High-performance synaptic transistors for neuromorphic computing

doi:10.1088/1674-1056/ab7806

中国物理B ›› 2020, Vol. 29 ›› Issue (4): 40703-040703.doi: 10.1088/1674-1056/ab7806

所属专题： SPECIAL TOPIC — Physics in neuromorphic devices

• SPECIAL TOPIC—Ultracold atom and its application in precision measurement • 上一篇下一篇

High-performance synaptic transistors for neuromorphic computing

1 Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;
2 School of Physical Sciences, University of Chinese Academy of Science, Beijing 100049, China;
3 Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China;
4 Songshan Lake Materials Laboratory, Dongguan 523808, China

收稿日期:2020-01-10 修回日期:2020-02-15 出版日期:2020-04-05 发布日期:2020-04-05
通讯作者: Chen Ge, Kui-Juan Jin E-mail:gechen@iphy.ac.cn;kjjin@iphy.ac.cn
基金资助:
Project supported by the National Key R&D Program of China (Grant Nos. 2017YFA0303604 and 2019YFA0308500), the Youth Innovation Promotion Association of Chinese Academy of Sciences (Grant No. 2018008), the National Natural Science Foundation of China (Grant Nos. 11674385, 11404380, 11721404, and 11874412), and the Key Research Program of Frontier Sciences of Chinese Academy of Sciences (Grant No. QYZDJSSW-SLH020).

High-performance synaptic transistors for neuromorphic computing

Hai Zhong(钟海)¹, Qin-Chao Sun(孙勤超)¹, Guo Li(李果)¹, Jian-Yu Du(杜剑宇)^1,2, He-Yi Huang(黄河意)^1,2, Er-Jia Guo(郭尔佳)^1,3, Meng He(何萌)¹, Can Wang(王灿)^1,2,4, Guo-Zhen Yang(杨国桢)¹, Chen Ge(葛琛)^1,2, Kui-Juan Jin(金奎娟)^1,2,4

1 Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;
2 School of Physical Sciences, University of Chinese Academy of Science, Beijing 100049, China;
3 Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China;
4 Songshan Lake Materials Laboratory, Dongguan 523808, China

Received:2020-01-10 Revised:2020-02-15 Online:2020-04-05 Published:2020-04-05
Contact: Chen Ge, Kui-Juan Jin E-mail:gechen@iphy.ac.cn;kjjin@iphy.ac.cn
Supported by:
Project supported by the National Key R&D Program of China (Grant Nos. 2017YFA0303604 and 2019YFA0308500), the Youth Innovation Promotion Association of Chinese Academy of Sciences (Grant No. 2018008), the National Natural Science Foundation of China (Grant Nos. 11674385, 11404380, 11721404, and 11874412), and the Key Research Program of Frontier Sciences of Chinese Academy of Sciences (Grant No. QYZDJSSW-SLH020).

摘要/Abstract

摘要： The further development of traditional von Neumann-architecture computers is limited by the breaking of Moore's law and the von Neumann bottleneck, which make them unsuitable for future high-performance artificial intelligence (AI) systems. Therefore, new computing paradigms are desperately needed. Inspired by the human brain, neuromorphic computing is proposed to realize AI while reducing power consumption. As one of the basic hardware units for neuromorphic computing, artificial synapses have recently aroused worldwide research interests. Among various electronic devices that mimic biological synapses, synaptic transistors show promising properties, such as the ability to perform signal transmission and learning simultaneously, allowing dynamic spatiotemporal information processing applications. In this article, we provide a review of recent advances in electrolyte- and ferroelectric-gated synaptic transistors. Their structures, materials, working mechanisms, advantages, and disadvantages will be presented. In addition, the challenges of developing advanced synaptic transistors are discussed.

关键词: synaptic transistor, artificial synapse, synaptic plasticity, electrolyte gating, ferroelectric gating

Abstract: The further development of traditional von Neumann-architecture computers is limited by the breaking of Moore's law and the von Neumann bottleneck, which make them unsuitable for future high-performance artificial intelligence (AI) systems. Therefore, new computing paradigms are desperately needed. Inspired by the human brain, neuromorphic computing is proposed to realize AI while reducing power consumption. As one of the basic hardware units for neuromorphic computing, artificial synapses have recently aroused worldwide research interests. Among various electronic devices that mimic biological synapses, synaptic transistors show promising properties, such as the ability to perform signal transmission and learning simultaneously, allowing dynamic spatiotemporal information processing applications. In this article, we provide a review of recent advances in electrolyte- and ferroelectric-gated synaptic transistors. Their structures, materials, working mechanisms, advantages, and disadvantages will be presented. In addition, the challenges of developing advanced synaptic transistors are discussed.

Key words: synaptic transistor, artificial synapse, synaptic plasticity, electrolyte gating, ferroelectric gating

中图分类号: (Neural networks, fuzzy logic, artificial intelligence)

07.05.Mh

73.40.Mr (Semiconductor-electrolyte contacts) 85.30.Tv (Field effect devices) 85.50.Gk (Non-volatile ferroelectric memories)

钟海, 孙勤超, 李果, 杜剑宇, 黄河意, 郭尔佳, 何萌, 王灿, 杨国桢, 葛琛, 金奎娟. High-performance synaptic transistors for neuromorphic computing[J]. 中国物理B, 2020, 29(4): 40703-040703.

Hai Zhong(钟海), Qin-Chao Sun(孙勤超), Guo Li(李果), Jian-Yu Du(杜剑宇), He-Yi Huang(黄河意), Er-Jia Guo(郭尔佳), Meng He(何萌), Can Wang(王灿), Guo-Zhen Yang(杨国桢), Chen Ge(葛琛), Kui-Juan Jin(金奎娟). High-performance synaptic transistors for neuromorphic computing[J]. Chin. Phys. B, 2020, 29(4): 40703-040703.

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High-performance synaptic transistors for neuromorphic computing

High-performance synaptic transistors for neuromorphic computing

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