Magnetic Skyrmion as a Nonlinear Resistive Element: A Potential Building Block for Reservoir Computing

Prychynenko D, Sitte M, Litzius K, Krueger B, Bourianoff G, Klaeui M, Sinova J, Everschor-Sitte K (2018)


Publication Type: Journal article

Publication year: 2018

Journal

Book Volume: 9

Article Number: 014034

Journal Issue: 1

DOI: 10.1103/PhysRevApplied.9.014034

Abstract

Inspired by the human brain, there is a strong effort to find alternative models of information processing capable of imitating the high energy efficiency of neuromorphic information processing. One possible realization of cognitive computing involves reservoir computing networks. These networks are built out of nonlinear resistive elements which are recursively connected. We propose that a Skyrmion network embedded in magnetic films may provide a suitable physical implementation for reservoir computing applications. The significant key ingredient of such a network is a two-terminal device with nonlinear voltage characteristics originating from magnetoresistive effects, such as the anisotropic magnetoresistance or the recently discovered noncollinear magnetoresistance. The most basic element for a reservoir computing network built from "Skyrmion fabrics" is a single Skyrmion embedded in a ferromagnetic ribbon. In order to pave the way towards reservoir computing systems based on Skyrmion fabrics, we simulate and analyze (i) the current flow through a single magnetic Skyrmion due to the anisotropic magnetoresistive effect and (ii) the combined physics of local pinning and the anisotropic magnetoresistive effect.

Involved external institutions

How to cite

APA:

Prychynenko, D., Sitte, M., Litzius, K., Krueger, B., Bourianoff, G., Klaeui, M.,... Everschor-Sitte, K. (2018). Magnetic Skyrmion as a Nonlinear Resistive Element: A Potential Building Block for Reservoir Computing. Physical Review Applied, 9(1). https://doi.org/10.1103/PhysRevApplied.9.014034

MLA:

Prychynenko, Diana, et al. "Magnetic Skyrmion as a Nonlinear Resistive Element: A Potential Building Block for Reservoir Computing." Physical Review Applied 9.1 (2018).

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