High-performance synaptic transistors for neuromorphic computing (Invited Review)
High-performance synaptic transistors for neuromorphic computing (Invited Review)
复制标题
用于神经形态计算的高性能突触晶体管
DOI：
10.1088/1674-1056/ab7806
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发表时间：
2020
影响因子：
1.7
通讯作者：
Jin Kui-juan
中科院分区：
文献类型：
--
作者：
Zhong Hai;Sun Qin-chao;Li Guo;Du Jian-yu;Huang He-yi;Guo Er-jia;He Meng;Wang Can;Yang Guo-zhen;Ge Chen;Jin Kui-juan
传统的冯·诺依曼体系结构计算机的进一步发展受到摩尔定律的突破和冯·诺依曼瓶颈的限制，这使得它们不适合未来的高性能人工智能（AI）系统。因此，迫切需要新的计算范式。受人类大脑的启发，提出了神经形态计算，以实现AI，同时降低功耗。人工突触作为神经形态计算的基本硬件单元之一，近年来引起了世界范围内的研究兴趣。在模拟生物突触的各种电子器件中，突触晶体管显示出有前途的特性，例如同时执行信号传输和学习的能力，允许动态时空信息处理应用。在这篇文章中，我们提供了一个审查的最新进展电解质和铁电门控突触晶体管。介绍了它们的结构、材料、工作机理和优缺点。此外，发展先进的突触晶体管的挑战进行了讨论。
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.