Li Tiantian, Li Yijie, Wang Yuteng, Liu Yuxin, Liu Yumeng, Wang Zhan, Miao Ruixia, Han Dongdong, Hui Zhanqiang, Li Wei
School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China.
College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.
Nanomaterials (Basel). 2023 May 29;13(11):1756. doi: 10.3390/nano13111756.
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 (GeSbTe), GeTe-SbTe, GSST (GeSbSeTe), SbS/SbSe, ScSbTe (SST), and InSe, 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.
基于相变材料（PCM）和硅光子技术的神经形态光子器件，已成为解决传统脉冲神经网络在可扩展性、响应延迟和能耗方面局限性的有前景的解决方案。在本综述中，我们对神经形态器件中使用的各种PCM进行了全面分析，比较了它们的光学特性并讨论了它们的应用。我们探讨了诸如GST（GeSbTe）、GeTe-SbTe、GSST（GeSbSeTe）、SbS/SbSe、ScSbTe（SST）和InSe等材料，突出了它们在擦除功耗、响应速率、材料寿命和片上插入损耗方面的优势和挑战。通过研究不同PCM与硅基光电子学的集成，本综述旨在确定光子脉冲神经网络在计算性能和可扩展性方面的潜在突破。进一步的研究和开发对于优化这些材料并克服其局限性至关重要，为人工智能和高性能计算应用中更高效、高性能的光子神经形态器件铺平道路。
