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

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 x 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 thinfilm transistors. PRF with self-formed microstructures exhibited high pressure-sensitivity of similar to 385 kPa-1 for multi walled carbon nanotubes concentration of 6%, while the 14% one exhibited fast response time of similar to 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.

Automated summary

This study demonstrates a flexible tactile sensor array with high spatial resolution and showcases its applications in identifying footprints and recognizing handwritten digits and Chinese calligraphy. The integration of sensor networks with deep learning hardware shows potential for reducing power consumption and latency in edge computing. This work is significant for the development of large-scale intelligent sensor networks for next-generation smart robotics.