AdjointPlugin7Metalens.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inverse design optimization of a metalens\n",
    "\n",
    "In this notebook, we will use inverse design and the Tidy3D `adjoint` plugin to design a high numerical aperture (NA) metalens for optimal focusing to a point. This demo also introduces how to use the `adjoint` plugin for objective functions that depend on the `FieldMonitor` outputs.\n",
    "\n",
    "We will follow the basic set up from Mansouree et al. \"Large-Scale Parametrized Metasurface Design Using Adjoint Optimization\". The published paper can be found [here](https://pubs.acs.org/doi/abs/10.1021/acsphotonics.0c01058) and the arxiv preprint can be found [here](https://arxiv.org/abs/2101.06292).\n",
    "\n",
    "<img src=\"img/adjoint_7.png\" width=400 alt=\"Schematic of the metalens\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- View this project in [Tidy3D Web App](https://tidy3d.simulation.cloud/workbench?taskId=d226dea4-9a65-4481-83d0-7f7d4cbfd388). -->\n",
    "\n",
    "## Setup\n",
    "\n",
    "We first perform basic imports of the packages needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-19T03:36:26.506739Z",
     "iopub.status.busy": "2023-08-19T03:36:26.506470Z",
     "iopub.status.idle": "2023-08-19T03:36:28.343725Z",
     "shell.execute_reply": "2023-08-19T03:36:28.343097Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# standard python imports\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tidy3d as td\n",
    "from tidy3d import web\n",
    "\n",
    "import tidy3d.plugins.adjoint as tda\n",
    "from tidy3d.plugins.adjoint.web import run as run_adj\n",
    "import jax\n",
    "import jax.numpy as jnp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The metalens design consists of a rectangular array of Si rectangular prisms sitting on an SiO2 substrate.\n",
    "\n",
    "Here we define all of the basic parameters of the setup, including the wavelength, NA, geometrical dimensions, and material properties.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-19T03:36:28.346378Z",
     "iopub.status.busy": "2023-08-19T03:36:28.346103Z",
     "iopub.status.idle": "2023-08-19T03:36:28.369227Z",
     "shell.execute_reply": "2023-08-19T03:36:28.368625Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for diameter of 6.0 um, have 18 cells per side\n",
      "full metalens has area of 36.0 um^2 and 324 total cells\n"
     ]
    }
   ],
   "source": [
    "# 1 nanometer in units of microns (for conversion)\n",
    "nm = 1e-3\n",
    "\n",
    "# free space central wavelength\n",
    "wavelength = 850 * nm\n",
    "\n",
    "# desired numerical aperture\n",
    "NA = 0.94\n",
    "\n",
    "# shape parameters of metalens unit cell (um) (refer to image above and see paper for details)\n",
    "H = 430 * nm\n",
    "S = 320 * nm\n",
    "\n",
    "# space between bottom PML and substrate (-z)\n",
    "space_below_sub = 1 * wavelength\n",
    "\n",
    "# thickness of substrate between source and Si unit cells\n",
    "thickness_sub = 100 * nm\n",
    "\n",
    "# side length of entire metalens (um)\n",
    "side_length = 6\n",
    "\n",
    "# Number of unit cells in each x and y direction (NxN grid)\n",
    "N = int(side_length / S)\n",
    "\n",
    "print(f\"for diameter of {side_length:.1f} um, have {N} cells per side\")\n",
    "print(f\"full metalens has area of {side_length**2:.1f} um^2 and {N*N} total cells\")\n",
    "\n",
    "# Define material properties at 600 nm\n",
    "n_Si = 3.84\n",
    "n_SiO2 = 1.46\n",
    "air = td.Medium(permittivity=1.0)\n",
    "SiO2 = td.Medium(permittivity=n_SiO2**2)\n",
    "Si = td.Medium(permittivity=n_Si**2)\n",
    "\n",
    "# define symmetry\n",
    "symmetry = (-1, 1, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will compute some important quantities derived from these parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-19T03:36:28.371454Z",
     "iopub.status.busy": "2023-08-19T03:36:28.371257Z",
     "iopub.status.idle": "2023-08-19T03:36:28.396257Z",
     "shell.execute_reply": "2023-08-19T03:36:28.395630Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# using the wavelength in microns, one can use td.C_0 (um/s) to get frequency in Hz\n",
    "# wavelength_meters = wavelength * meters\n",
    "f0 = td.C_0 / wavelength\n",
    "\n",
    "# Compute the domain size in x, y (note: round down from side_length)\n",
    "length_xy = N * S\n",
    "\n",
    "# focal length given diameter and numerical aperture\n",
    "focal_length = length_xy / 2 / NA * np.sqrt(1 - NA**2)\n",
    "\n",
    "# total domain size in z: (space -> substrate -> unit cell -> 1.7 focal lengths)\n",
    "length_z = space_below_sub + thickness_sub + H + 1.7 * focal_length\n",
    "\n",
    "# construct simulation size array\n",
    "sim_size = (length_xy, length_xy, length_z)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Metalens Geometry\n",
    "\n",
    "Now we will define the structures in our simulation. We will first generate the substrate as a regular `td.Box`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-19T03:36:28.399033Z",
     "iopub.status.busy": "2023-08-19T03:36:28.398796Z",
     "iopub.status.idle": "2023-08-19T03:36:28.426601Z",
     "shell.execute_reply": "2023-08-19T03:36:28.425142Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# define substrate\n",
    "substrate = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-td.inf, -td.inf, -1000),\n",
    "        rmax=(+td.inf, +td.inf, -length_z / 2 + space_below_sub + thickness_sub)\n",
    "    ),\n",
    "    medium=SiO2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will write a function to make a list of `JaxStructure` objects corresponding to each unit cell.\n",
    "\n",
    "> Note that the adjoint plugin does not yet support `GeometryGroup` for `JaxBox`, so we will keep them as individual `JaxStructure` objects for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-19T03:36:28.429393Z",
     "iopub.status.busy": "2023-08-19T03:36:28.429213Z",
     "iopub.status.idle": "2023-08-19T03:36:28.792709Z",
     "shell.execute_reply": "2023-08-19T03:36:28.791587Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n"
     ]
    }
   ],
   "source": [
    "# define coordinates of each unit cell\n",
    "centers_x = S * np.arange(N) - length_xy / 2.0 + S / 2.0\n",
    "centers_y = S * np.arange(N) - length_xy / 2.0 + S / 2.0\n",
    "center_z = -length_z / 2 + space_below_sub + thickness_sub + H / 2.0\n",
    "\n",
    "focal_z = center_z + H / 2 + focal_length\n",
    "\n",
    "\n",
    "x_centers, y_centers = np.meshgrid(centers_x, centers_y, indexing=\"ij\")\n",
    "xs = x_centers.flatten()\n",
    "ys = y_centers.flatten()\n",
    "\n",
    "def get_sizes(params):\n",
    "    \"\"\"Returns the actual side lengths of the boxes as a function of design parameters from (-inf, +inf).\"\"\"\n",
    "    return S * (jnp.tanh(params) + 1.0) / 2.0\n",
    "\n",
    "# initially, start with parameters of 0 (all boxes have side length S/2)\n",
    "params0 = 0 * np.ones(x_centers.shape)\n",
    "\n",
    "def make_structures(params, apply_symmetry: bool = True):\n",
    "    \"\"\"Make the JaxStructure objects that will be used as .input_structures.\"\"\"\n",
    "    \n",
    "    sizes = get_sizes(params)\n",
    "    nx, ny = sizes.shape\n",
    "    geometries = []\n",
    "    \n",
