Infrared light-responsive CuSbS2 optoelectronic artificial synapses enabling high-accuracy color image recognition...

Area of Science:

• Materials Science
• Neuroscience
• Computer Engineering

Background:

• Neuromorphic computing requires efficient artificial synapses for perception and memory.
• Optoelectronic synaptic memristors integrate light and electrical signals for advanced computing.
• Developing novel materials is crucial for high-performance artificial synaptic devices.

Purpose of the Study:

• To introduce copper antimony sulfide (CuSbS2) as a material for near-infrared (NIR) optoelectronic synaptic memristors.
• To investigate the synaptic plasticity emulation capabilities of CuSbS2-based devices.
• To evaluate the performance of a neuromorphic system utilizing these artificial synapses for visual tasks.

Main Methods:

• Fabrication of CuSbS2-based memristors exhibiting bipolar resistive switching.
• Stimulation of devices using electrical signals and NIR light to emulate synaptic behaviors.
• Implementation of a neuromorphic computing system for color image recognition and classification.

Main Results:

• CuSbS2 memristors showed stable non-volatile resistive switching with a high on/off ratio (~99) and endurance (>10^4 s).
• The devices successfully emulated various synaptic plasticity behaviors, including short-term potentiation/depression and spike-timing-dependent plasticity.
• A CuSbS2 memristor-based system achieved 92.3% accuracy in color image recognition, demonstrating efficient learning rate regulation.

Conclusions:

• CuSbS2 is a promising material for NIR optoelectronic artificial synapses, advancing neuromorphic computing.
• The developed devices integrate sensing, storage, and processing for brain-inspired AI.
• This work offers a new material platform for intelligent visual perception and future AI systems.