On-chip phonon-magnon reservoir for neuromorphic computing

Dmytro D. Yaremkevich, Alexey V. Scherbakov (), Luke Clerk, Serhii M. Kukhtaruk, Achim Nadzeyka, Richard Campion, Andrew W. Rushforth, Sergey Savel’ev, Alexander G. Balanov and Manfred Bayer
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Dmytro D. Yaremkevich: Experimentelle Physik 2, Technische Universität Dortmund
Alexey V. Scherbakov: Experimentelle Physik 2, Technische Universität Dortmund
Luke Clerk: Loughborough University
Serhii M. Kukhtaruk: V. E. Lashkaryov Institute of Semiconductor Physics
Achim Nadzeyka: Raith GmbH
Richard Campion: University of Nottingham
Andrew W. Rushforth: University of Nottingham
Sergey Savel’ev: Loughborough University
Alexander G. Balanov: Loughborough University
Manfred Bayer: Experimentelle Physik 2, Technische Universität Dortmund

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: Abstract Reservoir computing is a concept involving mapping signals onto a high-dimensional phase space of a dynamical system called “reservoir” for subsequent recognition by an artificial neural network. We implement this concept in a nanodevice consisting of a sandwich of a semiconductor phonon waveguide and a patterned ferromagnetic layer. A pulsed write-laser encodes input signals into propagating phonon wavepackets, interacting with ferromagnetic magnons. The second laser reads the output signal reflecting a phase-sensitive mix of phonon and magnon modes, whose content is highly sensitive to the write- and read-laser positions. The reservoir efficiently separates the visual shapes drawn by the write-laser beam on the nanodevice surface in an area with a size comparable to a single pixel of a modern digital camera. Our finding suggests the phonon-magnon interaction as a promising hardware basis for realizing on-chip reservoir computing in future neuromorphic architectures.

Date: 2023
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DOI: 10.1038/s41467-023-43891-y

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