Ultrathin Gallium Nitride Quantum-Disk-in-Nanowire-Enabled Reconfigurable Bioinspired Sensor for High-Accuracy Human Action Recognition
Zhixiang Gao, Xin Ju, Huabin Yu, Wei Chen, Xin Liu, Yuanmin Luo, Yang Kang, Dongyang Luo, Jikai Yao, Wengang Gu, Muhammad Hunain Memon, Yong Yan, Haiding Sun
Abstract
Human action recognition (HAR) is crucial for the development of efficient computer vision, where bioinspired neuromorphic perception visual systems have emerged as a vital solution to address transmission bottlenecks across sensor-processor interfaces. However, the absence of interactions among versatile biomimicking functionalities within a single device, which was developed for specific vision tasks, restricts the computational capacity, practicality, and scalability of in-sensor vision computing. Here, we propose a bioinspired vision sensor composed of a GaN/AlN-based ultrathin quantum-disks-in-nanowires (QD-NWs) array to mimic not only Parvo cells for high-contrast vision and Magno cells for dynamic vision in the human retina but also the synergistic activity between the two cells for in-sensor vision computing. By simply tuning the applied bias voltage on each QD-NW-array-based pixel, we achieve two biosimilar photoresponse characteristics with slow and fast reactions to light stimuli that enhance the in-sensor image quality and HAR efficiency, respectively. Strikingly, the interplay and synergistic interaction of the two photoresponse modes within a single device markedly increased the HAR recognition accuracy from 51.4% to 81.4% owing to the integrated artificial vision system. The demonstration of an intelligent vision sensor offers a promising device platform for the development of highly efficient HAR systems and future smart optoelectronics.