A Chemically Programmable Retinomorphic GaN p–n Diode for Multimode Visual Sensing

Real-world visual scenes include wide illumination conditions with rapid fluctuations, demanding vision sensors that integrate multiple visual dynamics through diverse modulation strategies. However, most vision sensors suffer from limited tunability, mainly modulated via external physical stimuli (e.g., electric or light) while lacking chemical programmability critical for biorealistic vision. Here, we report a chemically programmable retinomorphic p-n diode that supports spiking response and light adaptation together with chemical tunability in an ultracompact device platform. Governed by the interplay between photoelectric carrier separation at the internal p-n junction and electrochemical carrier consumption at the semiconductor/electrolyte interface, the retinomorphic diode demonstrates switchable response modes with chemical programmability. Specifically, the device generates spikes upon illumination transitions under closed-circuit operation, while varying ion/molecule species enables bidirectional tuning of steady-state current, transforming dynamic-only sensing into static-dynamic fused sensing. Moreover, it spontaneously shifts from scotopic to photopic adaptation with increasing illumination under open-circuit operation, while both processes can be bidirectionally regulated through chemical programming. Leveraging this adaptive behavior, we further construct a proof-of-concept visual-motor pathway that exhibits chemically programmable, illumination-dependent actuation. Together, this work demonstrates a chemically programmable retinomorphic platform combining physical and chemical modulation for bioinspired multimode visual sensing.

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Publication Details

Journal
Advanced Materials
Published
2026-06-30
DOI
https://doi.org/10.1002/adma.73892
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

A Chemically Programmable Retinomorphic GaN p–n Diode for Multimode Visual Sensing

Guofeng Yang, Shi Fang, Dongyang Luo, Yong Yan et al.
Advanced Materials
Advanced Memory and Neural Computing
article

A Chemically Programmable Retinomorphic GaN p–n Diode for Multimode Visual Sensing

Guofeng Yang, Shi Fang, Dongyang Luo, Yong Yan, W Chen, Xiaomin Yang, Haiding Sun, Yuanmin Luo, M Wang, Wengang Gu, Xin Liu, Zhixiang Gao, Yang Kang, Zixun Wang
article en

Abstract

Real-world visual scenes include wide illumination conditions with rapid fluctuations, demanding vision sensors that integrate multiple visual dynamics through diverse modulation strategies. However, most vision sensors suffer from limited tunability, mainly modulated via external physical stimuli (e.g., electric or light) while lacking chemical programmability critical for biorealistic vision. Here, we report a chemically programmable retinomorphic p-n diode that supports spiking response and light adaptation together with chemical tunability in an ultracompact device platform. Governed by the interplay between photoelectric carrier separation at the internal p-n junction and electrochemical carrier consumption at the semiconductor/electrolyte interface, the retinomorphic diode demonstrates switchable response modes with chemical programmability. Specifically, the device generates spikes upon illumination transitions under closed-circuit operation, while varying ion/molecule species enables bidirectional tuning of steady-state current, transforming dynamic-only sensing into static-dynamic fused sensing. Moreover, it spontaneously shifts from scotopic to photopic adaptation with increasing illumination under open-circuit operation, while both processes can be bidirectionally regulated through chemical programming. Leveraging this adaptive behavior, we further construct a proof-of-concept visual-motor pathway that exhibits chemically programmable, illumination-dependent actuation. Together, this work demonstrates a chemically programmable retinomorphic platform combining physical and chemical modulation for bioinspired multimode visual sensing.

Advanced Materials
Jiangnan University (CN), University of Science and Technology of China (CN)
National Natural Science Foundation of China, Natural Science Foundation of Anhui Province
Openalex Percentile: Top 12%
Advanced Memory and Neural Computing
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