    "    for i in range(nx):\n",
    "        i_quad = max(i, nx - 1 - i)\n",
    "        for j in range(ny):\n",
    "            j_quad = max(j, ny - 1 - j)\n",
    "            size = sizes[i_quad, j_quad]\n",
    "            x0 = x_centers[i, j]\n",
    "            y0 = y_centers[i, j]\n",
    "\n",
    "            if apply_symmetry and symmetry[0] != 0 and x0 < -S/2:\n",
    "                continue\n",
    "\n",
    "            if apply_symmetry and symmetry[1] != 0 and y0 < -S/2:\n",
    "                continue        \n",
    "\n",
    "            geometry = tda.JaxBox(\n",
    "                center=(x0, y0, center_z),\n",
    "                size=(size, size, H)\n",
    "            )\n",
    "        \n",
    "            geometries.append(geometry)\n",
    "    medium = tda.JaxMedium(permittivity=n_Si**2)\n",
    "    return [tda.JaxStructure(medium=medium, geometry=geo) for geo in geometries]\n",
    "\n",
    "structures = make_structures(params0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define grid specification\n",
    "\n",
    "We define the grid based on the properties of the geometry. The metalens is quasi-periodic in x and y, in that we have clearly defined unit cells, but each is slightly modified from its neighbors. Such structures are best resolved with a grid that matches the periodicity, which is why we use a uniform grid in x and y. In z, we use the automatic nonuniform grid that will place a higher grid density around the metalens region, and a lower one in the air region away from the metalens. To speed up the auto meshing in the region with the pillars, we put an override box in the grid specification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-19T03:36:28.795984Z",
     "iopub.status.busy": "2023-08-19T03:36:28.795510Z",
     "iopub.status.idle": "2023-08-19T03:36:28.828796Z",
     "shell.execute_reply": "2023-08-19T03:36:28.828262Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# steps per unit cell along x and y\n",
    "grids_per_unit_length = 10\n",
    "\n",
    "# uniform mesh in x and y\n",
    "grid_x = td.UniformGrid(dl=S / grids_per_unit_length)\n",
    "grid_y = td.UniformGrid(dl=S / grids_per_unit_length)\n",
    "\n",
    "# in z, use an automatic nonuniform mesh with the wavelength being the \"unit length\"\n",
    "grid_z = td.AutoGrid(min_steps_per_wvl=grids_per_unit_length)\n",
    "\n",
    "# we need to supply the wavelength because of the automatic mesh in z\n",
    "grid_spec = td.GridSpec(\n",
    "    wavelength=wavelength, grid_x=grid_x, grid_y=grid_y, grid_z=grid_z\n",
    ")\n",
    "\n",
    "# put an override box over the pillars to avoid parsing a large amount of structures in the mesher\n",
    "grid_spec = grid_spec.copy(\n",
    "    update=dict(\n",
    "        override_structures=[\n",
    "            td.Structure(\n",
    "                geometry=td.Box.from_bounds(\n",
    "                    rmin=(-td.inf, -td.inf, -length_z / 2 + space_below_sub),\n",
    "                    rmax=(td.inf, td.inf, center_z + H / 2),\n",
    "                ),\n",
    "                medium=Si,\n",
    "            )\n",
    "        ]\n",
    "    )\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Source\n",
    "\n",
    "Now we define the incident fields.  We simply use an x-polarized, normally incident plane wave with Gaussian time dependence centered at our central frequency.  For more details, see the [plane wave source documentation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PlaneWave.html#tidy3d-planewave) and the [gaussian source documentation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.GaussianPulse.html#tidy3d-gaussianpulse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-19T03:36:28.831433Z",
     "iopub.status.busy": "2023-08-19T03:36:28.831223Z",
     "iopub.status.idle": "2023-08-19T03:36:28.857655Z",
     "shell.execute_reply": "2023-08-19T03:36:28.856805Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Bandwidth in Hz\n",
    "fwidth = f0 / 10.0\n",
    "\n",
    "# time dependence of source\n",
    "gaussian = td.GaussianPulse(freq0=f0, fwidth=fwidth, phase=0)\n",
    "\n",
    "source = td.PlaneWave(\n",
    "    source_time=gaussian,\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    center=(0, 0, -length_z / 2 + space_below_sub / 10.0),\n",
    "    direction=\"+\",\n",
    "    pol_angle=0,\n",
    ")\n",
    "\n",
    "run_time = 50 / fwidth\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Monitors\n",
    "\n",
    "Now we define the monitor that measures field output from the FDTD simulation.  For simplicity, we use measure the fields at the central frequency at the focal spot.\n",
    "\n",
    "This will be the monitor that we use in our objective function, so it will go into `JaxSimulation.output_monitors`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-19T03:36:28.862048Z",
     "iopub.status.busy": "2023-08-19T03:36:28.861617Z",
     "iopub.status.idle": "2023-08-19T03:36:29.103638Z",
     "shell.execute_reply": "2023-08-19T03:36:29.103119Z"
    }
   },
   "outputs": [],
   "source": [
    "# To decrease the amount of data stored, only store the E fields\n",
    "fields = [\"Ex\", \"Ey\", \"Ez\"]\n",
    "\n",
    "monitor_focal = td.FieldMonitor(\n",
    "    center=[0.0, 0.0, focal_z],\n",
    "    size=[0, 0, 0],\n",
    "    freqs=[f0],\n",
    "    name=\"focal_point\",\n",
    "    fields=fields,\n",
    "    colocate=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Simulation\n",
    "\n",
    "Now we can put everything together and define a [JaxSimulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.adjoint.JaxSimulation.html) object to be run.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a number of warnings about structures being too close to the PML. In FDTD simulations, this can result in instability, as PML are absorbing for propagating fields, but can be amplifying for evanescent fields. This particular simulation runs without any issues even with PML on the sides, but it is best to heed these warnings to avoid problems. There are two ways that we can fix the simulation: one is to just put some space between the last of the metalens boxes and the PML. The other is to use adiabatic absorbers on the sides, which are always stable. The only downside of the absorbers is that they are slightly thicker than the PML, making the overall simulation size slightly larger. This is why we only put them along x and y, while we leave the PML in z.\n",
    "\n",
    "> Note: we add symmetry of (-1, 1, 0) to speed up the simulation by approximately 4x taking into account the symmetry in our source and dielectric function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-19T03:36:29.106084Z",
     "iopub.status.busy": "2023-08-19T03:36:29.105876Z",
     "iopub.status.idle": "2023-08-19T03:36:29.715014Z",
     "shell.execute_reply": "2023-08-19T03:36:29.714183Z"
    },
    "tags": []
   },
