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

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 () and Ronggui Yang
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Zhiyuan Li: Huazhong University of Science and Technology
Zhongshao Li: 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
Rui Yang: Huazhong University of Science and Technology
Xiangshui Miao: Huazhong University of Science and Technology
Ronggui Yang: Huazhong University of Science and Technology

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: 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 (

Date: 2024
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DOI: 10.1038/s41467-024-51609-x

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