16 research outputs found
Topological melting of the metastable skyrmion lattice in the chiral magnet CoZnMn
In a -Mn-type chiral magnet CoZnMn, 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
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 CuOSeO 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 CoZnMn
(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
A Rotary Magnetic Damper Consisting of Several Sector Magnets and an Arbitrarily Shaped Conductor With a Circular Cavity
A Rotary Magnetic Damper or Brake Consisting of a Number of Sector Magnets and a Circular Conductor
Ferromagnetic critical behavior and critical universality in itinerant-electron metamagnet UCoAl
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 ∇ 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