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

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.

Authors

Institutions

Publication Details

Journal
Applied Physics Letters
Published
2025-10-20
DOI
https://doi.org/10.1063/5.0290759
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Jian Li, Yuhan Zhang, Zhi Chen, Ting Zhang et al.
Applied Physics Letters
Advanced Memory and Neural Computing
article

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

Jian Li, Yuhan Zhang, Zhi Chen, Ting Zhang, Lu Chen, Shibin Li, Qien Xu
article en

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.

Applied Physics LettersVol. 127(16)
University of Kentucky (US), University of Electronic Science and Technology of China (CN), Chengdu University of Information Technology (CN), Tianjin Tianhe Hospital (CN), Tianjin Medical University (CN)
Openalex Percentile: Top 70%
Advanced Memory and Neural Computing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

High-photosensitivity optoelectronic synaptic transistors based on triple-cation perovskite for neuromorphic vision system — Jian Li, Yuhan Zhang, et al. · Applied Physics Letters (2025) | TGRS Research Map | TGRS