Graphene quantum dots induced performance for intelligence memristors

Abstract

With the rapid development of information technology, the demand for miniaturization, integration, and intelligence of electronic devices is growing rapidly. As a key device in the non-Von Neumann architecture, memristors can perform computations while storing data, enhancing computational efficiency and reducing power consumption. Memristor has become pivotal in driving the advancement of artificial intelligence (AI) and Internet of Things technologies. Combining the electronic properties of graphene with the size effects of quantum dots, graphene quantum dots (GQDs)-based memristors exhibit potential applications in constructing brain-inspired neuromorphic computing systems and achieving AI hardware acceleration, making them a focal point of research interest. This review provides an overview of the preparation, mechanism, and application of GQDs-based memristors. Initially, the structure, properties, and synthesis methods of GQDs are introduced in detail. Subsequently, the memristive mechanisms of GQDs-based memristors are presented from three perspectives: metal conductive filament mechanism, electron trapping and detrapping mechanism, and oxygen vacancy conductive filament mechanism. Furthermore, the different application scenarios of GQDs-based memristors in both digital and analog types are summarized, encompassing information storage, brain-like artificial synapses, visual perception systems, and brain-machine interface. Finally, the challenges and future development prospects of GQDs-based memristors are discussed.