Optically tunable synaptic transistors based on AlGaN/GaN heterostructure for neuromorphic vision processing

X Xiaoqi Li (State Key Laboratory of Functional Crystals and Devices, Fujian Institute of Research on the Structure of Matter) H Huazhen Sun M Mei Ge L Leyang Qian (School of Science, Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Jiangnan University 1 , Wuxi 214122,) X Xuyang Ge (School of Science, Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Jiangnan University 1 , Wuxi 214122,) X Xuekun Hong (School of Electronic Information Engineering, Suzhou University of Technology 3 , Changshu, Jiangsu 215500,) W Weiying Qian (School of Science, Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Jiangnan University 1 , Wuxi 214122,) X Xiangyang Zhang J Jun-Ge Liang (RFIC Center, Kwangwoon University; Department of Medical and Digital Engineering, Hanyang University 4 , Seoul 04763,) X Xinyi Shan (School of Science, Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Jiangnan University 1 , Wuxi 214122,) J Jian Guo G Guofeng Yang

Abstract

Optoelectronic synaptic devices are a promising technology for overcoming the von Neumann bottleneck, meeting the demand from rapidly advancing artificial intelligence for faster, more energy-efficient neuromorphic computing. This study fabricated an optically tunable synaptic transistor based on an AlGaN/GaN heterostructure, which enables the implementation of neuromorphic vision processing. The device exhibits a low dark current in the cutoff region and a high photo-to-dark current ratio of 1.47 × 108, highlighting its excellent photoresponsivity. Under UV illumination, the device demonstrates synaptic behaviors such as excitatory postsynaptic current and paired-pulse facilitation. By tuning the time, power, and number of optical pulses, dynamic transitions from short-term memory to long-term memory are achieved, effectively emulating visual persistent memory. Furthermore, an optically modulated convolutional neural network is implemented, achieving a classification accuracy of 93.86% on the Fashion-MNIST dataset. These results validate the potential of the proposed device for neuromorphic vision processing applications.

Article Details

Volume / Issue Vol. 127, Issue 21
Published November 24, 2025
ISSN 0003-6951
Publisher American Institute of Physics

Journal Info

Applied Physics Letters

American Institute of Physics

ISSN: 0003-6951 Physical Sciences

Authors (12)

X

Xiaoqi Li

State Key Laboratory of Functional Crystals and Devices, Fujian Institute of Research on the Structure of Matter

H

Huazhen Sun

M

Mei Ge

L

Leyang Qian

School of Science, Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Jiangnan University 1 , Wuxi 214122,

X

Xuyang Ge

School of Science, Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Jiangnan University 1 , Wuxi 214122,

X

Xuekun Hong

School of Electronic Information Engineering, Suzhou University of Technology 3 , Changshu, Jiangsu 215500,

W

Weiying Qian

School of Science, Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Jiangnan University 1 , Wuxi 214122,

X

Xiangyang Zhang

J

Jun-Ge Liang

RFIC Center, Kwangwoon University; Department of Medical and Digital Engineering, Hanyang University 4 , Seoul 04763,

X

Xinyi Shan

School of Science, Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Jiangnan University 1 , Wuxi 214122,

J

Jian Guo

G

Guofeng Yang