Memristor-Based Smart Image Sensor

National Science Review paper "Networking retinomorphic sensor with memristive crossbar for brain-inspired visual perception" by Shuang Wang, Chen-Yu Wang, Pengfei Wang, Cong Wang, Zhu-An 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, and Feng Miao from Nanjing University, China, and National Institute for Materials Science, Japan, proposes a pixel array that can recognaze objects:

"Comparing to human vision, conventional machine vision composed of image sensor and processor suffers from high latency and large power consumption due to physically separated image sensing and processing. Neuromorphic vision system with brain-inspired visual perception provides a promising solution to solve the challenge. Here we propose and demonstrate a prototype neuromorphic vision system by networking 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 such sensor with a large-scale Pt/Ta/HfO2/Ta one-transistor-one-memristor (1T1R) memristive crossbar, which serves as the role similar to the visual cortex in 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 neuromorphic vision system may open up unprecedented opportunities in future visual perception applications."