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Understanding memristive switching via in situ characterization and device modeling

Author

Listed:
  • Wen Sun

    (Institute of Microelectronics, Tsinghua University
    Oak Ridge National Laboratory
    University of Massachusetts)

  • Bin Gao

    (Institute of Microelectronics, Tsinghua University)

  • Miaofang Chi

    (Oak Ridge National Laboratory)

  • Qiangfei Xia

    (University of Massachusetts)

  • J. Joshua Yang

    (University of Massachusetts)

  • He Qian

    (Institute of Microelectronics, Tsinghua University)

  • Huaqiang Wu

    (Institute of Microelectronics, Tsinghua University)

Abstract

Owing to their attractive application potentials in both non-volatile memory and unconventional computing, memristive devices have drawn substantial research attention in the last decade. However, major roadblocks still remain in device performance, especially concerning relatively large parameter variability and limited cycling endurance. The response of the active region in the device within and between switching cycles plays the dominating role, yet the microscopic details remain elusive. This Review summarizes recent progress in scientific understanding of the physical origins of the non-idealities and propose a synergistic approach based on in situ characterization and device modeling to investigate switching mechanism. At last, the Review offers an outlook for commercialization viability of memristive technology.

Suggested Citation

  • Wen Sun & Bin Gao & Miaofang Chi & Qiangfei Xia & J. Joshua Yang & He Qian & Huaqiang Wu, 2019. "Understanding memristive switching via in situ characterization and device modeling," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11411-6
    DOI: 10.1038/s41467-019-11411-6
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    Cited by:

    1. Peng Chen & Fenghao Liu & Peng Lin & Peihong Li & Yu Xiao & Bihua Zhang & Gang Pan, 2023. "Open-loop analog programmable electrochemical memory array," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    2. Yan Wang & Yue Gong & Shenming Huang & Xuechao Xing & Ziyu Lv & Junjie Wang & Jia-Qin Yang & Guohua Zhang & Ye Zhou & Su-Ting Han, 2021. "Memristor-based biomimetic compound eye for real-time collision detection," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    3. Bassem Tossoun & Di Liang & Stanley Cheung & Zhuoran Fang & Xia Sheng & John Paul Strachan & Raymond G. Beausoleil, 2024. "High-speed and energy-efficient non-volatile silicon photonic memory based on heterogeneously integrated memresonator," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    4. Mahata, Chandreswar & Kim, Sungjun, 2021. "Electrical and optical artificial synapses properties of TiN-nanoparticles incorporated HfAlO-alloy based memristor," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    5. Konlechner, Roland & Allagui, Anis & Antonov, Vladimir N. & Yudin, Dmitry, 2023. "A superstatistics approach to the modelling of memristor current–voltage responses," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 614(C).
    6. Ying Zhang & Ge-Qi Mao & Xiaolong Zhao & Yu Li & Meiyun Zhang & Zuheng Wu & Wei Wu & Huajun Sun & Yizhong Guo & Lihua Wang & Xumeng Zhang & Qi Liu & Hangbing Lv & Kan-Hao Xue & Guangwei Xu & Xiangshui, 2021. "Evolution of the conductive filament system in HfO2-based memristors observed by direct atomic-scale imaging," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    7. Liang Yang & Hongrong Hu & Alexander Scholz & Florian Feist & Gabriel Cadilha Marques & Steven Kraus & Niklas Maximilian Bojanowski & Eva Blasco & Christopher Barner-Kowollik & Jasmin Aghassi-Hagmann , 2023. "Laser printed microelectronics," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    8. Koryazhkina, M.N. & Filatov, D.O. & Shishmakova, V.A. & Shenina, M.E. & Belov, A.I. & Antonov, I.N. & Kotomina, V.E. & Mikhaylov, A.N. & Gorshkov, O.N. & Agudov, N.V. & Guarcello, C. & Carollo, A. & S, 2022. "Resistive state relaxation time in ZrO2(Y)-based memristive devices under the influence of external noise," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).

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