arXiv subjects

Hidekazu Kurebayashi

Publications and source records attributed to Hidekazu Kurebayashi.

At least 19 recordsLinked to original sources

arXiv2026

Magnetoelastic coupling in stripe-domain states of yttrium iron garnet

We study magnetoelastic coupling in stripe-domain magnetic states of $3\,\mathrm{μm}$-thick YIG thin films grown on a GGG substrate. Broadband ferromagnetic resonance reveals low-frequency stripe-domain magnon branches modulated by a field-independent phonon comb with a frequency spacing of $3.5\,\mathrm{MHz}$, matching the value predicted for confined thickness-shear modes of the GGG substrate. Analytical fitting yields coupling rates that vary between $0.33$--$0.54\,\mathrm{MHz}$ and cooperativities of order $10^{-1}$, indicating that the system is in the weak-coupling regime without resolvable avoided-crossing gaps. Magnon-phonon mode-overlap calculations using finite-element simulations show that the weak coupling arises from phase and domain-sign cancellation: the local magnetoelastic coupling is sizable, but more than $99\%$ of the coherent overlap cancels across the stripe texture. Fully coupled simulations further demonstrate phonon-mediated excitation of a remote YIG layer and show that efficient propagating-phonon generation requires spatially asymmetric magnon modes, establishing magnetic texture as a control parameter for magnon--phonon coupling.

cond-mat.mes-hall↗

arXiv2026

The 2026 Skyrmionics Roadmap

Magnetic skyrmions and related topological spin textures have emerged as a central topic in condensed-matter physics, combining fundamental significance with potential for transformative applications in spintronics, magnonics, and beyond. Over the past decade, advances in material platforms, imaging techniques, theoretical modeling, and device concepts have established skyrmionics as a rapidly expanding field. At the same time, challenges remain in stabilizing, controlling, and integrating such textures into functional architectures, while novel phenomena such as antiskyrmions, higher-order skyrmions, hopfions, and antiferromagnetic textures arise. The 2026 Skyrmionics Roadmap represents a collective effort of many authors, providing a comprehensive perspective on the current state-of-the-art and the outlook for the coming years. In 33 focused sections, each co-authored by two researchers, we chart progress in theory and modeling, material systems, skyrmion dynamics, and skyrmion technologies. By offering a consolidated vision, this Roadmap aims to guide both fundamental research and application-driven efforts, accelerating the transition of skyrmionics from conceptual breakthroughs toward practical technologies.

cond-mat.mes-hall↗

arXiv2026

Dynamical stability by spin transfer in nearly isotropic magnets

Spin transfer torques (STTs) control magnetisation by electric currents, enabling a range of nano-scale spintronic applications. They can destabilise the equilibrium magnetisation state by counteracting magnetic relaxation. Here, we maximise the STT effect through a dedicated growth-annealing protocol for CoFeB thin films, such that magnetic anisotropies originating from the interface and shape almost cancel each other. The nearly isotropic magnets enable low-current dynamical stabilisation of the magnetisation in the direction opposite to an applied magnetic field, thereby realising a spintronic analogue of the Kapitza pendulum. In an intermediate current regime, the STT drives large magnetisation vector fluctuations that cover the entire Bloch sphere. The continuous variable associated with the stochastic magnetisation direction may serve as a resource for probabilistic computing and neuromorphic hardware. Our results establish isotropic magnets as a platform to study as-yet-uncharted, far-from-equilibrium spin dynamics including anti-magnonics, with promising implications for unconventional computing paradigms.

cond-mat.mes-hall↗

arXiv2026

Metrics for spin-based computing

Spin-based computing is emerging as a powerful approach for energy-efficient and high-performance solutions to future data processing hardware. Spintronic devices function by electrically manipulating the collective dynamics of the electron spin, that is inherently non-volatile, nonlinear and fast-operating, and can couple to other degrees of freedom such as photonic and phononic systems. This review explores key advances in integrating magnetic and spintronic elements into computational architectures, ranging from fundamental components like radio-frequency neurons/synapses and spintronic probabilistic-bits to broader frameworks such as reservoir computing and magnetic Ising machines. We discuss hardware-specific and task-dependent metrics to evaluate the computing performance of spin-based components and associate them with physical properties. Finally, we discuss challenges and future opportunities, highlighting the potential of spin-based computing in next-generation technologies.