   "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\">[20:38:51] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Override structures take no effect along   </span> <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">grid_spec.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">555</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">x-axis. If intending to apply override structures to</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">this axis, use </span><span style=\"color: #008000; text-decoration-color: #008000\">'AutoGrid'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                          </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:38:51]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Override structures take no effect along   \u001b[0m \u001b]8;id=691081;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=950426;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\u001b\\\u001b[2m555\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mx-axis. If intending to apply override structures to\u001b[0m \u001b[2m                \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mthis axis, use \u001b[0m\u001b[32m'AutoGrid'\u001b[0m\u001b[31m.                          \u001b[0m \u001b[2m                \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Override structures take no effect along   </span> <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">grid_spec.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">555</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">y-axis. If intending to apply override structures to</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">this axis, use </span><span style=\"color: #008000; text-decoration-color: #008000\">'AutoGrid'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                          </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Override structures take no effect along   \u001b[0m \u001b]8;id=631222;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=453319;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\u001b\\\u001b[2m555\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[31my-axis. If intending to apply override structures to\u001b[0m \u001b[2m                \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mthis axis, use \u001b[0m\u001b[32m'AutoGrid'\u001b[0m\u001b[31m.                          \u001b[0m \u001b[2m                \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def make_sim(angles, apply_symmetry: bool=True):\n",
    "    metalens = make_structures(angles, apply_symmetry=apply_symmetry)\n",
    "    sim = tda.JaxSimulation(\n",
    "        size=sim_size,\n",
    "        grid_spec=grid_spec,\n",
    "        structures=[substrate],\n",
    "        input_structures=metalens,\n",
    "        sources=[source],\n",
    "        monitors=[],\n",
    "        output_monitors=[monitor_focal],\n",
    "        run_time=run_time,\n",
    "        boundary_spec=td.BoundarySpec(\n",
    "            x=td.Boundary.absorber(), y=td.Boundary.absorber(), z=td.Boundary.pml()\n",
    "        ),\n",
    "        symmetry=symmetry,\n",
    "    )\n",
    "    return sim\n",
    "\n",
    "sim = make_sim(params0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The warnings are just letting us know that we are using uniform grid along x and y (per our spec) even though the override structures have some extent in these dimensions. We can ignore as this is intended."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Geometry\n",
    "\n",
    "Lets take a look and make sure everything is defined properly.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Override structures take no effect along   </span> <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">grid_spec.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">555</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">x-axis. If intending to apply override structures to</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">this axis, use </span><span style=\"color: #008000; text-decoration-color: #008000\">'AutoGrid'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                          </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Override structures take no effect along   \u001b[0m \u001b]8;id=950010;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=320445;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\u001b\\\u001b[2m555\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mx-axis. If intending to apply override structures to\u001b[0m \u001b[2m                \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mthis axis, use \u001b[0m\u001b[32m'AutoGrid'\u001b[0m\u001b[31m.                          \u001b[0m \u001b[2m                \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Override structures take no effect along   </span> <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">grid_spec.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">555</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">y-axis. If intending to apply override structures to</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">this axis, use </span><span style=\"color: #008000; text-decoration-color: #008000\">'AutoGrid'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                          </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Override structures take no effect along   \u001b[0m \u001b]8;id=916929;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=988817;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\u001b\\\u001b[2m555\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[31my-axis. If intending to apply override structures to\u001b[0m \u001b[2m                \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mthis axis, use \u001b[0m\u001b[32m'AutoGrid'\u001b[0m\u001b[31m.                          \u001b[0m \u001b[2m                \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Override structures take no effect along   </span> <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">grid_spec.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">555</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">x-axis. If intending to apply override structures to</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">this axis, use </span><span style=\"color: #008000; text-decoration-color: #008000\">'AutoGrid'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                          </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Override structures take no effect along   \u001b[0m \u001b]8;id=875009;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=490146;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\u001b\\\u001b[2m555\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mx-axis. If intending to apply override structures to\u001b[0m \u001b[2m                \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mthis axis, use \u001b[0m\u001b[32m'AutoGrid'\u001b[0m\u001b[31m.                          \u001b[0m \u001b[2m                \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Override structures take no effect along   </span> <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">grid_spec.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">555</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">y-axis. If intending to apply override structures to</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">this axis, use </span><span style=\"color: #008000; text-decoration-color: #008000\">'AutoGrid'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                          </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Override structures take no effect along   \u001b[0m \u001b]8;id=464190;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=522801;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\u001b\\\u001b[2m555\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[31my-axis. If intending to apply override structures to\u001b[0m \u001b[2m                \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mthis axis, use \u001b[0m\u001b[32m'AutoGrid'\u001b[0m\u001b[31m.                          \u001b[0m \u001b[2m                \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Override structures take no effect along   </span> <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">grid_spec.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">555</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">x-axis. If intending to apply override structures to</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">this axis, use </span><span style=\"color: #008000; text-decoration-color: #008000\">'AutoGrid'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                          </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Override structures take no effect along   \u001b[0m \u001b]8;id=425266;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=728338;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\u001b\\\u001b[2m555\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mx-axis. If intending to apply override structures to\u001b[0m \u001b[2m                \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mthis axis, use \u001b[0m\u001b[32m'AutoGrid'\u001b[0m\u001b[31m.                          \u001b[0m \u001b[2m                \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Override structures take no effect along   </span> <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">grid_spec.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">555</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">y-axis. If intending to apply override structures to</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">this axis, use </span><span style=\"color: #008000; text-decoration-color: #008000\">'AutoGrid'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                          </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Override structures take no effect along   \u001b[0m \u001b]8;id=133106;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=827272;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\u001b\\\u001b[2m555\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[31my-axis. If intending to apply override structures to\u001b[0m \u001b[2m                \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mthis axis, use \u001b[0m\u001b[32m'AutoGrid'\u001b[0m\u001b[31m.                          \u001b[0m \u001b[2m                \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Override structures take no effect along   </span> <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">grid_spec.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">555</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">x-axis. If intending to apply override structures to</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">this axis, use </span><span style=\"color: #008000; text-decoration-color: #008000\">'AutoGrid'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                          </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Override structures take no effect along   \u001b[0m \u001b]8;id=874587;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=80363;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\u001b\\\u001b[2m555\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mx-axis. If intending to apply override structures to\u001b[0m \u001b[2m                \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mthis axis, use \u001b[0m\u001b[32m'AutoGrid'\u001b[0m\u001b[31m.                          \u001b[0m \u001b[2m                \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\">[20:38:52] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Override structures take no effect along   </span> <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">grid_spec.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">555</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">y-axis. If intending to apply override structures to</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">this axis, use </span><span style=\"color: #008000; text-decoration-color: #008000\">'AutoGrid'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                          </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:38:52]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Override structures take no effect along   \u001b[0m \u001b]8;id=898787;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=264049;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\u001b\\\u001b[2m555\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[31my-axis. If intending to apply override structures to\u001b[0m \u001b[2m                \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mthis axis, use \u001b[0m\u001b[32m'AutoGrid'\u001b[0m\u001b[31m.                          \u001b[0m \u001b[2m                \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Override structures take no effect along   </span> <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">grid_spec.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">555</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">x-axis. If intending to apply override structures to</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">this axis, use </span><span style=\"color: #008000; text-decoration-color: #008000\">'AutoGrid'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                          </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Override structures take no effect along   \u001b[0m \u001b]8;id=331177;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=396481;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\u001b\\\u001b[2m555\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mx-axis. If intending to apply override structures to\u001b[0m \u001b[2m                \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mthis axis, use \u001b[0m\u001b[32m'AutoGrid'\u001b[0m\u001b[31m.                          \u001b[0m \u001b[2m                \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Override structures take no effect along   </span> <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">grid_spec.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">555</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">y-axis. If intending to apply override structures to</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">this axis, use </span><span style=\"color: #008000; text-decoration-color: #008000\">'AutoGrid'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                          </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Override structures take no effect along   \u001b[0m \u001b]8;id=622284;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=909390;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\u001b\\\u001b[2m555\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[31my-axis. If intending to apply override structures to\u001b[0m \u001b[2m                \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mthis axis, use \u001b[0m\u001b[32m'AutoGrid'\u001b[0m\u001b[31m.                          \u001b[0m \u001b[2m                \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Override structures take no effect along   </span> <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">grid_spec.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">555</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">x-axis. If intending to apply override structures to</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">this axis, use </span><span style=\"color: #008000; text-decoration-color: #008000\">'AutoGrid'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                          </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Override structures take no effect along   \u001b[0m \u001b]8;id=113968;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=829402;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\u001b\\\u001b[2m555\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mx-axis. If intending to apply override structures to\u001b[0m \u001b[2m                \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mthis axis, use \u001b[0m\u001b[32m'AutoGrid'\u001b[0m\u001b[31m.                          \u001b[0m \u001b[2m                \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Override structures take no effect along   </span> <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">grid_spec.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">555</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">y-axis. If intending to apply override structures to</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">this axis, use </span><span style=\"color: #008000; text-decoration-color: #008000\">'AutoGrid'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                          </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Override structures take no effect along   \u001b[0m \u001b]8;id=453090;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=640490;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/components/grid/grid_spec.py#555\u001b\\\u001b[2m555\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[31my-axis. If intending to apply override structures to\u001b[0m \u001b[2m                \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mthis axis, use \u001b[0m\u001b[32m'AutoGrid'\u001b[0m\u001b[31m.                          \u001b[0m \u001b[2m                \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 6))\n",
    "\n",
    "sim.plot(x=0.1, ax=ax1)\n",
    "sim.plot(y=0.1, ax=ax2)\n",
    "sim.plot(z=-length_z / 2 + space_below_sub + thickness_sub + H / 2, ax=ax3)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective Function\n",
    "\n",
    "Now that our simulation is set up, we can define our objective function over the `JaxSimulationData` results.\n",
    "\n",
    "We first write a function to take a `JaxSimulationData` object and return the intensity at the focal point.\n",
    "\n",
    "Next, we write a function to \n",
    "\n",
    "1. Set up our simulation given our design parameters.\n",
    "\n",
    "2. Run the simulation through the adjoint `run` function.\n",
    "\n",
    "3. Compute and return the intensity at the focal point.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# turn off warnings as we understand they are just about AutoGrid and can be ignored in our case\n",
    "td.config.logging_level = \"ERROR\"\n",
    "\n",
    "def measure_focal_intensity(sim_data: tda.JaxSimulationData) -> float:\n",
    "    \"\"\"Measures electric intensity at focal point.\"\"\"\n",
    "    return jnp.sum(sim_data.get_intensity('focal_point').values)\n",
    "\n",
    "def J(params) -> float:\n",
    "    \"\"\"Objective function, returns intensity at focal point as a function of params.\"\"\"\n",
    "    sim = make_sim(params)\n",
    "    sim_data = run_adj(sim, task_name='metalens_adj', verbose=False)\n",
    "    return measure_focal_intensity(sim_data)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first run our function to test that it works and see the starting value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array(11.112829, dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "J(params0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we use `jax` to get a function returning the objective value and its gradient, given some parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dJ = jax.value_and_grad(J)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And try it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "val, grad = dJ(params0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.112829\n",
      "[[ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          8.052231    1.5407639  -2.633849\n",
      "  -1.7117175   1.5994252   0.37416115 -1.1727542   0.6812252   0.05781552]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          1.170242   -0.38775107 -1.3131913\n",
      "  -0.13359495  0.8831538  -0.1225284  -0.49174562  0.42683142 -0.06464756]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.         -1.4127594  -0.9652585  -0.50624955\n",
      "   0.6102758   0.50805783 -0.4469478  -0.17206581  0.43278244 -0.22273514]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.         -0.7552655  -0.19202262  0.47696215\n",
      "   0.4672531  -0.21933757 -0.3337388   0.23176189  0.20692322 -0.3084491 ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.6831305   0.33028147  0.37160286\n",
      "  -0.21041775 -0.41866505  0.13355729  0.28937018 -0.13324808 -0.16658844]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.11585653 -0.07828572 -0.16567144\n",
      "  -0.2921306   0.05472778  0.24623685 -0.02784408 -0.21342102  0.09299421]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.         -0.3484568  -0.17058687 -0.03697217\n",
      "   0.09520242  0.26396352 -0.07381138 -0.20417789  0.02290697  0.15813121]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.1712473   0.10132807  0.15602311\n",
      "   0.08441953 -0.0273253  -0.17447795  0.01955975  0.12130421 -0.02494766]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.08062559  0.01353269 -0.03312859\n",
      "  -0.09126864 -0.07907826  0.02552689  0.10252865 -0.02282601 -0.07314368]]\n"
     ]
    }
   ],
   "source": [
    "print(val)\n",
    "print(grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize Objective\n",
    "\n",
    "To normalize our objective function value to something more understandable, we first run a simulation with no boxes to compute the focal point intensity in this case. Then, we construct a new objective function value that normalizes the raw intensity by this value, giving us an \"intensity enhancement\" factor. In this normalization, if our objective is given by \"x\", it means that the intensity at the focal point is \"x\" times stronger with our design than with no structures at all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8701368\n"
     ]
    }
   ],
   "source": [
    "params_empty = -1e5 * np.ones_like(params0)\n",
    "J_empty = np.array(J(params_empty))\n",
    "\n",
    "def J_normalized(params):\n",
    "    return J(params) / J_empty\n",
    "\n",
    "val_normalized = val / J_empty\n",
    "\n",
    "dJ_normalized = jax.value_and_grad(J_normalized)\n",
    "\n",
    "print(val_normalized)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimization\n",
    "\n",
    "With our objective function set up, we can now run the optimization.\n",
    "\n",
