaHfdLwKO9wLPDQIzM3BSlboTbJj8gSt0YWvrGKFO2JIzKNYWevj5H3HwlfN+rHhZG8jAsjOR9uD/zfp/SY4Z8Qf45sQXXX99P9p2mugaaV6iP/9Vpg8ZlakJDXabtCHt5FVcv3wSiAlEl9CisY91kG+hbAFV6AtV6IUhigYCgEBQtYg9NDY1sdaa9/voBDt09g2P3LwhBRRkSIyoXLYv+jX5C26pNoaUhM9cIEmKBN+cgvbcNeHEMcdDCbZP2CNewkH2vpQ81syKo0agpTK2ss+0YChp836seFkbyMCyM5H24P/N2n95zf4qZ+5Z9IYRYG1tgQJPO6Fa3HcwNTD5/EeQBnJ6OZ+6BwgnVJvYNKoceB4wdgQa/AlV6AJq5Z96g0OFrr+5hx7UjQjCJTYj74rj6NPwRPet3gIWhabI+DXB/CguvU0i8ux2v420RrGmLCA1zEanTOHI/dGr2BGoPAXQ/b8ekDt/3qoeFkTwMCyN5H+7PvNmn5BMy5+BqHL53NtnyYtbOGNa8OzrWaAltcgSVExMGXFwAXFsBJMbBX8sJ73TLw1k7GBb1egIVOwPqShqHPEBwZCj23TqFbVcO4pn3m2TfaWtooUud7zGieU84Wdgl71Na4c0Z4MoySN0uCrONXmKI0LAkaZvgSdnxKFK/A4ws7cS+wiOi4O39EaFhEShdyiVZ+vrCCt/3qoeFkTwMCyN5H+7PvNWnUXExWHJsI1ac2oqEJFkeEKKopQMmtB+CtlWafLnP58eAw6OBMJ/Py/TMgaaTgWp9gE+mm7wKPUZvvHmAded24viDS8LpVY66mjp+rNECI5r3gLGa3pd96vMQuPoP8HgvkJSIN/o18Fq/JtTVgCKONgi0qImQiDhhCypT2hWO9tZ8nfN9X+CEkbx9hzMMk684//QGJuyYD88Ab8UyMlWM+b4/6jhUQ0R4FBISEqGl9elBF/4RODoOeLL/8040tIHaw4CGowEdY+QHyPm0dvHK4vMuwAfrL+zG1ssHERkbJfxNdt84JqKGmpepi8mdhqOEbdHPG9tVBH5aBzSbClxcCKf7uxGkaY9ALQe89fQF3h+HxMIVlsUqwtBAjwdepkDCmpEMYM1I3of7M/f7lEJzp+5ajAN3TidzTB3evAdGtOgJXS1dnDp9TSw3MjZArWrlof7oP+DYREWkiaB4M8S0+AvvwjVhb2+Vr0NfyYSz7twurD+/GyFRYYrlahI1dK7dWghojua2X24Y5IGkCwvx6rUbgjRsYRv7GnYxL6BtbA00ny7ylSRJpUIAKswROHzfqx420+RhWBjJ+3B/5m6fnnp0BaP/nZ0swoTCcv/qNi6ZBoAq4F6/8RBIioeN/2VUerXgc5J0PTOg9TzhF+Lm4S3ydpiYGKF2zQrI70TERGLTxX1YeXobAj9llJULa70adMCo1v2SOboqCHIHzswEHu1Jttjb8Qe8du6GctVqwsKi8Dq68n1fsIQRPoMMw2SJyNhojNs2D71W/KYQRCjvxpJeU7B/9IrkpggAJsaGqGGXCIn7ZfiGxOKlQV3ZF+SY+ssdoFIXkRjsvY+fWOxgb4WCgIGOvtAO3fhjD4Y36Q4jXQOxnDLNktak9tRO+OfkFsTExybf0Kwo0HkDMPQCULSBYnFYcCCiX13B6xMbIQ2X9RXD5HdYGGEY5qt54Pkc383uhS2XP/t6fFe+Li5O2y7yhXxhPiBH1rN/wnxXe5QPOiwWuRvUhGeLjTJ/CcodAiA0NAKREVFQU1eDjfWnPBwFBH0dPQxs8BNuzNyDX1v1ga6WrIpveEwkZu9fgbrTumDvzROKGjYK7KsA/Q4DPXcDFsXhEnUXatIEhAQFImB5O+D6Kln/Mkw+hoURhmEyDVl1/718AO3mD4Kbn5dYRoPq/O4TsGXYAlgaydKwKxMd/BFXNs3By1uXIJUmwT7mJYrrBgFF6+J1vH2ywfe9z0fx38bKHJqaBdO/3kTPSEQVkVDSvW474UNCeAf5YvjG6Wi3YDAeer5IvhEJdyVbACOuQ7vFFDjFvxaL32iWg5QcgFc1AbzvZfjbMTFxePbCDfHxyev/MExuw8IIwzCZIjouBqP+nY2x2+YiLiFeLKvkXAZnJm9Bz/o/pO5M6fsEAVsGITwiGkFa9pDQwNt0Clz7/QOnoi5wdLRR2KYTE5Pg5eUrpu3tC34mUkqOtrDnJJydsgWNy9ZSLL/j9hgt5/bFb1vnfJHpFRpaQN0RcOm7FGrGdgjRtEaAlhPgc18mkBwZK8vXooSyW6C6uhqCgkLxxk0mSDJMXoGFEYZhMsQzwEdoQ/67dkSxbEDjzjg0djVcrZ1S34gcL1c3Q0Cstpi1QBDQ9xDQeBwk6hooW8YVxV0/b+v9yVdER1cb5mb5I6RXFZS2L4YdI5eIDyWEkwsQW68cRN1pnbHp4l4RHqyMtrkDnGq2Apxq4YlVR0SrGQKU2+TGamBpdZG3hfbh4emDy1fviXBqIjgkDOERkXjv/RGJiWzaYfIO+UIY8fDwQP/+/VG0aFHo6urC1dUV06ZNQ1xc8jTMKWnUqJEi/E3+GTJkSI61m2EKApTGveWcPnjs9UphllnZ/w/M6jI6eQ0WOfQmfmYWsKsfpPHRCNRyFPlCLH6aB7h8dsQklD32yVyjqaUpBJTCGLJK2pFzU7diWseRMNDRE8tCo8JF3pbv5w0UVYSVcXVxhL6FHWKKNkNI3cmfU+SHf0DStq54um0Gnj99gcjIaPh88BdfWVqYQldHBwnxCfD5EJDzB8kwaZAvjLIvXrwQD6rVq1ejWLFiePLkCQYOHIjIyEgsWLAg3W1pvT/++EMxr6cnu8kZhsmY3TeO47dtc0TkB+Fi5Yj1g+eitL1r6htQUbj9I4CHO8VsqIYl4k2LQcO+PIwdiqX7W0Wc7cSnMEPC3dDvZKnyZ+5fLpKlEQ88n6HlnH7o16gTxrcbDENdfWhpaaJ6tXLC6dfGuj5Q43vg0CgkvLmI+8atEBAUD4ReRqmyZeBoX0fsh4Q8J0cbETr9zuuDiFgqjIIfk/fIF5qRli1bYuPGjWjevDlcXFzQrl07/Pbbb9i3b1+G25LwYWNjo/ioKlcIwxRkSMW/4tx2/LJlpkIQoTf3ExM3pimISKOC4L1pGKIfH5UtkEgQWHM8YFMeZubmnAviK7AyNsc/fX7H/jErFSHSlGJ+3fldaDDjfzj58JJYpqujDRvrT07Dps4I+2EzrlRZgQDdYlCTJqJK4D4UPdMfkv96AhEy7YjDp3TyYWERotYNw+QF8oVmJDUogYqZmVmG623btg1bt24Vgkjbtm0xderUdLUjsbGx4qOcsIUgzcwXIXdZhPZDD3tV7a+ww/2pWmLj4/Drlpk4ePdzgbve9TtgZudR0FDXSL2fgz3xascMuCfZw9KwCapEnQY6roF/VFFIg0NhbmZU6M9PVq7Tmq4VcWriJqw59x8WHd0gcpF8CPFH75Xj0KZyY8zqPFoILkRMTCyu3HgA6NlBq7g1qvr/B1N/d9mOnh2G1OMapO0WQ6NMO1hbm8Png5/wKalQrrji96h95E9iaKgv8sLkZfi+zx99mtl95csMrG/evEHVqlWFiYbMMGmxZs0aODs7w87ODo8ePcL48eNRo0aNdDUq06dPx4wZM75Y/urVKxgaGqrs5JAwRVnp+G3x2+H+VG0is1H//Ylb7o/FvAQSjGnRF91rtU1TnR/q+QyGl2bBMMId18y6IFFDF0XLV4NJ8Spw8/BBcEgEypUuCh0dpQq9hZBvvU69gz/iz6OrcfXN5xBeQx19jG7eBz9UbobomFg8f+kJDQ11lCzuBB1tLWi/OQnjC79DLeZzVE50ibbwrjoej9/6Q01NgqqVSoptkpKkeOP2HoFBYdDUUEeVSiXy9P3E933+6NPw8HCUKFEib6eDnzBhAubNm5fuOs+fP0epUqUU897e3mjYsKFwTl23bt1X/d65c+fQtGlTIcyQE2xmNSOOjo4IDg5WaTp4f39/WFpa8k2kArg/VQOlKu+5fIxIaEboaGpjZb8ZaFExudOpMlLv+7hyYBuipLooF3YOccZF8bJIH2joGaJencpiQKRHDPslqOY6pb7cf+c0ft+9BEFKqeXrl6qOBd0nwN7U+suaNRH+kBz+FZLnRz+3xcgObvWWwKpcPRgY6Imw6oePX8LPP0ixTs3q5WFqknfN2nzf548+pTHU1NQ0bwsjdNCBgYHprkM+IlpasjcqHx8fIYTUqlULmzZt+urOIodXAwMDnDhxAi1atMjUNlybJu/D/fnt+AT7ocvSn/Ha10PMm+gZ4u+uU9Csav20+9T9Kvx2jsFdvabQkMahseYdqPXchRtPvYVTpZWVGapUKs2CSDZcpyQ4Ttu9VFQClqOvrYcpHYahd4Mfv9w/PebJqfjIuOSFCesMR0KTKbj3xB2BgSEi861LUQcUcbLL80nn+L4vWLVpcvVqI+mLPpmBNCKNGzcW5hlyZs1KRz148ED8t7VNpVImwxRS3D6+w09LfxYZQOXJuHaMXAwzjXTMki9PAjt6wt2glZh11IuBRs8DIoS3fFk9XL3xAH5+QSKk1N6uYNSYyUuYG5hgWd9p6FD9O5GEjoTJyNgoTPxvAQ7fO4fFvabA2UIpMokEwkr/A4rWB/YNA96eF4ul11bgoWcUAu1bQl3fBFUrlylUOV6YvEO+ECdJECGNiJOTk/ATIY2Kr6+v+CivQ+acW7duifm3b99i5syZuHv3rshTcujQIfTq1QsNGjRAhQr5vxIow6iCN76e6LBomEIQKWrpgENj16CUXRqhu8TLE8D27giDAYK0HCDRt4Bzlz+EIEKQ8yPlwCAePX6FRHYwzDaalquDC79vR4967RXLrr26h8Yzu2PLpf3Jsq8KjO2B3vuB1nMADW1EqRtBPdwH6h6XUEP9McxNk7+5RkfH5NShMIWcvK2H+8Tp06eFnwd9HBwckn0nv9ni4+Px8uVLREVFiXky7Zw5cwZLliwR5hny++jYsSOmTJmSK8fAMHkNMsl0XDQcfmEyU2kZ+2LY+ctSUV8mTQ940ohs7wEkxsHDqBJgaAvrSo2ha5S8lL1rUQdR/0RPVwfqrELPVqgK8IIeE9GualOM+vdPIVhGUUXl7fNw7MEFLOwxCfZmSun16XzUGQ64NobW7iHQCw9BncDtMDgTDLw7Bfy4EvE6prhz96kI/W3csAa0tVJJbscwKiRfRtPkJOwzkvfh/vx6Xn1wR8fFw+EfJnNYLOdYArt++QdmBsZp96kQRLoLQSRWTQ/nXSZDalsJtWtWgolJ3g4DLSzXaXh0JKbvWYptVw8lE1ZmdxmDTjVbfum/Q0nqTk0Hri3/vMzACui4GteDLBASEo7ixZ1R7JOmi4iIjIK3tx9cXBygqZF777N83xcsnxE+gwxTyHjp444fFw1TCCLlHUtg96+fBZFUeXVKIYgQsaV/hFHJujAxMWJBJA9BmVmp+N62EYtgYyzzxwuLjsDITTMwYM0k4fiaDA1tmcmm115A/5P/XoQfsLkDnHxP0PAkihdS2K8cPV1dBAWH4dHj11+agRgmi7AwwjCFzFm105LhimqwFZxKYdev/8BUPx1B5O3FZIIIyv8Io85/o3atyqhWpWwOtZz5el+SbUIbIufo/fNo/Ed3nH1y7csNSnwHjLwOFG+mWGRzey60PC8jJjwEnu98FKY70oxQgjU/v0CRNI1hVAELIwxTSHgf5IufloxUaESEIPLL3+kLIl53gG3/k6nziXI/Ap3WAeoaQuWf18M/CzMm+kZY1nc61g78E6b6MvU4+Qd1XzZa+JNExaVwTiXzTM89QMvZgLom1JEIx8BLgMdVvLh7XdSzIYwM9YWJhqBlQcGhOX9wTIGDhRGGKQT4hQbip8UjRBZPgurLkLMqDVhpoRHwEpKtnYC4SNmCUq0R98MKvPH8IJxTmfxB26pNRMRNk7K1Fcso0qb57N6KBHcKyE+g3khg4CnAtAgcox9DkhQHeN9H2P1DSIqVBQg4OdjA1tZSmGkePHqJ2Nj0K6gzTEawMMIwBZygiFB0Xvoz3P3fKyrvZqgRCXSD6aG+kER/8jEo2gDosgkeXv54/doT9x6kGMSYPA3ljiE/knndxkFXU1sse/PRE23mDcDS45uQmJSYfAOHqsDwy9At2xxVQw6jfNgZ1Hg6DWrrWgCBb4VWrFyZYtA30ENsTJwQSNh/hPkWWBhhmAJMZEwUuv0zCi983op5ezMb4SNC4btpEuYDyeb2UI8KkM07VAN67ECcVB0e72Q+As5OSgm1mHwBCRCUnfX05C2o6FxaLEtISsScg6uEQ7NX4IfkG1DemM4bYdlqDBwS3CGBFPjwEFjRAHi8V9SzqVKxFNQ11BEUFAp3D+/cOTCmQMDCCMMUUOITE0QExQPPZ2Leysgce379Bw5mNmlvFBMKbO4ISYiXmJValQF67QG0DYWzYmJCokhqZm2VccVsJm9SzMYZR8atxajWfaEmkQ0BN988RNNZPXHg9unkK1MocPU+wJBzgEUx2bLYcGBnX+DwaBjoqKN82eKwsDCBo0M61xXDZAALIwxTAKHIh1FbZuP8sxti3ljPEDt/+RtFrT7ni/gCclLd1h34+FQ2a+QAae99gJ4Z4uLiFVqR4sWcuN5MPkdTXQPj2w3GgTEr4WhuqwgBHrJ+KkZsnCHylSTDphww9CJQsfPnZTfXAWuaw1YrXERVsTMz8y2wMMIwBZBZ+5criqhpa2hh89D5wmk1TShsc+9gwP2SmJXqmSO47XrAUPa2Syp4oRUx0oeVJWtFCgo1ilXE2Sn/omONz4VD6bppOrsn7ro9Sb6ytiHQaS3wwz+Aho5smc99YbaRVwQmvxHPdx8QESFzdGWYzMLCCMMUMFaf2YEVp7eJaVLDrxowE7WKV0p/o5NTgMf7ZNOaupB234lE06JiNiwsAu6eMn+A4q6sFSloUIbW5f1mYHnf6TDU0RfL3gX4oN2CwVhybGNy51Y699V6A4PPAuaun01727sBxyfB08MLz56/xZ17TxEbF59LR8TkR1gYYZgCxOG75zBtz1LF/NyuY9GqUsP0N7q2Ari6TDatpi6iZuBYTfG1rq4ObK0tYG1tzlqRAkzHmi1xZsq/qO5SXsyTEDL30Gp0opDwIFlIuALb8jKzTbkOn5ddXQa700Ogq5GI6OhYXLpyF89euCE8PIXJh2FSgYURhikgkFqd0n7LGfN9f/RqoDRYpMaL48DxiZ/n2y0BSrVKtgr5AlSsUFJ8WCtSsHG2sMP+MSvFtSN3br3++r5wbj12/0LylXWMZIJrm/kiSRqh9e4Kqt3+BXoJQUiIT4Cnpw+uXLuP6zcfISAwRSp6hlGChRGGKQB4Bvig18qxiImXZUrtUvt7/NZmQPob+TwEdvUjQ79svtFYmQr+E+S0qpw7gqvvFg401DUwtu1A7Bu9Avamsmq/IVFh6Ld6gsjcGq2cuZWE01qDgYEnAWOZc7RBhBsa3OiJakm3RNQVCbAhIWF4+OglEhNT5DNhmE/w04Vh8jkhkWHosWwUAj/Vm6lbsirmd5+QvhYjzAf4t7PIrhor0YV7qWHwKjUQYeGRIhInLj4ej5+54f7DF0IoYQof5GdEzq1tqzRNlrm15dx+eO4ty1ujgHLRDL8ElGguZiVSKSyvTkeVR5PRqJorypR2RcP61aCurp7Th8HkE1gYYZh8TFxCPPqvmYjXvp5ivriNM9YPmgMtDZnaPDViw4Phv3UEEC5LciV1qIYXFq3x5OkbXL12H6fP3cC16w8RF5eAiMhoLs9eiKFyAWsGzsLCHhMVmVtf+rih1dx+2HRxb/Ksq3rmQI9dQLPfgU8mHrw+C531TeAseS+SpDFMWvBThmHyKTQQTNwxH1df3hXz5oam2Dpicbr1ZiiE123fX/CI0kMi1EX9EZ3um+DgYAszM2MxYCQlJiEmNlZo4CuUK86DSCGHNGzd67XHyUmbUMZelviMzIETdszHgDUTERypVCiPBNdGvwF9DgL6lrJloe+BdS2B66sgTUriukZMqnCWGobJp6w7twvbrh4S0zqa2tgydL5wQEwP6ZlZ+BgUiVhNBwQZlIJlz42AgSXKl7NUCDhRUTEICQ1HVFQETIwNc+RYmLxPCduiODZhPf7Y+w82XNgjlh29fwEPPJ5jRf8ZqFlMKXzctSEw/Aqwsw/geR1IjEfIyYV48iocWqWaokbNKrl3IEyehDUjDJMPOf/0RrIQ3sW9JqOqS7n0N3q8F+HXNiJaXSZgmHaYCViV+uItWF9fF7Y2FjA00MuexjP5FhJ6//zfb9g89C+YftLAUSXoDguHYeHR9clzkhjZAv2OAHVHilntpCiEh4Yg8P5JRHg+zq1DYPIoLIwwTD7jta8HBq+bgiRpkpj/tVUfdKgucxxME+/7wN6h8NOWJaqyKFISGiWb5URzmQJIi4oNcHbKVtQuXlnM07U4//BakZPEJ9jv84oU8ttqNtBtG3Q1JbCKdQfiIvBu9zTg4e7cOwAmz8HCCMPkI8g+32v5b6KOCNGqYkOMazso/Y3CPwLbugIJMfio