IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-39452-y.html
   My bibliography  Save this article

Pattern recognition in reciprocal space with a magnon-scattering reservoir

Author

Listed:
  • Lukas Körber

    (Helmholtz-Zentrum Dresden - Rossendorf
    Technische Universität Dresden)

  • Christopher Heins

    (Helmholtz-Zentrum Dresden - Rossendorf
    Technische Universität Dresden)

  • Tobias Hula

    (Helmholtz-Zentrum Dresden - Rossendorf
    Technische Universität Chemnitz)

  • Joo-Von Kim

    (Université Paris-Saclay)

  • Sonia Thlang

    (Université Paris-Saclay)

  • Helmut Schultheiss

    (Helmholtz-Zentrum Dresden - Rossendorf
    Technische Universität Dresden)

  • Jürgen Fassbender

    (Helmholtz-Zentrum Dresden - Rossendorf
    Technische Universität Dresden)

  • Katrin Schultheiss

    (Helmholtz-Zentrum Dresden - Rossendorf)

Abstract

Magnons are elementary excitations in magnetic materials and undergo nonlinear multimode scattering processes at large input powers. In experiments and simulations, we show that the interaction between magnon modes of a confined magnetic vortex can be harnessed for pattern recognition. We study the magnetic response to signals comprising sine wave pulses with frequencies corresponding to radial mode excitations. Three-magnon scattering results in the excitation of different azimuthal modes, whose amplitudes depend strongly on the input sequences. We show that recognition rates as high as 99.4% can be attained for four-symbol sequences using the scattered modes, with strong performance maintained with the presence of amplitude noise in the inputs.

