Roadmap on Spin-Wave Computing
Abstract
Magnonics is a field of science that addresses the physical properties of spin waves and utilizes them for data processing. Scalability down to atomic dimensions, operations in the GHz-to-THz frequency range, utilization of nonlinear and nonreciprocal phenomena, and compatibility with CMOS are just a few of many advantages offered by magnons. Although magnonics is still primarily positioned in the academic domain, the scientific and technological challenges of the field are being extensively investigated, and many proof-of-concept prototypes have already been realized in laboratories. This roadmap is a product of the collective work of many authors that covers versatile spin-wave computing approaches, conceptual building blocks, and underlying physical phenomena. In particular, the roadmap discusses the computation operations with Boolean digital data, unconventional approaches like neuromorphic computing, and the progress towards magnon-based quantum computing. The article is organized as a collection of sub-sections grouped into seven large thematic sections. Each sub-section is prepared by one or a group of authors and concludes with a brief description of the current challenges and the outlook of the further development of the research directions.
Keywords
Cite
@article{arxiv.2111.00365,
title = {Roadmap on Spin-Wave Computing},
author = {A. V. Chumak and P. Kabos and M. Wu and C. Abert and C. Adelmann and A. Adeyeye and J. Åkerman and F. G. Aliev and A. Anane and A. Awad and C. H. Back and A. Barman and G. E. W. Bauer and M. Becherer and E. N. Beginin and V. A. S. V. Bittencourt and Y. M. Blanter and P. Bortolotti and I. Boventer and D. A. Bozhko and S. A. Bunyaev and J. J. Carmiggelt and R. R. Cheenikundil and F. Ciubotaru and S. Cotofana and G. Csaba and O. V. Dobrovolskiy and C. Dubs and M. Elyasi and K. G. Fripp and H. Fulara and I. A. Golovchanskiy and C. Gonzalez-Ballestero and P. Graczyk and D. Grundler and P. Gruszecki and G. Gubbiotti and K. Guslienko and A. Haldar and S. Hamdioui and R. Hertel and B. Hillebrands and T. Hioki and A. Houshang and C. -M. Hu and H. Huebl and M. Huth and E. Iacocca and M. B. Jungfleisch and G. N. Kakazei and A. Khitun and R. Khymyn and T. Kikkawa and M. Kläui and O. Klein and J. W. Kłos and S. Knauer and S. Koraltan and M. Kostylev and M. Krawczyk and I. N. Krivorotov and V. V. Kruglyak and D. Lachance-Quirion and S. Ladak and R. Lebrun and Y. Li and M. Lindner and R. Macêdo and S. Mayr and G. A. Melkov and S. Mieszczak and Y. Nakamura and H. T. Nembach and A. A. Nikitin and S. A. Nikitov and V. Novosad and J. A. Otalora and Y. Otani and A. Papp and B. Pigeau and P. Pirro and W. Porod and F. Porrati and H. Qin and B. Rana and T. Reimann and F. Riente and O. Romero-Isart and A. Ross and A. V. Sadovnikov and A. R. Safin and E. Saitoh and G. Schmidt and H. Schultheiss and K. Schultheiss and A. A. Serga and S. Sharma and J. M. Shaw and D. Suess and O. Surzhenko and K. Szulc and T. Taniguchi and M. Urbánek and K. Usami and A. B. Ustinov and T. van der Sar and S. van Dijken and V. I. Vasyuchka and R. Verba and S. Viola Kusminskiy and Q. Wang and M. Weides and M. Weiler and S. Wintz and S. P. Wolski and X. Zhang},
journal= {arXiv preprint arXiv:2111.00365},
year = {2023}
}
Comments
74 pages, 57 figures, 500 references