Quantum dot-based memristors for information processing and artificial intelligence applications

  • Dingshu Tian
  • , Chuan Ke
  • , Bai Sun
  • , Haotian Liang
  • , Ziran Qian
  • , Qifan Wen
  • , Xueqi Chen
  • , Chuan Yang
  • , Min Xu
  • , Yong Zhao

Research output: Contribution to journalReview articlepeer-review

3 Scopus citations

Abstract

Traditional computing systems struggle to keep pace with the development of artificial intelligence, as well as the development of the economy and continuous innovation in science and technology. Therefore, there is an urgent need for a new generation of powerful yet low-power computing technologies to replace them. Quantum dots have been incorporated into memristors due to their unique electrical properties, and the development of quantum dot memristors is expected to solve the problems faced by traditional memristors, including cycle stability, high energy consumption, and conductivity uniformity. This article reviews the research progress of quantum dot memristors and their simulation applications in artificial synapses. It summarizes some of the current challenges faced in the development of quantum dot memristors and discusses the potential future applications of these memristors in the field of artificial intelligence.

Original languageEnglish
Pages (from-to)10485-10505
Number of pages21
JournalNanoscale
Volume17
Issue number17
DOIs
StatePublished - 11 Apr 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

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