Suggested Citation

  • Lukas Körber & Christopher Heins & Tobias Hula & Joo-Von Kim & Sonia Thlang & Helmut Schultheiss & Jürgen Fassbender & Katrin Schultheiss, 2023. "Pattern recognition in reciprocal space with a magnon-scattering reservoir," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39452-y
    DOI: 10.1038/s41467-023-39452-y
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-39452-y
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-39452-y?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. Jacob Torrejon & Mathieu Riou & Flavio Abreu Araujo & Sumito Tsunegi & Guru Khalsa & Damien Querlioz & Paolo Bortolotti & Vincent Cros & Kay Yakushiji & Akio Fukushima & Hitoshi Kubota & Shinji Yuasa , 2017. "Neuromorphic computing with nanoscale spintronic oscillators," Nature, Nature, vol. 547(7664), pages 428-431, July.
    2. Kristof Vandoorne & Pauline Mechet & Thomas Van Vaerenbergh & Martin Fiers & Geert Morthier & David Verstraeten & Benjamin Schrauwen & Joni Dambre & Peter Bienstman, 2014. "Experimental demonstration of reservoir computing on a silicon photonics chip," Nature Communications, Nature, vol. 5(1), pages 1-6, May.
    3. L. Appeltant & M.C. Soriano & G. Van der Sande & J. Danckaert & S. Massar & J. Dambre & B. Schrauwen & C.R. Mirasso & I. Fischer, 2011. "Information processing using a single dynamical node as complex system," Nature Communications, Nature, vol. 2(1), pages 1-6, September.
    4. Yanan Zhong & Jianshi Tang & Xinyi Li & Bin Gao & He Qian & Huaqiang Wu, 2021. "Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    5. Ádám Papp & Wolfgang Porod & Gyorgy Csaba, 2021. "Nanoscale neural network using non-linear spin-wave interference," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. Zhuohui Liu & Qinghua Zhang & Donggang Xie & Mingzhen Zhang & Xinyan Li & Hai Zhong & Ge Li & Meng He & Dashan Shang & Can Wang & Lin Gu & Guozhen Yang & Kuijuan Jin & Chen Ge, 2023. "Interface-type tunable oxygen ion dynamics for physical reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    4. Yiming Sun & Tao Lin & Na Lei & Xing Chen & Wang Kang & Zhiyuan Zhao & Dahai Wei & Chao Chen & Simin Pang & Linglong Hu & Liu Yang & Enxuan Dong & Li Zhao & Lei Liu & Zhe Yuan & Aladin Ullrich & Chris, 2023. "Experimental demonstration of a skyrmion-enhanced strain-mediated physical reservoir computing system," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    5. Min Yan & Can Huang & Peter Bienstman & Peter Tino & Wei Lin & Jie Sun, 2024. "Emerging opportunities and challenges for the future of reservoir computing," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    6. Ali Momeni & Romain Fleury, 2022. "Electromagnetic wave-based extreme deep learning with nonlinear time-Floquet entanglement," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    7. Xing Chen & Flavio Abreu Araujo & Mathieu Riou & Jacob Torrejon & Dafiné Ravelosona & Wang Kang & Weisheng Zhao & Julie Grollier & Damien Querlioz, 2022. "Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    8. 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.
    9. Laura E. Suárez & Agoston Mihalik & Filip Milisav & Kenji Marshall & Mingze Li & Petra E. Vértes & Guillaume Lajoie & Bratislav Misic, 2024. "Connectome-based reservoir computing with the conn2res toolbox," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    10. Minati, Ludovico & Mancinelli, Mattia & Frasca, Mattia & Bettotti, Paolo & Pavesi, Lorenzo, 2021. "An analog electronic emulator of non-linear dynamics in optical microring resonators," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    11. Changsong Gao & Di Liu & Chenhui Xu & Weidong Xie & Xianghong Zhang & Junhua Bai & Zhixian Lin & Cheng Zhang & Yuanyuan Hu & Tailiang Guo & Huipeng Chen, 2024. "Toward grouped-reservoir computing: organic neuromorphic vertical transistor with distributed reservoir states for efficient recognition and prediction," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    12. Ren, Jinfu & Liu, Yang & Liu, Jiming, 2023. "Chaotic behavior learning via information tracking," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    13. Oleksii M. Volkov & Oleksandr V. Pylypovskyi & Fabrizio Porrati & Florian Kronast & Jose A. Fernandez-Roldan & Attila Kákay & Alexander Kuprava & Sven Barth & Filipp N. Rybakov & Olle Eriksson & Sebas, 2024. "Three-dimensional magnetic nanotextures with high-order vorticity in soft magnetic wireframes," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    14. Klaus Raab & Maarten A. Brems & Grischa Beneke & Takaaki Dohi & Jan Rothörl & Fabian Kammerbauer & Johan H. Mentink & Mathias Kläui, 2022. "Brownian reservoir computing realized using geometrically confined skyrmion dynamics," Nature Communications, Nature, vol. 13(1), pages 1-6, December.
    15. Suresh, R. & Senthilkumar, D.V. & Lakshmanan, M. & Kurths, J., 2016. "Emergence of a common generalized synchronization manifold in network motifs of structurally different time-delay systems," Chaos, Solitons & Fractals, Elsevier, vol. 93(C), pages 235-245.
    16. Yang, J. & Primo, E. & Aleja, D. & Criado, R. & Boccaletti, S. & Alfaro-Bittner, K., 2022. "Implementing and morphing Boolean gates with adaptive synchronization: The case of spiking neurons," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    17. Hu, Wancheng & Zhang, Yibin & Ma, Rencai & Dai, Qionglin & Yang, Junzhong, 2022. "Synchronization between two linearly coupled reservoir computers," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    18. H. Merbouche & B. Divinskiy & D. Gouéré & R. Lebrun & A. El Kanj & V. Cros & P. Bortolotti & A. Anane & S. O. Demokritov & V. E. Demidov, 2024. "True amplification of spin waves in magnonic nano-waveguides," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    19. Yuchun Zhang & Lin Liu & Bin Tu & Bin Cui & Jiahui Guo & Xing Zhao & Jingyu Wang & Yong Yan, 2023. "An artificial synapse based on molecular junctions," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    20. Liao, Zhiqiang & Ma, Kaijie & Tang, Siyi & Sarker, Md Shamim & Yamahara, Hiroyasu & Tabata, Hitoshi, 2021. "Phase locking of ultra-low power consumption stochastic magnetic bits induced by colored noise," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).

    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:14:y:2023:i:1:d:10.1038_s41467-023-39452-y. 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.