An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements

Chao Wei; Wansheng Lin; Shaofeng Liang; Mengjiao Chen; Yuanjin Zheng; Xinqin Liao; Zhong Chen

doi:10.1007/s40820-022-00875-9

An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements

Nanomicro Lett. 2022 Jun 14;14(1):131. doi: 10.1007/s40820-022-00875-9.

Authors

Chao Wei¹, Wansheng Lin¹, Shaofeng Liang¹, Mengjiao Chen¹, Yuanjin Zheng², Xinqin Liao^{3

4}, Zhong Chen^{5

6}

Affiliations

¹ Department of Electronic Science, Xiamen University, Xiamen, 361005, People's Republic of China.
² School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
³ Department of Electronic Science, Xiamen University, Xiamen, 361005, People's Republic of China. liaoxinqin@xmu.edu.cn.
⁴ Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province, Xiamen, 361005, People's Republic of China. liaoxinqin@xmu.edu.cn.
⁵ Department of Electronic Science, Xiamen University, Xiamen, 361005, People's Republic of China. chenz@xmu.edu.cn.
⁶ Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province, Xiamen, 361005, People's Republic of China. chenz@xmu.edu.cn.

Abstract

Carbon-based gradient resistance element structure is proposed for the construction of multifunctional touch sensor, which will promote wide detection and recognition range of multiple mechanical stimulations. Multifunctional touch sensor with gradient resistance element and two electrodes is demonstrated to eliminate signals crosstalk and prevent interference during position sensing for human-machine interactions. Biological sensing interface based on a deep-learning-assisted all-in-one multipoint touch sensor enables users to efficiently interact with virtual world. Human-machine interactions using deep-learning methods are important in the research of virtual reality, augmented reality, and metaverse. Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes, signal crosstalk, propagation delay, and demanding configuration requirements. Here, an all-in-one multipoint touch sensor (AIOM touch sensor) with only two electrodes is reported. The AIOM touch sensor is efficiently constructed by gradient resistance elements, which can highly adapt to diverse application-dependent configurations. Combined with deep learning method, the AIOM touch sensor can be utilized to recognize, learn, and memorize human-machine interactions. A biometric verification system is built based on the AIOM touch sensor, which achieves a high identification accuracy of over 98% and offers a promising hybrid cyber security against password leaking. Diversiform human-machine interactions, including freely playing piano music and programmatically controlling a drone, demonstrate the high stability, rapid response time, and excellent spatiotemporally dynamic resolution of the AIOM touch sensor, which will promote significant development of interactive sensing interfaces between fingertips and virtual objects.

Keywords: Carbon functional material; Gradient resistance element; Human–machine interaction; Multifunctional touch sensor; Paper-based device.