2D Reconfigurable Memory Device Enabled by Defect Engineering for Multifunctional Neuromorphic Computing

In this era of artificial intelligence and Internet of Things, emerging new computing paradigms such as in-sensor and in-memory computing call for both structurally simple and multifunctional memory devices. Although emerging two-dimensional (2D) memory devices provide promising solutions, the most reported devices either suffer from single functionalities or structural complexity. Here, this work reports a reconfigurable memory device (RMD) based on MoS2/CuInP2S6 heterostructure, which integrates the defect engineeringenabled interlayer defects and the ferroelectric polarization in CuInP2S6, to realize a simplified structure device for all-in-one sensing, memory and computing. The plasma treatment-induced defect engineering of the CuInP2S6 nanosheet effectively increases the interlayer defect density, which significantly enhances the charge-trapping ability in synergy with ferroelectric properties. The reported device not only can serve as a non-volatile electronic memory device, but also can be reconfigured into optoelectronic memory mode or synaptic mode after controlling the ferroelectric polarization states in CuInP2S6. When operated in optoelectronic memory mode, the all-in-one RMD could diagnose ophthalmic disease by segmenting vasculature within biological retinas. On the other hand, operating as an optoelectronic synapse, this work showcases in-sensor reservoir computing for gesture recognition with high energy efficiency.

Citation

If you use this work, please cite the following paper:

@article{xia20242d,
  title={2D Reconfigurable Memory Device Enabled by Defect Engineering for Multifunctional Neuromorphic Computing},
  author={Xia, Yunpeng and Lin, Ning and Zha, Jiajia and Huang, Haoxin and Zhang, Yiwen and Liu, Handa and Tong, Jinyi and Xu, Songcen and Yang, Peng and Wang, Huide and others},
  journal={Advanced Materials},
  pages={2403785},
  year={2024},
  publisher={Wiley Online Library}
}

Software and Hardware Informations

Python version: 3.9.12 (main, Apr  5 2022, 06:56:58) 
[GCC 7.5.0]
CUDA version: 11.7
PyTorch version: 2.0.1
GPU model: NVIDIA GeForce RTX 3090 Ti
CPU model: AMD Ryzen 9 7950X 16-Core Processor

Environment Configuration

conda create --name newenv python=3.7
conda activate newenv
pip install -r requirements.txt

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