Recent Advances on Neuromorphic Devices Based on Chalcogenide Phase‐Change Materials

• Published in: Advanced functional materials Vol. 30; no. 50
• Main Authors: Xu, Ming, Mai, Xianliang, Lin, Jun, Zhang, Wei, Li, Yi, He, Yuhui, Tong, Hao, Hou, Xiang, Zhou, Peng, Miao, Xiangshui
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01.12.2020
• Subjects: Amorphous materials; artificial neurons, artificial synapses; Brain; Chalcogenides; Computation; Data processing; Design optimization; Electronic devices; Materials science; memory; neurocomputing; Neuromorphic computing; Optical properties; phase change materials; Storage units; Synapses
• ISSN: 1616-301X; 1616-3028
• DOI: 10.1002/adfm.202003419

Traditional von Neumann computing architecture with separated computation and storage units has already impeded the data processing performance and energy efficiency, calling for emerging neuromorphic electronic and optical devices and systems which can mimic the human brain to shift this paradigm. Material‐level innovation has become the key component to this revolution of information technology. Chalcogenide phase‐change material (PCM) as a well‐acknowledged data‐storage medium is a promising candidate to tackle this challenge. In this review, the use of PCMs to implement artificial neurons and synapses from both the electronic and optical respects is discussed, and in particular, the structure–property physics and transition dynamics that enable such brain‐inspired and in‐memory computing applications are emphasized. Recent advances on the atomic‐level amorphous and crystalline structures, transition mechanisms, materials optimization and design, neural and synaptic devices, brain‐inspired chips, and computing systems, as well as the future opportunities of PCMs, are summarized and discussed. Phase‐change materials (PCM) utilizing the rapid transition between amorphous and crystalline chalcogenides enable precise and reliable brain‐inspired computing in both electronic and photonic neural networks. The recent advances in PCM‐based neuromorphic devices, as well as the related physics and transition dynamics, are surveyed and discussed in this article.