Adv. Mater.: Quantitative Tactile Sensing of Surface Microstructures Through Time-Domain Analysis of Piezoelectric Twin Signals

Authors：

Jiaqi Tu^1,2, Zheren Cai², Zhihua Liu³, Jiangtao Su², Yanzhen Li², Xue Feng^1,4,5*, Zequn Cui^2,6*, and Xiaodong Chen²*

Institutions：

¹Institute of Flexible Electronics Technology of THU Jiaxing, Zhejiang 314000, China

²Innovative Centre for Flexible Devices (iFLEX) Max Planck–NTU Joint Lab for Artificial Senses School of Materials Science and Engineering Nanyang Technological University50 Nanyang Avenue, Singapore 639798, Singapore

³Institute of Materials Research and Engineering the Agency for Science Technology and Research2 Fusionopolis Way, Innovis, #08-03, Singapore 138634, Singapore

⁴Department of Engineering Mechanics Tsinghua University Beijing 100084, China

⁵State Key Laboratory of Flexible Electronics Technology Tsinghua University Beijing 100084, China

⁶State Key Laboratory of Bioinspired Interfacial Materials Science Institute of Functional Nano & Soft Materials (FUNSOM)Soochow University Suzhou 215123, China

Abstract：

Tactile sensors enabling human-like behavior to identify surface microstructures are essential for humanoid robots to interact precisely with complex environments. Most existing approaches use materials responding to dynamic forces and rely on machine learning methods to distinguish various types of surface microstructures. Quantitatively profiling the surface microstructures is significant but challenging, especially under the requirement of eliminating external bulky motion-control systems. Here, a quantitative tactile surface profiling strategy is presented through time-domain analysis of the signal of a piezoelectric twin-film architecture. The architecture uses two parallel piezoelectric films with a fixed interlayer distance, generating twin voltage signals with a time delay, which is inversely proportional to the scanning speed, and consequently removes the need for motion control. The microstructure heights correlate with the peak voltages, whereas widths and edge profiles are derived from the temporal analysis of distinct signal features. Tactile and in situ measurement of surface microstructures is demonstrated with high accuracy (>99.2%) over a broad height range of 1–1000 µm. Furthermore, in-line quality inspection during additive manufacturing is realized by quantitatively profiling the surface microstructures. This work will drive innovations in tactile technologies that emulate and potentially surpass human capabilities and advance in situ surface characterization methods.

IF：

26.8

Link：

https://doi.org/10.1002/adma.202510393