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An optoelectronic synapse based on electrochemically deposited CuI thin film for neuromorphic visual processing
Qiufei Yu1, Zhongao Yang1, Xiaojian Chen1
1Key Laboratory of Polar Materials and Devices (MOE), Department of Electronics, School of Information and Electronic Engineering, East China Normal University, Shanghai 200241, People's Republic of China.
Nanotechnology
|April 14, 2026
Summary
Researchers developed a low-cost optoelectronic synaptic device using copper iodide (CuI) thin films. This device mimics brain functions and achieves high accuracy in image recognition, paving the way for efficient neuromorphic computing.
Area of Science:
- Materials Science
- Neuroscience
- Computer Science
Background:
- Optoelectronic synaptic devices are crucial for developing efficient neuromorphic computing systems.
- Existing devices often face challenges with cost, scalability, and integration with flexible electronics.
- Copper iodide (CuI) presents a promising material for low-cost, high-performance optoelectronic applications.
Purpose of the Study:
- To report a novel, low-cost optoelectronic synaptic device based on electrochemically deposited CuI thin films.
- To demonstrate the device's capability in emulating various biological synaptic functions.
- To validate the device's potential for practical neuromorphic computing applications, particularly in image recognition.
Main Methods:
- Electrochemical deposition of CuI thin films for large-area, uniform fabrication under ambient conditions.
- Characterization of the optoelectronic properties of the CuI thin-film device.
- Emulation of synaptic plasticity mechanisms (paired-pulse facilitation, spike-width/frequency/number-dependent plasticity) under light stimulation.
- Integration with a convolutional neural network (CNN) for image classification tasks.
Main Results:
- Successful emulation of key biological synaptic functions by the CuI-based device under 445 nm light stimulation.
- Demonstration of device compatibility with flexible substrates due to low-temperature, ambient-pressure processing.
- Achieved 95.2% recognition accuracy in clothing image classification with a CNN, even with 50% noise.
- The device exhibits potential for low-power, highly parallel neuromorphic computing.
Conclusions:
- The developed CuI-based optoelectronic synaptic device offers a cost-effective and scalable solution for neuromorphic computing.
- The device effectively mimics synaptic plasticity and demonstrates robust performance in complex tasks like image recognition.
- This work highlights the potential of electrochemical deposition and CuI materials for next-generation, brain-inspired computing architectures.
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