Shows improved accuracy in image recognition using low-energy photonic synapses, suggesting advancements in computing technology.
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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Wang et al. (2026) studied this question.
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