Low-power optoelectronic synaptic devices with ZnMgO in deep-ultraviolet encryption computation

Abstract

The realization of vision-based neuromorphic computing relies significantly on advancements in optoelectronic synaptic chip technology. Currently, the development of optoelectronic devices is mainly limited by high power consumption due to high bias voltage and low recognition rates caused by the background noise. A low-power deep-ultraviolet optoelectronic synapse device is developed by doping Mg into zinc oxide to modulate oxygen-vacancy defects. Specifically, the synaptic behavior still has excellent persistent photoconductivity response at 12 mV bias, and the low single synaptic energy consumption is 2.34 pJ. Meanwhile, the artificial neural network of the device is constructed according to the excitation and inhibition characteristics, and the recognition rate of handwritten digits is as high as 95.41%. In addition, on the basis of demonstrating ultraviolet image visual learning and memory, the device provides an encryption algorithm verification array integrating sensing and storage with energy consumption as low as 20 nJ. This low-power and strong anti-interference deep-ultraviolet optoelectronic synapse device supports the development of high-performance visual neural-state computing.