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


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 (<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.

SUBMITTER: Li Z 

PROVIDER: S-EPMC11344147 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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

Li Zhiyuan Z   Li Zhongshao Z   Tang Wei W   Yao Jiaping J   Dou Zhipeng Z   Gong Junjie J   Li Yongfei Y   Zhang Beining B   Dong Yunxiao Y   Xia Jian J   Sun Lin L   Jiang Peng P   Cao Xun X   Yang Rui R   Miao Xiangshui X   Yang Ronggui R  

Nature communications 20240823 1


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<sub>2</  ...[more]

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