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Surface diffusion-limited lifetime of silver and copper nanofilaments in resistive switching devices

Author

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  • Wei Wang

    (Politecnico di Milano and IUNET, Piazza L. da Vinci)

  • Ming Wang

    (Nanyang Technological University)

  • Elia Ambrosi

    (Politecnico di Milano and IUNET, Piazza L. da Vinci)

  • Alessandro Bricalli

    (Politecnico di Milano and IUNET, Piazza L. da Vinci)

  • Mario Laudato

    (Politecnico di Milano and IUNET, Piazza L. da Vinci)

  • Zhong Sun

    (Politecnico di Milano and IUNET, Piazza L. da Vinci)

  • Xiaodong Chen

    (Nanyang Technological University)

  • Daniele Ielmini

    (Politecnico di Milano and IUNET, Piazza L. da Vinci)

Abstract

Silver/copper-filament-based resistive switching memory relies on the formation and disruption of a metallic conductive filament (CF) with relatively large surface-to-volume ratio. The nanoscale CF can spontaneously break after formation, with a lifetime ranging from few microseconds to several months, or even years. Controlling and predicting the CF lifetime enables device engineering for a wide range of applications, such as non-volatile memory for data storage, tunable short/long term memory for synaptic neuromorphic computing, and fast selection devices for crosspoint arrays. However, conflictive explanations for the CF retention process are being proposed. Here we show that the CF lifetime can be described by a universal surface-limited self-diffusion mechanism of disruption of the metallic CF. The surface diffusion process provides a new perspective of ion transport mechanism at the nanoscale, explaining the broad range of reported lifetimes, and paving the way for material engineering of resistive switching device for memory and computing applications.

Suggested Citation

  • Wei Wang & Ming Wang & Elia Ambrosi & Alessandro Bricalli & Mario Laudato & Zhong Sun & Xiaodong Chen & Daniele Ielmini, 2019. "Surface diffusion-limited lifetime of silver and copper nanofilaments in resistive switching devices," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-018-07979-0
    DOI: 10.1038/s41467-018-07979-0
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    Cited by:

    1. Kwon, Osung & Kim, Sungjun & Agudov, Nikolay & Krichigin, Alexey & Mikhaylov, Alexey & Grimaudo, Roberto & Valenti, Davide & Spagnolo, Bernardo, 2022. "Non-volatile memory characteristics of a Ti/HfO2/Pt synaptic device with a crossbar array structure," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    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. Sebastian Jenderny & Rohit Gupta & Roshani Madurawala & Thomas Strunskus & Franz Faupel & Sören Kaps & Rainer Adelung & Karlheinz Ochs & Alexander Vahl, 2024. "Stimulus-dependent spiking and bursting behavior in memsensor circuits: experiment and wave digital modeling," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(9), pages 1-11, September.
    4. Sang Hyun Sung & Tae Jin Kim & Hyera Shin & Tae Hong Im & Keon Jae Lee, 2022. "Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    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. 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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