Large-Scale Integrated Flexible Tactile Sensor Array for Sensitive Smart Robotic Touch

作者：Zhenxuan Zhao, Jianshi Tang, Jian Yuan, Yijun Li, Yuan Dai, Jian Yao, Qingtian Zhang, Sanchuan Ding, Ting Li, Ruirui Zhang, Yu Zheng, Zhengyou Zhang, Song Qiu, Qingwen Li, B. Gao, Ning Deng, H. Qian, F. Xing, Zheng You, Huaqiang Wu · 发表于：ACS Nano · 年份：2022 · DOI：10.1021/acsnano.2c06432 · 被引用次数：125 · 研究领域：Medicine、Computer Science、Physics、Engineering

In the long pursuit of smart robotics, it has been envisioned to empower robots with human-like senses, especially vision and touch. While tremendous progress has been made in image sensors and computer vision over the past decades, tactile sense abilities are lagging behind due to the lack of large-scale flexible tactile sensor array with high sensitivity, high spatial resolution, and fast response. In this work, we have demonstrated a 64 × 64 flexible tactile sensor array with a record-high spatial resolution of 0.9 mm (equivalently 28.2 pixels per inch) by integrating a high-performance piezoresistive film (PRF) with a large-area active matrix of carbon nanotube thin-film transistors. PRF with self-formed microstructures exhibited high pressure-sensitivity of ∼385 kPa-1 for multi-walled carbon nanotubes concentration of 6%, while the 14% one exhibited fast response time of ∼3 ms, good linearity, broad detection range beyond 1400 kPa, and excellent cyclability over 3000 cycles. Using this fully integrated tactile sensor array, the footprint maps of an artificial honeybee were clearly identified. Furthermore, we hardware-implemented a smart tactile system by integrating the PRF-based sensor array with a memristor-based computing-in-memory chip to record and recognize handwritten digits and Chinese calligraphy, achieving high classification accuracies of 98.8% and 97.3% in hardware, respectively. The integration of sensor networks with deep learning hardware may enable edge o...