Multi-defect-engineering in ZnO/GO heterostructures for optoelectronic synaptic devices with ultra-high dynamic range and low energy consumption

Abstract

In artificial visual systems, optimizing the dynamic range (DR) of optoelectronic synapses is essential for achieving robust and environment-adaptive perception. However, the inherent trade-off between photoresponse and dark current noise presents significant challenges in realizing a high DR. This study introduces a flat-band heterojunction strategy to achieve high DR optoelectronic synapses through a zinc oxide (ZnO) nanowires and graphene oxide (GO) sheets heterostructure, which enables efficient minority carrier trapping under minimal external bias. Through multi-defect-engineering in the heterojunction structure, the device demonstrates enhanced persistent photoconductivity (PPC), improved photocurrent gain, and significantly suppressed dark current, achieving an ultra-high DR of 74.9 dB in two-terminal optoelectronic synaptic devices while reducing energy consumption to 23 fJ/spike at a bias voltage of 1 mV. Additionally, the devices can emulate typical synaptic functionalities and attain 92.84% pattern recognition accuracy in artificial neural network simulations, offering an energy-efficient platform for advanced neuromorphic systems. This work offers a generalizable strategy for low-power, high-fidelity visual perception systems, advancing intelligent sensing and neuromorphic computing.

Keywords

dynamic range / low power / neuromorphic computing / optoelectronic synapses / oxide semiconductors

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