    "As before, we will `optax`'s \"adam\" optimization with initial parameters of all zeros (corresponding to boxes of side length `S/2`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 1\n",
      "\tJ = 8.7014e-01\n",
      "\tgrad_norm = 7.5746e-01\n",
      "step = 2\n",
      "\tJ = 1.9787e+00\n",
      "\tgrad_norm = 1.0905e+00\n",
      "step = 3\n",
      "\tJ = 4.7533e+00\n",
      "\tgrad_norm = 1.5273e+00\n",
      "step = 4\n",
      "\tJ = 7.5140e+00\n",
      "\tgrad_norm = 1.8209e+00\n",
      "step = 5\n",
      "\tJ = 1.3054e+01\n",
      "\tgrad_norm = 2.8733e+00\n",
      "step = 6\n",
      "\tJ = 2.0436e+01\n",
      "\tgrad_norm = 3.2374e+00\n",
      "step = 7\n",
      "\tJ = 2.7075e+01\n",
      "\tgrad_norm = 2.9542e+00\n",
      "step = 8\n",
      "\tJ = 3.1865e+01\n",
      "\tgrad_norm = 2.3616e+00\n",
      "step = 9\n",
      "\tJ = 3.2881e+01\n",
      "\tgrad_norm = 3.0067e+00\n",
      "step = 10\n",
      "\tJ = 3.6570e+01\n",
      "\tgrad_norm = 2.5087e+00\n",
      "step = 11\n",
      "\tJ = 3.9969e+01\n",
      "\tgrad_norm = 2.7586e+00\n",
      "step = 12\n",
      "\tJ = 4.2636e+01\n",
      "\tgrad_norm = 2.6617e+00\n",
      "step = 13\n",
      "\tJ = 4.3821e+01\n",
      "\tgrad_norm = 2.7791e+00\n",
      "step = 14\n",
      "\tJ = 4.5719e+01\n",
      "\tgrad_norm = 2.6449e+00\n",
      "step = 15\n",
      "\tJ = 4.7921e+01\n",
      "\tgrad_norm = 2.1662e+00\n",
      "step = 16\n",
      "\tJ = 4.8882e+01\n",
      "\tgrad_norm = 2.0180e+00\n",
      "step = 17\n",
      "\tJ = 4.8722e+01\n",
      "\tgrad_norm = 2.4410e+00\n",
      "step = 18\n",
      "\tJ = 4.9012e+01\n",
      "\tgrad_norm = 2.9489e+00\n"
     ]
    }
   ],
   "source": [
    "import optax\n",
    "\n",
    "# hyperparameters\n",
    "num_steps = 18\n",
    "learning_rate = 0.02\n",
    "\n",
    "# initialize adam optimizer with starting parameters\n",
    "params = np.array(params0)\n",
    "optimizer = optax.adam(learning_rate=learning_rate)\n",
    "opt_state = optimizer.init(params)\n",
    "\n",
    "# store history\n",
    "J_history = [val_normalized]\n",
    "params_history = [params0]\n",
    "\n",
    "for i in range(num_steps):\n",
    "\n",
    "    # compute gradient and current objective funciton value\n",
    "    value, gradient = dJ_normalized(params)\n",
    "\n",
    "    # outputs\n",
    "    print(f\"step = {i + 1}\")\n",
    "    print(f\"\\tJ = {value:.4e}\")\n",
    "    print(f\"\\tgrad_norm = {np.linalg.norm(gradient):.4e}\")    \n",
    "\n",
    "    # compute and apply updates to the optimizer based on gradient (-1 sign to maximize obj_fn)\n",
    "    updates, opt_state = optimizer.update(-gradient, opt_state, params)\n",
    "    params = optax.apply_updates(params, updates)\n",
    "\n",
    "    # save history\n",
    "    J_history.append(value)\n",
    "    params_history.append(params)    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_after = params_history[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(J_history)\n",
    "plt.xlabel('iterations')\n",
    "plt.ylabel('objective function (focusing intensity enhancement)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_before = make_sim(0 * params_after, apply_symmetry=False).to_simulation()[0]\n",
    "sim_after = make_sim(params_after, apply_symmetry=False).to_simulation()[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2)\n",
    "\n",
    "sim_before.plot(z=center_z, ax=ax1)\n",
    "sim_after.plot(z=center_z, ax=ax2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_after_mnt = sim_after.updated_copy(monitors=list(sim_after.monitors) + \n",
    "[\n",
    "    td.FieldMonitor(\n",
    "        size=(0, td.inf, td.inf),\n",
    "        center=(0,0,0),\n",
    "        freqs=[f0],\n",
    "        name='fields_yz',\n",
    "    ),\n",
    "    td.FieldMonitor(\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        center=(0,0,focal_z),\n",
    "        freqs=[f0],\n",
    "        name='far_field',\n",
    "    ),    \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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\">[21:11:10] </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'meta_near_field_after'</span> with task_id       <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#188\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">188</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-f56dc12f-663c-43ca-85f5-32f01b3062e7v1'</span>.          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[21:11:10]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'meta_near_field_after'\u001b[0m with task_id       \u001b]8;id=330946;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=598583;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[32m'fdve-f56dc12f-663c-43ca-85f5-32f01b3062e7v1'\u001b[0m.          \u001b[2m             \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 <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-f56dc12f-663c-43ca-85f5-32f01b3062e7v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cl</span></a> <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#190\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">190</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-f56dc12f-663c-43ca-85f5-32f01b3062e7v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">oud/workbench?taskId=fdve-f56dc12f-663c-43ca-85f5-32f01</span></a> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-f56dc12f-663c-43ca-85f5-32f01b3062e7v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">b3062e7v1'</span></a>.                                             <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=260043;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f56dc12f-663c-43ca-85f5-32f01b3062e7v1\u001b\\\u001b[32m'https://tidy3d.simulation.cl\u001b[0m\u001b]8;;\u001b\\ \u001b]8;id=32844;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=392722;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#190\u001b\\\u001b[2m190\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b]8;id=260043;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f56dc12f-663c-43ca-85f5-32f01b3062e7v1\u001b\\\u001b[32moud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=912680;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f56dc12f-663c-43ca-85f5-32f01b3062e7v1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=260043;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f56dc12f-663c-43ca-85f5-32f01b3062e7v1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=912680;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f56dc12f-663c-43ca-85f5-32f01b3062e7v1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=260043;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f56dc12f-663c-43ca-85f5-32f01b3062e7v1\u001b\\\u001b[32m-f56dc12f-663c-43ca-85f5-32f01\u001b[0m\u001b]8;;\u001b\\ \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b]8;id=260043;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f56dc12f-663c-43ca-85f5-32f01b3062e7v1\u001b\\\u001b[32mb3062e7v1'\u001b[0m\u001b]8;;\u001b\\.                                             \u001b[2m             \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd506158944f4f0dbc0fd01b7ba00edf",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\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\">[21:11:11] </span>status = queued                                         <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#361\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">361</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[21:11:11]\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                         \u001b]8;id=895032;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=197946;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#361\u001b\\\u001b[2m361\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "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\">[21:11:20] </span>status = preprocess                                     <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#355\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">355</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[21:11:20]\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                     \u001b]8;id=705967;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=86178;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#355\u001b\\\u001b[2m355\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\"></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\">[21:11:24] </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Use                     <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#341\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">341</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed FlexCredit   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>cost after a simulation run.                            <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[21:11:24]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use                     \u001b]8;id=65467;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=546477;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#341\u001b\\\u001b[2m341\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\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 FlexCredit   \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0mcost after a simulation run.                            \u001b[2m             \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>starting up solver                                      <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#377\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">377</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                      \u001b]8;id=440173;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=409697;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#377\u001b\\\u001b[2m377\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>running solver                                          <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#386\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">386</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                          \u001b]8;id=974698;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=53756;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#386\u001b\\\u001b[2m386\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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or   <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#387\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">387</span></a>\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   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>web UI. Terminating the Python script will not stop the <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>job running on the cloud.                               <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\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   \u001b]8;id=971038;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=738490;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\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   \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0mweb UI. Terminating the Python script will not stop the \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0mjob running on the cloud.                               \u001b[2m             \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "103e5e5eb7ce47a890ef2ceecf36e890",
       "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\">[21:11:34] </span>early shutoff detected, exiting.                        <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#404\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">404</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[21:11:34]\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected, exiting.                        \u001b]8;id=857352;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=749118;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#404\u001b\\\u001b[2m404\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\"></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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\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>status = postprocess                                    <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#419\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">419</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                    \u001b]8;id=94457;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=906019;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#419\u001b\\\u001b[2m419\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "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\">[21:11:38] </span>status = success                                        <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#426\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">426</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[21:11:38]\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                        \u001b]8;id=593268;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9455;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#426\u001b\\\u001b[2m426\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\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df91e93714c94252aeeb6fd0f8258546",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\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\">[21:11:44] </span>loading SimulationData from simulation_data.hdf5        <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#590\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">590</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[21:11:44]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from simulation_data.hdf5        \u001b]8;id=83865;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=680344;file:///home/momchil/Drive/flexcompute/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#590\u001b\\\u001b[2m590\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data_after_mnt = web.run(sim_after_mnt, task_name='meta_near_field_after')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 4))\n",
    "sim_data_after_mnt.plot_field('far_field', 'int', vmax=105, ax=ax1)\n",
    "sim_data_after_mnt.plot_field('fields_yz', 'int', vmax=180, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "We notice that our metalens does quite well at focusing at this high NA! For the purposes of demonstration, this is quite a small device, but the same the same principle can be applied to optimize a much larger metalens.\n",
    "\n",
    "For more case studies using the `adjoint` plugin, see the\n",
    "\n",
