Affiliation:
1. School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2. College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
3. Los Alamos National Laboratory, Computer, Computational, and Statistical Sciences Division, Los Alamos, NM 87545, USA
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.
Funder
National Natural Science Foundation of China
Natural Science Basic Research Program of Shaanxi
Young Talent fund of University Association for Science and Technology in Shaanxi, China
Young Talent fund of Xi’an Association for science and technology
Scientific Research Program Foundation of Shaanxi Provincial Education Department
Laboratory Directed Research and Development Program of Los Alamos National Laboratory
Triad National Security, LLC, for the National Nuclear Security Administration of the U.S. Department of Energy
Subject
General Materials Science,General Chemical Engineering
Reference53 articles.
1. Le, Q.V. (2013, January 26–31). Building high-level features using large scale unsupervised learning. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.
2. Computing on Silicon Photonic Platform;Zhou;Chin. J. Lasers,2020
3. Photonics for artificial intelligence and neuromorphic computing;Shastri;Nat. Photonics,2021
4. Development trends in silicon photonics for data centers;Zhou;Opt. Fiber Technol.,2018
5. Deep learning with coherent nanophotonic circuits;Shen;Nat. Photon,2017
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献