arXiv subjects

Robin Msiska

Publications and source records attributed to Robin Msiska.

5 recordsLinked to original sources

arXiv2026

Planar Interfaces for Transmission of Chiral Spin Textures

Lateral magnetic interfaces provide a direct way to test whether skyrmions remain robust when driven across abrupt changes in material parameters and magnetic order. Here we study skyrmion transmission across planar ferromagnet-ferromagnet (FM-FM), antiferromagnet-antiferromagnet (AFM-AFM), ferromagnet-antiferromagnet (FM-AFM), and antiferromagnet-ferromagnet (AFM-FM) interfaces using micromagnetic simulations and analytic reduced-coordinate criteria. The outcomes are organized into phase diagrams according to the morphology formed in the receiving region, distinguishing compact transmission from deformed skyrmions, stripe-domain states, amorphous textures, and relaxation into the background. Same-order FM-FM and AFM-AFM skyrmion transmission is captured by an analytically defined range of the reduced Dzyaloshinskii-Moriya interaction, identifying the wall-softening regime that supports compact transmission without stripe formation. Mixed-order FM-AFM and AFM-FM interfaces are directionally distinct, requiring conversion between ferromagnetic magnetization and antiferromagnetic N\'eel textures. These results show that planar interfaces act as active transport elements and provide reduced design criteria for heterogeneous skyrmion tracks.

cond-mat.mes-hall↗

arXiv2023

Perspective on unconventional computing using magnetic skyrmions

Learning and pattern recognition inevitably requires memory of previous events, a feature that conventional CMOS hardware needs to artificially simulate. Dynamical systems naturally provide the memory, complexity, and nonlinearity needed for a plethora of different unconventional computing approaches. In this perspective article, we focus on the unconventional computing concept of reservoir computing and provide an overview of key physical reservoir works reported. We focus on the promising platform of magnetic structures and, in particular, skyrmions, which potentially allow for low-power applications. Moreover, we discuss skyrmion-based implementations of Brownian computing, which has recently been combined with reservoir computing. This computing paradigm leverages the thermal fluctuations present in many skyrmion systems. Finally, we provide an outlook on the most important challenges in this field.

cs.ET↗

arXiv2022

Audio Classification with Skyrmion Reservoirs

Physical reservoir computing is a computational paradigm that enables spatio-temporal pattern recognition to be performed directly in matter. The use of physical matter leads the way towards energy-efficient devices capable of solving machine learning problems without having to build a system of millions of interconnected neurons. We propose a high performance "skyrmion mixture reservoir" that implements the reservoir computing model with multi-dimensional inputs. We show that our implementation solves spoken digit classification tasks at the nanosecond timescale, with an overall model accuracy of 97.4% and a less that 1% word error rate; the best performance ever reported for in-materio reservoir computers. Due to the quality of the results and the low power properties of magnetic texture reservoirs, we argue that skyrmion fabrics are a compelling candidate for reservoir computing.

cond-mat.mes-hall↗

arXiv2021

Nonzero Skyrmion Hall Effect in Topologically Trivial Structures

It is widely believed that the skyrmion Hall effect, often disruptive for device applications, vanishes for overall topologically trivial structures such as (synthetic) antiferromagnetic skyrmions and skyrmioniums due to a compensation of Magnus forces. In this manuscript, however, we report that in contrast to the case of spin-transfer torque driven skyrmion motion, this notion is generally false for spin-orbit torque driven objects. We show that the skyrmion Hall angle is directly related to their helicity and imposes an unexpected roadblock for developing faster and lower input racetrack memories based on spin-orbit torques.

cond-mat.mes-hall↗

arXiv2021

Spatial Analysis of Physical Reservoir Computers

Physical reservoir computing is a computational framework that implements spatiotemporal information processing directly within physical systems. By exciting nonlinear dynamical systems and creating linear models from their state, we can create highly energy-efficient devices capable of solving machine learning tasks without building a modular system consisting of millions of neurons interconnected by synapses. To act as an effective reservoir, the chosen dynamical system must have two desirable properties: nonlinearity and memory. We present task agnostic spatial measures to locally measure both of these properties and exemplify them for a specific physical reservoir based upon magnetic skyrmion textures. In contrast to typical reservoir computing metrics, these metrics can be resolved spatially and in parallel from a single input signal, allowing for efficient parameter search to design efficient and high-performance reservoirs. Additionally, we show the natural trade-off between memory capacity and nonlinearity in our reservoir's behaviour, both locally and globally. Finally, by balancing the memory and nonlinearity in a reservoir, we can improve its performance for specific tasks.

cs.LG↗