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

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.

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Publication Details

Journal
Laser & Photonics Review
Published
2026-05-08
DOI
https://doi.org/10.1002/lpor.202502765
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Shun‐Xin Li, Yue Wang, Bo Zou, Guang Zu et al.
Laser & Photonics Review
Neural Networks and Reservoir Computing
article

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

Shun‐Xin Li, Yue Wang, Bo Zou, Guang Zu, Xin Chen
article en

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.

Laser & Photonics Review
Jilin University (CN), Changchun University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 4%
Neural Networks and Reservoir Computing
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