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

• Published in: Nature communications Vol. 15; no. 1; pp. 7275 - 11
• Main Authors: Li, Zhiyuan, Li, Zhongshao, Tang, Wei, Yao, Jiaping, Dou, Zhipeng, Gong, Junjie, Li, Yongfei, Zhang, Beining, Dong, Yunxiao, Xia, Jian, Sun, Lin, Jiang, Peng, Cao, Xun, Yang, Rui, Miao, Xiangshui, Yang, Ronggui
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.08.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/925/357/995; 639/925/927/1007; Action Potentials - physiology; Coding; Computation; Cross-modal; Data conversion; Energy efficiency; Equipment Design; Feedback; Firing pattern; Human performance; Humanities and Social Sciences; Humans; Information processing; Interfaces; Man-machine interfaces; Man-Machine Systems; Memristors; multidisciplinary; Real time; Science; Science (multidisciplinary); Sensors; Sensory neurons; Sensory Receptor Cells - physiology; Signal processing; Spiking; Vanadium oxides; Wearable Electronic Devices; Wearable technology
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-024-51609-x

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 VO 2 memristor, and demonstrate a crossmodal in-sensor encoding and computing system for wearable human-machine interfaces. We demonstrate excellent performance in the VO 2 memristor including endurance (>10 12 ), uniformity (0.72% for cycle-to-cycle variations and 3.73% for device-to-device variations), speed (<30 ns), and flexibility (bendable to a curvature radius of 1 mm). A flexible hardware processing system is implemented based on the CSSN, which can directly perceive and encode pressure and temperature bimodal information into spikes, and then enables the real-time haptic-feedback for human-machine interaction. We successfully construct a crossmodal in-sensor spiking reservoir computing system via the CSSNs, which can achieve dynamic objects identification with a high accuracy of 98.1% and real-time signal feedback. This work provides a feasible approach for constructing flexible bio-inspired crossmodal in-sensor computing systems for wearable human-machine interfaces. Constructing crossmodal in-sensor processing system based on high-performance flexible devices is important for the development of wearable human-machine interfaces. This work reports a bio-inspired spiking sensory neuron based on a flexible VO2 memristor and demonstrates a crossmodal in-sensor encoding and computing system.