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<p>Dynamic vision processing at the edge requires in-sensor spiking neural networks (SNNs) to achieve high energy efficiency and rapid processing. Although optoelectronic leaky integrate-and-fire (LIF) neurons are essential for optical sensing and sparse coding, their practical utility has been hindered by incomplete emulation of biological behaviors and integration difficulties with synaptic devices. Here, we show an optoelectronic LIF neuron based on a MoS<sub>2</sub> phototransistor that reproduces key neuronal features, including multispectral sensing, capacitor-less integration, and threshold-triggered spiking. This neuron supports complementary rate and time-to-first-spike coding, enabling versatile visual information processing at the hardware level. Furthermore, we achieve the homogeneous integration of these neurons with MoS<sub>2</sub> ferroelectric synapses on a single substrate, unifying volatile optical encoding with non-volatile weight storage. The integrated SNN system attains recognition accuracies of 91.7% for color recognition and 93.5% for object detection, indicating its potential for scalable, high-performance next-generation neuromorphic vision systems.</p>

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Homogeneous integration of two-dimensional material-based optoelectronic neurons and ferroelectric synapses for neuromorphic vision

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月9日

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  • Jiarong Wang,
  • Keqin Liu,
  • Pek Jun Tiw,
  • Dawei He,
  • Lianfeng Yu,
  • Bowen Wang,
  • Jinxuan Bai,
  • Teng Zhang,
  • Xin Shan,
  • Yang Yang,
  • Yuzhe Wang,
  • Yongsheng Wang,
  • Yaoyu Tao,
  • Xiaoxian Zhang,
  • Yuchao Yang

摘要

Dynamic vision processing at the edge requires in-sensor spiking neural networks (SNNs) to achieve high energy efficiency and rapid processing. Although optoelectronic leaky integrate-and-fire (LIF) neurons are essential for optical sensing and sparse coding, their practical utility has been hindered by incomplete emulation of biological behaviors and integration difficulties with synaptic devices. Here, we show an optoelectronic LIF neuron based on a MoS2 phototransistor that reproduces key neuronal features, including multispectral sensing, capacitor-less integration, and threshold-triggered spiking. This neuron supports complementary rate and time-to-first-spike coding, enabling versatile visual information processing at the hardware level. Furthermore, we achieve the homogeneous integration of these neurons with MoS2 ferroelectric synapses on a single substrate, unifying volatile optical encoding with non-volatile weight storage. The integrated SNN system attains recognition accuracies of 91.7% for color recognition and 93.5% for object detection, indicating its potential for scalable, high-performance next-generation neuromorphic vision systems.