Magnetic skyrmions for unconventional computing and revealing latent information

Karin Everschor-Sitte, University of Duisburg-Essen

Novel computational paradigms in combination with proper hardware solutions are required to overcome the limitations of our state-of-the-art computer technology. [1-3] In this talk, I will focus on the potential of topologically stabilized magnetic whirls – so-called skyrmions for reservoir computing. Reservoir computing is a computational scheme that allows to drastically simplify spatial-temporal recognition tasks. We have shown that random skyrmion fabrics provide a suitable physical implementation of the reservoir. [4,5] They allow to classify patterns via their complex resistance responses either by tracing a signal over time or by a single spatially resolved measurement. [6] In a second part of the talk, I will introduce two recently developed data analysis tools. [7, 8] While often a significant effort is made in enhancing the resolution of an experimental technique to obtain further insight into the sample and its physical properties, an advantageous data analysis has the potential to provide deep insights into given data set.

[1] J. Grollier, D. Querlioz, K.Y. Camsari, KES, S. Fukami, M.D. Stiles, Nat. Elect. 3, 360 (2020)
[2] E. Vedmedenko, R. Kawakami, D. Sheka, ..., KES, et al., J. of Phys. D 53, 453001 (2020)
[3] G. Finocchio, M. Di Ventra, K.Y. Camsari, KES, P. K. Amiri, Z. Zeng, JMMM 521, 167506 (2021)
[4] D. Prychynenko, M. Sitte, et al, KES, Phys. Rev. Appl. 9, 014034 (2018)
[5] G. Bourianoff, D. Pinna, M. Sitte and KES, AIP Adv. 8, 055602 (2018)
[6] D. Pinna, G. Bourianoff and KES, Phys. Rev. Appl. 14, 054020 (2020)
[7] I. Horenko, D. Rodrigues, T. O’Kane and KES, arXiv:1907.04601
[8] D. Rodrigues, KES, S. Gerber, I. Horenko, iScience 24, 102171 (2021)

PDF file of the talk available here