FS-02 All-Magnonic Neurons Based on Chiral Magnonic Resonators
Creators: Kevin Fripp
Andrey Shytov
Volodymyr Kruglyak
Publisher: name: IEEE
Year: 2025

We demonstrate chiral magnonic resonators [1,2] as building blocks of artificial neural networks. Using micromagnetic simulations and analytical modelling, we show that chiral magnonic resonators can concentrate energy of incident linear spin waves, leading to a strongly nonlinear response of the resonators’ confined modes to the excitation. For modest excitation levels, the effect is described in terms of a nonlinear shift of the resonant frequency (’detuning’), which results in amplitude-dependent scattering of monochromatic spin waves. We show how this behavior can be harnessed to realize a sigmoid-like activation and so to implement artificial neurons in a deep neural network linked by spin waves propagating in a linear medium. Figures 1 and 2 exemplify a numerically simulated linear excitation pattern and the corresponding nonlinear response, respectively, of a magnonic neuron formed by a Permalloy nanodisk placed above a YIG film (inset in Fig.1). Our numerical results are in good agreement with a phenomenological model in which the nonlinear detuning of the confined mode is quadratic in its amplitude, while the propagation in the medium is linear. The research leading to these results has received funding from the UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee (Grant No. 10039217) as part of the Horizon Europe (HORIZON-CL4-2021-DIGITAL-EMERGING-01) under Grant Agreement No. 101070347. Yet, views and opinions expressed are those of the authors only and do not necessarily reflect those of the EU, and the EU cannot be held responsible for them.References: [1] V. V. Kruglyak, Appl. Phys. Lett. 119, 200502 (2021). [2] K. G. Fripp, Y. Au, A. V. Shytov, and V. V. Kruglyak, Appl. Phys. Lett. 122, 172403 (2023).
We demonstrate chiral magnonic resonators [1,2] as building blocks of artificial neural networks. Using micromagnetic simulations and analytical modelling, we show that chiral magnonic resonators can concentrate energy of incident linear spin waves, leading to a strongly nonlinear response of the resonators’ confined modes to the excitation. For modest excitation levels, the effect is described in terms of a nonlinear shift of the resonant frequency (’detuning’), which results in amplitude-dependent scattering of monochromatic spin waves. We show how this behavior can be harnessed to realize a sigmoid-like activation and so to implement artificial neurons in a deep neural network linked by spin waves propagating in a linear medium. Figures 1 and 2 exemplify a numerically simulated linear excitation pattern and the corresponding nonlinear response, respectively, of a magnonic neuron formed by a Permalloy nanodisk placed above a YIG film (inset in Fig.1). Our numerical results are in good agreement with a phenomenological model in which the nonlinear detuning of the confined mode is quadratic in its amplitude, while the propagation in the medium is linear. The research leading to these results has received funding from the UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee (Grant No. 10039217) as part of the Horizon Europe (HORIZON-CL4-2021-DIGITAL-EMERGING-01) under Grant Agreement No. 101070347. Yet, views and opinions expressed are those of the authors only and do not necessarily reflect those of the EU, and the EU cannot be held responsible for them.References: [1] V. V. Kruglyak, Appl. Phys. Lett. 119, 200502 (2021). [2] K. G. Fripp, Y. Au, A. V. Shytov, and V. V. Kruglyak, Appl. Phys. Lett. 122, 172403 (2023).