IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v12y2021i1d10.1038_s41467-021-26711-z.html
   My bibliography  Save this article

Nanoscale neural network using non-linear spin-wave interference

Author

Listed:
  • Ádám Papp

    (Pázmány Péter Catholic University)

  • Wolfgang Porod

    (Center for Nano Science and Technology University of Notre Dame (NDnano))

  • Gyorgy Csaba

    (Pázmány Péter Catholic University)

Abstract

We demonstrate the design of a neural network hardware, where all neuromorphic computing functions, including signal routing and nonlinear activation are performed by spin-wave propagation and interference. Weights and interconnections of the network are realized by a magnetic-field pattern that is applied on the spin-wave propagating substrate and scatters the spin waves. The interference of the scattered waves creates a mapping between the wave sources and detectors. Training the neural network is equivalent to finding the field pattern that realizes the desired input-output mapping. A custom-built micromagnetic solver, based on the Pytorch machine learning framework, is used to inverse-design the scatterer. We show that the behavior of spin waves transitions from linear to nonlinear interference at high intensities and that its computational power greatly increases in the nonlinear regime. We envision small-scale, compact and low-power neural networks that perform their entire function in the spin-wave domain.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26711-z
    DOI: 10.1038/s41467-021-26711-z
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-021-26711-z
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-021-26711-z?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. Qi Wang & Andrii V. Chumak & Philipp Pirro, 2021. "Inverse-design magnonic devices," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    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. 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.
    2. K. An & M. Xu & A. Mucchietto & C. Kim & K.-W. Moon & C. Hwang & D. Grundler, 2024. "Emergent coherent modes in nonlinear magnonic waveguides detected at ultrahigh frequency resolution," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    3. Korbinian Baumgaertl & Dirk Grundler, 2023. "Reversal of nanomagnets by propagating magnons in ferrimagnetic yttrium iron garnet enabling nonvolatile magnon memory," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    4. 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.
    5. 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.
    6. Qi Wang & Roman Verba & Kristýna Davídková & Björn Heinz & Shixian Tian & Yiheng Rao & Mengying Guo & Xueyu Guo & Carsten Dubs & Philipp Pirro & Andrii V. Chumak, 2024. "All-magnonic repeater based on bistability," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    7. Kilian D. Stenning & Jack C. Gartside & Luca Manneschi & Christopher T. S. Cheung & Tony Chen & Alex Vanstone & Jake Love & Holly Holder & Francesco Caravelli & Hidekazu Kurebayashi & Karin Everschor-, 2024. "Neuromorphic overparameterisation and few-shot learning in multilayer physical neural networks," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    8. 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.
    9. Davide Girardi & Simone Finizio & Claire Donnelly & Guglielmo Rubini & Sina Mayr & Valerio Levati & Simone Cuccurullo & Federico Maspero & Jörg Raabe & Daniela Petti & Edoardo Albisetti, 2024. "Three-dimensional spin-wave dynamics, localization and interference in a synthetic antiferromagnet," 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. Davide Girardi & Simone Finizio & Claire Donnelly & Guglielmo Rubini & Sina Mayr & Valerio Levati & Simone Cuccurullo & Federico Maspero & Jörg Raabe & Daniela Petti & Edoardo Albisetti, 2024. "Three-dimensional spin-wave dynamics, localization and interference in a synthetic antiferromagnet," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    2. K. An & M. Xu & A. Mucchietto & C. Kim & K.-W. Moon & C. Hwang & D. Grundler, 2024. "Emergent coherent modes in nonlinear magnonic waveguides detected at ultrahigh frequency resolution," Nature Communications, Nature, vol. 15(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:12:y:2021:i:1:d:10.1038_s41467-021-26711-z. 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.