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<p>Next-generation fire safety systems demand precise detection and motion recognition of flames. In-sensor computing, which integrates sensing, memory, and processing capabilities, has emerged as a key technology in flame detection. However, the implementation of hardware-level functional demonstrations based on artificial vision systems in the solar-blind ultraviolet (UV) band (200–280&#xa0;nm) is hindered by the weak detection capability. Here, we propose Ga<sub>2</sub>O<sub>3</sub>/In<sub>2</sub>Se<sub>3</sub> heterojunctions for the ferroelectric (abbreviation: Fe) optoelectronic sensor (abbreviation: OES) array (5 × 5 pixels), which is capable of ultraweak UV light detection with an ultrahigh detectivity through ferroelectric regulation and features in configurable multimode functionality. The Fe-OES array can directly sense different flame motions and simulate the non-spiking gradient neurons of insect visual system. Moreover, the flame signal can be effectively amplified in combination with leaky integration-and-fire neuron hardware. Using this Fe-OES system and neuromorphic hardware, we successfully demonstrate three flame processing tasks: achieving efficient flame detection across all time periods with terminal and cloud-based alarms; flame motion recognition with a lightweight convolutional neural network achieving 96.47% accuracy; and flame light recognition with 90.51% accuracy by means of a photosensitive artificial neural system. This work provides effective tools and approaches for addressing a variety of complex flame detection tasks.</p>

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Ferroelectric Optoelectronic Sensor for Intelligent Flame Detection and In-Sensor Motion Perception

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月13日

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  • Jiayun Wei,
  • Guokun Ma,
  • Runzhi Liang,
  • Wenxiao Wang,
  • Jiewei Chen,
  • Shuang Guan,
  • Jiaxing Jiang,
  • Ximo Zhu,
  • Qian Cheng,
  • Yang Shen,
  • Qinghai Xia,
  • Shiwen Wu,
  • Houzhao Wan,
  • Longhui Zeng,
  • Mengjiao Li,
  • Yi Wang,
  • Liangping Shen,
  • Wei Han,
  • Hao Wang

摘要

Next-generation fire safety systems demand precise detection and motion recognition of flames. In-sensor computing, which integrates sensing, memory, and processing capabilities, has emerged as a key technology in flame detection. However, the implementation of hardware-level functional demonstrations based on artificial vision systems in the solar-blind ultraviolet (UV) band (200–280 nm) is hindered by the weak detection capability. Here, we propose Ga2O3/In2Se3 heterojunctions for the ferroelectric (abbreviation: Fe) optoelectronic sensor (abbreviation: OES) array (5 × 5 pixels), which is capable of ultraweak UV light detection with an ultrahigh detectivity through ferroelectric regulation and features in configurable multimode functionality. The Fe-OES array can directly sense different flame motions and simulate the non-spiking gradient neurons of insect visual system. Moreover, the flame signal can be effectively amplified in combination with leaky integration-and-fire neuron hardware. Using this Fe-OES system and neuromorphic hardware, we successfully demonstrate three flame processing tasks: achieving efficient flame detection across all time periods with terminal and cloud-based alarms; flame motion recognition with a lightweight convolutional neural network achieving 96.47% accuracy; and flame light recognition with 90.51% accuracy by means of a photosensitive artificial neural system. This work provides effective tools and approaches for addressing a variety of complex flame detection tasks.