Artificial Synapses: A Reliable All‐2D Materials Artificial Synapse for High Energy‐Efficient Neuromorphic Computing (Adv. Funct. Mater. 27/2021). (2nd July 2021)

• Record Type: Journal Article
• Authors: Tang, Jian; He, Congli; Tang, Jianshi; Yue, Kun; Zhang, Qingtian; Liu, Yizhou; Wang, Qinqin; Wang, Shuopei; Li, Na; Shen, Cheng; Zhao, Yanchong; Liu, Jieying; Yuan, Jiahao; Wei, Zheng; Li, Jiawei; Watanabe, Kenji; Taniguchi, Takashi; Shang, Dashan; Wang, Shouguo; Yang, Wei; Yang, Rong; Shi, Dongxia; Zhang, Guangyu
• Is Part Of: Advanced functional materials. Volume 31:Number 27(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-02
• Subjects: 2D materials -- artificial synapse -- linear weight update -- MoS 2; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adfm.202170197
• Languages: English
• ISSNs: 1616-301X

Abstract

Abstract : In article number 2011083, Congli He, Guangyu Zhang, and co‐workers report an all‐2D materials two‐terminal floating‐gate memory as an artificial synapse device for high energy‐efficient neuromorphic computing. It exhibits linear and symmetric weight update behaviors with high reliability and tunability. A large number of states up to 3000, high switching speed of 40 ns, and low energy consumption of 18 fJ for a single pulse event are realized, demonstrating great potential for high‐speed and low‐power neuromorphic computing applications.