7QIYO8CqavJcIgzztdiZWmHPqGUiDFg5J0mzWT1x6tHl5CuXaQsMvQAnPVmaeG9NVyTsGQwcHiOricQUelgYYZh8QkJiAoasmwp3//dinpwJl/Wdln60Cz3ot3cXobzRagYIMywB2JSFlZV5zjWcKbCoq6mLBGmUk4SEEyKIBOYVYzFl5yLExsd9XtmiGCwGbIOeiSUSJFr4oF0SuLlW5tz6qUo0U3hhYYRh8gl/HliJi89vKSJnNg+bD32dDPw6jowDvGTb+JlWB+wrw9TUBNpaaYf+MkxWc5K0rNhAsWzd+V1o89cAvP34TrFMoq0Pp/qdAJvyeG7UCCIw+P1dYEV9PL90FDExSsILU6hgYYRh8gEHbp9WFL/TUFPHukF/Kkq/p8ntTcCdjbJpDR3oNJ8Acytr2Fhb5ECLmcKGqb4xNg6ZJxxcqVI08djrFb77szd23TimWM/e3hpqZk7QLtkI0k/FGBOjQuF/9xheH1sLKDvBMoUGFkYYJo/zxOsVRm2ZpZif8dOvCsfBNCFtyJHfPs+3XwrrsrVRo1o5FHFOP/yXYbIKRWP1a9RJhAAXs3YWy6Jio/Hzpj8wYuN0RMREQktLE2VKucLEtgjUhl0ASrVGmKYVItVN4OPlhbhNnYHIgNw+FCaHYWGEYfIwQRGh6LtqPKI/1Zz5X5024mGfocPq9p5A4ieVd+0hQOWuOdBahpFR1qG4SJLWtU5bxbI9N0/gu9m98dDzBRwdrEXhReiaAt13wKTxUBgmBCBJog4v3yBgeT3g3c1cPQYmZ2FhhGHysMPq4HWT4RUoS9teuUgZzO06Nv2aM4nxwH+9FaneUaQu0HI2AgKC2R7P5Cj62roi/83K/n/A4JNvEzlfkx/J6jM7PqeSl0ggafArijTsDGho451uBSSFfQDWtQKuLf9cyJEp0LAwwjB5lHmH1uDyizti2sLQFOsHzxVJp9Ll5FTA85ps2sgO+N9mJErUce/hC5y/eAvh4ZE50HKG+QzlwDkzeQsqOZcR8/GJCSJhX4/lYxDwqbgjYVupMbSKN0CMoRM+Uj6cpATg2ETgv15AjFLKeaZAwsIIw+RBjt2/gH9OblGET64bNEcROpkmj/cB11bIptW1gG5bAQMrBAWFIjEhEdo6WjDgrKpMLlDE0gGHxq7GsO+6K5adfXINTWf1xJVPtZXU1dXgWKQo4FgDniUGfN746UFgZUPgw6PcaDqTQ7AwwjB5DAqF/HnzH4r5aR1HitDJdPF7Cewf/nn++3mysu4AfHxkGTGtLM3SN/EwTDZClaR/7zgSO0YuEaHpxMfQAPy0ZATmHlwlzJJOjjaQqKkh2KQCIjvvAHRMZBsHugGrmwJ3NguzDZl47tx7igePXiMuPj53D4xRCSyMMEweIjI2Gv1WT0BEjCxTZftqzTCwSZf0N4oNB3Z0B+I+mWAqdQWq9xOT770/wueDv5i2s81As8IwOUDjsrVwfspWNChVXcyTYLHk+Cb8sHAo/CODUK5MMdSvVxX6Fb4Hhl0C7Cp/TuB3YCSwdzC8PDwREBiC6Jg4vHnLCdMKAiyMMEwegR7Kv22dg5c+booqqYt6TEpfm0HOfftHAv6vZPM25YB2i4VTYEhIOJ4+eysWu7o6wsxUVtiMYXIbK2Nz/PfzUkzuMEzkzSHuuD1Gs9m9cN/3KQz0dWUrmhUBBp0Can4228Q/3I9XJzcDsbKSCO+8fNkXqgDAwgjD5BGoLPv+26fENEUfbBg8N+MMq9dXAk/2yaa1jYCu/wJaekhISMS9h8+RlJQEKyszFHd1yoEjYJjMQ2UMRrbohYO/rYaThSz3TWhUOAaunYSx2+YKLWFgYIiIsEHbRUDnDYCWARIkmjCKcIOB23FYJZBWRIrnL90/R+cw+RIWRhgmD3DP/Qmm71mqmF/aeyqK2ciSRqWb2OzElM/znVYD5rKqvBoa6ihZvAiMjA1QsXxJ9hVh8ixVXcqJaBsyScr59/IBDFs6HWev3MRbt09mmAqdgKEXoWvpjOoh+1Er8D9UfjgFar6PER0Zgbj4hNw7CKbwCCPt2rWDk5MTdHR0YGtri549e8LHxyfdbWJiYjB8+HCYm5vDwMAAHTt2xMePH3OszQyT2cRmA9dMFiGPxNDvuuP7yo3T3ygqEPivjyz8kaj3C1D6e6EJkWNvZ4U6NSsKwYRh8jJGugZY1X8mFvWcDF0tHbHMO9gXN988xJkbN/DqtadM82FZHBh8FpKqvaAljYFeYhhqePyDeo9GQzvUPbcPgykMwkjjxo2xa9cuvHz5Env37sXbt2/RqVP6mShHjRqFw4cPY/fu3bh48aIQXn788cccazPDZAQJD8M3ToN3sExIrlmsIib9MDSjjQAqvx4qq94L59qQNpsKT68PuHD5DiKjohWrskaEyS/QtdqtblucmrRJZHB9EvwS9wMe44XPW+w5fxJXbt7D0+dvEQdNoMMyJP24CkkaujCL94b6xyey8N8HOxEbGwev9x9x9/5znL94W+HAzeRtJNJ8amg7dOgQfvjhB8TGxkJT88sKpKGhobC0tMT27dsVQsuLFy9QunRpXL9+HbVq1crU74SFhcHY2Fjsz8jISGUDkJ+fH6ysrNIv/84U+P5cfGyDSG5GULjjmUmbYZtRPpGLC4HTM2TT+hYI6nEKz7zCFU58zs52KFPKpdD2aV6F+zTzxMTHYvb+FVh7bidKGruiimV5UXyvvGMJVK9YHiWKOYv+DHxxDRZnxkDi9xxJUMMt0x8RbFEDsC4NSGQaQXUNddSvWwW6OhkkDGSQHddoZsdQDeRDgoKCsG3bNtSpUydVQYS4e/cu4uPj0azZZztkqVKlhKknPWGEhBv6KHek/CQpq8C/BdoPyYCq2l9hJ7/255WXd/DX4bWKt8IVfWfA2tgi/eNwvwLJmZkgfYeUImZar8LN577i+DU1NFC8mBMc7G2+uS/ya5/mZbhPM4+WuiZmdPoF9UtWw6//zsZtvweoblUJl17fhjs8MdZ5INQlakgwdUXigNNQPzEBj9/4IVjTFgj1glGUOyxrtEdAvB5CQsPx/IUbKlEtHCbHr9HM7itfCSPjx4/HsmXLEBUVJYSJI0eOpLmur68vtLS0YGLyKWnOJ6ytrcV3aTFnzhzMmPHprVMJf39/4YOiqpNDUiKddH5DKpz96R8ehCHrpioiAIY16oqSZk7irSQt1KICYb6zH9Sksps7oupw3Ak0QExMNEyMDVDMxR6amuoICPAvlH2a1+E+/XoqWBfHzsGLMGX/UhzxPI3YxDjEecTj/NMbmP3jrzBW15P1Z53fYWt8AJY3N8MsxgM6SZGQHt4IgzrT8UavGizMDNK9t5SJjo5FVHQszM0KXyh8UjZco+Hh4XnfTDNhwgTMmzcv3XWeP38uNBpEQECA0Ip4enoKgYFUPySQpGYXJ/NM3759k2k5iBo1agj/k7R+NzXNiKOjI4KDg1VqpiHhhsxI/FAqfP1JmSb/9/evuPb6nphvVKYmtg5bmH7bk5Ig+bcjJG/Pi1mpS0N4NV2FJy/coKGujnp1q0BHW6vQ9ml+gPv02/pu1dkdskytSYmKQnzjWg5Av2adoa7+yUnb/xUku/pA8vGZYltphc6Qtl0IaBtm+DukRblx67Fw+m5Yryo0NfPV+3qevEZpDDU1Nc3bZpoxY8agT58+6a7j4vLZ9m1hYSE+JUqUEL4fJCTcuHEDtWvX/mI7GxsbxMXFISQkJJl2hKJp6Lu00NbWFp+U0IlR5QOEBChV77Mwk5/6c/GRjQpBxNbEEsv7zYCGRga34qWFwCdBBAbWkHRej8C3QZBAguLFnaGnK4tAKKx9ml/gPs0a1F8jWvREvVLVMGz973Dz8xJ5SKYd/Ad33z/D/O4TYKxnCFiXAoaclxXYu71BbCt5tAsS7ztA540IMywOIyODVH/D3cMbpqZGon5TZEQUvLw/opiLIwobEhVfo5ndT64KIyR90edb7FApNR9yqlYlqVYTZ8+eFSG9BEXivHv3LlXhhWFyAlIvU+preQG81QNmwdwguSnxC9yvAmdny6YlakDn9aIAXqUKlvC3s4KFeQbbM0wBoZJzaZyetBlTdi3GjmuHxbJDd8/irvtTLO87XVbDSVMXaL8EcKkPHPgFiA2DNNANj7f/CW/H9qjapBWsrMyT7TcyMhovXrqDnLFKlSgqpj08fVDEyY5D43OIfCGe37x5U/iKPHjwQJhozp07h65du8LV1VUhWHh7ewtzzq1bt8Q8mXD69++P0aNH4/z588Khlcw2tH5mI2kYRpV8CPbD8I3TFX4iE9sPQY1iFdPfKDIA2NUP+OQngsbjAZcGijcYKn7Hb9lMYYKyEi/uNVnkJTHU0RfLvIN88eOiYZh3aLUiXw/KdwSGXwYcqgqHb63ECMDvOZ4eWY/40OT5prw/FZO0MDeFs5MddPV0EB8XDy/vtP0LGdXyzU+xtDQTqkRPTw/79u1D06ZNUbJkSSFkVKhQQeQOkZtUKHKGNB/k3Cpn8eLFaNOmjdCMNGjQQJhnaD8Mkxt+IkPWT0VQRIiYb1a+brJy6mnnExkEhH+Qzbs0hLThWLi5v0c8Z5tkCjntqjbFriFLUKuYrKJ1kjQJi49tRLv5g+Du9ylrq1lRYMBJkRSweMRN6CWGIiYiFK83jgbeXpBtlyTFex+ZcOJgbw01NQlcizooTDeJHP2UI3y1A+vx48fx33//4fLly/Dy8hLmEn19fVSuXBnNmzcX2gc7O1mdgYIA5xnJ++SH/pxzYCWWntgspu1NrXF68haYGRinv9GlxcCpabJpAytg+FV8iFTHg4cvxJtbg7pVxYOzsPZpfoP7NHv609zCHCtOb8P8w2sVzq162rqY1Xk0utZp8znA4dVpBByYgds6DcVsreA9MK31P/hVGo67D15BU0sDjRvWgLqamhBALl6+g9iYOJQtUwxOjmn7GRYkknIxz0imf23//v3CcbRfv37C2Y7CbEnLcPLkSaxbtw4NGzbEmTNnhMPpkCFDhEcuwzAyP5G/T24R01ShdPXAWRkLIu9uAmf+kE3Tw7TTWsDQWqFOtrMhb3fOrsow5Hv1S6s+ODR2DYpayjQaUbHRGP3vbAxYM0mUWxCU+A4WQ/fC3kCmVXxi2ARJl5fi/YF5QHwk7G2thCAi26caXIo4CAGFyRky3dN//fWXMHu0atUqVYmpc+fOCt+Nf/75B1u3bhXp2BmmMOMb4o8Rm2Z89hP5YSiquZRPf6OoIGBnP+DTWx4a/gYUa4y4uHgEBAaLRXa2WXP8ZpiCSpWiZUXBvam7F2P7VZlz69H753HX7QmW9p6ChmVqCoG+VPc/4H9wJyI+vsYLg3rwi9ECPK7B3jwAkBaVCf8AHB1shNmGHVjzmDBCWUszg729PebOnfstbWKYAuMnMnT97wgMlwkQTcvVwdBm3dLfiISW/SNEFkmBcx2g8UQx6fsxENIkKQyN9EX4IcMwXzq3UrE9utd+2zoHwZFh8A31R5e/f8HAJl1E3SddbR2Urt8KLx5YQe2NJ7QSo6ETGwCjo4uBd6eBdksAXROoq7MpLSfh3maYbGLh0Q24/vq+mLYztcLffX7P2A57YzXw/FNmYT0zWRivuuyd4YOvv8JEwzBM2lDV63NTtqJh6RqKZVTnpuXcfnji9Qq2NhZo8N13KDVoBRo5xqNy6FHZSo/3AcvqysLpP0FazY9+gXjvzRXf85Qw8vr1a1E1191dVq756NGjIlKlevXqmD17tkIdzTCFmcsvbmPJ8Y0KmzaFIWaYT8TnAXBiyuf5jqsAY3sxGR0Ti6Bgme3blk00DJMhVHByx8glmPnTKFFkj3jp44ZWc/th+amtImUPtA2g1nE5dDsvB3Q+3Z+kldzQGjg1HUiIQ0BgCO7dfy7q28TGxefuQRVgvkoYISfWMmXKoFu3biID6pYtW0RFXIqmoZov06dPF74lDFOY8Q8LxLAN0xSC+YR2gzPOJxIbDuzsAyTGyebrjgBKtlR8HRoaLqICzMyMufoow2QS0kQObNoFJydtQlmH4mIZ5SGZtX85Oi4ajncBPrIVy3UARlwDitSTzdO9e2kRsKYZLJL8RNbWhIREvH37LhePpmDzVcIIaT7GjRsnCsatXLlSRM1QYTkK96UaMcuXL8emTbLskgxTGElMSsTwDdPhHxYk5huXqYXhzXukvxE9+A6NAgLdZPP2VYDvpidbxcbaAk0a1kDZ0q7Z1naGKaiUsnPBsfHrxb0oD/W98eYBmszqgf+uHZG9OJg4AP0OAy3+ANQ/VYP3eQDJygYoFXtXzL5774uIyOjcPJQCy1cJI5RUjEJ76WT27t1b1H5p1qyZ4nvKM0IZUhmmsPLPiS249OK2mLY2tsicn8i9bcDDXbJpbSOgy0bgk1pZGS0tTXZcZZgsoq2phak/jsC+0SvgYCbLGxIRE4Vft8xCv1UTZC8QaupA/V+BwWcByxKyDeOjYX5mFKy8T0IaF42Xrzxy90AKKF8ljERGRsLQUFb5kB6wurq6IjuqHJrPiYysDJMXufH6Af46vFZMq0nUsKLfDFgamaW/kd9L4MhvitnYNksQLDFP5ntF6mGGYVRD7eKVcX7qNnSp/b1i2fGHF9FoZneceHBJtsCuEjD0ElBzoGKdkp5bIHG/DL+3TxAY9Cl3SSrEJyQgOprHwWwVRkgjoshml8o8wxRWAiNCMHT9VJGSmhjTpj/qlqya/kbx0TI/kfhPJQyq9UFS6ba4c/8Zbtx6JBznSCi5duMBrt98yOphhlERhrr6WNp7KjYMnguzT47lFILfZ9U4oSkJj44EtPSAtguB3vsAQxsYJAbDMfK+MN08PbwOUsoHlAK6Zy9euoPLV+/y/Zqdwgg9GCkLq5mZmfhERESINPDyeSpUxzCFDUqh/POmP/AhRBZ6S0LIr636ZLzh8UnAx6eIl2gBVqWB1nOFKYaq8IaEhOP2nSe4dvOhqCgaFh4JHe0vTTcMw2Sd1pUb4eLv29CiQn3FMvIhIS3JlRd3ZAuKNwNGXhdOrsUjbsA4/iPKeqyD5J/awOszirGRakbdvvtE1I1KTEwS9dKYzPNVuW43bpSFKjIM85lVZ3bg7JNrYtrC0FSYZyicN12e7EfirU3w0SmLF0YNUalFZ1hq6YG2KlPKBdraWnjn9QFhoRFidarOy5kgGUb1WBqZY9PQv/Df9aOYumuR8COhKsCdloxA/8Y/YXKH4dDTMwe6bILWw12ofWQsJPEhAMkam39ESOVhcC/SFb4B4YrQ+/Jli3PStOwURshplWGYz1Cq6T8PrBDTZLJc3ne6cFxNC3qDCvV6ifcn/8MHywFIIK2ITXm8i9KBPHsICSIkkBQtYo+3bl4IDg5TVBFlGEb10L1LRfXqlqiCUf/OxtWXsuiZ9ed3i9pSZNKp7loBqNQFkqL1gH3DgLfnEaFuiuve6oDfEUhsK6B0lWqiqB67L3w9LLoxTBYJiQzD4HVTFJVCf27RS1b/Ig2otswN8v84cRhemsWEIKJrbI5S1euhcsXSX6xP+UTKlSmG+nWriDwHDMNkL04Wdtj9yz+Y1WU0dDVl+Xzc/LzQfsEQ/LH3H8TEx8oSEfY5ALRdhEhtG6hJE6EVG4gaj8bB+fESSMgXjFzC4hPg84ELxqpcM2JqapppaS8o6EvHHoYpSJCGY9SW2Xgf5Cvma7hWwNi2nz3vU4PSSYe8vQe16EBYx76Fg2YIzDvvhkQ3g8ysDMPkGBQpOqBxZ5EjiHzB7ro/EY7pK05vw+nHV0W4fuUiZYCaA2BdrDEa7/0Z6u+uQx0JwPWVwKtTSPhhJS64AwnxCSIc38hQ/4uIG00NrgisTKZ7Y8mSJYrpwMBAzJo1Cy1atEDt2rUVhfROnjyJqVOnZnaXDJNvWX9+lwgHJEz1jbCy/0xofKohkxZGwY9h/eE0LGM94Bj/Ghh4ShTkYhgm7+Fq7YRDY1dj5ent+OvwGsQlxOO1rwfa/DUQI5r3wOjv+0Pb3BVaAw7JhJDTfwAJMUDgW2hsaAGL8n/A16gSvLx8UbbM52SFb9y88PqNJ4o42aF0KZdcPca8hESahWIyHTt2ROPGjTFixIhky5ctW4YzZ87gwIEDKCiEhYXB2NgYoaGhMDIyUln0hZ+fH6ysrDJOiMXkuf584Pkcbf8aKNJKE1uGLUDzCp/SSKdF2AdgWR0gKlA23+pPWcr3PApfo6qH+zT/9ucLHzehJXn07oViWSk7VyzpPQWVnD+ZWP1fAXuHAO9lUTiBmg64Zd0D6vYV0KRVK+GAHhIajus3Hir2UbVKGeGcXpD7NLNjaJZ+jTQgLVt+rpshh5aRMMIwBZWw6AgMXjtFIYgMadYtY0GEfEp2D/wsiJRoDtQelgOtZRhGVenkj45fh/HtBkHzkwb0hc9bfD9vAOYcWInY+DhZxtZBp2Xp5DW0YRb/HnrR3kh0v4EPh+YBcVEwMTZEmdKuCh+wx09ec/G9bxFGzM3NcfDgwS+W0zL6jmEKIqREHP3vn/AM8BbzVYqWxeQOmRAqLsyHj48vItVNAENboONqMkxnf4MZhlEZJISMat0PJyduRHnHEopaVEtPbEbzOX1w3+PZ53Tyw69C4lgdTtFP6MkBT493wLLagNtlODvZolaNCsKXhJzanzx9w9Xuvza0V86MGTMwYMAAXLhwATVryqIHbt68iRMnTmDtWlk6bIYpaGy6uBdH7p0T08Z6hljVf6biLSlN3K8g7sJiPLLoDynU0KhFc+jqs8DOMPmVMg7FcWzCBiw7uQWLjm4QWtKXPm5CSzKseXf81mYAdEhLMvAU7C+vxItHfgjXMEdoUCSMN3wP1OgP9eYzULF8CZHU0M8vEN4+fnCwt0ZhJkuvZ3369MHVq1eF/Wffvn3iQ9NXrlwR3zFMQePxu5eYtmepYn5JrykiDDBdIgOBXf3hp1UEUkhgaFMEuiUbZH9jGYbJES3JqUmbUMGppFhGETfLTv6LZrN64fbbR0JLotVwBBxqtAR0TRGk+el5cWs98E8tGPleQ/FiztDS1hS5hQo7WY4tIo3Itm3bVNsahsmDUJ2KQWsnC296YmCTLmhVqWH6GyUlAfuGAOEf8NG4KqBnDuvKnytcMwyT/yltXwxHx6/HilNbsfDoevGMePPRE+0WDBbhwRPaD0GZ6rVQtHQFGDwzBE69BuIigdD3wJZOcKnQGY7NZ0PLxBSFHbWvqdj7NXzt+gyTFyFb7pitf8Ld/72Yr+hcWpQhz5Bry4CXJ5Eg0UCAXnHAriKsbeQ5VhmGKUhakl9a9cGpSZtl+Uc+PTfWntuJxjO749qruzAgh9Vag4GRNwDXxoptJY92QWtFLeDhLtoIhZlMCyPFihXD3Llz8eHDhzTXoRNw+vRptGrVCn///beq2sgwucbmS/tw6O5ZMW2ka4A1A2dDS0Mz/Y28bgGnpotJf60iSLKtDD1DYxga6OVEkxmGyaWIm8Nj1+D3H0dC51P21ncBPvhpyUj8tnWOiMSDqbMse2uHFYDOpxxDUYFI2DMEXptHwv3Z57DfwkamzTTkrDpp0iRMnz4dFStWRLVq1WBnZwcdHR0EBwfj2bNnIvGZhoYGJk6ciMGDB2dvyxkmm6GcAr/vXpLMT8Q5Iz8RKiv+X18gSRb6+7F0P0DfAtbW5lyvgmEKOJT4kJxYW1asLyLvbrx5IJZvvXIQZx5fw7xuY9GiYgOgag+gxHfAkbHA0wMI1rTDkzBjaJzdAaegq1CvM1gWmVOIyLRmpGTJkti7dy9evXqFzp07w9vbG3v27BHRMySo2Nvbi2kPDw8MGzYM6uqFqyOZggW9xQxaO0XhJzKoyf9EufF0ITUrFdAK9ZLNOtdGkHk1MW1txRE0DFNYcLF2wr7RKzC361joa8s0or6h/ui9cpyoZ+UfFgQYWgNdtwDdtsNCOx66iWFIkKrjw4XNwOqmwIfHKExkKQNrYYIzsOZ9VN2fdEsMXDtZEcZbybmMSAudoXnm2nLg2ETZtJ6ZyDWQaGCDgMBQWFlmvrZTXoCvUdXDfVo4+5PqV43bNg/nnl5XLKMSEjN++hU/1Wwley7EhOLtoX/wyjcaxvEfUSd4l0wzUnckpI3G4+nbD/Dx8YOpqbF4saHKwNlBvsvAyjAFve6Mcj6RNQNnZSyIUArok79/nu+4SlT3JA2htZVZvhJEGIZRHQ5mNtg2YhGW9Z0GM31jsSw4Mkykl+/6z6/CrwQ6xnD4YTwkzrURqu+KUA1LWebmy0vguaoXvF48QWJiEgICguHnXzAL0bIwwjBK3HN/ghl7/1HM/917asb5RISfSB8g8VNa53o/46NpdSQmJmZzaxmGyQ/Qy0inmq1wcdoO/FDtO8XyC89uouEf3bDm7H/Q0FCDTdHiQJG68Ko4BlDXQpCmPV5ISgLvb8M14ARKOZvB0eGzVoQElMioaBQEWBhhmE8ERYRioFLdmaHfdZc5m2XGTyTknWzeqSYCq/6Kew+e4+r1B0hIYIGEYRgZlkZmWDVgJrYMmw87UyuxLDouRjjKUzXgeM1YQKIGH6PKiOh/Effte4mEibYxr1D8+d8ouqclrD0PijxGlEr+9t0nuHn7MaJjYpHfYWGEYT7ZSkdumgHvIF8xX8O1Aib9MDTjDa8uA14ck03rmSGuwzo8fOZG5ShgZmosKnUyDMMo07xCfVz8fQf6NOyoWHbf4xk6/DMUr/zdYWSkj9fB6ohzrA9Dp3IoF38XwtAbEwIc/AVY1wISvxeIj09AbEwc7tx9KqbzMyyMMAyA5ae24uyTa2LazMAEqwbMyrjuzLubwKlpillpx9V4/D5KPBz09XVRqmTR7G42wzD5FENdfRFtc/C3VShu4yyWJSQl4q+rKzHmyCxE6USKdPGVm/8AjV9uApX+93njdzehuaYhqoUegbamGiIiooQ2ll6qCl06+JCQENy6dUt43qbsgF69eqmibQyTI1x9eRdzDq5S2HZX9JuhUKGmW3dm5+d8ItL6Y+CmVRZ+Hh6QqElQqUJJ1oowDJMhNYtVwpnJ/4rqv/+c2CzMxG8/vkPHxcPRo157WcZnA0ug0xqgcnfg0K9A4Fvx7NG9tgDVzE7hhssYBAUBl67eg7mpMeztrWFmqprozzwtjBw+fBjdu3dHRESECNVRjhSgaRZGmPzCx9AADFk/VRS5Iqj4VaMyskrUaULC954BsvoSAOKKNMRD8x8R8MpDzJcsXgRGlP6ZYRgmE2hramFc24FoV7Upxvz7J+66P1EkSzv16ApmdxmDNlUaQ+LaEBhxHbi0SPZJjINR0CNUjpiK+44DEW1VFu+jYmBiYqgQRqKiYuAfGCzMxgb6unk2si9LZpoxY8agX79+QhghDQllYJV/gkg8ywbatWsHJycnkfHV1tYWPXv2hI+PT7rbNGrUSHS88mfIkCHZ0j4m/0FvIFQATyQgouulTE2M+b5fxhtenA+8lqWIh74l1DutQmxcgojLJ9NMEecMom8YhmHSSCl/aOxqIXzIk6X5hQVi4NpJ6LNyHHyC/QBNHaDpJGDkdcBFlojRMu4dGrvNQpVHU1A04QXMjD+/DPkHUIb0t7hy9R6uXLuP2LhPUX8FQRih7Ks///wz9PRyrtZG48aNsWvXLrx8+VJkgn379i06deqU4XYDBw4U9XTkn7/++itH2svkfWbvX4Gbb2S1IMgss7zfDKhnlIL5zXng3J+yaYka0Hk91E3sUbF8CdStXQlFi9jn2TcPhmHyPupq6ujf+CdcmrYDzSvUUyw/+egyGsz4HzZc2CNzjbAoDvQ9CHRaK16KNKWxsI58hlLXR0N/cwtZjSzSumhrwtzcBGrqMt8Sz3fpv8TnK2GkRYsWuHPnDnKSUaNGoVatWnB2dkadOnUwYcIE3LhxA/Hx6Ut5JDDZ2NgoPqrKosrkb47eP49VZ7aLaXJUXTvwT5gbfCpclRah3sCufiKc1123EjxrzYLUpaH4ytBQHwZcCI9hGBVhb2aNzUPni+KcFBJMRMREYdJ/C9BuwWA8935LfhFApS7Ar3eA6v1k84TvY2DNd8DBX2FjpI4a1cqhQrkS4isvL18kpuLoGhMTi8jI6PzlM/L9999j7Nixojhe+fLloamp+YVJJTshU9C2bduEUJLyt1NC623dulUIIm3btsXUqVPT1ejExsaKj3IqW4IkUVV5KtN+KOV4fvZ8zkt8bX+Sc9ivm2cp5qd1/FmU/k53+8R4SP7rA0lUIII1bfHCpgOk6hWhGxAMC/MMhJh8CF+jqof7VLUUlv5sU7kx6pWsiln7l2P71cNi2R23x2j+Z28Mb94DP7fsDR1tY6DtIqBSV0gOj4LE94ksB9LtDZA+OwRpi5mwLN8Z2jpaQuig1PL2dsmd9LW0NBEbG4eo6BiV9mlm95Wl2jTp5awnFXV2ZZ4cP348li1bhqioKKElOXLkCMzN0y5AtmbNGqFJoerCjx49EtvXqFED+/btS3Mbqko8Y8aML5ZTgUBDQ0OVnRzK00/5+vNyTYX8wtf0Z1RsNHquG4+3/rIkZS3L1cecjqMzNK0YXv4T+g83IU6igyvW/RHm0BDmlhYo5lIwzTJ8jaoe7lPVUhj7847HE8w8vAKegZ9NLUXM7TG17TBULVJWtiApAXqPtsLg5lKoxUcq1ou1r4lnZcfBI0QNBvo6KFfG5YtnV3b0aXh4OEqUKJFhbZpcLZRHppZ58+alu87z589RqlQpMR0QECC0Ip6enkJgoA4jgSSzg8G5c+fQtGlTvHnzBq6urpnWjDg6OgrnXFUWyvP394elpWWhuYmyk8z2J13qQzf8jkN3Zc6nJWyL4ujYtdDXycC88ngf1Hb3ozxmuGvaHn6uXaBvboPaNSsW2PBdvkZVD/epaims/RkTH4ulxzeJ3EiUl0RO97rtMKXDcFFPSxDqDcmJSZA8PahYJ1bDEJdLTIdt+booWbqE6DdKKU/aEsqNlB19SmOoqalp3hZG6KADAwPTXcfFxQVaWlpfLH///r0QEq5du4batWtn6vciIyNhYGCAEydOCL+XzMBVe/M+me3PtWd3YuruxWLaQEcPJyZsRLFPyYbSxO8FsKoxEBcJd73KeOE6GGpmRVC7VkUYGeqjoMLXqOrhPlUthb0/n3u/xW9b5yjCgAkrI3P8+b8x+L5y488v6a9OAYd/A4JlqQeSoAY1E0eg7XygZEu4e3rj5UsPuBR1QDFXx1yr2pvlpGcXL17EggULhOaCKFOmjPAjqV+/fqb3QdIXfb7FDqWsxciIBw8eiP8UGswULm68foAZe/9WzP/d+/eMBZGYMGB7dyGIfNAuhpd2nQETJ5Qu5VKgBRGGYfI+pe1dRRjwpov78OeBlYiMjRJhwAPWTEKrig2FUGJLyRtLNAd+vglcXCCqAKtRQc8QT+Dfzogv3QFvLfpACk3o6urk6vFkSfQhh9BmzZoJR1AK8aWPrq6uMIFs3y6LUFAlN2/eFL4iJEyQiYbMLV27dhWmFrlWhMKNyZxDWWEJCv2dOXMm7t69Cw8PDxw6dEgkY2vQoAEqVKig8jYyeTuxGeUTkas0R7bohdaVZfH5GRbAC3gtmzdzhdSmHOztreDoYJ0DrWYYhslaGPDxhxfRYEZXbL60T/birqkLNJsqS5hWtIEwOQdp2uGanwHiX1+Afvhb2FvLInZyiyyZaUqXLo1BgwaJcFtlFi1ahLVr1yq0Jari8ePH+OWXX/Dw4UNhaiHNRsuWLTFlyhTY29uLdUjgKFq0KM6fPy+SnXl5eaFHjx548uSJ2IZMOh06dBDbfI25hc00eZ/0+jMuIR4dFw3DbbfHYr5+qWrYMXIJNDKqO3Plb+DEFNm0jjEw9CKCJOYwNU2ecbigwteo6uE+VS3cn8mhofzwvXOYvHOhIpEjUatYJSzoMfGzJlgqRcD1Xbh9/yWQKLMsVA49BhszfSS1XQw/LcdcMdNkSRjR1tbG06dPUaxYsWTLyTG0XLlyiImJQUGBhZG8T3r9OX77X+LtgLA3tcbJSZtgYWia/g7fnId0cwe80asGh+hn0O22HijVCoUJvkZVD/epauH+TJ2QyDD8se8fRRgwoa2hhdHf98Ow5j1EXiUa9k+cuAD4v4JW0Cs0CVgrqgJLJRJEl+0KnbZ/Qk3fLEfH0CydQdIynD37KR22EmfOnBHfMUxeYPvVQwpBhG7GDUPmZSyIBHsiaVd/PDJshjf6NXGn7HQklcicszPDMExuY6JvhEU9J2PPr8vgbCGzHMQmxIlioC3n9MVDzxdCu1upcnkYuFZH1ZY/QmJTXqwnkUqh92Q74HU7x9utkdXaNOQnQj4clHiMuHr1KjZt2oSlS5equo0M89Xcc3+KCTvmK+bndRuHis6yEPE0iY9G4vZeuK9ZG/7aRSDRt4BLvfb81sUwTL6jXqlqOP/7Nsw/vBarz+wQxUCfvn+N1vP6Y9h33TGmTX/Ut6kiW7n4ReDGKkjPzkasY31olfgufwgjQ4cOFRlNFy5cKOrFyP1Idu7cifbt26u6jQzzVfiHBaL/6gnCX4To27AT/lenTfobSaWI3z8K96KLIkjbHmqa2qjcogOs7NlZlWGY/Imelg6mdRyJ9tWaYfSW2Xjm/QaJSYn45+QWHHtwAYt6TkLNYpUA8qGrOwLSMu0QFhQCi1xoa5ZDe8kZlD4Mk5cgAWTgmsn4EOIv5msWq4gZP/2S4XaxV1fjzvskhGnZQ0OShKrN2sDMoUgOtJhhGCZ7qeRcGicmbsTyU/9i8bGN4jlJZTF+WDgU/Rp1wqQfhkFfWxcwdkBS7Jd5vXIC1j8zBYqpuxbjxhtZPhkbY0tRAE9LI/36RXC7hOfXzyNMwxJaSdGoWasazIp/Ul8yDMMUALQ0NDGqdT+cnrQFVYrKUseTI+v687vReGZ3XHl5N1fbl2lhxMzMTKRjJyi1K82n9WGY3GDrlQMpHFbnwso47dpFgiAP4L9ecI56AKfoR6hSzBxG1TvmTIMZhmFymJJ2RXF47BrM6PQLdDW1xbJ3AT7otHi48LOLjM2dyr2ZNtMsXrxYUSiOpgtDrgUm/3Df8xkm71ykmP+r+3iF9J8msRHA9m5AVBAoxsaUCk21npD9jWUYhsnlZGmDm3UVidJGbZmt0CZvubwfpx9dwZpBs1HdtULeFEZ69+6tmO7Tp092tYdhvhrvoI8Ys2se4hMTxPygJv9Dl9rfZyLD6lCASm0T5q5A5w2AWsEsfMcwDJOSolaO2Dd6BTZe3ItZ+5cjOi4GwVFhGadAyCs+I/fu3RNZUeUcPHgQP/zwAyZNmoS4uDhVto9h0iUqLgb910xEUGSoIsPq7x1HZLzhhfnA04OIl2jjqWlLBLXbBCllWmUYhilEqKmpiZTyF6ZuQ90SVfBz056K/CQ52o6sbDR48GC8evVKTLu5uaFLly6iTs3u3bsxbtw4VbeRYVKFnK8oXO3Ruxdi3sncDqsHzM441fuzI8DZWWLyo44r3hX5H559TGLTI8MwhRZnS3vs/Plv/K9G61z5/SwJIySIVKpUSUyTANKwYUNRII+Snu3du1fVbWSYVFl6fBMO3DmtiKffOGQezAwy0G58eAzsGfh5tlR/wMAKNja5EVnPMAyTdyAtSW4leVTL6hupqAT4KQV869YySYpSwcsjbhgmOzl2/wLmHlotpkmj8eePo0VJ7bSg61Ua/hHY2gWIixTLYst3RaBhaTFty8IIwzBMrpGlpGfVqlXDrFmz0KxZM1y8eBErV64Uy93d3WFtzRkrmezl2fvXGLFphmJ+QtvBaFSqRprrx8TE4cbN+9B4dRTVwoOhQwsdqsK3xkRIX72HkbEB9PV0c6bxDMMwjGo0I0uWLBFOrCNGjMDkyZMV1Xv37NmjqFXDMNkBlcbutWIsoj7Fwv9YvTlGtOiZrhbv6bM3iHa/i/DoRNww7YRo4+JA9x3wDQgX69jZWOZY+xmGYRgVaUYqVKiQLJpGzvz586GuzqGRTPYQGx+HfqvG432Qr5iv5FwGC3tOStfx1PdjIPxe3oEk7D10EiMQrWGKG6X/ROUkfQQFvxHr2FiziYZhGCbfaUa8vLzw/v17xfytW7fw66+/YsuWLdDUzCD1NsNkAdJw/LZtLm67PVaket84ZC50tXTS3eblrYuA/0u4RN5GreDd0HeuCBNbZyQmJkFHRxsmJkbQ1ZVlIWQYhmHykTDSrVs3nD9/Xkz7+vriu+++EwIJmWz++OMPVbeRYbDs5L/YfeOYmKYUxpuHzYetqVW620h87qPGw/FwjH4C18g70GkyGrVatkPF8iVgbmaMRvWroUplmQMrwzAMk8+EkSdPnqBGDZnD4K5du1CuXDlcu3YN27ZtE+G9DKPqyJnZB1Yo5v/uMw0VnUulv1HIe+DfLtCL9UO58PNQr9QZaPgbtLQ0FaFrZN7R1mJNHsMwTL4URuLj46Gtra0I7W3Xrp2YLlWqFD58+KDaFjKFmsfvXmL4xumK+QntBqNt1SbpbpMQFYKgbUOAiI+yBc51gB/+Jukju5vLMAzD5JQwUrZsWaxatQqXL1/G6dOn0bJlS7Hcx8cH5uYZVEllmEzyIdgPvVb8JuolyCNnfmmVQV2kxAS83vknbiZWxGv9GoBZUaDbNkCD/UIYhmEKlDAyb948rF69Go0aNULXrl1RsWJFsfzQoUMK8w3DfAtUxpoEkQ8h/mK+mkt5LOo1Of2U7VIpQg9OgUe4zPRiIokCeu4B9FlAZhiGKXChvSSEUKbVsLAwmJp+ru43aNAgUaOGYb6FxKREDN8wDY+9ZPWPHM1tRap3Hc30tRtJV5bhiXsgoGkF27i3sOw8F7AsnkOtZhiGYXJUGCEon4iyIEIUKVIkyw1hGDmz9q/AiYeXxLShjj62Dl8ESyOzdLfRfnMCXlf2IsywPjSkcSjdsD3g0iCHWswwDMPkiDBSpUoVnD17VggglStXTlddTtlZGSYr/Hv5AFae3iam1dXUsW7QnyhpVzT9jd7dgvbZGXht0lnMlnR1gHb1rjnRXIZhGCYnhZH27dsrImh++OEHVfw2wyTjwrObmLBjvmJ+zv9+Q8MyNdPfKOANsL0rnuvVRqJEAyampnBsPTL7G8swDMPkvDAybdq0VKcZRhU8936LgWsmCX8RYnCzrujVoEP6G0X4A5t/hCQqEDY6rxFqWh7l2w2EJJdKYDMMwzA57DMiJyIiQpRnV8bIyOhbd8sUIvxCA9Fj+RiEx0SK+VYVG+L3H0ekv1FcJPDvT0CwB8hgaKUPWP9vEDQMjHOm0QzDMIzKyNIrpLu7O77//nvo6+vD2NhY+JHQx8TE5AunVoZJj6i4GPRc8Ru8PxW/q+hcGsv6TRf+ImmSmADs7At4y3yTpEZ2CG67Fmp6fO0xDMMUGs1Ijx49RBGyDRs2wNraOv3cDwyTBmSSGbFhGh56Phfz9mY2+HfYAuhr66a9kVQKHPkNeHlCzL40aQrdFuOhoZd+nRqGYRimgAkjDx8+xN27d1GyZEnVt4gpNPyx9x8ce3BRKYR3IayMM0hQduEv4PYGMRmlaQY31z6Q+sSjlEFcTjSZYRiGyStmmurVq8PLy0v1rWEKDevP78bqs/+JaTLJrB30J0rbu6a/0Z0twNnZilnPOvMAPXNYmJtAT1cnu5vMMAzD5CXNyLp16zBkyBB4e3uLir2amskrn1aoUEFV7WMKIKceXcHUXYsV8391G4dGGYXwklnm0C+K2YTms/A+3glISoSzkx2kSfHZ2WSGYRgmrwkj/v7+ePv2Lfr27atYRn4j5EdC/xMTZeGZDJOSR+9eYPC6KUiSyiKwRrbohe712qe/kddt4L/eQvAQ1BmG984dkfDCHfr6ukIzQtckwzAMU4iEkX79+oksrDt27GAHVibTvA/yRc/ln6vwtq/WDBPbD0m2Dgm0H3wDEBsr8wGRhH0ATv0OXTVbmEu8oFGuLaQtZsPz2n3xvbOzHV9/DMMwhVEY8fT0FBV6ixUrhpwmNjYWNWvWFE609+/fR6VKldJcNyYmBmPGjMF///0ntmvRogVWrFghBCgmZwmLjkCPZWPwMTRAzFd3KY+lvadCLUWCso9+gXj46KVsJiEGeHcD0KoCaAGl9IJQtOMS+AeFIioqBhqaGrC35SgahmGYQunA2qRJEyEM5Abjxo2DnZ1dptYdNWoUDh8+jN27d+PixYvw8fHBjz/+mO1tZJITn5iAAasn4oXPWzFf1NIBm4bNT7UKr7uHj/hvYqAFu3cHYBv+EDYxb6CnpQ6rH2cAGtrQ0tSEhYUJHB1soKGRTj4ShmEYpuBqRtq2bSsG+sePH6N8+fJfOLC2a9cO2cHx48dx6tQp7N27V0ynR2hoKNavX4/t27cL4YnYuHEjSpcujRs3bqBWrVrZ0kYGX5hdxm+fh0svbot5M31jbBuxCOYGJqmu6+RoAyTGo/KzP6DjfV72hWkRSLstgcRIpgUxMTFE9arlxPoMwzBMIRVGKJKG+OOPP774LrscWD9+/IiBAwfiwIED0NPTy3B9yoMSHx+PZs2aKZaVKlUKTk5OuH79eprCCJlz6CMnLCxM/KeU9ynT3mcV2g8NpKraX17m7xObsf3qYTGtraGFDUPmoYilQ5rHbmtlCrvzv0LiIRNEpPoWkPbaCxhYQ5rKNvJ+LCz9mVNwn6oe7lPVwv2ZP/o0s/vKkjCS0yefOqdPnz5CCKpWrRo8PDwy3MbX1xdaWloiRb0y5C9C36XFnDlzMGPGjC+WU7QG+aCoAuo/0tzQcaX0mShIHH98CXMPrVbM//HDzyhiaAM/P7/UN5BKYXRhGvSeHxGzSZp6CGq9GgmJhkBa2xSi/sxJuE9VD/epauH+zB99Gh4enjOF8uSEhIR8MfBnxIQJEzBv3rx013n+/LkwzdABTZw4EdkN/cbo0aOTaUYcHR1haWmpsgKAdMJJg0T7LKg30c03DzHt4D+K+YnthqBnk7T9dTw8fSB5vAfmz/aJeam6JtB1G8yKNc7wtwpDf+Y03Keqh/tUtXB/5o8+1dHRyT5hhASIIkWKoEuXLmL+p59+En4ctra2OHbsGCpWrJip/VCkC2k80sPFxQXnzp0TphVt7eQOj6Ql6d69OzZv3vzFdjY2NoiLi/tCSCJzD32XFvQbKX+HoBOjygueTriq95lXcPv4Dv1Wj0dcgiwRWfe67fBzq95phuAmJCTC7c5FxH/wh56mPazj3CH5cRUkJZpm+jcLcn/mFtynqof7VLVwf+b9Ps3sfrIkjKxatQrbtm0T06dPn8aZM2dw4sQJ7Nq1C2PHjhWajMxA0hd9MuLvv//GrFmzFPMUFUNhujt37hRhvqlRtWpV4Vh79uxZdOzYUSx7+fIl3r17h9q1a2fySJmvJSgiFN2XjUZwpMzXhjKrzu02Lt1cIN7XDyD+w3PoJYbCKs4D+H4eUPGnHGw1wzAMk5tkSRghnwsyXRBHjhxB586d0bx5c6EtSUs4+BbI6VQZAwMD8d/V1RUODg5imlLTN23aFFu2bEGNGjVgbGyM/v37C5OLmZmZMLGMHDlSCCIcSZM9xMTHou+qcXD3fy/mS9m5Ys3A2dBUT/syk765AI+7FwE1QzhHPYSkwWig9tAcbDXDMAyT22RJD2NqaqoolEcaEXnECjm95FYqeIqcIc1HVFSUYtnixYvRpk0boRlp0KCBMM/s2yfzSWBUC5370VtmC18RwsrIHFtHLISRrkxwTBWfB/DfMwFRaobQkMbBoXQl4Lvfc67RDMMwTP7VjFDisG7duqF48eIIDAxEq1atxHLKiJoTWVlJA5Myx0Rqy8hxZvny5eLDZC8LjqzDvtsy85yulg7+Hb4QDmZp++Yg0A3Y3BEf1GUZdO2N1aHRfjEZLHOqyQzDMEx+FkZI40CDP2lH/vrrL4XZ5MOHDxg2bJiq28jkcfbcPI6FR9eLafINWdnvD1R0LpX2BhF+wKYfII30R4CFM6BrButWPYB0zDkMwzBMwSVLT39yDP3tt9++WE5ZWZnCxY3XDzD63z8V89M6jkTLSg3S3iAmTGhEEOyBeIkOTLQSEepSE6aZcGRmGIZhCiZZfhV9/fo1zp8/LxJYpUyC9vvvbPcvDLj7eQmHVXkIb68GHTC4ade0N0iIBbZ3Bz7I/Eq0jCxRtcd4SA1tufIuwzBMISZLwsjatWsxdOhQWFhYCKdQ5YGEplkYKfiERIahx/IxyUJ4Z3cZk7ZQQQLr3sGA20XZvK4p0Gc/YGQHFkMYhmEKN1kSRijnx+zZszF+/HjVt4jJF1V4B66dhLcf34n5ErZF0w/hJcfi4xOBx58imTR1kdB9J2L1HaGfg+1mGIZhClBob3BwsMi6yhQ+KGJp8n8LcfnFHTFvZmAiImfSDeG98jdwfaVsWk0d6LIJftquuHT5Lu7ef5ZDLWcYhmEKlDBCgkhms6wyBYt153Zhy+X9YlpLQxObhsyDs4Vd2hvc3wGcnPp5vv3fQKlW8A8IFrP6+hlXYGYYhmEKNlky01AukalTp+LGjRsoX768iK5R5ueff1ZV+5g8xNkn1zBtz1LF/MIek1CjWDp1iN6cA/YP/zzfbCpQtafQrgR8EkYsLUyztc0MwzBMARVG1qxZI3KLXLx4UXyUIQdGFkYKHq8+uGPwuilIksoip35u2Rs/1ZIlu0uVD4+AHT2BpATZfM0BQENZOHhoWATi4uKhrqEOUxPDHGk/wzAMU8CEEXd3d9W3hMmzBEeGoveKsYiIkaXab12pISa0G5z2BiFewJZOQGy4bL7098D38xXZVeVaEQtzE662yTAMw2TNZ4QpPCQkJmDw2imK4ndlHYrjn77T0xYiooOBLR2BcF/ZvGN14Kf1MsfVT8j9RdhEwzAMw3xT0rP379/j0KFDePfuHeLi4pJ9t2jRIu7dAsKMvf/g0ovbYtrc0BSbhv4FfW3d1FdOiEXSth6Q+L2Q5Q4xdwV67AK0PjupknkmJFSmMbFgYYRhGIbJqjBy9uxZtGvXDi4uLnjx4gXKlSsHDw8P4ZhYpUoV1beSyRW2Xz2Eted2imkNNXWsHzQHjua2qa8slSJ+7whcCS8GqUVxFEt4AYcey6Gmb55sNXV1dVSpVBphYZHQ1dHOicNgGIZhCqKZZuLEiaI2zePHj0Vl3L1794qieQ0bNuT8IwWEO26PMX77X4r5uV3HolZxWYXdVDk7G5qPd6Ji2CnEqRvgafERuPQ8CO+9PyI6JhbePn7CV0RdXQ3WVuYoXswpZw6EYRiGKZiakefPn2PHjh2yHWhoIDo6WkTX/PHHH2jfvr1IFc/kXz6GBqD/6oki0yrRr1En9Kj/Q9ob3NsGXJAJLsYJH1GqSnW8jbdEdHQsHj95rVjN0tKMTTMMwzCMajQj+vr6Cj8RW1tbvH37VvFdQEBAVnbJ5BGo6N2A1ROFQELULl4ZM376Ne0N3C4BB39G0qdLSb3VTBSp2x6N6ldDqZJFoaklk3eNjAxgYpxOllaGYRim0JIlzUitWrVw5coVlC5dGq1bt8aYMWOEyWbfvn3iOyb/MnXXYtx2eyym7U2t06854/cS2N4DSIzHXZP2kNpXRelyvWH4yTekaBF7ODvZIjExCZqaWfaVZhiGYQo4WRohKFomIiJCTM+YMUNM79y5E8WLF+dImnzusLr5kqyYnbaGFtYPmQtLI7PUV44MBP79CYgJQYS6CQLMqgFmVaCRQuigEGDOJcIwDMOoVBhJTEwUYb0VKlRQmGxWrVr1tbth8hj3PZ5hwo75ivl53cahknPpZOtQtFRMTCx0NCSQUHbVYA+x3MumFWBbEZaW5tDV1cnxtjMMwzCFTBgh9Xvz5s2FE6uJiUn2tIrJUYIiQjFgzSThLyJ3WP1fnTZfrPfqjSfc3N7DLugqynlcB6UxSzSwhbdLF3EpOTna5ELrGYZhmPxOlvTnlFfEzc1N9a1hcpykpCQM3zgN3kGyjKk1XCuk6rBKWhEK0yVtiI9/GEI1rQENbXxouRbx0IKurjZnVGUYhmFyThiZNWuWyDNy5MgRfPjwAWFhYck+TP5h8fGNOP/0hpi2MDTF6gGzUnVYDQoKRVyQDzQ+PkSZ8Iswi/cBOizHuxhj8b2jo60oksgwDMMw2WqmoTwiFDlDETQEZWFVHoDo7Znmya+EyftceHYTC46sE9NqEjWs6j8TtqZWqa774c0zwOchbGJewzn6EdBoLKJLtof641fiewd76xxtO8MwDFNIhRGKnBkyZAjOnz+ffS1icgTvoI8Ytv53IUAS49sNQr1S1VJdNykqBL73TwNJ8bCNeQWUaQc0mQwtqazWjKOjDbS1NHP4CBiGYZhCKYzIBy5K+87kXyiz6pB1UxAUGSrmvytfFyNb9Ep95aQkBO6ZgPg4c2glRcPMzBjouIpidqEmlaJalTLQ4RozDMMwTE5G07BfQP7nr0NrFInNqPDdP32mpZ0L5PxcmL7ejQraLkjUNoFaz22AtoHiWuBQXoZhGCbHhZESJUpkKJAEBQV9S5uYbIScVf85uUVRiZccVk30jVJf+flRIYzQRWIf+xrosgcwd8nZBjMMwzAFnq8WRshvxNhYFkHB5C+o3syITTMU85M7DEOVomVTX9n/NbBn0Of55tOB4s1yoJUMwzBMYeOrhZH//e9/sLJKPeKCybskJiVi2IZpCAwPFvPNytfF4KZdU185LhLY0QOIDcdr/ZpQd6wK++pDwZ4hDMMwTK4LI+wvkn9Zenwzrr68K6ZtTSyxtPfU1P1EyEn54K+A33MkQgPupg2RaFEfZjFx0GZHVYZhGCa3k57Jo2mY/MXtt4+S5RNZ2X8mzA3SSOV/ewPwcKeY9DMojUT76tDV04OxscxplWEYhmFyVTNCqcOZ/EV4dCSGb5yOJKns3I1p0x+1ildKfWXve8DR8WIyRk0fHuVHARr6sLWxZK0YwzAMk3d8Rpj8xeSdC/EuwEdRd+aXlr1TXzEqCNjRC3FJanAzqAdPh/ZI0rCBRE0COzv2EWIYhmHyWG2a3CQ2NhaVKlUSb+oPHjxId91GjRqJ9ZQ/lEG2sHDgzmnsunFMTBvo6OGfvtOhkUrdGeEnsn8EEPIOPjol4W7eBEkWpWFqaoSa1crD0EAv5xvPMAzDFBrynWZk3LhxsLOzw8OHDzO1/sCBA0VNHTl6eoVjYH0f5Ivx2/9SzM/tOhbOFnapr3xzLfD8iJh0lPggsGw9OJUoCQtzEzbPMAzDMNlOvhJGjh8/jlOnTmHv3r1iOjOQ8GFjY4PCFsY7cuMMhEaFi/kO1ZujY42Wqa/84TFwYrJiVv3HZahaqlZONZVhGIZh8o8w8vHjR6HlOHDgwFdpN7Zt24atW7cKgaRt27aYOnVqutuTGYg+csLCwhTOu6py4KX9UGRSdjkErzn7H66/vi+m7c2s8WeXMeL3voiGiouEZGcfSBJi4aFbEUZlG8O4eHNI8pmjcnb3Z2GE+1T1cJ+qFu7P/NGnmd1XvhBGqHP69Okj/D2qVasGDw+PTG3XrVs3ODs7C7POo0ePMH78eLx8+RL79u1Lc5s5c+aILLMp8ff3R0xMDFR1ckJDQ8VxpVkTJou4+XthzoFVYloCCWa0G4nYiGj4RUR/sa7R2UnQC3iNWDVdPDP/HrGSqqji7ZPvKvBmZ38WVrhPVQ/3qWrh/swffRoeLtPQ52lhZMKECZg3b1666zx//lyYZuiAJk6c+FX7HzToczrz8uXLw9bWFk2bNsXbt2/h6uqa6jb0G6NHj06mGXF0dISlpSWMjNKo4ZKFE06+GLRPVd5ECYkJ6LNxIuIS48X8gCad0bpGk9RXfrwXas/3iEk//dKAfWVYWljA0cEe+Y3s6s/CDPep6uE+VS3cn/mjT3V0dPK+MDJmzBih8UgPFxcXnDt3DtevX4e2dvIMoKQl6d69OzZv3pyp36tZs6b4/+bNmzSFEfqNlL9D0IlR5QVPJ1zV+1xxYhseeD4X08WsnTHph6Gp7z/kPXD4s8DlV34IJOr6sLEyz7c3dXb0Z2GH+1T1cJ+qFu7PvN+nmd1PrgojJH3RJyP+/vtvzJo1SzHv4+ODFi1aYOfOnQoBIzPIQ4FJQ1LQePr+NRYeXa/Isvp3n6nQ1UpFIiX73b6hQEyomE0o3xkBWk7k9QorK/OcbjbDMAzD5A+fEScnp2TzBgay1OSk3XBwcBDT3t7ewgSzZcsW1KhRQ5hitm/fjtatW8Pc3Fz4jIwaNQoNGjRAhQoVUJCIS4gX0TPxiQlifmSLnqhStFzqK99YCbhdlE0b2SOg9mQkPfeGrq425xNhGIZhcoV8IYxkhvj4eOGcGhUVJea1tLRw5swZLFmyBJGRkcLvo2PHjpgyZQoKGn+f2Ixn3m/EdGl7V4z+vn/qK358Dpya/nm+4yr4hcn8S0grwjlFGIZhmNwgXwojRYoU+SJMNeUyEj4uXvykASjAvPBxw9Ljm8S0hpo6/u79O7Q1tb5YL+CjP54e2AEXDVc4JjwD6gyH1KUBQq/LQoCtLc1yvO0MwzAMk2+FEeZzcrPR/85WmGeGN++B8k4lU13345XtiIpNxBOjpkgwdUHR76YJTUi92pURGhoBIyOuysswDMPkDuyCnI/ZcH4P7rk/VUTPjPq+X+orvr+DMg+mwT7mOSBRwwvHHnjzzk9okkggMTExhJoam2gYhmGY3IGFkXyKZ4AP/jy4UjG/sOdE6Gh+GZKMhFhg3zBIpEkoH3YGxYvYANpGeP3mHV6+8vgyKyvDMAzD5DBspsmHkAAxbttcRMfJMsL2bdgJNYtVSn3l8/Mg9XsB0ntI7CqhWOtBUH/vhxcv3OHu4Y3IqGhUrVwmZw+AYQoIiYmJwnk+swmlaF3K5Mx5Mb4d7s+80aeamppQV1f/5t9mYSQfsuvGMVx8fktM25taY3KHoWJabnZR4PMAuLwYd0zaQSrRRMkWI2GsromizvbQUFfH02dvoaHBlwDDfC10r/n6+iIkJOSrtqGHPWWT5si1b4f7M+/0qYmJiaj/9i3ngUeifEZQRChm7P1HMT+v23joa+vh+s2HiI9PQJ1alaChoQ4kxAnzTGKSBIGajpBaloSGTWnFdo4ONrC2MoemJl8CDPO1yAURKysrUXgzMw9hetAnJCSIFwAePL8d7s/c71Nan9Jp+Pn5fXNCUR6J8hmz9y9HUITsbax9tWZoVr4OwsIiEBIiK0YUFRUti4y5tBDwfYJgLSdIdUygY1caenrJM7Jq5bOCeAyTV0wzckGEEipmFh48VQv3Z97oU11dXfGfBBK6J7JqsmFDWz7i9ttH2Hb1kJg21NHHjE6/iGn/QJlwYmFhKhNEKLnZxQViWYB2EcC2PCwszPiGZRgVIPcRIY0IwzBQ3AuZ9Z9KDRZG8gmUS2Tc9s8Vjse3HwwbE1ldn4CAYPHfihKXUe2ZQ78Cnyr3BhRpK6JnzM1NcqnlDFMwYeGeYVR3L7Awkk9Ye3Ynnnu/FdMVnEqhb8OOYjohIRHBIWFi2sLCBIl3tiDI552YjzMvhXBjmZ+IuZlxrrWdYRiGYdKDhZF8wPsgXyw4uk4hgf7VfTzU1WR2ucCgEEiTpMIfRDM2GBduvsAt0w6IUdNHYIPZIsmZoaE+tLW/TBHPMAyTFaZPnw5ra2vxPDpw4AAKClRWhOqZfWvfVKqURqoFFeHh4SH6Xl6JviDAwkg+4PddixEVGy2m+zToiErOn6Ni/D+ZaCwtTKF1eioMYn0hhQTvSg2EZpHqsLQ0lZlvGIYp1PTp00cMYPIPOd+2bNlSVDT/Gp4/f44ZM2Zg9erV+PDhA1q1aoX8xqZNm0Q4akpu376NQYMGfdO+f/vtN5w9exaqPG8//PBDsmVUe436vly5NKqz50NYGMnjXHx2E8ceyAr+WRqZYUL7wcm+d3ayQ4kSRWAb9xZ4tBtO0Y8AdS14WTWDmakRqlUpixLFnXOp9QzD5CVI+KBBjD40YFLURJs2bb5qH2/fyszF7du3F7kltLVTyfycCb7F2TG7sLS0/GbHZAMDg6+KssoK6urqou8LUp4oFkbyuNPqlF2LFfNTfxwBYz3DZOsYGujB1cEcpmd+E/PWsW7Qti2JuEQ1+H4MzPE2MwyTdyHBgQYx+pApYcKECfDy8oK/v79iHZrv3Lmz0ByYmZkJoYPMAnITRNu2bcU0ZeiUOy5Soqw//vgDDg4O4jdo3ydOnPjCrLBz5040bNgQOjo62LZtm/hu3bp1KF26tFhWqlQprFixIt1jiI2Nxc8//yzMRIaGhqhfv77QaMi5cOGC+K2jR4+iQoUKYr+1atXCkydPFN/37dsXoaGhCi0RHVdqZhr6jjRAJLCRkELtvH79Ot68eYNGjRpBX18fderUUQhoqZlplLVR8g/9jjxMvH///ihatKgIkS1ZsiSWLl2abF+bN2/GwYMHFdtS+1Mz01CV+ho1aoj+p3wfdG4pTFcOtZf6bdy4ceK80jUgP+68QMERqwogmy7sxWtf2UOgatFy6FSjZeorXloMBMvWUytSG04V6uD123d4+doDdrayiBuGYbKP5n/2gX9YxsI/VYJSZQyOpZE5Tk3alKVtIyIisHXrVhQrVkzxJk/aihYtWqB27dq4fPmyePOeNWuWwpxDJggaSGkwJ+2KHBpAFy5cKAbuypUrY8OGDWjXrh2ePn2K4sWLK9ajAZLWo3XkAsnvv/+OZcuWiWX379/HwIEDxSDfu3fvVNtNg+nevXuFqcXe3h6LFy8WbSYBgQZZOWPHjhXtokF30qRJQoh69eqVEB5I4KDfffnypUKbkRYzZ87EokWLxGf8+PHo1q0bXFxcMHHiRDg5OaFfv34YMWIEjh8/nur2yv0UGRkp+pL6Vy7EkQC3e/ducQ6uXbsmzEQkTJBASP1NZrGwsDBs3LhRbEPH6OPjk+w3vL290bp1a2HS2bJlC168eCH6kfpYWeAgwWb06NG4efOmEKpo/bp16+K7775DbsPCSB7FPywI84+sFdMkAc/qMvqLWgFUW0Y7NhCWV5ZBpC9T0wDaLYaDkY0QRmKiY0VBvOLFnHLnIBimkECCyIeQz9qFvMqRI0cUAy8NjDTo0TL5s4U0FzRAkrZCrvWgQZC0JPRG3rx5c4WvBQ3ychYsWCAG6v/9739ift68eTh//rwY9JcvX65Y79dff8WPP/6omJ82bZoQTuTLSEPw7NkzIdSkJoxQm1euXCkEEfJVoTf/NWvW4PTp01i/fr0QQJT3LR9kaRCmQX///v1ikDc2NhbHp3wMaUGCF21D0DGSIDF16lQhABG//PKLWCct5L9BCcU6duwofpuOT17Xhfxv5NDxk5Cwa9cu8Zt0rkhjQtqg9NpK2iTyIyGhjo6LNEwksFB7SeiSn1/SFFG/ECQk0vpkrmNhhEmTuQdXISw6Qkx3rdMGlYskL2aXmJiEV288kfTuDuolaUMTEUDtoYBVKVCe1aJF7PHe5yNrRhgmByANRWbIDs3I19C4cWMxmBPBwcFiEKNB/datW3B2dsbDhw+FhoHMH8pQ4TRlU4Qy9NZOAx+9YStD87Q/ZapVq5ZMsKB9kpmC3uLlkIBBA3Zq0PqkvVH+LRrQyTxBGgRl5NoHuTaBTCAp18kMNIDLIdMQUb58+WTLqH+oH4yMjNLcD2lnSNC4c+eOImspQcLahg0b8O7dO0RHRyMuLu6ro3HouOh4lfN9UB+R9uv9+/dCg5PyWAgSRuWp3HMbFkbyIA88n2P7tcOKTKsT28sK4SkTFByKpDA/aIe5wSAxEDCwBhqPV3xfqmRR8WEYJvvJjKkkL6QvJ/MHmWXkkAaEBv61a9cKcwwNXlWrVlX4c6R07lTF78uh3yLot2vWrJlsPVVUgVUVJOzIkZ+31JaRRiktyBxG5iTSLpFpSc5///0nTDELFy4UwgQJgfPnzxdmlOw+Fnnb02t3TsLCSB6DHlhTdi4S/4mxbQeKKJqUBPgFAH7PYBnrKXvTavEHoJO2VM4wDJMSGoxIhU9v5ESVKlWEqYZqjKT3lq8MrWdnZ4erV68K51Q5NE8ai7QgjQJt5+bmhu7du2fqt1xdXaGlpSX2LX/bJ00JObCSCUiZGzduKNYhLRD5i5ADKkH7IOfRnIC0IQMGDBCmGXKkVYaOg3xYhg0bpliWUgOVmbbScZEfjXLldto3CTdknsoPcDRNHuPg3TO44/ZYTBe3KYK+jTqlup7/sytAXCQs4jwBp5pAJZmtlmEYJi3I94AqDtOHVPsjR44UGgp5hAwJBRYWFiKChhxY3d3dxds8RWGQuj8tyFeD/ERIkCGnUHJUpUgP8qdID/KXmDNnDv7++28hLDx+/Fj4qJCzaFqalaFDh4rfo2gd8i8hh0+qHEvmHmUouof8ISiKhhw16bjk+TrICZeOm74PCAgQ22cH1M8dOnQQvjTkYyLve3n0EvltkNnm5MmT4vjJF0U5MkjeVnIepn6ltqYWEk3CDEVB0fkk51WKviHfEHJWTelrmFdhzUgeIiY+FrP3fw5rm97pZ2iqf3mKov08EOnrAQmksIj3AtpspFecHG4twzD5DRrA5WXe6a2ZHB0pkoPCPgkKX7106ZJwfCSn0vDwcGFWaNq0abqaEhJWKFR2zJgxwgehTJkyOHToULJImtQgjQH9JpkmSMAgYYP8MVJqOZSZO3euMC306tVLtI/8UGgwNzU1/WI9EoZev34tfDAOHz4stAwEaSOGDBmCLl26IDAwUAzc2RHmSoLBx48fhQMtfeSQfw6F5w4ePFhEEHXp0kVoNLp27SoEC+XIHPKnIYGQjpMEKHIMlocGy6FzdOzYMdGHFStWFD4yJJxNmTIF+QWJVG4PYFKFnJLIpko3WmbVlhlBN5K83LKy1Lr81FbM3LdMTDcsXQP//bw0Vdvyq+3T8DYgAWZx71GzhJmIoCnMpNWfTNbhPk0bclYkjQFFPlDoZGbJCz4jBYm0+pMGbnLUJdNMallWGdVfo+ndE5kdQ/kpk0cIjAjB0uMyJzi6CKZ1HJn6xeB9H9Hvn4pJpyQPoFn+kXwZhmEYJjXYTJNHWHhkXbJQ3jIOqag3SYl1fDIqhl2Ba+Qt6DYfD+hlb9phhmEYhsluWBjJA7zx9cSWS/vFtJ62Lsa3S15/RsHzo4DHFTFpYGIG1Pwcm88wDMPI0p6z90H+g800eQDyE0lIkoVuDW/eA9bGFl+ulBCH2JOzEKv2qYhTi5mAhswZi2EYhmHyM6wZyWWuv76Pk48ui2kbY0sMadYt9RVvrYN7rCk8LJqguF4wXEt/XaVNhmEYhsmrsGYkFyFV4p9Kobzj2w+GvvbnNMEKooKQeP4vvNcpAykkMKjVi0N5GYZhmAIDCyO5yKnHV3D7U4KzErZF0blWq9RXvLgAH5MsEK+mA21jK1iWSZ7Fj2EYhmHyMyyM5BKJSYmiGJ6cSe2HQl0tlXoMwe+AG2vgpVsOkKjDsXJDqKmxVoRhGIYpOLAwkksce3QJLz+4i+mqRcuhRcX6qa94bg4ioI8gLXtIzJzhWEJWW4FhGIZhCgosjOQCsfFxWHF+u2J+codhqSc4+/gc0gf/4alhY0BdE5YlKkNHRztnG8swTKHyY6NaL5ROnJ5JVF8mP0PHcODAgW/aB9W1kde0yS4uXLgg2hoSEoLCCgsjucC/Vw7gQ6isUFLjsrVQp0SV1Fc88wei1AwQqWECdXMXlCrDWhGGYb69iqy6ujq+//77VGvXbNq0CUeOHMGHDx9Qrlw5lQzo2Q3VlaH6MymhY2jVKg1fvEyydOlS0SeqzIOSsvZOnTp1RFspbXphhYWRHCYiJlKR9l3uK5Iq726KJGf6iaGoG3cOVRo1h75+KpE2DMMwX8H69etFdVcqiOfj45PsOypfT4X0aHC0sbERNUpURWrVZrMbOgZt7W/TJpOAkN01brS0tERbC3PNonwjjFCVQjpRyh+qyphR8Z7hw4fD3NwcBgYG6Nixo6igmJusPvufqENDtK/WDOWdSn65EmUPPPW5gqR241GwsLHJyWYyDFMAoaqvO3fuxNChQ4VmRPmNn8wRJKS8e/dOPF/pmSuvDtuhQwfFMjlUpr5KlSqiMJqLiwtmzJghiqzJofVXrlyJdu3aiWq8s2fPTrVNVNCOKvBS1V2q4EuaDKq0K4faSMIAaWeoCjD9XosWLeDl5aX4nn774cOHirFBflzKWh2qkkvzu3btQv369aGrq4vq1avj1atXuH37tqiKS+ME/b6/v3+qZhr5PlJ+5FWPqQIwVd6lKrp0LFSBeMeOHcn2dfHiRaFtkW9L+7yQiplm7969KFu2rBCmqN8XLlyYrN9o2Z9//ol+/fqJCsxOTk5Ys2YN8iv5KunZH3/8Icopy6ETkB6jRo3C0aNHRYlskm5HjBghymJfvXoVucXH0ABx0alL1DCuzaBU15G+OoXHQbow1ykJe4NEoErPHG8nwzBfwYqGQETGLzoa9KKhyrdfA2tg2MVMr04DcalSpVCyZEn06NFDmAsmTpwonkk0QLq6uooBjQZnMuUQVLl548aNaNmypWLZ5cuXhQDx999/i4GdNCrka0JMmzYtmfmEXhqXLFmSppaFBmgSPg4dOiSquo4fPx6tW7fGs2fPoKmpKdaJiooSwsyWLVuEFmHYsGFi0KdBvEuXLnj69KkwMZ05c0asn565g9pH7aHBmwbybt26ibGEjp8EiM6dO+P3338XglRKHB0dhTlFjq+vL5o1a4YGDRooXoCrVq0qjoGOhcafnj17in6tUaOG+A0Sfsj8ReMZYWlpKQQSZe7evSvaQf1Hx3ft2jVxzPRiTf0lhwSUmTNnYtKkSdizZ48QMhs2bCjOb34jXwkjdMGQKiszULliUkdu374dTZo0EcvohipdujRu3LiBWrVyJ1fHX93Go1e9Djj/8BqKWjl8uUJSEt6f3QxvndLw0S4J0/rVoaeer04TwxQ+SBAJS27ySEleUMDTM5GEEIKEC3pO0ps6vdnTAE7PWBI4Uj5nSTOhvIw0ERMmTEDv3r3FPGlGaFAcN25cMmGEBvq+ffum2R65EEIviGQaIrZt2yYGfdJo/PTTTwoTz7Jly1CzZk0xv3nzZvEsJ6Gpdu3aQqNBwk5mxofffvtNaFaIX375RQg1Z8+eRd26dcWy/v37p+kjotw3JHiQxoR+n4QGgjQitH85pGk6efKkEAJJGKE+JmGKhJ702rpo0SI0bdoUU6dOFfMlSpQQwtn8+fOTCSMktJGQQpAAtHjxYpw/f56FkeyGJGy64EmipYucNB9pSdskWdIFTFKrHHojoG3JgSstYSQ2NlZ85ISFhYn/SUlJ4qMKStm5wFzTMNX9SZ8cgFusGaAOFNfyhU6Fdir73YIK9Q9FAXA/qQ7u04z7Rv4RGFhlbmOVa0asZPvMBC9fvsStW7ewb98+0W4aWOntmwQUepuWNU+2r5SF5pIdKyBMIiRAKJteEhMTxQAdGRkpBluCtATpFa2jAZae4TRQy9ejSB4aTOk7+e/SOmRGka9D35OA9Pz5cyEMpNVu5bbLvyPTiXyatD4EaSqUl/n5+aXaB8qQViU8PBynTp0SmiX6nvqATCekjff29kZcXJwYT6g/lLdP2Z9SpfbTh46LzFvK65CwRhodMoXJNVTKx0KQgEOuCN9SKDC9vkxvG/nzIuUzI7PPkHwjjPz888/CPkkXKqmsSLVI6jKSIFOD1GckgaZ0PLK2thbfpcWcOXOE1J8SsiHSjaYK6OTQGwmdPDU1JbcdaRIk59YgSr0q1KUJMKrUEn4BASr5zYJMmv3JZBnu07ShlxzqHxoUFD4SA89muJ18sKKBRKWOikp+Gumxdu1a0V56e1duE/kk0Bs1vbXLBw5l3w+C2q28jHxPyJSRWsgrCQ7ydcm/I+W+Uu5X/nspB2d5Hyu3KeW1SN/Jzwdtk9pvydsu/476Xj4t37fyMuXflq+jPE+QwEEaDxLIyPdE/t1ff/0lTFcLFiwQAg75ypCmhMYO5f2nbGuiUj/I+yLlb6bWV3QtpTzmZNflVyK/RuV9klnk54l8ZuSmNTkksOV5YYTUfPPmzUt3HZIQSaMxevRoxbIKFSoIQWPw4MFCePhWb2llSMhR/i3SjJDKkOx6ZANUBXTS6ETTPpPdXE/240mcHqAD2OjEwLxGJ65B8y39yWQZ7tO0oYGFHrA06GYl2iTlwzonoMGCzB80SDZv3jzZd+ScSm/yQ4YMUZxr5eOSt1d5Gb0YkomFns3pQYNlen1EAza1jTTZcjMNDWhyvwraltpE61DOE9KgyLU85OxZpkwZ0T4SeuiaTe235G2Qf6c8LdcwKC9L2Qc0Tx/5PDmWkkbo2LFjX5hDyAWANBpy8xW1ifqJ2infnsarlG1VT9EOWp/2pbwOzZO5Rnm8U24XQfdsymVZ4WuvUfl5Ip8WOhfKpJxPcx/IRcaMGZPM/pUaZItMDbId0gVKjj+p2cdIXUUqMrpglbUjpMJKz1ZHJzo14UZ+QaoK+UWj2GdSIhLOL4CvtswRyrHm91D7dIEyWehP5pvhPk0d6g/lSIqveeuUr5/TIZzkSElRKwMGDPjCuZOiDDds2CCcH1NrH0VtnDt3DvXq1RPPRop6Ia1ImzZt4OzsjE6dOok+IdPNkydPMGvWLMW2GfURDa7t27cXzq+rV68WPiv0kkraG9K6yLenwZG046R1oIGPghHI1C4XTooWLQp3d3fRBgcHB7Ef+XM85blKOZ3eMmVono6PBA3yzyBhSR6dSS/HpLWnaB9yJCVXAOon0tzTOiRcyPdH/UnmMk9PT+HrIk8wp9wOGhsp0of6khxYaX/Lly/HihUrkrUrtf792utSFdeo/DdTe15k9vmRq08ZeusiyTq9D53k1CApmQ5SbvNLCdkq6QImxyQ5JE1T2BrZGPMcj/fhQ7gUiRIN6Otqw6TCZ18XhmGYb4H8Qsh/LrUoExJG7ty5g0ePHqW6LUVsnD59WmiIK1euLJaRAyglRiN/CRo0STAgUw8JJ18LBRbQ85qEG7n/B2kdlN/OyeeCBADyFSRHUxrE//vvv2THQA65jRs3FuOKcjitKqF+osgeEhIoH4v8Q1GaxJQpU4TWiPqHnILpxTelKYvMNqQJIQGF2kpjUkpoH+T0SsdIQg8JfxR9k9HLe35GIv0WT5ccgqTCmzdviguNJF6aJ+dVigcnr2qCnIXI+5hCv+TSMkn6dFGTZzSZWMizmSCfk8xCZhq6gcl+rkozDTlIkSAlpMakRODvGggOjYC7XmWY1u+LorVk3t5MFvqT+Wa4T9M309BbOL2NZ1YFTcj9BOjNvjAnt/pa6PlNIcgpU6Vzf6qerPZpevdEZsfQfOHASuo2khApfIo8k+mASRhR9u0gJybSfJDUKockdXqQktRM25G0SmquPMej3UDAa5gCMDV1AWomt+kyDMMwTEEmXwgjpLIi5530IDtcSiUPSWhkZ6NPniUxATivlEm2ySR2WmUYhmEKFax/zW0e70FSoDve6FVHZJEWgEv93G4RwzBMnoH8JApzNdvCAgsjuQnFuF9ciAAtJ7w2qIXr1r2QlJTnXXgYhmEYRqWwMJKbPD8M+L+Et24ZQNcMdsXKQE2NTTQMwzBM4YKFkdyC4rkvLUQi1OGnVRQwd4GDvXVut4phGIZhchwWRnIJLc+LkHx4hGAtOyTpmkHb3B6Ghvq53SyGYRiGyXFYGMkNpFIY3JGVpw7QcgbMXWFhbsqx8gzDMEyhhIWR3MD9MrR874vJAOOKovKmhXnygn4MwzAMU1hgYSQXIF8RIl6ihSiryiKvCAsjDMMw2QelZ6dMrhnRoEEDbN++HbmdddYkRcV5VYZKp1ZtOTUCAgJEFub3798ju2FhJKfxugWJ20UxqWFqj6btfkDtGhWhpZXzlTwZhik80CBEpmCqzpuS4cOHi+8Kcu2TzHDo0CFR2O5///tfjv0mJexcsmSJyvdLRWTpnFIdN2WWLl0qhJ3MYGFhgV69emHatGnIblgYyWkuLFBMSuuPgrqmFkxMDHO1SQzDFA6o2B2V1oiOjk5WV4Q0AU5OTsjrUCX27ISqAvft27dA12MyNjb+Kq0L9ce2bdsQFBSUre0quD2eV2k4BtISLZBoYAtUzDnpm2GY7CUhITHNT2JiksrXzWppDRJI9u3bp1hG0ySIyCvyKhdLnDNnjqgFpquri4oVK2LPnj2K7xMTE9G/f3/F9yVLlhRv3cpcuHBBFC7V19cXAyBV3PX09EzTXEBmFDKnyKHpESNGiOX0lk71xYgnT56gdevWMDU1FZVxe/bsKUwKciIjI8UbPVX3paq6VHk4I/z9/XHu3Dm0bdtWsYxKjFBNNOofqpFmZ2eHn3/+WXxHVXSpom5KKlWqhKlTpyY7xgULFoh2mJubCy0U1VKTHx/1x6hRo4QWI2UQw8mTJ1G6dGlxHFSV+MOHD8m+X7dunfieSp9QlXvl2mt0Xgg6r7Rfeb+m7Hc6z3/99ReKFSsm9uPq6orZs2crvi9btqw47v379wOFvTZNgcKpJqQ9dsLz5VN43XoCW2tLlCj+9WW3GYbJW5w+ez3N7ywtTVG1chnF/LkLN78QOuSYmRmjZvXyivkLl+8gPk42eCnTqkW9LLWzX79+2LhxI7p37y7mN2zYIN5+SXBQhgSRrVu3YtWqVShevDguXbqEHj16iLL3DRs2FIOYg4MDdu/eLQZZqoY+aNAgMeh27txZVH+lQW/gwIHYsWOH0GrcunXrq6MGqTI7VWC/evWqmKfU8E2aNBGCEA2iNLBPmDBB/CYJE8TYsWNx8eJFHDx4UPg8TJo0Cffu3ROCQlpcuXIFenp6YnCXs3fvXlFwlbRJNCj7+vri4cOHin6cMWMGbt++jerVq4tl9+/fx6NHj5IJe+fPnxd9Qv/fvHmDLl26iHZQv9B6JOQNGjRIzCtDRV9JiPn333+Fpob6/rfffhNaCoL+//7771i2bJkQOOi3aR8k+PXu3Vv0NQmCZ86cEW3X0tJK9bgnTpyItWvXiuMkYZH8Q16/fp1sHdrP5cuXRZ9nFyyM5BLBMRJERkYjLDwyt5vCMEwhggY1GoDkGgoa5GmwVRZGqMr5n3/+KQay2rVri2UuLi5iwF69erUQRjQ1NcVgrPwmfv36dezatUsIBlQ6nsrGt2nTRrxtE8oDfWYhQYiEDjmzZs0Sgy+1T17ungQq0vi8evVKvMWvX79eCFJNmzZVCDQkOKUH9Ye1tXUyE827d++E5qVZs2bieElDQgMzQfsjTQ0JdnJhhKapb6iv5JD2hgQGdXV1ob34/vvvcfbsWSE4mJmZieWGhobid5QhIYsEQXnfkYaItDFyyI+DND4//vijov+fPXsmzg8JIyQ0EiQopty3nPDwcKHNovbRNqQJcnZ2FsegDPUpCTvZCQsjuURoaIT4z1E0DFMw+K6pbNBOjZTagCaNamZ63Ub1q0GV0CBFAyI5MdLgQ9NkAlGG3uDpzfy7775Ltpy0G8rmHKqIToIADdrkh0Lfy7UPNNCSSYAGbNoPDegkpJCW4GuoWrVqsnnSTJCWgQbwlLx9+1bRjpo1P/cxtYXMSOlB25GZQpmffvpJOJeScEFmEjINkRmHBCCCBArSkCxatEgIMeR7QxoGZUgrQQKHHDr+x48fZ3jcenp6CkFEvp2fn5/CDEXHSpoKZY0KCWfkE5JZnj9/LgRPudCWFmSGo+shO2FhJBcge294eBQ0tTRhYWGa281hGEYFaGh8HnBSgwb+zK77NfvNCjSA0pu2XKBISUSE7GXp6NGjsLe3T/Yd+U4QpE0hswG9nZP2hISD+fPn4+bNm4p1SVNAPhYnTpzAzp07MWXKFJw+fRq1atUSg7dynxByXwplyOyQsm0kEMydO1ehGZELcDRgkyCVFUggCw4OTraMtC0vX74UGiJq97Bhw8QxkgmINCXUDuoP8qcgMwi1v1OnTsn2QespQ20lE1dGaKaynby/5OeHzCvKQhehLPhkBAkZmYGcV+WaluyChZFcIDgkDElSKXR1dKCvl1wSZxiGyW7oLZ+0BzTAyZ1ClSlTpowYZEnjkVJlL4fMO3Xq1BEDtBx6W08JaVLoQ6YhElpIe0DCCA1u5IiqDIWhphyEU3PCJV8OCokllIURgrQJtA8SiuQRQiRkkAknrWORt5N8QmhdMq0oD9gkdNCHnE/J1EKaDWoH/TaZN0joImGEQoIzO8DLoe0SE7/OIZnMSWQ6cXNzU/j+pLZfIr19kwmM2ktmowEDBqS5Hp0nZcfi7ICFkVwgMDBE/Dc3N+YU8AzD5Dj09kwqevl0SkjLQVoPivKgt/h69eoJ/w8SQIyMjMQATAPZli1bRMQH+SuQoyU5c8qjONzd3bFmzRq0a9dODJykYSDHSIpyIcgJlbQMtA8SUsjHgwa9lFE9KSGBgDQC3bp1w+jRo4VQQ0IQaWoouoQiT8h8QU6s5C9BDqyTJ0/OMFyXfpe0I3SM5OdCkCmLBnPSPpDZhNpIgzf5VcihQVzuCyN3sv0aSKi6dOmSEGRIAExpMksL8tchrROZZUi4JHPLnTt3hDBF/ULHTW0lrRT5t5AJKqUJh5aNHz8e48aNE8ILCZckkL148UIhnJB55u7du8JHJzvh0N5cIOCTMML+IgzD5BYkVNAnLWbOnClCVCmqhgZbGvDIbCMXNgYPHiycJyk6hAbrwMDAZFoSGrxpUOvYsSNKlCghIkZIkKDtCNLI0P5pICQHUHKmlAsq6UGCDQ36JCSQD0eFChVE6C+FDssFDhJy6tevL7QZ5KtCwlRK35OUkFAmz6khh/ZJgg9FmdDvkLnm8OHDQsiRQ0IZDeKkMUlpMskM5JTq4eEhNDpfYwohYYGEL9LKlC9fXmh9SHiSnx/S2lDeFHJopT5r3759qvuhczBmzBgRmUMaMdK0yH1TCIpIIg0T9Wd2IpGmNNoxySCPcJIm6a0gvRs3s9BbxpNnb+Dt7YsmjWpBWzv1cCvm6/qUbh56EyjIyYpyEu7TtKEkYfTWTw/9lA6P6UGP2pQ+DkzWyY7+JK0AOZxSGLCy9iOjdpBAQoIYaSQKWp/WqlVLaGBIE5WVeyKzYyibaXIYerCXK1MMVhZG0NTk7mcYhskrUAgshQWTr0xmhBFKlEbmIRJiSKtS0AgICBDar65du2b7b/FoyDAMwzCfyGwROYI0h+TjQb4xyk6vBQULCwthRssJWBhhGIZhmCzAXg6qg43BDMMwDMPkKiyMMAzDZAF+K2YY1d0LLIwwDMN8BfKkXNmdHpth8gvyeyGjhHXpwT4jDMMwXwHlo6D8E/JcDJRPIzOhpRzaq1q4P3O/T2l9EkToXqB74mtS0aeEhRGGYZivRF4FVTk5VGYe3JS/hcL7efD8drg/806fkiCSVmXgzMLCCMMwzFdCD2oqykahnakVd0sNeshTllLK3smJ5L4d7s+80adkmvkWjYgcFkYYhmGyCD2EM/sgpgc9PbgpQyUPnt8O92fB6lM+gwzDMAzD5CosjDAMwzAMk6uwMMIwDMMwTK7CPiOZTOZClQdVaZejctls61QN3J+qh/tU9XCfqhbuz/zRp/KxM6PEaCyMZACdGMLR0TG3m8IwDMMw+XYsNTY2TvN7iZRzGmcoKfr4+MDQ0FBlsewkKZJw4+XlBSMjI5XsszDD/al6uE9VD/epauH+zB99SiIGCSJ2dnbpaltYM5IB1HkODg7Zsm862XwTqQ7uT9XDfap6uE9VC/dn3u/T9DQictjQxjAMwzBMrsLCCMMwDMMwuQoLI7mAtrY2pk2bJv4z3w73p+rhPlU93KeqhfuzYPUpO7AyDMMwDJOrsGaEYRiGYZhchYURhmEYhmFyFRZGGIZhGIbJVVgYYRiGYRgmV2Fh5Bu5dOkS2rZtK7LLUYbWAwcOpLv+hQsXxHopP76+vsnWW758OYoUKSJqBNSsWRO3bt1CYSE7+nTOnDmoXr26yKRrZWWF/7d3PkBVVF8cP4xAEo2klpaYlCTgX9QaFE2kBM3oj9mIGpalNVqmhulkoVma+WcqHckQsTCboLQiyaQ0JVIRIzPUxKwgKbUowuKPIjL3N9/z++3+3iKQwD6X9975zCzs3b1793Le8u7Zc869Z9SoUfT999+TK2CvZ1Rj6dKlfP7JJ58kV8FeMj1x4gRNmDCB2rdvT15eXtS7d2/6+uuvydmxhzxrampo/vz5dMMNN7As/f39adGiRf+aI8VVZQqqqqooLi6O/Pz8eEYNxqA333yTbNm0aRMFBQXx2ITnc+vWrWQGoow0k4qKCgoODmbloTFgIDx16pS+YYDUeO+992jWrFk8xeqbb77h9keMGEHFxcXkCthDpllZWTRt2jTKycmh7du3U3V1NQ0fPpzv5ezYQ54aubm5lJiYSH369CFXwh4yLS0tpcGDB5OHhwdlZGTQkSNH6JVXXqG2bduSs2MPeS5btowSEhLotddeo/z8fC4vX76c4uPjyRWoaIJMo6OjaceOHfTGG2+wbFNTUykwMFA/n52dTePHj6fJkyfTgQMH+KUO2+HDh5vfYUztFcwB4kxLS2uwTmZmJtcrLS2tt05ISIiaNm2aXq6pqVGdOnVSS5YsUa6GWTKtTXFxMV+TlZWlXAkz5VlWVqa6deumtm/froYOHapmzpypXBGzZPr000+rW265Rbk6ZskzKipKTZo0yXBs9OjRKiYmRrkadBEyzcjIUD4+PqqkpKTeOtHR0SxXWwYMGKCmTJnS7D6KZcQi+vbtS9deey1FRkbSnj179OPnzp2j/fv3U0REhCE/Dsp79+61qLeOLdO6+Pvvv/l3u3btLlHvnE+esDRFRUUZnlWh6TJNT0+nm2++mcaMGcNv+P369aOkpCTL+uro8hw0aBC/5R87dozLeXl5tHv3bho5cqRFvW3ZpP/v+YP1yNfXlwICAmj27Nl05swZvQ7GoNr/77DamzE2SaK8Swz+cdasWcMfOvxz69ato/DwcNq3bx/179+f/vzzT/Z1duzY0XAdykePHrWs344s07oyMSO+ASbxXr16WdJnR5fnu+++yy5EuGkEc2RaUFDAbgW4aJ999lmW7YwZM8jT05MmTpxo9Z/gcPKcO3cuZ6FFfEOrVq34e3Xx4sUUExNjdfdbJAUFBaysIRYkLS2Nx6LHH3+cSkpKKDk5mesgJqeusam+eLJG0WzbitAoU1hdhIWFqQkTJvD+iRMnuJ3s7GxDnTlz5rD7xtUwQ6a1mTp1qvLz81O//PKLcjXMkGdRUZHq0KGDysvL08+Lm6b5z6iHh4cKDQ011Jk+fboaOHCgciXMkmdqaqrq3Lkz/z548KDasGGDateunVq/fr1yNegiZBoZGalat26tTp8+rR/74IMPlJubm6qsrNSf0ZSUFMN1q1ev5u+D5iJumhZASEgI/fjjj7x/1VVXsRb/+++/G+qgfM0111jUQ8eWqS1PPPEEbdmyhTIzM6lz586W9M3R5Qk3IoKp8Qbq7u7OGwKEV61axft4AxUa/4zibb9Hjx6GOt27d6eioiILeuf48pwzZw5bR8aNG8ezPh544AGKjY3lmXXCheD5g3vGx8fH8PxBl/n111+5jDHIXmOTKCMtgG+//ZYfBACT7E033cS+Tlu3AsqhoaEW9tJxZQrwDwVFBObHnTt38nQ/oWnyHDZsGB06dIiPaRvM5TB/Yx/KtND4ZxRuw9rTzRHvgGmWQuPlWVlZyfF2tuDZxPepcCF4/k6ePEnl5eWG5w8y1F7cMAbZjk0AsxPNGJskZqSZ4IOz1cYLCwv5nwKBkV26dKFnnnmG1w7YsGEDn1+5ciUPhD179qSzZ8+yrxOD47Zt2/Q24DOGjxhf8ND2cQ2maT388MPkCthDpgi2TElJoc2bN/NaI5qPE28BWIPAmTFbnpBf7Vgbb29vXhvDVWJw7PGM4q0dQZcvvfQST7HE2kJr167lzdmxhzyxxgZiRHA96mEq6quvvkqTJk0iV6C8kTK9//77eR0WjDMvvPACx4zAugR5ad+RM2fOpKFDh/KUcwSvI3YM6+CY8ow229Hj4mhTzGpvEydO5PP4DX+6xrJly5S/vz/75uC/DA8PVzt37ryg3fj4eNWlSxfl6enJsSI5OTnKVbCHTOtqD1tycrJyduz1jNriajEj9pLpxx9/rHr16qUuu+wyFRQUpNauXatcAXvI859//uFnEt+jqNe1a1cVFxenqqqqlCuQ2UiZgvz8fBUREaG8vLw43mbWrFl6vIjGxo0bVUBAAI9NPXv2VJ988okp/XXDj+arNIIgCIIgCE1DYkYEQRAEQbAUUUYEQRAEQbAUUUYEQRAEQbAUUUYEQRAEQbAUUUYEQRAEQbAUUUYEQRAEQbAUUUYEQRAEQbAUUUYEQRAEQbAUUUYEQbhkPP/889S3b19qKbi5udFHH33U6OuQQwbJwcrKysieYEnuDh066InKBMFZEWVEEJyMNWvWcP6Y8+fPG/JUeHh4UHh4uKHuF198wQPyTz/9RM6M2UoQ8npMnz6d5WxPkMX7wQcfpAULFtj1PoJgNaKMCIKTceutt7LygQRWGrt27eI3+X379nFiMY3MzExOmuXv729Rbx2PoqIi2rJlCz300EOX5H5IXPbOO+/QX3/9dUnuJwhWIMqIIDgZgYGBnEodVg8N7N9zzz2c6TQnJ8dwHMoLePvttzlTNN72obggi2dxcTGfQ9p1pBFPSEgw3AuZUJFi/Pjx41w+ffo0PfLII3T11VdTmzZt6LbbbqO8vLwG+4uMq927d6fWrVtTUFAQvf766/q5n3/+mS03H374Iffz8ssvp+DgYNq7d6+hjaSkJLruuuv4/L333svZWa+88ko+t379es5Cin6gLWw4ZusKwTW4tlu3bpSent5gfzdu3Mh98PX1bdDygsyy119/vV6G8jJq1CjOytuxY0fu38KFC9mCheyoyKYKGScnJxvaQcbZTp06UVpaWoP9EgRHRpQRQXBCMHDD6qGBfbhokP5bO37mzBm2lGjKSHV1NacQx6CNOAooAtrbPxSO8ePHU0pKiuE+eGMfPHgw+fn5cXnMmDGswGRkZND+/fupf//+NGzYsHrf6nH9c889x6ne8/PzeaCeP38+vfXWW4Z6cXFxNHv2bE6BHhAQwH3R3FB79uyhqVOncnpznI+MjOT2NMaOHUtPPfUUD+qnTp3iDcc0oKhER0fTwYMH6Y477qCYmJgGrRCwMkFpawpIc3/y5En68ssvWWGC++XOO++ktm3b8meBv2PKlCkXxIiEhITwfQXBaTEl968gCC2KpKQk5e3traqrqzmVuru7uyouLlYpKSkqLCyM6+zYsYNTih8/frzONnJzc/l8WVkZlw8cOKDc3Nz0+jU1NcrX11clJCRwedeuXapNmzbq7NmzhnaQ6j0xMZH3FyxYoIKDgw3n0CdbFi1apEJDQ3m/sLCQ+7Bu3Tr9/HfffcfHkO4cjB07VkVFRRnaiImJUT4+Pnq59n010M68efP0cnl5OR/LyMioV7ZoZ+HChYZjdbW/YsUK5efnp5eRsh1lyE0jMDBQDRkyRC+fP3+eP7fU1FRDW7GxsSo8PLzePgmCoyOWEUFwQmAFqaiooNzcXH6jhjUBrhNYRrS4EbhounbtyjEjAJaMu+66i8tw1aCuFiMB4IaAO0WzjmRlZbEVBNYQAIsKYlXat29PV1xxhb4VFhbWGSCL/uH45MmTDfVffPHFC+r36dNH34cLCmguJMxsgeXAltrlhrBt29vbm91LWtt1AYsSXEpNAdYZWJk04K7p3bu3Xm7VqhXLr/b9vby8qLKyskn3FARHwN3qDgiCYD433ngjxx/AJVNaWqorFog9QGxFdnY2n0NMh6YYjBgxgje4TqC4QAlB+dy5c3q7cGFAGZk7dy7/vv3223nwBFBEaseqaGjxG7agvhbvMWDAAMM5DMq2YCaQBmI+tDgWM7BtW2u/obYxwwUytQUKxn8NLf8Hbq+LudfF3B9uI3wmguCsiDIiCE4KYkGgGGDgRICkRlhYGMd0fPXVV/TYY4/xsaNHj1JJSQktXbqUlRVgOxtHA0Gt8+bNYyvK+++/z9OINRAf8ttvv5G7u7shcLM+YBWAclRQUMBKTnMCdmEBsqV22dPTk2pqasgM+vXrR0eOHDEcg6KAvx0KiaYsIX7FLA4fPnzBtGxBcCbETSMITqyM7N69mwdFzTICsJ+YmMgWDy14Fa4ZDNjx8fGsHGBGCYJZawMlY9CgQexaweB+99136+ciIiIoNDSUZ4xs27aNA2BhgUHwaV2KjRY8umTJElq1ahUdO3aMDh06xLNJENx5sWC9j61bt/I1P/zwA/9tULY0pUDrN9xFkAVmz1RVVVFTgbUIs3lslRsoCn/88QctX76cXUyrV6/mPpgB3DNQ/oYPH25Ke4LQEhFlRBCcFCgaiG+AywZWCFtlBCuHalOAtTd7THfdtGkT9ejRgy0kL7/8cp3twoqB+BBMh0UsgwYGfygFsLxgbQzEqYwbN46n/dre3xZMA8bUXiggiJ1A39APTEG+WDCbBxYaKCOYcvvpp59SbGysIa7jvvvuY5cSZIK/NTU1lZrKyJEj2frz+eef68cQS4MpyVBC0AdYnTD7xww2b97MyuKQIUNMaU8QWiJuiGK1uhOCIAhm8uijj7LryV7TYaF0wHr02Wefkb0ZOHAgzZgxg11kguCsSMyIIAgOD6w4WF8Es2HgHsE6JbaLp5kN1gLBAm+wMNlzSXi4lEaPHs3rqgiCMyOWEUEQHB4sWoZgXSgHmK6MOBIsICYIgmMgyoggCIIgCJYiAayCIAiCIFiKKCOCIAiCIFiKKCOCIAiCIFiKKCOCIAiCIFiKKCOCIAiCIFiKKCOCIAiCIFiKKCOCIAiCIFiKKCOCIAiCIJCV/AfRdbb2vtP3jwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "ax.plot(\n",