cond-mat.mes-hall↗

arXiv2025

Laser-Induced Topological Toggle Switching at Room Temperature in the van der Waals Ferromagnet Fe3GaTe2

We demonstrate room-temperature nucleation and manipulation of topological spin textures in the van der Waals (vdW) ferromagnet, Fe3GaTe2, through laser pulse excitation. By leveraging laser-induced heating and subsequent cooling, we access the skyrmion/bubble state at low fields and achieve toggle switching between two topological spin textures - skyrmion/bubble and labyrinth. Micromagnetic simulations reveal that this switching behaviour arises from laser-induced heating and cooling. Our findings highlight the potential of vdW ferromagnets for room temperature laser-controlled non-volatile memory storage applications.

cond-mat.mtrl-sci↗

arXiv2025

Spin wave resonance in yttrium iron garnet stripe domains

We study a thin film yttrium iron garnet sample that exhibits magnetic stripe domains due to a small perpendicular magnetic anisotropy. Using wide-field magneto-optic Kerr effect measurements we reveal the domain pattern evolution as a function of applied field and discuss the role of the cubic anisotropy in the domain formation. Rich magnon spectra are observed in the stripe domain states, with a range of excitation conditions providing distinct spectra. The measurements are interpreted using micromagnetic simulations to provide the spatial profiles of each resonance mode. We further simulate domain patterns and resonance spectra accounting for the cubic anisotropy,with good correlation to experiment. This study highlights how non-collinear magnetic domain structures can host complex resonant behaviour in a low-damping magnetic material, with potential use in future magnonic applications.

cond-mat.mtrl-sci↗

arXiv2024

PRCpy: A Python Package for Processing of Physical Reservoir Computing

Physical reservoir computing (PRC) is a computing framework that harnesses the intrinsic dynamics of physical systems for computation. It offers a promising energy-efficient alternative to traditional von Neumann computing for certain tasks, particularly those demanding both memory and nonlinearity. As PRC is implemented across a broad variety of physical systems, the need increases for standardised tools for data processing and model training. In this manuscript, we introduce PRCpy, an open-source Python library designed to simplify the implementation and assessment of PRC for researchers. The package provides a high-level interface for data handling, preprocessing, model training, and evaluation. Key concepts are described and accompanied by experimental data on two benchmark problems: nonlinear transformation and future forecasting of chaotic signals. Throughout this manuscript, which will be updated as a rolling release, we aim to facilitate researchers from diverse disciplines to prioritise evaluating the computational benefits of the physical properties of their systems by simplifying data processing, model training and evaluation.

cs.CE↗

arXiv2024

Neuromorphic Overparameterisation and Few-Shot Learning in Multilayer Physical Neural Networks

Physical neuromorphic computing, exploiting the complex dynamics of physical systems, has seen rapid advancements in sophistication and performance. Physical reservoir computing, a subset of neuromorphic computing, faces limitations due to its reliance on single systems. This constrains output dimensionality and dynamic range, limiting performance to a narrow range of tasks. Here, we engineer a suite of nanomagnetic array physical reservoirs and interconnect them in parallel and series to create a multilayer neural network architecture. The output of one reservoir is recorded, scaled and virtually fed as input to the next reservoir. This networked approach increases output dimensionality, internal dynamics and computational performance. We demonstrate that a physical neuromorphic system can achieve an overparameterised state, facilitating meta-learning on small training sets and yielding strong performance across a wide range of tasks. Our approach's efficacy is further demonstrated through few-shot learning, where the system rapidly adapts to new tasks.

cond-mat.mes-hall↗

arXiv2024

Ultrastrong magnon-magnon coupling and chiral spin-texture control in a dipolar 3D multilayered artificial spin-vortex ice

Strongly-interacting nanomagnetic arrays are ideal systems for exploring reconfigurable magnonics. They provide huge microstate spaces and integrated solutions for storage and neuromorphic computing alongside GHz functionality. These systems may be broadly assessed by their range of reliably accessible states and the strength of magnon coupling phenomena and nonlinearities. Increasingly, nanomagnetic systems are expanding into three-dimensional architectures. This has enhanced the range of available magnetic microstates and functional behaviours, but engineering control over 3D states and dynamics remains challenging. Here, we introduce a 3D magnonic metamaterial composed from multilayered artificial spin ice nanoarrays. Comprising two magnetic layers separated by a non-magnetic spacer, each nanoisland may assume four macrospin or vortex states per magnetic layer. This creates a system with a rich $16^N$ microstate space and intense static and dynamic dipolar magnetic coupling. The system exhibits a broad range of emergent phenomena driven by the strong inter-layer dipolar interaction, including ultrastrong magnon-magnon coupling with normalised coupling rates of $\frac{Δω}γ = 0.57$, GHz mode shifts in zero applied field and chirality-selective magneto-toroidal microstate programming and corresponding magnonic spectral control.

