High-photosensitivity optoelectronic synaptic transistors based on triple-cation perovskite for neuromorphic vision system

L Lu Chen T Ting Zhang Q Qien Xu (School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China 1 , Chengdu 611731,) J Jian Li Y Yuhan Zhang (Department of Chemistry) Z Zhi David Chen (Department of Electrical and Computer Engineering and the Center for Nanoscale Science and Engineering, University of Kentucky 4 , Lexington, Kentucky 40506,) S Shibin Li

Abstract

Optoelectronic synaptic transistors are considered as a key hardware foundation for building neuromorphic vision systems. Due to the lack of photosensitive materials with high optical absorption coefficients and excellent charge-trapping ability, research focusing on optoelectronic synaptic transistors for colored pattern recognition remains limited. In this study, based on the high optical absorption coefficient and broad spectral response range of triple-cation perovskite materials, a non-volatile optoelectronic synaptic transistor was developed. Notably, since the proposed device exhibits an excellent photogenerated charge-trapping effect, it maintains high photosensitivity of up to 5.6 × 104 even under weak light intensity (0.081 mW/cm2). These transistors mimic the colored pattern recognition capability of human retina and demonstrate excellent recognition accuracy under dim lighting conditions when integrated into artificial neural networks. This device holds significant potential for the development of neuromorphic vision systems with advanced colored image discrimination.

Article Details

Volume / Issue Vol. 127, Issue 16
Published October 20, 2025
ISSN 0003-6951
Publisher American Institute of Physics

Journal Info

Applied Physics Letters

American Institute of Physics

ISSN: 0003-6951 Physical Sciences

Authors (7)

L

Lu Chen

T

Ting Zhang

Q

Qien Xu

School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China 1 , Chengdu 611731,

J

Jian Li

Y

Yuhan Zhang

Department of Chemistry

Z

Zhi David Chen

Department of Electrical and Computer Engineering and the Center for Nanoscale Science and Engineering, University of Kentucky 4 , Lexington, Kentucky 40506,

S

Shibin Li