Chip, Volume. 3, Issue 2, 100086(2024)

Carbon-based memristors for resistive random access memory and neuromorphic applications

Fan Yang1,†... Zhaorui Liu2,†, Xumin Ding3, Yang Li4,*, Cong Wang1,** and Guozhen Shen5,*** |Show fewer author(s)
Author Affiliations
  • 1School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
  • 2School of Information Science and Engineering, University of Jinan, Jinan 250022, China
  • 3School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
  • 4School of Integrated Circuits, Shandong University, Jinan 250101, China
  • 5School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
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    As a typical representative of nanomaterials, carbon nanomaterials have attracted widespread attention in the construction of electronic devices owing to their unique physical and chemical properties, multi-dimensionality, multi-hybridization methods, and excellent electronic properties. Especially in the recent years, memristors based on carbon nanomaterials have flourished in the field of building non-volatile memory devices and neuromorphic applications. In the current work, the preparation methods and structural characteristics of carbon nanomaterials of different dimensions were systematically reviewed. Afterwards, in depth discussion on the structural characteristics and working mechanism of memristors based on carbon nanomaterials of different dimensions was conducted. Finally, the potential applications of carbon-based memristors in logic operations, neural network construction, artificial vision systems, artificial tactile systems, and multimodal perception systems were also introduced. It is believed that this paper will provide guidance for the future development of high-quality information storage, high-performance neuromorphic applications, and high-sensitivity bionic sensing based on carbon-based memristors.

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    Fan Yang, Zhaorui Liu, Xumin Ding, Yang Li, Cong Wang, Guozhen Shen. Carbon-based memristors for resistive random access memory and neuromorphic applications[J]. Chip, 2024, 3(2): 100086

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    Paper Information

    Category: Research Articles

    Received: Dec. 4, 2023

    Accepted: Jan. 28, 2024

    Published Online: Jan. 23, 2025

    The Author Email: Li Yang (yang.li@sdu.edu.cn), Wang Cong (kevinwang@hit.edu.cn), Shen Guozhen (gzshen@bit.edu.cn)

    DOI:10.1016/j.chip.2024.100086

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