cond-mat.mes-hall↗

arXiv2023

Spin Dynamics in van der Waals Magnetic Systems

The discovery of atomic monolayer magnetic materials has stimulated intense research activities in the two-dimensional (2D) van der Waals (vdW) materials community. The field is growing rapidly and there has been a large class of 2D vdW magnetic compounds with unique properties, which provides an ideal platform to study magnetism in the atomically thin limit. In parallel, based on tunneling magnetoresistance and magneto-optical effect in 2D vdW magnets and their heterostructures, emerging concepts of spintronic and optoelectronic applications such as spin tunnel field-effect transistors and spin-filtering devices are explored. While the magnetic ground state has been extensively investigated, reliable characterization and control of spin dynamics play a crucial role in designing ultrafast spintronic devices. Ferromagnetic resonance (FMR) allows direct measurements of magnetic excitations, which provides insight into the key parameters of magnetic properties such as exchange interaction, magnetic anisotropy, gyromagnetic ratio, spin-orbit coupling, damping rate, and domain structure. In this review article, we present an overview of the essential progress in probing spin dynamics of 2D vdW magnets using FMR techniques. Given the dynamic nature of this field, we focus mainly on broadband FMR, optical FMR, and spin-torque FMR, and their applications in studying prototypical 2D vdW magnets. We conclude with the recent advances in laboratory- and synchrotron-based FMR techniques and their opportunities to broaden the horizon of research pathways into atomically thin magnets.

cond-mat.mtrl-sci↗

arXiv2023

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).

cond-mat.mtrl-sci↗

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↗

arXiv2023

Probing spin dynamics of ultra-thin van der Waals magnets via photon-magnon coupling

Layered van der Waals (vdW) magnets can maintain a magnetic order even down to the single-layer regime and hold promise for integrated spintronic devices. While the magnetic ground state of vdW magnets was extensively studied, key parameters of spin dynamics, like the Gilbert damping, crucial for designing ultra-fast spintronic devices, remains largely unexplored. Despite recent studies by optical excitation and detection, achieving spin wave control with microwaves is highly desirable, as modern integrated information technologies predominantly are operated with these. The intrinsically small numbers of spins, however, poses a major challenge to this. Here, we present a hybrid approach to detect spin dynamics mediated by photon-magnon coupling between high-Q superconducting resonators and ultra-thin flakes of Cr$_2$Ge$_2$Te$_6$ (CGT) as thin as 11\,nm. We test and benchmark our technique with 23 individual CGT flakes and extract an upper limit for the Gilbert damping parameter. These results are crucial in designing on-chip integrated circuits using vdW magnets and offer prospects for probing spin dynamics of monolayer vdW magnets.

cond-mat.mtrl-sci↗

arXiv2023

Laser-induced topological spin switching in a 2D van der Waals magnet

Two-dimensional (2D) van der Waals (vdW) magnets represent one of the most promising horizons for energy-efficient spintronic applications because their broad range of electronic, magnetic and topological properties. Of particular interest is the control of the magnetic properties of 2D materials by femtosecond laser pulses which can provide a real path for low-power consumption device platforms in data storage industries. However, little is known about the interplay between light and spin properties in vdW layers. Here, combining large-scale spin dynamics simulations including biquadratic exchange interactions and wide-field Kerr microscopy (WFKM), we show that ultrafast laser excitation can not only generate different type of spin textures in CrGeTe$_3$ vdW magnets but also induce a reversible transformation between them in a toggle-switch mechanism. Our calculations show that skyrmions, anti-skyrmions, skyrmioniums and stripe domains can be generated via high-intense laser pulses within the picosecond regime. The effect is tunable with the laser energy where different spin behaviours can be selected, such as fast demagnetisation process ($\sim$250 fs) important for information technologies. The phase transformation between the different topological spin textures is obtained as additional laser pulses are applied to the system where the polarisation and final state of the spins can be controlled by external magnetic fields. We experimentally confirmed the creation, manipulation and toggle switching phenomena in CrGeTe$_3$ due to the unique aspect of laser-induced heating of electrons. Our results indicate laser-driven spin textures on 2D magnets as a pathway towards ultrafast reconfigurable architecture at the atomistic level.

