Nonlinear Magnonics & Neuromorphic Computing in a Multilayered ‘3D’ Artificial Spin Ice
Dr. Jack N. Carter-Gartside, Imperial College London, UK

Strongly-interacting nanomagnetic arrays are ideal systems for exploring the frontiers of magnonic control. They provide functional reconfigurable platforms and attractive technological solutions across storage, GHz communications and neuromorphic computing. Typically, these systems are primarily constrained by their range of accessible states and the strength of magnon coupling phenomena. Increasingly, magnetic nanostructures have explored the benefits of expanding into three dimensions. This has broadened the horizons of magnetic microstate spaces and functional behaviours, but precise control of 3D states and dynamics remains challenging. Here, we introduce a 3D magnonic metamaterial compatible with widely-available fabrication and characterisation techniques. By combining independently-programmable artificial spin-systems strongly coupled in the z-plane, we create a system with a rich 16^N microstate space and intense static and dynamic dipolar magnetic coupling. The system exhibits a broad range of emergent phenomena including ultrastrong magnon-magnon coupling with normalised coupling rates of Δω/γ=0.57, GHz mode shifts in zero applied field and reconfigurable generation of magnon frequency combs.

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References:

1. Gartside, Jack C., Kilian D. Stenning, Alex Vanstone, Holly H. Holder, Daan M. Arroo, Troy Dion, Francesco Caravelli, Hidekazu Kurebayashi, and Will R. Branford. “Reconfigurable training and reservoir computing in an artificial spin-vortex ice via spin-wave fingerprinting.” Nature Nanotechnology 17, no. 5 (2022): 460-469.
2. Lee, Oscar, Tianyi Wei, Kilian D. Stenning, Jack C. Gartside, Dan Prestwood, Shinichiro Seki, Aisha Aqeel et al. “Task-adaptive physical reservoir computing.” Nature Materials (2023): 1-9.
3. Dion, T., Stenning, K., Vanstone, A., Holder, H., Sultana, R., Alatteili, G., Martinez, V., Kaffash, M., Kimura, T., Kurebayashi, H. and Branford, W., 2023. arxiv Ultrastrong magnon-magnon coupling and chiral symmetry breaking in a 3d magnonic metamaterial.
4. Stenning, K.D., Gartside, J.C., Manneschi, L., Cheung, C.T., Chen, T., Vanstone, A., Love, J., Holder, H.H., Caravelli, F., Everschor-Sitte, K. and Vasilaki, E., 2023 arxiv Neuromorphic Few-Shot Learning: Generalization in Multilayer Physical Neural Networks.