+    "    wl_base,\n",
+    "    base_db,\n",
+    "    label=\"Before optimization\",\n",
+    "    linewidth=2,\n",
+    ")\n",
+    "ax.plot(\n",
+    "    wl_calib,\n",
+    "    calib_db,\n",
+    "    label=\"After optimization\",\n",
+    "    linewidth=2,\n",
+    ")\n",
+    "ax.plot(\n",
+    "    wl_meas,\n",
+    "    meas_db,\n",
+    "    label=\"Measured (synthetic)\",\n",
+    "    linewidth=1.5,\n",
+    "    linestyle=\"--\",\n",
+    "    alpha=0.7,\n",
+    ")\n",
+    "ax.set_xlabel(\"Wavelength (um)\")\n",
+    "ax.set_ylabel(\"Transmission (dB)\")\n",
+    "ax.set_title(\"Spectrum Before vs After Calibration\")\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "030bb489",
+   "metadata": {},
+   "source": [
+    "## Takeaways\n",
+    "\n",
+    "By calibrating the simulation to match measurement we keep the model and fabricated hardware in sync. Combined with robust optimization this closes the loop between design, fabrication, and test, enabling faster debug and higher-yield deployment of inverse-designed photonics."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025-10-09-invdes-seminar/README.md b/2025-10-09-invdes-seminar/README.md
new file mode 100644
index 00000000..cbde69cf
--- /dev/null
+++ b/2025-10-09-invdes-seminar/README.md
@@ -0,0 +1,37 @@
+# Inverse Design Seminar Demos
+
+These notebooks track the inverse-designed dual-layer grating coupler workflow that we presented during the October 9, 2025 seminar. Start with the simulation setup, follow the optimization and robustness studies, and finish with a calibration example that ties measurements back into the digital twin.
+
+Seminar recording: https://www.youtube.com/watch?v=OpVBJmomzoo
+
+## Repository Layout
+- `00_setup_guide.ipynb`: builds the baseline Tidy3D simulation for a dual-layer grating coupler and visualizes the initial, uniform geometry.
+- `01_bayes.ipynb`: performs a five-parameter Bayesian optimization to locate a high-performing uniform grating without gradient information.
+- `02_adjoint.ipynb`: expands to per-tooth parameters and applies adjoint gradients with Adam to apodize the grating and boost peak efficiency.
+- `03_sensitivity.ipynb`: quantifies fabrication variability through ±20 nm bias sweeps, Monte Carlo sampling, and adjoint-based sensitivity analysis.
+- `04_adjoint_robust.ipynb`: optimizes the adjoint design against nominal/over/under etch corners by penalizing performance variance, yielding a fabrication-aware geometry.
+- `05_robust_comparison.ipynb`: reruns the Monte Carlo experiment with the robust and nominal designs side by side to measure yield improvements.
+- `06_measurement_calibration.ipynb`: demonstrates how adjoint gradients can back-fit SiN widths so simulated spectra line up with measured (synthetic) data.
+
+Supporting assets:
+- `nbconvert/`: Python exports of every notebook (`jupyter nbconvert --to python`) for quick diffing and review.
+- `setup.py`: shared simulation utilities, geometry constraints, and helper routines used across the series.
+- `optim.py`: lightweight, autograd-friendly Adam implementation plus parameter clipping helpers.
+- `results/`: JSON snapshots of intermediate designs (Bayesian best guess, adjoint refinements, robust solution) consumed by later notebooks.
+
+## Getting Started
+1. Install dependencies (Python 3.10+ recommended):
+   ```bash
+   pip install tidy3d bayes_opt autograd pandas matplotlib scipy
+   ```
+   You will also need an active Tidy3D account and API access since every notebook submits jobs with `tidy3d.web.run`.
+2. Launch Jupyter and open the notebooks in numerical order; each one assumes the prior results exist in `results/`.
+3. If you prefer scripts, run the equivalents under `nbconvert/`, but keep in mind they expect the same working directory layout.
+
+## Suggested Workflow
+- Use `00_setup_guide.ipynb` to verify your environment and understand the baseline geometry.
+- Iterate through optimization (`01`–`04`) to see how global and local methods complement each other.
+- Leverage the sensitivity and comparison notebooks (`03`, `05`) when you need wafer-level statistics.
+- Apply `06_measurement_calibration.ipynb` after you gather measured spectra to keep your model synced with hardware.
+
+Enjoy the seminar content, and reach out if you adapt these workflows to your own devices - we’d love to hear what you build.
diff --git a/2025-10-09-invdes-seminar/optim.py b/2025-10-09-invdes-seminar/optim.py
new file mode 100644
index 00000000..a73a2761
--- /dev/null
+++ b/2025-10-09-invdes-seminar/optim.py
@@ -0,0 +1,132 @@
+"""Utility routines for functional-style optimization in the tutorial notebooks.
+
+The helpers here avoid mutating inputs so they play nicely with autograd.
+"""
+
+import autograd.numpy as np
+from autograd.misc import flatten
+
+
+def clip_params(params, bounds):
+    """Clip a parameter dictionary according to per-key bounds.
+
+    Parameters
+    ----------
+    params : dict[str, np.ndarray]
+        Dictionary mapping parameter names to array values.
+    bounds : dict[str, tuple[float | None, float | None]]
+        Lower and upper limits for each parameter. Missing keys default to no
+        clipping. ``None`` disables a bound on that side.
+
+    Returns
+    -------
+    dict[str, np.ndarray]
+        New dictionary with values clipped to the requested interval.
+    """
+    clipped = {}
+    for key, value in params.items():
+        lo, hi = bounds.get(key, (None, None))
+        lo_val = -np.inf if lo is None else lo
+        hi_val = np.inf if hi is None else hi
+        clipped[key] = np.clip(value, lo_val, hi_val)
+    return clipped
+
+
+def _flatten(tree):
+    """Return a flat representation of a pytree and its inverse transform."""
+    flat, unflatten = flatten(tree)
+    return np.array(flat, dtype=float), unflatten
+
+
+def init_adam(params, lr=1e-2, beta1=0.9, beta2=0.999, eps=1e-8):
+    """Initialize Adam optimizer state for a parameter pytree.
+
+    Parameters
+    ----------
+    params : dict[str, np.ndarray]
+        Current parameter values used to size the optimizer state.
+    lr : float = 1e-2
+        Learning rate applied to each step.
+    beta1 : float = 0.9
+        Exponential decay applied to the first moment estimate.
+    beta2 : float = 0.999
+        Exponential decay applied to the second moment estimate.
+    eps : float = 1e-8
+        Numerical stabilizer added inside the square-root denominator.
+
+    Returns
+    -------
+    dict[str, object]
+        Dictionary holding the Adam accumulator vectors and hyperparameters.
+    """
+    flat_params, unflatten = _flatten(params)
+    state = {
+        "t": 0,
+        "m": np.zeros_like(flat_params),
+        "v": np.zeros_like(flat_params),
+        "unflatten": unflatten,
+        "lr": lr,
+        "beta1": beta1,
+        "beta2": beta2,
+        "eps": eps,
+    }
+    return state
+
+
+def adam_update(grads, state):
+    """Compute Adam parameter updates from gradients and state.
+
+    Parameters
+    ----------
+    grads : dict[str, np.ndarray]
+        Gradient pytree with the same structure as the parameters.
+    state : dict[str, object]
+        Optimizer state returned by :func:`init_adam`.
+
+    Returns
+    -------
+    updates : dict[str, np.ndarray]
+        Parameter deltas that should be subtracted from the current values.
+    new_state : dict[str, object]
+        Updated optimiser state after incorporating the gradients.
+    """
+    g_flat, _ = _flatten(grads)
+    t = state["t"] + 1
+
+    beta1 = state["beta1"]
+    beta2 = state["beta2"]
+    m = (1 - beta1) * g_flat + beta1 * state["m"]
+    v = (1 - beta2) * (g_flat * g_flat) + beta2 * state["v"]
+
+    m_hat = m / (1 - beta1**t)
+    v_hat = v / (1 - beta2**t)
+    updates_flat = state["lr"] * (m_hat / (np.sqrt(v_hat) + state["eps"]))
+
+    new_state = {
+        **state,
+        "t": t,
+        "m": m,
+        "v": v,
+    }
+    updates = state["unflatten"](updates_flat)
+    return updates, new_state
+
+
+def apply_updates(params, updates):
+    """Apply additive updates to a parameter pytree.
+
+    Parameters
+    ----------
+    params : dict[str, np.ndarray]
+        Original parameter dictionary.
+    updates : dict[str, np.ndarray]
+        Update dictionary produced by :func:`adam_update`.
+
+    Returns
+    -------
+    dict[str, np.ndarray]
+        New dictionary with ``updates`` subtracted element-wise.
