All‐Optically Controlled Synapse‐Based Neuromorphic Vision System for Bioinformation Recognition

All‐optically controlled artificial synapses for neuromorphic vision offer unique advantages in simplifying circuit design and minimizing power consumption, meeting the application demands of the current artificial intelligence era. However, developing all‐optically controlled devices that combine high performance and high reproducibility remains a significant challenge. In this work, we demonstrate an all‐optically controlled artificial synapse based on ZnO and Cs 2 CoCl 4 single crystal connected structure, which integrates light information sensing and processing capabilities. Relying on the simple series‐connected structure, as well as the positive photoconductance of ZnO and the negative photoconductance of Cs 2 CoCl 4 , the optically controlled bidirectional synaptic plasticity is realized under ultraviolet and visible light stimulation without additional voltage modulation in the all‐optically controlled synapse. In addition, leveraging its ultraviolet‐enhanced feature extraction and visible‐suppression capabilities, the all‐optically controlled synapse can act as denoising units in bioinformation preprocessing and weight‐updating units in feature recognition. The proposed all‐optically controlled synapses exhibit excellent information perception, low‐level noise reduction, and high‐level cognition functions for bioinformation recognition under complex light conditions. We believe that this work can provide structural‐level insights and inspirations in the design and fabrication of all‐optically controlled synapses to promote the application for efficient neuromorphic vision in the future.

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Publication Details

Journal
Energy & environment materials
Published
2025-09-08
DOI
https://doi.org/10.1002/eem2.70131
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73
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article

All‐Optically Controlled Synapse‐Based Neuromorphic Vision System for Bioinformation Recognition

Guozhen Shen, Zhifeng Shi, Zhuangzhuang Ma, Yancheng Chen et al.
1 citations
Energy & environment materials
Advanced Memory and Neural Computing
0.73
article

All‐Optically Controlled Synapse‐Based Neuromorphic Vision System for Bioinformation Recognition

Guozhen Shen, Zhifeng Shi, Zhuangzhuang Ma, Yancheng Chen, Di Chen, Ying Li, Xinmiao Li, Huifang Jiang
article en
1 citations

Abstract

All‐optically controlled artificial synapses for neuromorphic vision offer unique advantages in simplifying circuit design and minimizing power consumption, meeting the application demands of the current artificial intelligence era. However, developing all‐optically controlled devices that combine high performance and high reproducibility remains a significant challenge. In this work, we demonstrate an all‐optically controlled artificial synapse based on ZnO and Cs 2 CoCl 4 single crystal connected structure, which integrates light information sensing and processing capabilities. Relying on the simple series‐connected structure, as well as the positive photoconductance of ZnO and the negative photoconductance of Cs 2 CoCl 4 , the optically controlled bidirectional synaptic plasticity is realized under ultraviolet and visible light stimulation without additional voltage modulation in the all‐optically controlled synapse. In addition, leveraging its ultraviolet‐enhanced feature extraction and visible‐suppression capabilities, the all‐optically controlled synapse can act as denoising units in bioinformation preprocessing and weight‐updating units in feature recognition. The proposed all‐optically controlled synapses exhibit excellent information perception, low‐level noise reduction, and high‐level cognition functions for bioinformation recognition under complex light conditions. We believe that this work can provide structural‐level insights and inspirations in the design and fabrication of all‐optically controlled synapses to promote the application for efficient neuromorphic vision in the future.

Energy & environment materialsVol. 9(1)
Beijing Institute of Technology (CN), Zhengzhou University (CN)
Openalex Percentile: Top 23%
Advanced Memory and Neural Computing
0.73
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