ZnO nanowire optoelectronic synapse for neuromorphic computing. (19th November 2021)

• Record Type: Journal Article
• Authors: Shen, Cong; Gao, Xu; Chen, Cheng; Ren, Shan; Xu, Jian-Long; Xia, Yi-Dong; Wang, Sui-Dong
• Is Part Of: Nanotechnology. Volume 33:Number 6(2022)
• Publication Date: 2021-11-19
• Subjects: ZnO nanowires -- optoelectronic synapse -- charge trapping -- synaptic plasticity; Nanotechnology -- Periodicals; Nanotechnology -- Periodicals; Nanotechnology; Publications périodiques; Nanotechnologies; Periodicals; 620.5
• DOI: 10.1088/1361-6528/ac3687
• Languages: English
• ISSNs: 0957-4484

Abstract

Abstract: Artificial synapses that integrate functions of sensing, memory and computing are highly desired for developing brain-inspired neuromorphic hardware. In this work, an optoelectronic synapse based on the ZnO nanowire (NW) transistor is achieved, which can be used to emulate both the short-term and long-term synaptic plasticity. Synaptic potentiation is present when the device is stimulated by light pulses, arising from the light-induced O2 desorption and the persistent photoconductivity behavior of the ZnO NW. On the other hand, synaptic depression occurs when the device is stimulated by electrical pulses in dark, which is realized by introducing a charge trapping layer in the gate dielectric to trap carriers. Simulation of a neural network utilizing the ZnO NW synapses is carried out, demonstrating a high recognition accuracy over 90% after only 20 training epochs for recognizing the Modified National Institute of Standards and Technology digits. The present nanoscale optoelectronic synapse has great potential in the development of neuromorphic visual systems.