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

Optical diffractive neural networks have sparked extensive research due to their low power consumption and high-speed capabilities in image processing. Here we propose and design a reconfigurable all-optical diffractive neural network structure with digital non-volatile optical neurons. The optical neurons are built with Sb<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>Se<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> phase-change material and can switch between crystalline and amorphous states with no constant energy supply. Using three reconfigurable non-volatile digital diffractive layers and 10 photodetectors connected to a reconfigurable resistor network, our model achieves an accuracy of 94.46% in the handwritten digit recognition task. Moreover, the fabrication and assembly robustness of the proposed optical diffractive neural network is verified through full-vector diffractive simulation. Thanks to its reconfigurability and low energy supply, the digital optical diffractive neural network holds great potential to facilitate a programmable and low-power-consumption photonic processor for optical-artificial-intelligence.