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<p>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<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41467_2025_63873_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\times\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月2日

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  • Mengjiao Li,
  • Hongling Chu,
  • Caifang Gao,
  • Feng-Shou Yang,
  • Muyun Huang,
  • Lingling Miu,
  • Jun Li,
  • Ching-Hwa Ho,
  • Jingjing Liu,
  • Yen-Fu Lin,
  • Jianhua Zhang

摘要

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.