+    """
+    p_flat, unflatten = _flatten(params)
+    u_flat, _ = _flatten(updates)
+    return unflatten(p_flat - u_flat)
diff --git a/2025-10-09-invdes-seminar/results/gc_adjoint_best.json b/2025-10-09-invdes-seminar/results/gc_adjoint_best.json
new file mode 100644
index 00000000..a8d5bdaa
--- /dev/null
+++ b/2025-10-09-invdes-seminar/results/gc_adjoint_best.json
@@ -0,0 +1,73 @@
+{
+  "widths_si": [
+    0.4596210205783229,
+    0.619021827243859,
+    0.5091612364667776,
+    0.44862540195053635,
+    0.4519060476837924,
+    0.45990865650576407,
+    0.4707837996827998,
+    0.5045578907962605,
+    0.5736868046251105,
+    0.38818932960888375,
+    0.5103031259894519,
+    0.5644712722428283,
+    0.43499552429872157,
+    0.7128307820061767,
+    0.5880320841841612
+  ],
+  "gaps_si": [
+    0.5471012483789967,
+    0.5751743079764461,
+    0.7609864084716235,
+    0.6784088880064844,
+    0.7110294438923443,
+    0.7162660313191388,
+    0.6572002123756033,
+    0.6002624533497121,
+    0.5423082463648713,
+    0.6014877337345622,
+    0.47967659439980775,
+    0.4947734252066087,
+    0.8223426836734665,
+    0.3018370285344259,
+    0.674088964498069
+  ],
+  "widths_sin": [
+    0.8370101006141384,
+    0.7358141802861392,
+    0.8591427172226472,
+    0.7188178275200784,
+    0.7251240965135923,
+    0.6477434589338728,
+    0.630917116356729,
+    0.7967252173677236,
+    0.6712968595454194,
+    0.664276799817368,
+    1.0,
+    0.6604544789433066,
+    1.0,
+    0.9505322926528258,
+    0.6800512052506013
+  ],
+  "gaps_sin": [
+    0.4946890226723554,
+    0.5365379587726309,
+    0.46065692058517027,
+    0.42203895741407915,
+    0.4834780230256085,
+    0.5352412107737056,
+    0.6262985005842511,
+    0.46195800688016325,
+    0.30113583498689617,
+    0.44560626938492015,
+    0.3061735955224083,
+    0.39071041714258803,
+    0.4939209477233372,
+    0.312008460456016,
+    0.3677091398546308
+  ],
+  "first_gap_si": -0.6795213307403507,
+  "first_gap_sin": 0.36214218348124017,
+  "target_power": 0.5920533670750903
+}
\ No newline at end of file
diff --git a/2025-10-09-invdes-seminar/results/gc_adjoint_robust_best.json b/2025-10-09-invdes-seminar/results/gc_adjoint_robust_best.json
new file mode 100644
index 00000000..f93f4780
--- /dev/null
+++ b/2025-10-09-invdes-seminar/results/gc_adjoint_robust_best.json
@@ -0,0 +1,73 @@
+{
+  "widths_si": [
+    0.46178686916966183,
+    0.6208605639642829,
+    0.5023204446163415,
+    0.45004614671073134,
+    0.45596025644051,
+    0.4585759420364934,
+    0.4712105530469806,
+    0.5100867903239975,
+    0.5706512594868336,
+    0.38871925269320423,
+    0.5113155720985404,
+    0.5659665523699627,
+    0.4365234841396745,
+    0.714475132758153,
+    0.5937534510699842
+  ],
+  "gaps_si": [
+    0.5466794735975892,
+    0.5771193435069213,
+    0.7533781486901343,
+    0.685123665411786,
+    0.7095082956179035,
+    0.706653647605633,
+    0.6514719255254264,
+    0.5905499349639569,
+    0.5425124682940347,
+    0.6029380560625501,
+    0.48106002793948394,
+    0.49752227029601187,
+    0.827596920790486,
+    0.3073181132794698,
+    0.674088964498069
+  ],
+  "widths_sin": [
+    0.834943058398537,
+    0.7353263082825178,
+    0.8580322415966256,
+    0.7188867567232994,
+    0.726264496324061,
+    0.6481023720890084,
+    0.6319137099717905,
+    0.7981058278133871,
+    0.6755233787983985,
+    0.6694648977631461,
+    1.0,
+    0.66622242713095,
+    0.9909790766628245,
+    0.9415172852346699,
+    0.6734413979723209
+  ],
+  "gaps_sin": [
+    0.49254257832079273,
+    0.5375042420490991,
+    0.4607741395165546,
+    0.42353529383379157,
+    0.4850861138159179,
+    0.5366293370824164,
+    0.6290946074785405,
+    0.4656365963898396,
+    0.30596460535700865,
+    0.44925187170566083,
+    0.3109077179555021,
+    0.3812328426951072,
+    0.48474019558900283,
+    0.30278224480267296,
+    0.3677091398546308
+  ],
+  "first_gap_si": -0.679228403609027,
+  "first_gap_sin": 0.3604720266258309,
+  "etch_bias_modeled": 0.02
+}
\ No newline at end of file
diff --git a/2025-10-09-invdes-seminar/results/gc_bayes_opt_best.json b/2025-10-09-invdes-seminar/results/gc_bayes_opt_best.json
new file mode 100644
index 00000000..b1123aa6
--- /dev/null
+++ b/2025-10-09-invdes-seminar/results/gc_bayes_opt_best.json
@@ -0,0 +1,9 @@
+{
+  "width_si": 0.4488560097489734,
+  "gap_si": 0.674088964498069,
+  "width_sin": 0.8434351040463873,
+  "gap_sin": 0.3677091398546308,
+  "first_gap_si": -0.6329943025756396,
+  "target_power": 0.4515458912419618,
+  "coupling_loss_db": 3.4529810515037673
+}
\ No newline at end of file
diff --git a/2025-10-09-invdes-seminar/setup.py b/2025-10-09-invdes-seminar/setup.py
new file mode 100644
index 00000000..91f844ca
--- /dev/null
+++ b/2025-10-09-invdes-seminar/setup.py
@@ -0,0 +1,410 @@
+"""Utilities for constructing and analyzing dual-layer grating coupler simulations.
+
+This script acts as a centralized configuration file, defining the physical
+constants, geometric constraints, and core simulation-building functions used
+throughout the seminar notebooks.
+
+Notes
+-----
+* All lengths are specified in micrometers to match the fabrication-scale
+  discussion in the accompanying tutorial notebooks.
+* ``autograd.numpy`` is used instead of standard NumPy so that the same
+  functions seamlessly support gradient-based optimization workflows.
+* ``getval`` extracts plain floats from Autograd tracers whenever we pass
+  values into Tidy3D constructors that expect concrete numbers.
+"""
+
+from __future__ import annotations
+
+from typing import Sequence
+
+import autograd.numpy as np
+import tidy3d as td
+from autograd.tracer import getval
+
+inf = 1000
+buffer_left = 3.0
+buffer_right = 3.0
+buffer_bot = 2.0
+buffer_top = 0.5
+
+substrate_index = 3.47
+box_index = 1.44
+si_index = 3.47
+sin_index = 2.0
+
+substrate = td.Medium(permittivity=substrate_index**2)
+box = td.Medium(permittivity=box_index**2)
+si = td.Medium(permittivity=si_index**2)
+sin = td.Medium(permittivity=sin_index**2)
+
+# Number of grating elements balances efficiency gains with simulation cost and
+# a manageable optimization search space for a standard C-band coupler.
+num_elements = 15
+
+# Representative design-rule constraints to mirror ones typically found in silicon photonics processes.
+# Maximums are not strictly necessary but are included to keep things within reasonable bounds.
+min_width_si = 0.1
+min_gap_si = 0.2
+min_width_sin = 0.2
+min_gap_sin = 0.3
+max_width_si = 1.0
+max_gap_si = 1.0
+max_width_sin = 1.0
+max_gap_sin = 1.0
+
+first_gap_si = -0.7  # First gap in silicon is effectively the layer offset.
+first_gap_sin = 1.5 * min_gap_sin
+default_spacer_thickness = 0.3  # Vertical separation between the two functional layers.
+
+
+def _make_teeth_structure(
+    centers: np.ndarray,
+    widths: np.ndarray,
+    *,
+    center_z: float,
+    thickness: float,
+    medium: td.Medium,
+    name: str,
+) -> tuple[td.Structure, np.ndarray]:
+    """Construct a ``td.Structure`` representing repeating grating teeth."""
+    teeth = [
+        td.Box(center=(center, 0, center_z), size=(width, inf, thickness))
+        for center, width in zip(centers, widths)
+    ]
+    structure = td.Structure(
+        geometry=td.GeometryGroup(geometries=teeth),
+        medium=medium,
+        name=name,
+    )
+    extent = centers + widths / 2
+    return structure, extent
+
+
+center_wavelength = 1.55
+min_steps_per_wvl = 20
+run_time = 1e-12
+
+
+def widths_gaps_to_centers(
+    widths: Sequence[float],
+    gaps: Sequence[float],
+    *,
+    first_gap: float,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Convert widths and gaps into center locations for rectangular grating teeth.
+
+    Parameters
+    ----------
+    widths : Sequence[float]
+        Ordered list of grating tooth widths in micrometers.
+    gaps : Sequence[float]
+        Ordered list of gaps between adjacent teeth in micrometers. The
+        sequence can have the same length as ``widths``; any extra entry is
+        automatically ignored.
+    first_gap : float
+        Offset between the start of the simulation coordinate system and the
+        leading edge of the first tooth, in micrometers.
+
+    Returns
+    -------
+    centers : np.ndarray
+        Array of tooth center positions in micrometers, aligned with the
+        simulation ``x`` axis.
+    widths : np.ndarray
+        Copy of the widths as a NumPy array (matching the Autograd backend).
+    """
+    widths = np.array(widths)
+    gaps = np.array(gaps)
+    n = int(widths.size)
+
+    gaps_interior = gaps[: n - 1] if n > 1 else gaps[:0]
+    if n == 0:
+        return widths[:0], widths
+
+    combined = widths[:-1] + gaps_interior
+    cumulative = np.cumsum(combined)
+    prefix_offset = np.zeros(1, dtype=widths.dtype)
+    prefix = np.concatenate((prefix_offset, cumulative)) if cumulative.size else prefix_offset
+    centers = first_gap + prefix + widths / 2
+    return centers, widths
+
+
+def make_grating_structures(
+    widths_si: Sequence[float],
+    gaps_si: Sequence[float],
+    widths_sin: Sequence[float],
+    gaps_sin: Sequence[float],
+    *,
+    first_gap_si: float,
+    first_gap_sin: float,
+    box_thickness: float,
+    si_thickness: float,
+    spacer_thickness: float,
+    sin_thickness: float,
+) -> tuple[list[td.Structure], dict[str, float]]:
+    """Return tidy3d structures for the dual-layer grating.
+
+    Parameters
+    ----------
+    widths_si : Sequence[float]
+        Silicon tooth widths in micrometers.
+    gaps_si : Sequence[float]
+        Silicon gaps in micrometers measured along ``x``.
+    widths_sin : Sequence[float]
+        Silicon nitride tooth widths in micrometers.
+    gaps_sin : Sequence[float]
+        Silicon nitride gaps in micrometers.
+    first_gap_si : float
+        Offset from the waveguide start to the first silicon tooth, in
+        micrometers.
+    first_gap_sin : float
+        Offset from the silicon nitride waveguide to its first tooth, in
+        micrometers.
+    box_thickness : float
+        Buried oxide thickness in micrometers.
+    si_thickness : float
+        Silicon device layer thickness in micrometers.
+    spacer_thickness : float
+        Spacer between silicon and silicon nitride layers in micrometers.
+    sin_thickness : float
+        Silicon nitride layer thickness in micrometers.
+
+    Returns
+    -------
+    structures : list[td.Structure]
+        Collection of Tidy3D structures representing the full coupler stack.
+    geometry_info : dict[str, float]
+        Dictionary with geometry references used to size the simulation domain
+        and place sources/monitors. The keys are ``"c_sin"`` (center of the
+        silicon nitride waveguide) and ``"x_gc"`` (end of the patterned region).
+    """
+    c_si, w_si = widths_gaps_to_centers(widths_si, gaps_si, first_gap=first_gap_si)
+    c_sin, w_sin = widths_gaps_to_centers(widths_sin, gaps_sin, first_gap=first_gap_sin)
+
+    structures: list[td.Structure] = []
+    substrate_geom = td.Box.from_bounds((-inf, -inf, -inf), (inf, inf, 0))
+    structures.append(
+        td.Structure(
+            geometry=substrate_geom,
+            medium=substrate,
+            name="substrate",
+        )
+    )
+
+    si_center_z = substrate_geom.bounds[1][2] + box_thickness + si_thickness / 2
+    si_teeth, si_extents = _make_teeth_structure(
+        c_si,
+        w_si,
+        center_z=si_center_z,
+        thickness=si_thickness,
+        medium=si,
+        name="si_teeth",
+    )
+    structures.append(si_teeth)
+
+    sin_waveguide_geom = td.Box.from_bounds(
+        (-inf, -inf, si_center_z + si_thickness / 2 + spacer_thickness),
+        (0, inf, si_center_z + si_thickness / 2 + spacer_thickness + sin_thickness),
+    )
+    structures.append(
+        td.Structure(
+            geometry=sin_waveguide_geom,
+            medium=sin,
+            name="sin_waveguide",
+        )
+    )
+
+    sin_teeth, sin_extents = _make_teeth_structure(
+        c_sin,
+        w_sin,
+        center_z=sin_waveguide_geom.center[2],
+        thickness=sin_thickness,
+        medium=sin,
+        name="sin_teeth",
+    )
+    structures.append(sin_teeth)
+
+    return structures, {
+        "c_sin": sin_waveguide_geom.center,
+        "x_gc": np.maximum(sin_extents.max(), si_extents.max()),
+    }
+
+
+def make_simulation(
+    widths_si: Sequence[float],
+    gaps_si: Sequence[float],
+    widths_sin: Sequence[float],
+    gaps_sin: Sequence[float],
+    *,
+    first_gap_si: float = first_gap_si,
+    first_gap_sin: float = first_gap_sin,
+    box_thickness: float = 2.0,
+    si_thickness: float = 0.09,
+    spacer_thickness: float = default_spacer_thickness,
+    sin_thickness: float = 0.4,
+    center_wavelength: float = center_wavelength,
+    bandwidth: float = 0.1,
+    freq_points: int = 101,
+    beam_offset_x: float = 5.0,
+    beam_height: float = 2.0,
+    beam_mfd: float = 9.2,
+    beam_angle_deg: float = 10,
+    include_field_monitor: bool = False,
+) -> td.Simulation:
+    """Assemble a tidy3d simulation for the dual-layer grating coupler.
+
+    Parameters
+    ----------
+    widths_si, gaps_si, widths_sin, gaps_sin : Sequence[float]
+        Geometry parameters in micrometers defining the silicon and silicon
+        nitride tooth widths and gaps.
+    first_gap_si : float, optional
+        Offset between the waveguide start and first silicon tooth in
+        micrometers.
+    first_gap_sin : float, optional
+        Offset between the silicon nitride guide and its first tooth.
+    box_thickness : float, optional
+        Buried oxide thickness in micrometers.
+    si_thickness : float, optional
+        Silicon layer thickness in micrometers.
+    spacer_thickness : float, optional
+        Vertical spacing between silicon and silicon nitride layers in
+        micrometers.
+    sin_thickness : float, optional
+        Silicon nitride layer thickness in micrometers.
+    center_wavelength : float, optional
+        Central wavelength in micrometers.
+    bandwidth : float, optional
+        Spectral span around the central wavelength in micrometers.
+    freq_points : int, optional
+        Number of frequency samples across the bandwidth.
+    beam_offset_x : float, optional
+        Horizontal displacement of the incident Gaussian beam center, in
+        micrometers.
+    beam_height : float, optional
+        Distance between the silicon nitride surface and the beam waist center,
+        in micrometers.
+    beam_mfd : float, optional
+        Mode field diameter of the Gaussian beam in micrometers.
+    beam_angle_deg : float, optional
+        Incident angle of the Gaussian beam in degrees.
+    include_field_monitor : bool, optional
+        If ``True``, include a 2D field monitor slicing through ``x``-``z``.
+
+    Returns
+    -------
+    td.Simulation
+        Fully defined Tidy3D simulation ready to run.
+    """
+    structures, geometry_info = make_grating_structures(
+        widths_si,
+        gaps_si,
+        widths_sin,
+        gaps_sin,
+        first_gap_si=first_gap_si,
+        first_gap_sin=first_gap_sin,
+        box_thickness=box_thickness,
+        si_thickness=si_thickness,
+        spacer_thickness=spacer_thickness,
+        sin_thickness=sin_thickness,
+    )
+
+    freq0 = td.C_0 / center_wavelength
+    freqs = td.C_0 / np.linspace(
+        center_wavelength - bandwidth / 2,
+        center_wavelength + bandwidth / 2,
+        freq_points,
+    )
+
+    source_z = geometry_info["c_sin"][2] + sin_thickness / 2 + beam_height
+    source = td.GaussianBeam(
+        center=(beam_offset_x, 0, source_z),
+        size=(inf, inf, 0),
+        source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10),
+        pol_angle=np.pi / 2,
+        angle_theta=np.deg2rad(beam_angle_deg),
+        direction="-",
+        waist_radius=beam_mfd / 2,
+        name="input_beam",
+    )
+
+    monitors = [
+        td.ModeMonitor(
+            center=(-buffer_left + 0.5, 0, getval(geometry_info["c_sin"][2])),
+            size=(0, inf, 3),
+            freqs=freqs,
+            mode_spec=td.ModeSpec(num_modes=1),
+            name="mode_monitor",
+        )
+    ]
+
+    if include_field_monitor:
+        monitors.append(
+            td.FieldMonitor(
+                center=(0, 0, 0),
+                size=(inf, 0, inf),
+                freqs=freq0,
+                fields=("Ey",),
+                name="field_monitor",
+            )
+        )
+
+    x_min = getval(-buffer_left)
+    x_max = getval(geometry_info["x_gc"] + buffer_right)
+    z_min = getval(-buffer_bot)
+    z_max = getval(source_z + buffer_top)
+
+    simulation = td.Simulation(
+        center=((x_min + x_max) / 2, 0, (z_min + z_max) / 2),
+        size=(x_max - x_min, 0, z_max - z_min),
+        structures=structures,
+        sources=(source,),
+        monitors=monitors,
+        medium=box,
+        boundary_spec=td.BoundarySpec(
+            x=td.Boundary.pml(),
+            y=td.Boundary.periodic(),
+            z=td.Boundary.pml(),
+        ),
+        grid_spec=td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl),
+        run_time=run_time,
+    )
+    return simulation
+
+
+def get_mode_monitor_power(
+    sim_data: td.SimulationData,
+    *,
+    mode_index: int = 0,
+    direction: str = "-",
+    monitor_name: str = "mode_monitor",
+    power_floor: float = 1e-12,
+) -> td.DataArray:
+    """Return a clipped power spectrum from a mode monitor.
+
+    Parameters
+    ----------
+    sim_data : td.SimulationData
+        Simulation result returned by ``td.Simulation.run()``.
+    mode_index : int, optional
+        Index of the guided mode to extract.
+    direction : str, optional
+        Propagation direction label (``"+"`` or ``"-"``) used by Tidy3D.
+    monitor_name : str, optional
+        Name of the mode monitor inside the simulation.
+    power_floor : float, optional
+        Minimum power value (in Watts) used to avoid taking the logarithm of
+        zero downstream. Set to ``None`` to disable clipping.
+
+    Returns
+    -------
+    td.DataArray
+        Absolute squared amplitudes corresponding to the requested mode.
+    """
+    monitor = sim_data[monitor_name]
+    amps = monitor.amps.sel(mode_index=mode_index, direction=direction)
+    power = np.abs(amps) ** 2
+    if power_floor is not None:
+        power = power.clip(min=power_floor)
+    return power