journal article·2023-07-19Open access

Encoding and Multiplexing Information Signals in Magnetic Multilayers with Fractional Skyrmion Tubes

Runze Chen, Yu Li, Will Griggs, Yuzhe Zang, Vasilis F. Pavlidis, Christoforos Moutafis

ACS Applied Materials & Interfaces· Volume 15· Issue 28

Abstract

Tailored magnetic multilayers (MMLs) provide skyrmions with enhanced thermal stability, leading to the possibility of skyrmion-based devices for room-temperature applications. At the same time, the search for additional stable topological spin textures has been under intense research focus. Besides their fundamental importance, such textures may expand the information encoding capability of spintronic devices. However, fractional spin texture states within MMLs in the vertical dimension are yet to be investigated. In this work, we demonstrate numerically fractional skyrmion tubes (FSTs) in a tailored MML system. We subsequently propose to encode sequences of information signals with FSTs as information bits in a tailored MML device. Micromagnetic simulations and theoretical calculations are used to verify the feasibility of hosting distinct FST states within a single device, and their thermal stability is investigated. A multilayer multiplexing device is proposed, where multiple sequences of the information signals can be encoded and transmitted based on the nucleation and propagation of packets of FSTs. Finally, pipelined information transmission and automatic demultiplexing are demonstrated by exploiting the skyrmion Hall effect and introducing voltage-controlled synchronizers and width-based track selectors. The findings indicate that FSTs can be potential candidates as information carriers for future spintronic applications.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Magnetic properties of thin films · Magnetic and transport properties of perovskites and related materials · Physics of Superconductivity and Magnetism

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Nano Engineering and Spintronic Technologies (NEST) Research Group, Department of Computer Science, The University of Manchester, Manchester M13 9PL, U.K.

Advanced Processor Technologies (APT) Research Group, Department of Computer Science, The University of Manchester, Manchester M13 9PL, U.K.

Funding

Engineering and Physical Sciences Research Council · University of Manchester

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