Neuromorphic Computing-Assisted Triboelectric Capacitive-Coupled Tactile Sensor Array for Wireless Mixed Reality Interaction

作者：Xinkai Xie, Qinan Wang, Chun Zhao, Qilei Sun, Haicheng Gu, Junyan Li, Xin Tu, Baoqing Nie, Xuhui Sun, Yina Liu, Eng Gee Lim, Zhen Wen, Zhong Lin Wang · 发表于：ACS Nano · 年份：2024 · DOI：10.1021/acsnano.4c03554 · 被引用次数：83 · 研究领域：Advanced Sensor and Energy Harvesting Materials、Advanced Memory and Neural Computing、Tactile and Sensory Interactions

Flexible tactile sensors show promise for artificial intelligence applications due to their biological adaptability and rapid signal perception. Triboelectric sensors enable active dynamic tactile sensing, while integrating static pressure sensing and real-time multichannel signal transmission is key for further development. Here, we propose an integrated structure combining a capacitive sensor for static spatiotemporal mapping and a triboelectric sensor for dynamic tactile recognition. A liquid metal-based flexible dual-mode triboelectric-capacitive-coupled tactile sensor (TCTS) array of 4 × 4 pixels achieves a spatial resolution of 7 mm, exhibiting a pressure detection limit of 0.8 Pa and a fast response of 6 ms. Furthermore, neuromorphic computing using the MXene-based synaptic transistor achieves 100% recognition accuracy of handwritten numbers/letters within 90 epochs based on dynamic triboelectric signals collected by the TCTS array, and cross-spatial information communication from the perceived multichannel tactile data is realized in the mixed reality space. The results illuminate considerable application possibilities of dual-mode tactile sensing technology in human-machine interfaces and advanced robotics.