Bioinspired high-order in-sensor spatiotemporal enhancement in van der Waals optoelectronic neuromorphic electronics

Mengjiao Li, Hongling Chu, Caifang Gao, Feng-Shou Yang, Muyun Huang, Lingling Miu, Jun Li, Ching-Hwa Ho, Jingjing Liu (), Yen-Fu Lin () and Jianhua Zhang ()
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Mengjiao Li: Shanghai University
Hongling Chu: Shanghai University
Caifang Gao: Shanghai University
Feng-Shou Yang: National Chung Hsing University
Muyun Huang: Shanghai University
Lingling Miu: Shanghai University
Jun Li: Shanghai University
Ching-Hwa Ho: National Taiwan University of Science and Technology
Jingjing Liu: Shanghai University
Yen-Fu Lin: National Chung Hsing University
Jianhua Zhang: Shanghai University

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract In over-complicated machine vision, target tracking within deep learning paradigms yields inaccurate and energy-intensive outputs. Although spiking neural networks excel at processing dynamic information, challenging tracking environments demand further enhancement in feature correlation learning for efficient target tracking. Distinct from Paired-spike-timing-dependent-plasticity-based architectures, we demonstrate a visual sensor based on van der Waals phototransistors, leveraging Triplet-spike-timing-dependent plasticity to extract bioinspired high-order correlation information, through tunable light-electric cooperation and competition effect on synaptic plasticity originating from interfacial defects-dominated persistent photoconductance phenomena. The universal Triplet-spike-timing-dependent plasticity with enhanced spatiotemporal correlation learning characteristic renders spiking neural networks with better processing capabilities for confusing object classification and dynamic tracking (90.44%) tasks, excelling particularly in seamless tracking post-occlusion, furthermore experimentally validated through hardware implementation on a 6 $$\times$$ × 6 van der Waals phototransistor array. The offers a bottom-up methodology employing device physics to guide mapping of biorational learning for high-performance dynamic tracking towards advanced machine visual technologies.

Date: 2025
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DOI: 10.1038/s41467-025-63873-6

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