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arXiv · 2305.11196

Non-volatile Reconfigurable Digital Optical Diffractive Neural Network Based on Phase Change Material

Chu Wu·Jingyu Zhao·Qiaomu Hu·Rui Zeng·Minming Zhang
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Abstract

Optical diffractive neural networks have triggered extensive research with their low power consumption and high speed in image processing. In this work, we propose a reconfigurable digital all-optical diffractive neural network (R-ODNN) structure. The optical neurons are built with Sb2Se3 phase-change material, making our network reconfigurable, digital, and non-volatile. Using three digital diffractive layers with 14,400 neurons on each and 10 photodetectors connected to a resistor network, our model achieves 94.46% accuracy for handwritten digit recognition. We also performed full-vector simulations and discussed the impact of errors to demonstrate the feasibility and robustness of the R-ODNN.

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Chu Wu, Jingyu Zhao, Qiaomu Hu, Rui Zeng, Minming Zhang. 2023-05-18. Non-volatile Reconfigurable Digital Optical Diffractive Neural Network Based on Phase Change Material. https://arxiv.org/abs/2305.11196

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