Networking retinomorphic sensor with memristive crossbar for brain-inspired visual perception |
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| Authors: | 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 Feng Miao |
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| Institution: | National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China;School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China;National Institute for Materials Science, Tsukuba 305-0044, Japan |
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| 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. |
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| Keywords: | van der Waals heterostructure retinomorphic sensor memristive crossbar brain-inspired visual perception neuromorphic computing |
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