Topological melting of the metastable skyrmion lattice in the chiral magnet Co9_9Zn9_9Mn2_2

In a $\beta$-Mn-type chiral magnet Co$_9$Zn$_9$Mn$_2$, we demonstrate that the magnetic field-driven collapse of a room temperature metastable topological skyrmion lattice passes through a regime described by a partial topological charge inversion. Using Lorentz transmission electron microscopy, the magnetization distribution was observed directly as the magnetic field was swept antiparallel to the original skyrmion core magnetization, i.e. negative magnetic fields. Due to the topological stability of skyrmions, a direct transition of the metastable skyrmion lattice to the equilibrium helical state is avoided for increasingly negative fields. Instead, the metastable skyrmion lattice gradually transforms into giant magnetic bubbles separated by $2\pi$ domain walls. Eventually these large structures give way to form a near-homogeneously magnetized medium that unexpectedly hosts a low density of isolated skyrmions with inverted core magnetization, and thus a total topological charge of reduced size and opposite sign compared with the initial state. A similar phenomenon has been observed previously in systems hosting ordered lattices of magnetic bubbles stabilized by the dipolar interaction and called "topological melting". With support from numerical calculations, we argue that the observed regime of partial topological charge inversion has its origin in the topological protection of the starting metastable skyrmion state.Comment: 9 pages, 4 figure

Task-adaptive physical reservoir computing

Reservoir computing is a neuromorphic architecture that may offer viable solutions to the growing energy costs of machine learning. In software-based machine learning, computing performance can be readily reconfigured to suit different computational tasks by tuning hyperparameters. This critical functionality is missing in 'physical' reservoir computing schemes that exploit nonlinear and history-dependent responses of physical systems for data processing. Here we overcome this issue with a 'task-adaptive' approach to physical reservoir computing. By leveraging a thermodynamical phase space to reconfigure key reservoir properties, we optimize computational performance across a diverse task set. We use the spin-wave spectra of the chiral magnet Cu2OSeO3 that hosts skyrmion, conical and helical magnetic phases, providing on-demand access to different computational reservoir responses. The task-adaptive approach is applicable to a wide variety of physical systems, which we show in other chiral magnets via above (and near) room-temperature demonstrations in Co8.5Zn8.5Mn3 (and FeGe)

Task-adaptive physical reservoir computing

Reservoir computing is a neuromorphic architecture that potentially offers viable solutions to the growing energy costs of machine learning. In software-based machine learning, neural network properties and performance can be readily reconfigured to suit different computational tasks by changing hyperparameters. This critical functionality is missing in ``physical" reservoir computing schemes that exploit nonlinear and history-dependent memory responses of physical systems for data processing. Here, we experimentally present a `task-adaptive' approach to physical reservoir computing, capable of reconfiguring key reservoir properties (nonlinearity, memory-capacity and complexity) to optimise computational performance across a broad range of tasks. As a model case of this, we use the temperature and magnetic-field controlled spin-wave response of Cu$_2$OSeO$_3$ that hosts skyrmion, conical and helical magnetic phases, providing on-demand access to a host of different physical reservoir responses. We quantify phase-tunable reservoir performance, characterise their properties and discuss the correlation between these in physical reservoirs. This task-adaptive approach overcomes key prior limitations of physical reservoirs, opening opportunities to apply thermodynamically stable and metastable phase control across a wide variety of physical reservoir systems, as we show its transferable nature using above(near)-room-temperature demonstration with Co$_{8.5}$Zn$_{8.5}$Mn$_{3}$ (FeGe).Comment: Main manuscript: 14 pages, 5 figures. Supplementary materials: 13 pages, 10 figure

遍歴電子系メタ磁性体UCoAlにおける強磁性臨界現象と臨界普遍性

京都大学0048新制・課程博士博士(理学)甲第18777号理博第4035号新制||理||1581(附属図書館)31728京都大学大学院理学研究科物理学・宇宙物理学専攻(主査)教授 石田 憲二, 教授 田中 耕一郎, 教授 前野 悦輝学位規則第4条第1項該当Doctor of ScienceKyoto UniversityDFA

Heat current-driven topological spin texture transformations and helical q-vector switching

Abstract The use of magnetic states in memory devices has a history dating back decades, and the experimental discovery of magnetic skyrmions and subsequent demonstrations of their control via magnetic fields, heat, and electric/thermal currents have ushered in a new era for spintronics research and development. Recent studies have experimentally discovered the antiskyrmion, the skyrmion’s antiparticle, and while several host materials have been identified, control via thermal current remains elusive. In this work, we use thermal current to drive the transformation between skyrmions, antiskyrmions and non-topological bubbles, as well as the switching of helical states in the antiskyrmion-hosting ferromagnet (Fe0.63Ni0.3Pd0.07)3P at room temperature. We discover that a temperature gradient $${{{{{\boldsymbol{\nabla }}}}}}T$$ ∇ T drives a transformation from antiskyrmions to non-topological bubbles to skyrmions while under a magnetic field and observe the opposite, unidirectional transformation from skyrmions to antiskyrmions at zero-field, suggesting that the antiskyrmion, more so than the skyrmion, is robustly metastable at zero field