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

Journal: ACS nano
Published Date:

Abstract

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 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 or near-sensor computing with significantly reduced power consumption and latency. Our work could empower the building of large-scale intelligent sensor networks for next-generation smart robotics.

Authors

  • Zhenxuan Zhao
    School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China.
  • Jianshi Tang
    Institute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China. jtang@tsinghua.edu.cn.
  • Jian Yuan
    State Key Laboratory of Chirosciences and Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China.
  • Yijun Li
    School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China.
  • Yuan Dai
    Tencent Robotics X, Shenzhen 518000, China.
  • Jian Yao
    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China.
  • Qingtian Zhang
  • Sanchuan Ding
    Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing 100084, China.
  • Tingyu Li
    Jiangxi University of Chinese Medicine, School of Humanity, Nanchang 330004, China.
  • Ruirui Zhang
    National Agricultural Intelligent Equipment Technology Research Center, Beijing, China.
  • Yu Zheng
    Department of Thoracic Surgery, West China Hospital of Sichuan University, Chengdu 610041, China.
  • Zhengyou Zhang
    Tencent Robotics X, Shenzhen 518000, China.
  • Song Qiu
    Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China.
  • Qingwen Li
    College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.
  • Bin Gao
    Institute of Microelectronics, Tsinghua University, Beijing, 10084, China; Center for Brain-Inspired Computing Research, Tsinghua University, Beijing, 10084, China. Electronic address: gaob1@tsinghua.edu.cn.
  • Ning Deng
    1] Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China [2] Institute of Microelectronics, Tsinghua University, Beijing 100084, China.
  • He Qian
    Institute of Microelectronics, Tsinghua University, Beijing, 10084, China; Center for Brain-Inspired Computing Research, Tsinghua University, Beijing, 10084, China. Electronic address: qianh@tsinghua.edu.cn.
  • Fei Xing
    School of Aerospace Engineering, Xiamen University, Xiamen, Fujian 361005, China.
  • Zheng You
  • Huaqiang Wu
    Institue of Microelectronics, Tsinghua University, Beijing, 100084, China.