Hardware‐Level Image Recognition System Based on ZnO Photo‐Synapse Array with the Self‐Denoising Function

TLDR

Optoelectronic neuromorphic devices integrate sensing, memory, and processing, yet building hardware‑level image recognition systems with photo‑synapse arrays remains challenging despite advances in individual devices. The study demonstrates a crosstalk‑free, scalable 8×8 ZnO photo‑synapse crossbar array with self‑denoising for optical image sensing and storage. Peripheral circuits enable a complete hardware‑level artificial visual system that performs real‑time pattern recognition on 8×8 pixel images, and an in‑sensor reservoir computing module is added for handwritten‑digit recognition. The photo‑synapse array delivers highly efficient optic neuromorphic computing, achieving 95.1 % accuracy on handwritten‑digit recognition.

Abstract

Abstract The emerging optoelectronic neuromorphic devices are widely concerned due to their capability to integrate the functions of signal sensing, memory, and processing. Although significant advancements have been made in the study of individual optoelectronic synaptic devices, the development of hardware‐level image recognition systems based on photo‐synapse arrays remains a challenge. In this study, a crosstalk‐free, easy‐to‐integrate, and scalable 8 × 8 crossbar array for optical image sensing and storage is demonstrated using vertical two‐terminal ZnO photo‐synapses with the self‐denoising function. By designing peripheral circuits, a complete hardware‐level artificial visual system is constructed that successfully implements the real‐time pattern recognition tasks for 8 × 8 pixel images. The excellent performance of the photo‐synapse array shows its remarkable ability in highly efficient optic neuromorphic computing. Additionally, an in‐sensor reservoir computing (RC) system is constructed for image recognition of handwritten digits. The system achieves a high classification accuracy of 95.1%.