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Artificial Synapses: A Reliable All‐2D Materials Artificial Synapse for High Energy‐Efficient Neuromorphic Computing (Adv. Funct. Mater. 27/2021)
Author(s) -
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
Publication year - 2021
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.202170197
Subject(s) - neuromorphic engineering , materials science , reliability (semiconductor) , synapse , synaptic weight , energy (signal processing) , power consumption , energy consumption , power (physics) , artificial neural network , computer science , nanotechnology , optoelectronics , artificial intelligence , electrical engineering , neuroscience , physics , engineering , quantum mechanics , biology
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.