Neuromorphic Photonics Based on Phase Change Materials
Tiantian Li, Yijie Li, Yuteng Wang, Yuxin Liu, Yumeng Liu, Zhan Wang, Ruixia Miao, Dongdong Han, Zhanqiang Hui, Wei Li
Abstract
Neuromorphic photonics devices based on phase change materials (PCMs) and silicon photonics technology have emerged as promising solutions for addressing the limitations of traditional spiking neural networks in terms of scalability, response delay, and energy consumption. In this review, we provide a comprehensive analysis of various PCMs used in neuromorphic devices, comparing their optical properties and discussing their applications. We explore materials such as GST (Ge2Sb2Te5), GeTe-Sb2Te3, GSST (Ge2Sb2Se4Te1), Sb2S3/Sb2Se3, Sc0.2Sb2Te3 (SST), and In2Se3, highlighting their advantages and challenges in terms of erasure power consumption, response rate, material lifetime, and on-chip insertion loss. By investigating the integration of different PCMs with silicon-based optoelectronics, this review aims to identify potential breakthroughs in computational performance and scalability of photonic spiking neural networks. Further research and development are essential to optimize these materials and overcome their limitations, paving the way for more efficient and high-performance photonic neuromorphic devices in artificial intelligence and high-performance computing applications.