Non-volatile Reconfigurable Digital Optical Diffractive Neural Network Based on Phase Change Material
Emerging Technologies
2023-05-22 v1 Signal Processing
Optics
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.
Cite
@article{arxiv.2305.11196,
title = {Non-volatile Reconfigurable Digital Optical Diffractive Neural Network Based on Phase Change Material},
author = {Chu Wu and Jingyu Zhao and Qiaomu Hu and Rui Zeng and Minming Zhang},
journal= {arXiv preprint arXiv:2305.11196},
year = {2023}
}