Wen Huang: New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, 210023, People's Republic of China. wenhuang@njupt.edu.cn.
Xuwen Xia: New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, 210023, People's Republic of China.
Chen Zhu: College of Electronic and Optical Engineering and College of Microelectronics, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, 210023, People's Republic of China.
Parker Steichen: Department of Materials Science and Engineering, University of Washington, Seattle, WA, 98195-2120, USA.
Weidong Quan: New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, 210023, People's Republic of China.
Weiwei Mao: New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, 210023, People's Republic of China.
Jianping Yang: New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, 210023, People's Republic of China.
Liang Chu: New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, 210023, People's Republic of China. chuliang@njupt.edu.cn.
Xing'ao Li: New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, 210023, People's Republic of China. iamxali@njupt.edu.cn.
Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.
