Spike‐Timing‐Dependent Plasticity Realized in the Silicon Photonic Synapse Based on Phase Change Materials
Zixuan Wang, Tianci Wang, Yunxiao Dong, Gongmin Li, Jian Guo Xia, Wei Tai Tang, Zhiyuan Li, Jie Gong, Xiangshui Miao, Rui Yang · Laser & Photonics Review · 2025
Abstract Optical spiking neural networks (OSNNs) based on phase‐change materials (PCMs) synergistically integrate the energy‐efficient nature of spiking neural networks, the low‐latency, high‐bandwidth advantages offered by optical neural networks, with high optical contrast, nonvolatile storage of PCM, emerging as a pivotal technology to address data redundancy and elevated energy consumption challenges in the information processing. The synaptic connections within OSNNs are modulated by spike‐timing‐dependent plasticity (STDP) learning rules. However, the STDP learning rules are not realized in PCM‐based electrically programmable photonic devices, due to the challenge of precisely controlling the PCM modulation. Here, a Ge 2 Sb 2 Te 5 ‐based photonic device with the implementation of four STDP learning rules is demonstrated, enabled by the introduction of an Al 2 O 3 thermal conductive layer. The present photonic device shows outstanding performances, including an endurance of over 45000 switching cycles and 70 intermediate crystalline states, as well as 25 intermediate amorphous states. The OSNN employing the present device as synapses achieves an accuracy of up to 94.07% in the recognition task, outperforming counterparts based on unilateral modulation devices. This investigation indicates that the integration of phase‐change photonic devices with OSNN shows great potential for building the next generation of neuromorphic computing systems with high energy efficiency.