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Inverse-design magnonic devices

Author

Listed:
  • Qi Wang

    (University of Vienna)

  • Andrii V. Chumak

    (University of Vienna)

  • Philipp Pirro

    (Technische Universität Kaiserslautern)

Abstract

The field of magnonics offers a new type of low-power information processing, in which magnons, the quanta of spin waves, carry and process data instead of electrons. Many magnonic devices were demonstrated recently, but the development of each of them requires specialized investigations and, usually, one device design is suitable for one function only. Here, we introduce the method of inverse-design magnonics, in which any functionality can be specified first, and a feedback-based computational algorithm is used to obtain the device design. We validate this method using the means of micromagnetic simulations. Our proof-of-concept prototype is based on a rectangular ferromagnetic area that can be patterned using square-shaped voids. To demonstrate the universality of this approach, we explore linear, nonlinear and nonreciprocal magnonic functionalities and use the same algorithm to create a magnonic (de-)multiplexer, a nonlinear switch and a circulator. Thus, inverse-design magnonics can be used to develop highly efficient rf applications as well as Boolean and neuromorphic computing building blocks.

Suggested Citation

  • Qi Wang & Andrii V. Chumak & Philipp Pirro, 2021. "Inverse-design magnonic devices," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22897-4
    DOI: 10.1038/s41467-021-22897-4
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    Cited by:

    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. 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.

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