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Capacitive in-sensor tactile computing

Author

Listed:
  • Yan Chen

    (Fudan University
    Fudan University
    Zhangjiang Laboratory)

  • Jie Cao

    (Fudan University)

  • Jie Qiu

    (Fudan University
    Fudan University
    Zhangjiang Laboratory)

  • Dongzi Yang

    (Fudan University
    Fudan University)

  • Mengyang Liu

    (Fudan University)

  • Mengru Zhang

    (Fudan University
    Fudan University)

  • Chenyang Li

    (Fudan University
    Fudan University
    Zhangjiang Laboratory)

  • Zhongyuan Wu

    (Fudan University)

  • Jie Yu

    (Fudan University)

  • Xumeng Zhang

    (Fudan University)

  • Xianzhe Chen

    (Fudan University)

  • Zhangcheng Huang

    (Fudan University)

  • Enming Song

    (Fudan University)

  • Ming Wang

    (Fudan University
    Zhangjiang Laboratory)

  • Qi Liu

    (Fudan University
    Zhangjiang Laboratory)

  • Ming Liu

    (Fudan University
    Zhangjiang Laboratory)

Abstract

Real-time sensing and processing of tactile information are essential to enhance the capability of artificial electronic skins (e-skins), enabling unprecedented intelligent applications in tactile exploration and object manipulation. However, conventional tactile e-skin systems typically execute redundant data transfer and conversion for decision making due to their physical separation between sensors and processing units, leading to high transmission latency and power consumption. Here, we report an in-sensor tactile computing system based on a flexible capacitive pressure sensor array. This system utilizes multiple connected sensor networks to execute in-situ analog multiplication and accumulation operations, achieving both tactile sensing and computing functionalities. We experimentally implemented the in-sensor tactile computing system for low-level tactile sensory processing tasks including noise reduction and edge detection. The consumed power for single sensing-computing operation is over 22 times lower than that of a conventional mixed electronic system. These results demonstrate that our capacitive in-sensor computing system paves a promising way for power-constrained applications such as robotics and human-machine interfaces.

Suggested Citation

  • Yan Chen & Jie Cao & Jie Qiu & Dongzi Yang & Mengyang Liu & Mengru Zhang & Chenyang Li & Zhongyuan Wu & Jie Yu & Xumeng Zhang & Xianzhe Chen & Zhangcheng Huang & Enming Song & Ming Wang & Qi Liu & Min, 2025. "Capacitive in-sensor tactile computing," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-60703-7
    DOI: 10.1038/s41467-025-60703-7
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