Yue Wang; Lei Yin; Wen Huang; Yayao Li; Shijie Huang; Yiyue Zhu; Deren Yang; Xiaodong Pi · 2020 · Advanced Intelligent Systems
Paper
Neuromorphic computing can potentially solve the von Neumann bottleneck of current mainstream computing because it excels at self‐adaptive learning and highly parallel computing and consumes much less energy. Synaptic devices that mimic biological synapses are critical building blocks for neuromorphic computing. Inspired by recent progress in optogenetics and visual sensing, light has been increasingly incorporated into synaptic devices. This paves the way to optoelectronic synaptic devices with a series of advantages such as wide bandwidth, negligible resistance–capacitance (RC) delay and power loss, and global regulation of multiple synaptic devices. Herein, the basic functionalities of synaptic devices are introduced. All kinds of optoelectronic synaptic devices are then discussed by categorizing them into optically stimulated synaptic devices, optically assisted synaptic devices, and synaptic devices with optical output. Existing practical scenarios for the application of optoelectronic synaptic devices are also presented. Finally, perspectives on the development of optoelectronic synaptic devices in the future are outlined.
Analysis
This paper reviews optoelectronic synaptic devices, which are crucial for neuromorphic computing, highlighting their advantages and potential applications.
Discovery
Alex James
Emmanuel Joseph Shaji; Zhiyuan Li; Srikanth Doddapaneni; Bhavya Rakheja; Vikrant Chaudhary; Avantika Suthar; Lingyun Zhu; Jingxin Ma; Hongbin Zhang; Monojit Bag; Gerardo Hernandez-Sosa; Ramesh Kumar
Falihah Balqis; Jin Pyo Lee; Zhenxiang Xing; Hui Wang; Tupei Chen; Rong Ji; Pooi See Lee
Hanna Hamrell; Thomas Sjögren; Hannes Ovrén
J. Rajvel
Minsu Ko; Yongjin Byun; Sungjun Kim
Source record