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Memristive Device Characteristics Engineering by Controlling the Crystallinity of Switching Layer Materials

  • Hao Yang
    Hao Yang
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
    More by Hao Yang
  • Buyun Chen
    Buyun Chen
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
    More by Buyun Chen
  • Boxiang Song
    Boxiang Song
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
    More by Boxiang Song
  • Deming Meng
    Deming Meng
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
    More by Deming Meng
  • Subodh Tiwari
    Subodh Tiwari
    Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States
  • Aravind Krishnamoorthy
    Aravind Krishnamoorthy
    Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States
  • Xiaodong Yan
    Xiaodong Yan
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
    More by Xiaodong Yan
  • Zerui Liu
    Zerui Liu
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
    More by Zerui Liu
  • Yunxiang Wang
    Yunxiang Wang
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
  • Pan Hu
    Pan Hu
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
    More by Pan Hu
  • Tse-Hsien Ou
    Tse-Hsien Ou
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
    More by Tse-Hsien Ou
  • Paulo Branicio
    Paulo Branicio
    Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States
  • Rajiv Kalia
    Rajiv Kalia
    Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States
    More by Rajiv Kalia
  • Aiichiro Nakano
    Aiichiro Nakano
    Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States
  • Priya Vashishta
    Priya Vashishta
    Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States
  • Fanxin Liu
    Fanxin Liu
    Collaborative Innovation Center for Information Technology in Biological and Medical Physics, and College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China
    More by Fanxin Liu
  • Han Wang
    Han Wang
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
    More by Han Wang
  • , and 
  • Wei Wu*
    Wei Wu
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
    *Email: [email protected]
    More by Wei Wu
Cite this: ACS Appl. Electron. Mater. 2020, 2, 6, 1529–1537
Publication Date (Web):May 4, 2020
https://doi.org/10.1021/acsaelm.0c00148
Copyright © 2020 American Chemical Society

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    Abstract

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    Memristive devices (i.e., memristors) can be highly beneficial in many emerging applications that may play important roles in the future generations of electronic systems, such as bio-inspired neuromorphic computing, high density nonvolatile memory, and field programmable gate arrays. Therefore, the memristor characteristics (such as operation voltage, on/off ratio, and the number of conductance states) must be engineered carefully for different applications. Here, we demonstrate a method to modify the memristor characteristics specifically by controlling the crystallinity of the switching layer material. Through setting the temperature of atomic layer deposition, the crystallinity of deposited Al2O3 can be controlled. Using different crystalline Al2O3 as the memristor switching layer, the characteristics of the corresponding Pt/Al2O3/Ta/Pt cross-point memristors can be modified precisely. The high I-V linearity, high on/off ratio (around 108), low pulse operation voltage (2.5 V), and multilevel conductance states (314 states) of the Pt/Al2O3/Ta/Pt cross-point memristor are demonstrated. More importantly, the mechanism behind this phenomenon is studied. This work deepens our understanding of the working mechanism of memristors and paves the way for using memristors in a broad spectrum of applications.

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsaelm.0c00148.

    • HAADF STEM and EDS images of the deposited Al2O3 film; IV curves of multiple switching cycles of the Pt/Al2O3/Ta/Pt memristor; more pulse switching data of the Pt/Al2O3/Ta/Pt memristor from different devices; and repeatability and stability of multilevel conductance states of Pt/Al2O3/Ta/Pt memristors (PDF)

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    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

    Cited By

    This article is cited by 5 publications.

    1. Pan Hu, Sushmit Hossain, Zerui Liu, Deming Meng, Yunxiang Wang, Tse‐Hsien Ou, Hao Yang, Buyun Chen, Zhi Cai, Yudi Shi, Mark Barnell, Qing Wu, Stephen B. Cronin, Wei Wu. Hybrid Tuning of Sub‐Filaments to Improve Analog Switching Performance in Memristive Devices. Advanced Materials Technologies 2023, 8 (15) https://doi.org/10.1002/admt.202300109
    2. Zerui Liu, Deming Meng, Guangxu Su, Pan Hu, Boxiang Song, Yunxiang Wang, Junhan Wei, Hao Yang, Tianyi Yuan, Buyun Chen, Tse‐Hsien Ou, Sushmit Hossain, Matthew Miller, Fanxin Liu, Wei Wu. Ultrafast Early Warning of Heart Attacks through Plasmon‐Enhanced Raman Spectroscopy using Collapsible Nanofingers and Machine Learning. Small 2023, 19 (2) https://doi.org/10.1002/smll.202204719
    3. Xi Zhou, Liang Zhao, Linfeng Lu, Dongdong Li. CuAg/Al 2 O 3 /CuAg Threshold Switching Selector for RRAM Applications. 2021, 1-2. https://doi.org/10.1109/DRC52342.2021.9467128
    4. Buyun Chen, Hao Yang, Boxiang Song, Deming Meng, Xiaodong Yan, Yuanrui Li, Yunxiang Wang, Pan Hu, Tse-Hsien Ou, Mark Barnell, Qing Wu, Han Wang, Wei Wu. A memristor-based hybrid analog-digital computing platform for mobile robotics. Science Robotics 2020, 5 (47) https://doi.org/10.1126/scirobotics.abb6938
    5. Panagiotis Kassanos. Analog-digital computing let robots go through the motions. Science Robotics 2020, 5 (47) https://doi.org/10.1126/scirobotics.abe6818

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