Dual‐Band Laser Selective Etching for Stretchable and Strain Interference‐Free Pressure Sensor Arrays

Author:

Xie Hongwei1,Huang Zhenlong123ORCID,Wan Jiawei2,Zhou Rui2,Chen Tao2,Wang Yizhuo2,Cheng Dong2,Yang Longpeng1,Ji Junjie1,Jiang Yan2,Wu Tailong2,Liu Jing2,Pan Taisong13,Jiang Binbin12,Zhu Jia1,Yao Guang1,Gao Min1,Lin Yuan14ORCID

Affiliation:

1. School of Materials and Energy University of Electronic Science and Technology of China Chengdu 610054 China

2. Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen 518110 China

3. Research Centre for Information Technology Shenzhen Institute of Information Technology Shenzhen 518172 China

4. State Key Laboratory of Electronic Thin Films and Integrated Devices, Medico‐Engineering Cooperation on Applied Medicine Research Center University of Electronic Science and Technology of China Chengdu 610054 China

Abstract

AbstractStretchable pressure sensor arrays are ideal for biomimetic electronic skin (e‐skin). However, conventional pressure sensors exhibit noticeable strain interference when stretched. This article introduces an advanced fabrication method based on dual‐band laser selective etching to create stretchable pressure sensor arrays free from strain interference. An intact sensitive film is sandwiched between the top and bottom electrodes and then etched into separate sensing cells. Silicone gel with an extremely low Young's modulus (0.157 kPa) is employed as the strain buffer layer, filling the space within sensing cells to absorb device strain during stretching. The pressure sensor arrays demonstrate exceptional interference‐free performance, with the resistance of sensing cells remaining unchanged under strains exceeding 20%. Pressure sensor arrays of 32 × 32 units are manufactured, showcasing a resistive response time of ≈40 ms during compression and 20 ms during release, along with a sensitivity of 0.7702 kPa−1 within the range of 1–70 kPa. Finally, the sensor arrays are integrated into a robotic hand as biomimetic e‐skin, coupled with deep learning algorithms, successfully identifying static and dynamic pressure distributions and achieving an average precision rate of over 99% in recognizing 2D shapes and objects.

Funder

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

Natural Science Foundation of Sichuan Province

Publisher

Wiley

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