Artificial Visual Perception Nervous System Based on Low-Dimensional Material Photoelectric Memristors

• Published in: ACS nano Vol. 15; no. 11; pp. 17319 - 17326
• Main Authors: Pei, Yifei, Yan, Lei, Wu, Zuheng, Lu, Jikai, Zhao, Jianhui, Chen, Jingsheng, Liu, Qi, Yan, Xiaobing
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 23.11.2021
• Subjects: Artificial Intelligence; Humans; Neural Networks, Computer; Neurons - physiology; Synapses; Visual Perception; visual perception nervous system; photovoltaic memristor; threshold-switching memristor; driverless automobile; low-dimensional material
• ISSN: 1936-0851; 1936-086X
• DOI: 10.1021/acsnano.1c04676

The visual perception system is the most important system for human learning since it receives over 80% of the learning information from the outside world. With the exponential growth of artificial intelligence technology, there is a pressing need for high-energy and area-efficiency visual perception systems capable of processing efficiently the received natural information. Currently, memristors with their elaborate dynamics, excellent scalability, and information (e.g., visual, pressure, sound, etc.) perception ability exhibit tremendous potential for the application of visual perception. Here, we propose a fully memristor-based artificial visual perception nervous system (AVPNS) which consists of a quantum-dot-based photoelectric memristor and a nanosheet-based threshold-switching (TS) memristor. We use a photoelectric and a TS memristor to implement the synapse and leaky integrate-and-fire (LIF) neuron functions, respectively. With the proposed AVPNS we successfully demonstrate the biological image perception, integration and fire, as well as the biosensitization process. Furthermore, the self-regulation process of a speed meeting control system in driverless automobiles can be accurately and conceptually emulated by this system. Our work shows that the functions of the biological visual nervous system may be systematically emulated by a memristor-based hardware system, thus expanding the spectrum of memristor applications in artificial intelligence.