Emergent coherent modes in nonlinear magnonic waveguides detected at ultrahigh frequency resolution

K. An, M. Xu, A. Mucchietto, C. Kim, K.-W. Moon, C. Hwang and D. Grundler ()
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K. An: École Polytechnique Fédérale de Lausanne (EPFL)
M. Xu: École Polytechnique Fédérale de Lausanne (EPFL)
A. Mucchietto: École Polytechnique Fédérale de Lausanne (EPFL)
C. Kim: Korea Research Institute of Standards and Science
K.-W. Moon: Korea Research Institute of Standards and Science
C. Hwang: Korea Research Institute of Standards and Science
D. Grundler: École Polytechnique Fédérale de Lausanne (EPFL)

Nature Communications, 2024, vol. 15, issue 1, 1-9

Abstract: Abstract Nonlinearity of dynamic systems plays a key role in neuromorphic computing, which is expected to reduce the ever-increasing power consumption of machine learning and artificial intelligence applications. For spin waves (magnons), nonlinearity combined with phase coherence is the basis of phenomena like Bose–Einstein condensation, frequency combs, and pattern recognition in neuromorphic computing. Yet, the broadband electrical detection of these phenomena with high-frequency resolution remains a challenge. Here, we demonstrate the generation and detection of phase-coherent nonlinear magnons in an all-electrical GHz probe station based on coplanar waveguides connected to a vector network analyzer which we operate in a frequency-offset mode. Making use of an unprecedented frequency resolution, we resolve the nonlocal emergence of a fine structure of propagating nonlinear magnons, which sensitively depends on both power and a magnetic field. These magnons are shown to maintain coherency with the microwave source while propagating over macroscopic distances. We propose a multi-band four-magnon scattering scheme that is in agreement with the field-dependent characteristics of coherent nonlocal signals in the nonlinear excitation regime. Our findings are key to enable the seamless integration of nonlinear magnon processes into high-speed microwave electronics and to advance phase-encoded information processing in magnonic neuronal networks.

Date: 2024
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DOI: 10.1038/s41467-024-51483-7

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