    "* [Gradient Checking Notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin2GradientChecking/).\n",
    "\n",
    "* [Inverse Design Notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin3InverseDesign/).\n",
    "\n",
    "* [Multi-Objective Gradient Notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin4MultiObjective/).\n",
    "\n",
    "* [Boundary Gradients Notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin5BoundaryGradients/).\n",
    "\n",
    "* [Grating Coupler Notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin6GratingCoupler/).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "description": "This notebook demonstrates the adjoint optimization of a metalens in Tidy3D using the adjoint plugin.",
  "feature_image": "./img/adjoint_7.png",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "inverse design, metalens, design optimization, adjoint, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "nbdime-conflicts": {
   "local_diff": [
    {
     "diff": [
      {
       "diff": [
        {
         "diff": [
          {
           "key": 5,
           "op": "addrange",
           "valuelist": "9"
          },
          {
           "key": 5,
           "length": 1,
           "op": "removerange"
          }
         ],
         "key": 0,
         "op": "patch"
        }
       ],
       "key": "version",
       "op": "patch"
      }
     ],
     "key": "language_info",
     "op": "patch"
    }
   ],
   "remote_diff": [
    {
     "diff": [
      {
       "diff": [
        {
         "diff": [
          {
           "key": 5,
           "op": "addrange",
           "valuelist": "12"
          },
          {
           "key": 5,
           "length": 1,
           "op": "removerange"
          }
         ],
         "key": 0,
         "op": "patch"
        }
       ],
       "key": "version",
       "op": "patch"
      }
     ],
     "key": "language_info",
     "op": "patch"
    }
   ]
  },
  "title": "Adjoint Optimization of a Metalens in Tidy3D Using the Adjoint Plugin| Flexcompute",
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "221f7eb28bd24430bf845e5883144ef8": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_eaa507fb4cd549cf9c6104425d93874a",
       "msg_id": "",
       "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: #800000; text-decoration-color: #800000; font-weight: bold\">↑</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">simulation.json</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">629.0/629.0 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">2.1 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m629.0/629.0 kB\u001b[0m • \u001b[31m2.1 MB/s\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "386aecd9a2b643d99ee58ce9a2002752": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_e7d67843bb994e13a5ab6ccfc3f64dd1",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000; font-weight: bold\">↓</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">monitor_data.hdf5</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">2.7/2.7 MB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">20.9 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m2.7/2.7 MB\u001b[0m • \u001b[31m20.9 MB/s\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "3ae03f102f324415a305e6c002006df3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "48f8e276d091491fa7800774be9fadb4": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "4ae64695120949c2a91488d9f25f552d": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_3ae03f102f324415a305e6c002006df3",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000\">🚶 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'metalens'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mStarting 'metalens'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "51e705e82b59434f87e998f04fe828cd": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_f3bd11a740814984b1d5e9ed562ddcd8",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000; font-weight: bold\">↓</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">monitor_data.hdf5</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">23.5/23.5 MB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">20.3 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m23.5/23.5 MB\u001b[0m • \u001b[31m20.3 MB/s\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "5582ac28333f4e6aae6b70f836193e3b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "55c70db6254c4dfa8085c698d0f5405e": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_6447cfa42b8841b6bf46faded3b40218",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'metalens'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🏃 \u001b[0m \u001b[1;32mStarting 'metalens'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "59f7b4ed010849639dc31bade9b5012f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "6447cfa42b8841b6bf46faded3b40218": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "7d0b370f20a344d9832a2fdc7a279454": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_a3abab06426a4faaba4e1ba1b3115535",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'metalens'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'metalens'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "7fb022224f024af3ba9902ce8f805763": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_48f8e276d091491fa7800774be9fadb4",
       "msg_id": "",
       "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\">solver progress (field decay = 5.96e-06) <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "solver progress (field decay = 5.96e-06) \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "a3abab06426a4faaba4e1ba1b3115535": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "b76c41719c2f4649b27f05ad4868062a": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_b7d6fa93e6664924bcc34f4f894b65a5",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'metalens'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'metalens'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "b7d6fa93e6664924bcc34f4f894b65a5": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "c32f795b0a72480bac2a40f2db5ce901": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_59f7b4ed010849639dc31bade9b5012f",
       "msg_id": "",
       "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\">solver progress (field decay = 7.28e-06) <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "solver progress (field decay = 7.28e-06) \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "e25610dd4c954cbdbd5a71585cdd78e1": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_5582ac28333f4e6aae6b70f836193e3b",
       "msg_id": "",
       "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: #800000; text-decoration-color: #800000; font-weight: bold\">↑</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">simulation.json</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">616.9/616.9 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">1.8 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m616.9/616.9 kB\u001b[0m • \u001b[31m1.8 MB/s\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "e7d67843bb994e13a5ab6ccfc3f64dd1": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "eaa507fb4cd549cf9c6104425d93874a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "f3bd11a740814984b1d5e9ed562ddcd8": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}