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Special Issue:
SPECIAL TOPIC — Physics in neuromorphic devices
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| TOPICAL REVIEW—Physics in neuromorphic devices |
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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 |
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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.
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Received: 10 January 2020
Revised: 15 February 2020
Accepted manuscript online:
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PACS:
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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73.40.Mr
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(Semiconductor-electrolyte contacts)
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85.30.Tv
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(Field effect devices)
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85.50.Gk
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(Non-volatile ferroelectric memories)
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| Fund: 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). |
Corresponding Authors:
Chen Ge, Kui-Juan Jin
E-mail: gechen@iphy.ac.cn;kjjin@iphy.ac.cn
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Cite this article:
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 2020 Chin. Phys. B 29 040703
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