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Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing system

Author

Listed:
  • Zhiyuan Li

    (Huazhong University of Science and Technology
    Hubei Yangtze Memory Laboratories)

  • Zhongshao Li

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Wei Tang

    (Huazhong University of Science and Technology)

  • Jiaping Yao

    (Huazhong University of Science and Technology)

  • Zhipeng Dou

    (Chinese Academy of Sciences)

  • Junjie Gong

    (Huazhong University of Science and Technology)

  • Yongfei Li

    (Huazhong University of Science and Technology)

  • Beining Zhang

    (Huazhong University of Science and Technology)

  • Yunxiao Dong

    (Huazhong University of Science and Technology)

  • Jian Xia

    (Huazhong University of Science and Technology)

  • Lin Sun

    (Chinese Academy of Sciences)

  • Peng Jiang

    (Chinese Academy of Sciences)

  • Xun Cao

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Rui Yang

    (Huazhong University of Science and Technology
    Hubei Yangtze Memory Laboratories)

  • Xiangshui Miao

    (Huazhong University of Science and Technology
    Hubei Yangtze Memory Laboratories)

  • Ronggui Yang

    (Huazhong University of Science and Technology)

Abstract

Constructing crossmodal in-sensor processing system based on high-performance flexible devices is of great significance for the development of wearable human-machine interfaces. A bio-inspired crossmodal in-sensor computing system can perform real-time energy-efficient processing of multimodal signals, alleviating data conversion and transmission between different modules in conventional chips. Here, we report a bio-inspired crossmodal spiking sensory neuron (CSSN) based on a flexible VO2 memristor, and demonstrate a crossmodal in-sensor encoding and computing system for wearable human-machine interfaces. We demonstrate excellent performance in the VO2 memristor including endurance (>1012), uniformity (0.72% for cycle-to-cycle variations and 3.73% for device-to-device variations), speed (

Suggested Citation

  • Zhiyuan Li & Zhongshao Li & Wei Tang & Jiaping Yao & Zhipeng Dou & Junjie Gong & Yongfei Li & Beining Zhang & Yunxiao Dong & Jian Xia & Lin Sun & Peng Jiang & Xun Cao & Rui Yang & Xiangshui Miao & Ron, 2024. "Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing system," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51609-x
    DOI: 10.1038/s41467-024-51609-x
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