Networking retinomorphic sensor with memristive crossbar for brain-inspired visual perception
Shuang Wang, Chenyu Wang, Pengfei Wang, Cong Wang, Zhuan Li, Chen Pan, Yitong Dai, Anyuan Gao, Chuan Liu, Jian Liu, Huafeng Yang, Xiaowei Liu, Bin Cheng, Kunji Chen, Zhenlin Wang, Kenji Watanabe, Takashi Taniguchi, Shi‐Jun Liang, Feng Miao
Abstract
Abstract Compared to human vision, conventional machine vision composed of an image sensor and processor suffers from high latency and large power consumption due to physically separated image sensing and processing. A neuromorphic vision system with brain-inspired visual perception provides a promising solution to the problem. Here we propose and demonstrate a prototype neuromorphic vision system by networking a retinomorphic sensor with a memristive crossbar. We fabricate the retinomorphic sensor by using WSe2/h-BN/Al2O3 van der Waals heterostructures with gate-tunable photoresponses, to closely mimic the human retinal capabilities in simultaneously sensing and processing images. We then network the sensor with a large-scale Pt/Ta/HfO2/Ta one-transistor-one-resistor (1T1R) memristive crossbar, which plays a similar role to the visual cortex in the human brain. The realized neuromorphic vision system allows for fast letter recognition and object tracking, indicating the capabilities of image sensing, processing and recognition in the full analog regime. Our work suggests that such a neuromorphic vision system may open up unprecedented opportunities in future visual perception applications.