Reconfigurable binary diffractive optical neural network based on chalcogenide phase change material Ge₂Sb₂Se₄Te₁

Diffractive optical neural networks (DONNs) possess unique advantages such as light-speed computing, low energy consumption, and parallel processing, which have obtained increasing attention in recent years. However, once conventional DONNs are fabricated, their function remains fixed, which greatly limits the applications of DONNs. Thus, we propose a reconfigurable DONN framework based on a repeatable and non-volatile phase change material Ge₂Sb₂Se₄Te₁(GSST). By utilizing phase modulation units made of GSST to form the network's neurons, we can flexibly switch the functions of the DONN. Meanwhile, we apply a binary training algorithm to train the DONN weights to binary values of 0 and π, which is beneficial for simplifying the design and fabrication of DONN while reducing errors during physical implementation. Furthermore, the reconfigurable binary DONN has been trained as a handwritten digit classifier and a fashion product classifier to validate the feasibility of the framework. This work provides an efficient and flexible control mechanism for reconfigurable DONNs, with potential applications in various complex tasks.