All-Optically Controlled Artificial Synapse Based on Full Oxides for Low-Power Visible Neural Network Computing

Abstract

Artificial synapse devices are dedicated to overcoming the von Neumann bottleneck. Adopting light signals in visual information processing and computing is vital for developing next-generation artificial neuromorphic systems. A strategy to construct all-optically controlled artificial synaptic devices based on full oxides with amorphous ZnAlSnO/SnO heterojunction in a two-terminal planar configuration is proposed. All synaptic behaviors are operated in the visible optical pathway, with excitatory synapse under red (635 nm) light and inhibitory synapse under green (532 nm) and blue (405 nm) lights. Based on the different inhibitory effects, two modes of long-term depression (LTD) and RESET processes can be implemented through green and blue lights, respectively. The energy consumption of an event can be as low as 0.75 pJ. A three-layer perceptron model is designed to classify 28 × 28-pixel handwritten digital images and performed supervised learning using a backpropagation algorithm, demonstrating the bio-visually inspired neuromorphic computing with a training accuracy of 92.74%. The all-optically controlled artificial synapses with write/erasure behaviors in visible RGB region and rational microelectronic process, as presented in this work, are essential in developing future artificial neuromorphic systems and highlight the huge potential of next-generation computer systems.