Fu Ziwei, Fu Tingzhao, Wu Hao, Zhu Zhihong, Zhang Jianfa
Opt Express. 2024 Nov 4;32(23):41433-41444. doi: 10.1364/OE.539235.
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 GeSbSeTe(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.
衍射光学神经网络（DONNs）具有诸如光速计算、低能耗和平行处理等独特优势，近年来受到越来越多的关注。然而，一旦传统的DONNs被制造出来，其功能就保持固定，这极大地限制了DONNs的应用。因此，我们提出了一种基于可重复且非易失性相变材料GeSbSeTe（GSST）的可重构DONN框架。通过利用由GSST制成的相位调制单元来形成网络的神经元，我们可以灵活地切换DONN的功能。同时，我们应用一种二进制训练算法将DONN权重训练为0和π的二进制值，这有利于简化DONN的设计和制造，同时减少物理实现过程中的误差。此外，可重构二进制DONN已被训练为手写数字分类器和时尚产品分类器，以验证该框架的可行性。这项工作为可重构DONNs提供了一种高效且灵活的控制机制，在各种复杂任务中具有潜在应用。
