An optoelectronic synapse based on electrochemically deposited CuI thin film for neuromorphic visual processing

This work reports a low-cost optoelectronic synaptic device based on an electrochemically deposited CuI thin film. The electrochemical deposition technique enables large-area and uniform thin-film fabrication under low-temperature and ambient-pressure conditions, offering significant advantages of simple processing, cost-effectiveness, and compatibility with flexible substrates. Under 445 nm light stimulation, the device successfully emulates the biological synaptic functions, including paired-pulse facilitation, spike-width-dependent plasticity, spike-frequency-dependent plasticity, and spike-number-dependent plasticity. Furthermore, by implementing a convolutional neural network for backend processing of the device-generated optoelectronic pulse signals, a high training set recognition accuracy of 95.2% is achieved under 50% noise perturbation in clothing image classification tasks, validating its potential for low-power, highly parallel neuromorphic computing applications.

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Publication Details

Journal
Nanotechnology
Published
2026-04-14
DOI
https://doi.org/10.1088/1361-6528/ae5f25
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

An optoelectronic synapse based on electrochemically deposited CuI thin film for neuromorphic visual processing

X H Chen, Chunli Jiang, Hui Peng, Chunhua Luo et al.
Nanotechnology
Advanced Memory and Neural Computing
article

An optoelectronic synapse based on electrochemically deposited CuI thin film for neuromorphic visual processing

X H Chen, Chunli Jiang, Hui Peng, Chunhua Luo, Qiufei Yu, Xiaodong Tang, Zhiwei Zhu, Zhongao Yang
article en

Abstract

This work reports a low-cost optoelectronic synaptic device based on an electrochemically deposited CuI thin film. The electrochemical deposition technique enables large-area and uniform thin-film fabrication under low-temperature and ambient-pressure conditions, offering significant advantages of simple processing, cost-effectiveness, and compatibility with flexible substrates. Under 445 nm light stimulation, the device successfully emulates the biological synaptic functions, including paired-pulse facilitation, spike-width-dependent plasticity, spike-frequency-dependent plasticity, and spike-number-dependent plasticity. Furthermore, by implementing a convolutional neural network for backend processing of the device-generated optoelectronic pulse signals, a high training set recognition accuracy of 95.2% is achieved under 50% noise perturbation in clothing image classification tasks, validating its potential for low-power, highly parallel neuromorphic computing applications.

NanotechnologyVol. 37(19)
Shanxi University (CN), East China Normal University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 30%
Advanced Memory and Neural Computing
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