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In-sensor image memorization, low-level processing, and high-level computing by using above-bandgap photovoltages
Kun Liu1, Shan Tan1, Zhen Fan2
1Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China.
Nature Communications
|December 12, 2025
Summary
Researchers developed a versatile ferroelectric photosensor array for in-sensor computing. This system integrates image memorization, processing, and high-level computing, enabling ultrafast and energy-efficient machine vision applications.
Area of Science:
- Materials Science
- Computer Engineering
- Physics
Background:
- In-sensor computing promises efficient machine vision but requires versatile photosensors.
- Current photosensors lack dynamic photoresponse and programmable photoresponsivity for integrated functions.
Purpose of the Study:
- To develop a versatile in-sensor computing system using ferroelectric photosensors.
- To integrate image memorization, low-level processing, and high-level computing into a single array.
Main Methods:
- Utilized ferroelectric photosensors operating via the bulk photovoltaic effect.
- Engineered an array capable of dynamic photoresponse and electrically switchable photovoltages.
- Leveraged ferroelectric polarization for adjustable memory and pre-processing.
Main Results:
- The array demonstrated image memorization and pre-processing with adjustable effects.
- Achieved 100% accuracy in a 4-class image recognition task using in-sensor computing.
- High photovoltage from ferroelectric sensors ensured precision and reliability.
Conclusions:
- Ferroelectric photosensor arrays offer a pathway to versatile in-sensor computing.
- This technology enables ultrafast and energy-efficient machine vision.
- The developed system provides a foundation for advanced machine vision applications.
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