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<p>In-sensor computing has emerged as an ultrafast and low-power technique for next-generation machine vision. However, in situ training of in-sensor computing systems remains challenging due to the demands for both high-performance devices and efficient programming schemes. Here, we experimentally demonstrate the in situ training of an in-sensor artificial neural network (ANN) based on ferroelectric photosensors (FE-PSs). Our FE-PS exhibits self-powered, fast (&lt;30 μs), and multilevel (&gt;4 bits) photoresponses, as well as long retention (50 days), high endurance (10<sup>9</sup>), high write speed (100 ns), and small cycle-to-cycle and device-to-device variations (~0.66% and ~2.72%, respectively), all of which are desirable for the in situ training. Additionally, a bi-directional closed-loop programming scheme is developed, achieving a precise and efficient weight update for the FE-PS. Using this programming scheme, an in-sensor ANN based on the FE-PSs is trained in situ to recognize traffic signs for commanding a prototype autonomous vehicle. Moreover, this in-sensor ANN operates 50 times faster than a von Neumann machine vision system. This study paves the way for the development of in-sensor computing systems with in situ training capability, which may find applications in new data-streaming machine vision tasks.</p>

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In situ training of an in-sensor artificial neural network based on ferroelectric photosensors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月7日

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  • Haipeng Lin,
  • Jiali Ou,
  • Zhen Fan,
  • Xiaobing Yan,
  • Wenjie Hu,
  • Boyuan Cui,
  • Jikang Xu,
  • Wenjie Li,
  • Zhiwei Chen,
  • Biao Yang,
  • Kun Liu,
  • Linyuan Mo,
  • Meixia Li,
  • Xubing Lu,
  • Guofu Zhou,
  • Xingsen Gao,
  • Jun-Ming Liu

摘要

In-sensor computing has emerged as an ultrafast and low-power technique for next-generation machine vision. However, in situ training of in-sensor computing systems remains challenging due to the demands for both high-performance devices and efficient programming schemes. Here, we experimentally demonstrate the in situ training of an in-sensor artificial neural network (ANN) based on ferroelectric photosensors (FE-PSs). Our FE-PS exhibits self-powered, fast (<30 μs), and multilevel (>4 bits) photoresponses, as well as long retention (50 days), high endurance (109), high write speed (100 ns), and small cycle-to-cycle and device-to-device variations (~0.66% and ~2.72%, respectively), all of which are desirable for the in situ training. Additionally, a bi-directional closed-loop programming scheme is developed, achieving a precise and efficient weight update for the FE-PS. Using this programming scheme, an in-sensor ANN based on the FE-PSs is trained in situ to recognize traffic signs for commanding a prototype autonomous vehicle. Moreover, this in-sensor ANN operates 50 times faster than a von Neumann machine vision system. This study paves the way for the development of in-sensor computing systems with in situ training capability, which may find applications in new data-streaming machine vision tasks.