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

TLDR

The visual perception system, which receives over 80 % of learning information, is critical for human learning, and the rapid growth of AI demands high‑energy and area‑efficient visual perception systems, for which memristors—due to their dynamic behavior, scalability, and multimodal sensing—offer great potential. The authors propose a fully memristor‑based artificial visual perception nervous system (AVPNS) composed of a quantum‑dot photoelectric memristor and a nanosheet threshold‑switching memristor. The AVPNS implements synaptic behavior with a photoelectric memristor and leaky integrate‑and‑fire neuron behavior with a threshold‑switching memristor. The AVPNS successfully emulates biological image perception, integration, and firing, reproduces the biosensitization process, and accurately models the self‑regulation of speed‑meeting control in driverless cars, demonstrating that a memristor‑based hardware system can systematically replicate visual nervous system functions and broaden memristor AI applications.

Abstract

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.