Enhancing plasticity in optoelectronic artificial synapses: A pathway to efficient neuromorphic computing

Abstract

The continuous growth in artificial intelligence and high-performance computing has necessitated the development of efficient optoelectronic artificial synapses crucial for neuromorphic computing (NC). Ga2O3 is an emerging wide-bandgap semiconductor with high deep ultraviolet absorption, tunable persistent photoconductivity, and excellent stability toward electric fields, making it a promising component for optoelectronic artificial synapses. Currently reported Ga2O3 optoelectronic artificial synapses often suffer from complex fabrication processes and potential room for improvement due to plasticity. To address the issue of low device plasticity and practical application scenarios, we present an amorphous Ga2O3 (α-GaOx) flexible optoelectronic artificial synapse. This synapse modulates light stimulus signals using electron/oxygen vacancies and optical stimulation and operates as a visual storage device for information processing. We investigate the improvement of the optoelectronic synapses' plasticity by controlling the number of oxygen vacancies via a plasma treatment method and demonstrate its effective application in a three-layer backpropagation neural network for handwritten digit classification. Under the same stimulus conditions, the synaptic weight of samples treated with Ar plasma exhibits a higher rate of change, with the current levels increasing by 2–3 orders of magnitude, achieving greater plasticity. The improved optoelectronic synapses achieved an accuracy of 93.34%/94%, demonstrating their potential as efficient computing solutions and insights for future applications in NC chips.