Graphene quantum dots induced performance enhancement in memristors.

Journal: Nanoscale
Published Date: Jun 12, 2025

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. Memristors have 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 dot (GQD)-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 GQD-based memristors. Initially, the structure, properties, and synthesis methods of GQDs are introduced in detail. Subsequently, the memristive mechanisms of GQD-based memristors are presented from three perspectives: the metal conductive filament mechanism, the electron trapping and detrapping mechanism, and the oxygen vacancy conductive filament mechanism. Furthermore, the different application scenarios of GQD-based memristors in both digital and analog types are summarized, encompassing information storage, brain-like artificial synapses, visual perception systems, and brain-machine interfaces. Finally, the challenges and future development prospects of GQD-based memristors are discussed.

Authors

  • Jintao He
    MOE Key Laboratory of Interface Science and Engineering in Advanced Materials, College of Materials Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China. yangyongzhen@tyut.edu.cn.
  • Guangdong Zhou
    School of Artificial Intelligence, Southwest University, Chongqing 400715, China.
  • Bai Sun
    Department of Mechanical and Mechatronics Engineering, Waterloo Institute for Nanotechnology, Centre for Advanced Materials Joining, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
  • Lingpeng Yan
    MOE Key Laboratory of Interface Science and Engineering in Advanced Materials, College of Materials Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China. yangyongzhen@tyut.edu.cn.
  • Xiaochen Lang
    MOE Key Laboratory of Interface Science and Engineering in Advanced Materials, College of Materials Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China. yangyongzhen@tyut.edu.cn.
  • Yongzhen Yang
    MOE Key Laboratory of Interface Science and Engineering in Advanced Materials, College of Materials Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China. yangyongzhen@tyut.edu.cn.
  • Haotian Hao
    College of Artificial Intelligence, Institute of Biomedical Engineering, Taiyuan University of Technology, Taiyuan 030024, China. haohaotian@tyut.edu.cn.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals