VO 2 Nanoparticle‐Densely Packed Microwires for Flexible and Energy‐Efficient Photonic Synapses in Neuromorphic Computing

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Authors: Yue Wang, Guang Zu, Xin Chen, Shun‐Xin Li, Bo Zou
Cited by: 1
References: 19

Abstract

ABSTRACT The growing demand for brain‐inspired computing in wearable electronics necessitates systems with high mechanical stability, biocompatibility, and low‐power processing. However, most existing neuromorphic technologies suffer from limited flexibility, reliance on ultraviolet light, and high energy consumption. Here, we report a flexible photonic synapse based on densely packed VO 2 microwires. The device achieves ultra‐low energy consumption (5.76 pJ per pulse) and robust performance in MNIST digit recognition with 92.5% accuracy. Even after 2000 bending cycles, it maintains 92.4% accuracy, demonstrating exceptional durability. In addition, the device retains synaptic memory responses under visible and near‐infrared stimulation, enabling RGB‐fusion reservoir computing.

Fields of Study & Subjects

Neural Networks and Reservoir Computing Advanced Memory and Neural Computing Transition Metal Oxide Nanomaterials

Publication details

Journal / source
Laser & Photonics Review
Publisher
Wiley
Volume
20
Issue
17
Publication date
8 May 2026
Language
EN