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<p>Real-time perception of dynamic visual scenes requires efficient extraction of spatiotemporal features. However, conventional image sensors fail to capture inter-pixel correlations, leading to redundant data transfer, high power consumption and latency. Here, we present a non-pixelated <i>in-materia</i> retinomorphic sensor (IMRS) that exploits the intrinsic spatiotemporal dynamics and correlated distributions of photocarriers for visual information processing. Built on a large-area graphene/silicon heterostructure, the IMRS integrates circumferentially arranged sampling electrodes that harness the lateral photovoltaic effect to convert incident optical patterns into spatial carrier distributions, which are further encoded as object-shape-dependent photovoltages. Mimicking the lateral inhibition of biological retinas, this sensor enables in-sensor spatiotemporal perception without image reconstruction. We demonstrate human motion recognition with over 98% accuracy while compressing raw visual data from 10,000 to 48 bytes, reducing postprocessing networks parameters by two orders of magnitude. These results establish spatiotemporal photocarrier dynamics in low-dimensional heterostructures as a computational primitive for energy-efficient, ultralow-latency processing of high-dimensional spatiotemporal information.</p>

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Non-pixelated in-materia retinomorphic sensor via photocarrier dynamics for precise spatiotemporal perception

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月22日

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  • Kaiyang Liu,
  • Pengfei Wang,
  • Tao Zhou,
  • Ting Zheng,
  • Wenhui Wang,
  • Dingli Guo,
  • Peiyu Zeng,
  • Xinlei Zhang,
  • Yao Zhang,
  • Chen Pan,
  • Dongyang Wan,
  • Shi-Jun Liang,
  • Zhenhua Ni,
  • Feng Miao,
  • Junpeng Lu

摘要

Real-time perception of dynamic visual scenes requires efficient extraction of spatiotemporal features. However, conventional image sensors fail to capture inter-pixel correlations, leading to redundant data transfer, high power consumption and latency. Here, we present a non-pixelated in-materia retinomorphic sensor (IMRS) that exploits the intrinsic spatiotemporal dynamics and correlated distributions of photocarriers for visual information processing. Built on a large-area graphene/silicon heterostructure, the IMRS integrates circumferentially arranged sampling electrodes that harness the lateral photovoltaic effect to convert incident optical patterns into spatial carrier distributions, which are further encoded as object-shape-dependent photovoltages. Mimicking the lateral inhibition of biological retinas, this sensor enables in-sensor spatiotemporal perception without image reconstruction. We demonstrate human motion recognition with over 98% accuracy while compressing raw visual data from 10,000 to 48 bytes, reducing postprocessing networks parameters by two orders of magnitude. These results establish spatiotemporal photocarrier dynamics in low-dimensional heterostructures as a computational primitive for energy-efficient, ultralow-latency processing of high-dimensional spatiotemporal information.