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, Ronggui Yang, Ronggui Yang, Ronggui Yang
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 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.