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Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing

Author

Listed:
  • Jaehyun Kang

    (Korea Institute of Science and Technology
    Seoul National University)

  • Taeyoon Kim

    (Korea Institute of Science and Technology)

  • Suman Hu

    (Korea Institute of Science and Technology)

  • Jaewook Kim

    (Korea Institute of Science and Technology)

  • Joon Young Kwak

    (Korea Institute of Science and Technology)

  • Jongkil Park

    (Korea Institute of Science and Technology)

  • Jong Keuk Park

    (Korea Institute of Science and Technology)

  • Inho Kim

    (Korea Institute of Science and Technology)

  • Suyoun Lee

    (Korea Institute of Science and Technology)

  • Sangbum Kim

    (Seoul National University)

  • YeonJoo Jeong

    (Korea Institute of Science and Technology)

Abstract

Memristors, or memristive devices, have attracted tremendous interest in neuromorphic hardware implementation. However, the high electric-field dependence in conventional filamentary memristors results in either digital-like conductance updates or gradual switching only in a limited dynamic range. Here, we address the switching parameter, the reduction probability of Ag cations in the switching medium, and ultimately demonstrate a cluster-type analogue memristor. Ti nanoclusters are embedded into densified amorphous Si for the following reasons: low standard reduction potential, thermodynamic miscibility with Si, and alloy formation with Ag. These Ti clusters effectively induce the electrochemical reduction activity of Ag cations and allow linear potentiation/depression in tandem with a large conductance range (~244) and long data retention (~99% at 1 hour). Moreover, according to the reduction potentials of incorporated metals (Pt, Ta, W, and Ti), the extent of linearity improvement is selectively tuneable. Image processing simulation proves that the Ti4.8%:a-Si device can fully function with high accuracy as an ideal synaptic model.

Suggested Citation

  • Jaehyun Kang & Taeyoon Kim & Suman Hu & Jaewook Kim & Joon Young Kwak & Jongkil Park & Jong Keuk Park & Inho Kim & Suyoun Lee & Sangbum Kim & YeonJoo Jeong, 2022. "Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31804-4
    DOI: 10.1038/s41467-022-31804-4
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    References listed on IDEAS

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    1. Chao Du & Fuxi Cai & Mohammed A. Zidan & Wen Ma & Seung Hwan Lee & Wei D. Lu, 2017. "Reservoir computing using dynamic memristors for temporal information processing," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    2. Yuchao Yang & Peng Gao & Linze Li & Xiaoqing Pan & Stefan Tappertzhofen & ShinHyun Choi & Rainer Waser & Ilia Valov & Wei D. Lu, 2014. "Electrochemical dynamics of nanoscale metallic inclusions in dielectrics," Nature Communications, Nature, vol. 5(1), pages 1-9, September.
    3. Dmitri B. Strukov & Gregory S. Snider & Duncan R. Stewart & R. Stanley Williams, 2008. "The missing memristor found," Nature, Nature, vol. 453(7191), pages 80-83, May.
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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. Shuzhi Liu & Jianmin Zeng & Zhixin Wu & Han Hu & Ao Xu & Xiaohe Huang & Weilin Chen & Qilai Chen & Zhe Yu & Yinyu Zhao & Rong Wang & Tingting Han & Chao Li & Pingqi Gao & Hyunwoo Kim & Seung Jae Baik , 2023. "An ultrasmall organic synapse for neuromorphic computing," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    3. Alessandro Milozzi & Saverio Ricci & Daniele Ielmini, 2024. "Memristive tonotopic mapping with volatile resistive switching memory devices," Nature Communications, Nature, vol. 15(1), pages 1-9, December.

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