All-Optical Diffractive Deep Neural Networks Enabled Laser-Reduced Graphene Oxide Tactile Sensor for Braille Recognition

All-optical diffractive deep neural networks (D2NNs) show a wide range of applications in image recognition and artificial vision due to their advantages of high-speed parallel processing, low energy consumption, and excellent anti-interference ability. However, there is relatively limited research applying D2NNs for tactile perception. In this study, we propose an automatic Braille recognition method based on D2NNs and tactile sensors. A flexible molybdenum disulfide-doped laser-reduced graphene oxide (LRGO/MoS2) tactile sensor was fabricated with the laser direct writing method. The LRGO/MoS2 tactile sensor shows a sensitivity of 9.8 kPa–1, with a response/recovery time of 0.14/0.10 s and excellent cyclic stability. The tactile sensor can be employed to capture Braille character information in real time and convert it into digital signals as inputs for all-optical D2NNs. The automatic recognition of Braille characters is achieved in the all-optical D2NNs with five diffraction layers, and the system finally can realize a recognition accuracy of 100% for Braille recognition. The strategy of integrating flexible tactile sensors with all-optical deep learning paves a path for realizing a low-cost, fast, accurate, and efficient tactile recognition system.