  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, the 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 64x64
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 MWCNTs 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 pave the road to building
large-scale intelligent sensor networks for next-generation smart robotics.
