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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
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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
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