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<p>In-sensor computing holds great promise for ultrafast and energy-efficient machine vision. However, the development of a versatile in-sensor computing system that can integrate image memorization, low-level processing, and high-level computing functions remains a challenge, primarily due to the scarcity of photosensors that can offer both dynamic photoresponse and programmable photoresponsivity. Here, we successfully integrate these multi-functions into a ferroelectric photosensor-based array. The key enabler is the ferroelectric photosensor operating via the bulk photovoltaic effect, which exhibits above-bandgap, dynamically responding, and electrically switchable photovoltages. By using the dynamic photovoltage response, the array is capable of memorizing and pre-processing images, with the ability to adjust the memory and pre-processing effects by ferroelectric polarization. On the other hand, the electrically switchable photovoltages, featuring multi-level switchability and retrievability, enable the array to perform in-sensor high-level computing, achieving 100% accuracy in a 4-class image recognition task (noise level ≤ 10%). Notably, the high precision and reliability of photovoltage-based image memorization and processing greatly benefit from the high photovoltage produced by the ferroelectric photosensor — a distinct advantage for this application. This study lays the foundation for developing versatile in-sensor computing systems that could be utilized across a wide range of machine vision scenarios.</p>

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In-sensor image memorization, low-level processing, and high-level computing by using above-bandgap photovoltages

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月12日

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  • Kun Liu,
  • Shan Tan,
  • Zhen Fan,
  • Haipeng Lin,
  • Jiali Ou,
  • Haoyue Deng,
  • Jinghao Chen,
  • Wenjie Li,
  • Wenjie Hu,
  • Boyuan Cui,
  • Zhiwei Chen,
  • Ruiqiang Tao,
  • Guo Tian,
  • Xubing Lu,
  • Guofu Zhou,
  • Xingsen Gao,
  • Jun-Ming Liu

摘要

In-sensor computing holds great promise for ultrafast and energy-efficient machine vision. However, the development of a versatile in-sensor computing system that can integrate image memorization, low-level processing, and high-level computing functions remains a challenge, primarily due to the scarcity of photosensors that can offer both dynamic photoresponse and programmable photoresponsivity. Here, we successfully integrate these multi-functions into a ferroelectric photosensor-based array. The key enabler is the ferroelectric photosensor operating via the bulk photovoltaic effect, which exhibits above-bandgap, dynamically responding, and electrically switchable photovoltages. By using the dynamic photovoltage response, the array is capable of memorizing and pre-processing images, with the ability to adjust the memory and pre-processing effects by ferroelectric polarization. On the other hand, the electrically switchable photovoltages, featuring multi-level switchability and retrievability, enable the array to perform in-sensor high-level computing, achieving 100% accuracy in a 4-class image recognition task (noise level ≤ 10%). Notably, the high precision and reliability of photovoltage-based image memorization and processing greatly benefit from the high photovoltage produced by the ferroelectric photosensor — a distinct advantage for this application. This study lays the foundation for developing versatile in-sensor computing systems that could be utilized across a wide range of machine vision scenarios.