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Neuromorphic Vision Based on van der Waals Heterostructure Materials

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Near-sensor and In-sensor Computing

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Abstract

The human vision system represents the most intelligent camera capable of sensing and perceiving the world in a real-time manner. It has long been one of the human dreams to reversely engineer the intelligent vision system by exploiting physics properties of materials. Van der Waals (vdW) heterostructures stacked by two-dimensional (2D) materials possess unique gate-tunable electrical and optical properties and provide an ideal platform to emulate the organization and functionality of the human vision system. In this chapter, we first show how the electrically tunable vdW heterostructures can be exploited for mimicking the hierarchical organization and biological function of the retina. Afterward, we demonstrate a proof-of-concept neuromorphic vision system by networking the retinomorphic sensor based on vdW heterostructures with memristive crossbar array and explore its promising application in pattern recognition and object tracking. These results suggest that such neuromorphic vision system may open unprecedented opportunities in future visual perception applications, such as edge computing in Internet of Things (IoT).

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Authors and Affiliations

  1. National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China

    Shuang Wang, Shi-Jun Liang & Feng Miao

Authors
  1. Shuang Wang
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  2. Shi-Jun Liang
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  3. Feng Miao
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Corresponding authors

Correspondence to Shi-Jun Liang or Feng Miao .

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Editors and Affiliations

  1. Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China

    Yang Chai

  2. The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzen, China

    Fuyou Liao

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Wang, S., Liang, SJ., Miao, F. (2022). Neuromorphic Vision Based on van der Waals Heterostructure Materials. In: Chai, Y., Liao, F. (eds) Near-sensor and In-sensor Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-11506-6_4

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  • DOI: https://doi.org/10.1007/978-3-031-11506-6_4

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  • Online ISBN: 978-3-031-11506-6

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