cond-mat.mes-hall↗

arXiv2022

Nonlinear magnon polaritons

We experimentally and theoretically demonstrate that nonlinear spin-wave interactions suppress the hybrid magnon-photon quasiparticle or "magnon polariton" in microwave spectra of an yttrium iron garnet film detected by an on-chip split-ring resonator. We observe a strong coupling between the Kittel and microwave cavity modes in terms of an avoided crossing as a function of magnetic fields at low microwave input powers, but a complete closing of the gap at high powers. The experimental results are well explained by a theoretical model including the three-magnon decay of the Kittel magnon into spin waves. The gap closure originates from the saturation of the ferromagnetic resonance above the Suhl instability threshold by a coherent back reaction from the spin waves.

cond-mat.mes-hall↗

arXiv2022

Memristive, Spintronic, and 2D-Materials-Based Devices to Improve and Complement Computing Hardware

In a data-driven economy, virtually all industries benefit from advances in information technology -- powerful computing systems are critically important for rapid technological progress. However, this progress might be at risk of slowing down if we do not address the discrepancy between our current computing power demands and what the existing technologies can offer. Key limitations to improving energy efficiency are the excessive growth of data transfer costs associated with the von Neumann architecture and the fundamental limits of complementary metal-oxide-semiconductor (CMOS) technologies, such as transistors. In this perspective article, we discuss three technologies that will likely play an essential role in future computing systems: memristive electronics, spintronics, and electronics based on 2D materials. We present how these may transform conventional digital computers and contribute to the adoption of new paradigms, like neuromorphic computing.

cs.ET↗

arXiv2022

Controlling spin pumping into superconducting Nb by proximity-induced spin-triplet Cooper pairs

Proximity-induced long-range spin-triplet supercurrents, important for the field of superconducting spintronics, are generated in superconducting/ferromagnetic heterostructures when interfacial magnetic inhomogeneities responsible for spin mixing and spin flip scattering are present. The multilayer stack Nb/Cr/Fe/Cr/Nb has been shown to support such exotic currents when fabricated into Josephson junction devices. However, creating pure spin currents controllably in superconductors outside of the Josephson junction architecture is a bottleneck to progress. Recently, ferromagnetic resonance was proposed as a possible direction, the signature of pure supercurrent creation being an enhancement of the Gilbert damping below the superconducting critical temperature, but the necessary conditions are still poorly established. Consistent with theoretical prediction, we demonstrate conclusively that pumping pure spin currents into a superconductor is only possible when conditions supporting proximity-induced spin-triplet effects are satisfied. Our study is an important step forward for superconducting pure spin current creation and manipulation, considerably advancing the field of superconducting spintronics.

cond-mat.supr-con↗

arXiv2022

Reconfigurable Training and Reservoir Computing in an Artificial Spin-Vortex Ice via Spin-Wave Fingerprinting

Strongly-interacting artificial spin systems are moving beyond mimicking naturally-occurring materials to emerge as versatile functional platforms, from reconfigurable magnonics to neuromorphic computing. Typically artificial spin systems comprise nanomagnets with a single magnetisation texture: collinear macrospins or chiral vortices. By tuning nanoarray dimensions we achieve macrospin/vortex bistability and demonstrate a four-state metamaterial spin-system 'Artificial Spin-Vortex Ice' (ASVI). ASVI can host Ising-like macrospins with strong ice-like vertex interactions, and weakly-coupled vortices with low stray dipolar-field. Vortices and macrospins exhibit starkly-differing spin-wave spectra with analogue-style mode-amplitude control and mode-frequency shifts of df = 3.8 GHz. The enhanced bi-textural microstate space gives rise to emergent physical memory phenomena, with ratchet-like vortex training and history-dependent nonlinear fading memory when driven through global field cycles. We employ spin-wave microstate fingerprinting for rapid, scaleable readout of vortex and macrospin populations and leverage this for spin-wave reservoir computation. ASVI performs linear and non-linear mapping transformations of diverse input signals as well as chaotic time-series forecasting. Energy costs of machine learning are spiralling unsustainably, developing low-energy neuromorphic computation hardware such as ASVI is crucial to achieving a zero-carbon computational future.

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