IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-31804-4.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-31804-4
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-022-31804-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rohit Abraham John & Yiğit Demirağ & Yevhen Shynkarenko & Yuliia Berezovska & Natacha Ohannessian & Melika Payvand & Peng Zeng & Maryna I. Bodnarchuk & Frank Krumeich & Gökhan Kara & Ivan Shorubalko &, 2022. "Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Liying Xu & Jiadi Zhu & Bing Chen & Zhen Yang & Keqin Liu & Bingjie Dang & Teng Zhang & Yuchao Yang & Ru Huang, 2022. "A distributed nanocluster based multi-agent evolutionary network," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. 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).
    4. Zhongfang Zhang & Xiaolong Zhao & Xumeng Zhang & Xiaohu Hou & Xiaolan Ma & Shuangzhu Tang & Ying Zhang & Guangwei Xu & Qi Liu & Shibing Long, 2022. "In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    5. Dong, Shiyu & Zhu, Hong & Zhong, Shouming & Shi, Kaibo & Liu, Yajuan, 2021. "New study on fixed-time synchronization control of delayed inertial memristive neural networks," Applied Mathematics and Computation, Elsevier, vol. 399(C).
    6. S. S. Teja Nibhanupudi & Anupam Roy & Dmitry Veksler & Matthew Coupin & Kevin C. Matthews & Matthew Disiena & Ansh & Jatin V. Singh & Ioana R. Gearba-Dolocan & Jamie Warner & Jaydeep P. Kulkarni & Gen, 2024. "Ultra-fast switching memristors based on two-dimensional materials," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    7. Min, Fuhong & Zhang, Wen & Ji, Ziyi & Zhang, Lei, 2021. "Switching dynamics of a non-autonomous FitzHugh-Nagumo circuit with piecewise-linear flux-controlled memristor," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    8. Feng, Liang & Hu, Cheng & Yu, Juan & Jiang, Haijun & Wen, Shiping, 2021. "Fixed-time Synchronization of Coupled Memristive Complex-valued Neural Networks," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    9. Hu, Yongbing & Li, Qian & Ding, Dawei & Jiang, Li & Yang, Zongli & Zhang, Hongwei & Zhang, Zhixin, 2021. "Multiple coexisting analysis of a fractional-order coupled memristive system and its application in image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    10. Yan, Dengwei & Wang, Lidan & Duan, Shukai & Chen, Jiaojiao & Chen, Jiahao, 2021. "Chaotic Attractors Generated by a Memristor-Based Chaotic System and Julia Fractal," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    11. Luo, Mengzhuo & Cheng, Jun & Liu, Xinzhi & Zhong, Shouming, 2019. "An extended synchronization analysis for memristor-based coupled neural networks via aperiodically intermittent control," Applied Mathematics and Computation, Elsevier, vol. 344, pages 163-182.
    12. Liu, Shuxin & Yu, Yongguang & Zhang, Shuo & Zhang, Yuting, 2018. "Robust stability of fractional-order memristor-based Hopfield neural networks with parameter disturbances," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 845-854.
    13. Zhang, Ge & Ma, Jun & Alsaedi, Ahmed & Ahmad, Bashir & Alzahrani, Faris, 2018. "Dynamical behavior and application in Josephson Junction coupled by memristor," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 290-299.
    14. Chen, Qun & Li, Bo & Yin, Wei & Jiang, Xiaowei & Chen, Xiangyong, 2023. "Bifurcation, chaos and fixed-time synchronization of memristor cellular neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    15. Stavrinides, Stavros G. & Hanias, Michael P. & Gonzalez, Mireia B. & Campabadal, Francesca & Contoyiannis, Yiannis & Potirakis, Stelios M. & Al Chawa, Mohamad Moner & de Benito, Carol & Tetzlaff, Rona, 2022. "On the chaotic nature of random telegraph noise in unipolar RRAM memristor devices," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    16. Zhiwei Chen & Wenjie Li & Zhen Fan & Shuai Dong & Yihong Chen & Minghui Qin & Min Zeng & Xubing Lu & Guofu Zhou & Xingsen Gao & Jun-Ming Liu, 2023. "All-ferroelectric implementation of reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    17. Li, Liangchen & Xu, Rui & Lin, Jiazhe, 2020. "Lagrange stability for uncertain memristive neural networks with Lévy noise and leakage delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    18. Sakthivel, R. & Anbuvithya, R. & Mathiyalagan, K. & Ma, Yong-Ki & Prakash, P., 2016. "Reliable anti-synchronization conditions for BAM memristive neural networks with different memductance functions," Applied Mathematics and Computation, Elsevier, vol. 275(C), pages 213-228.
    19. Xiangpeng Liang & Yanan Zhong & Jianshi Tang & Zhengwu Liu & Peng Yao & Keyang Sun & Qingtian Zhang & Bin Gao & Hadi Heidari & He Qian & Huaqiang Wu, 2022. "Rotating neurons for all-analog implementation of cyclic reservoir computing," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    20. Hongwei Tan & Sebastiaan van Dijken, 2023. "Dynamic machine vision with retinomorphic photomemristor-reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-9, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31804-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.