arXiv subjects

Weichao Yu

Publications and source records attributed to Weichao Yu.

At least 19 recordsLinked to original sources

arXiv2026

Acoustic Tweezers for Magnetic Skyrmions

Current methods for driving magnetic skyrmions predominantly translate ensembles as a whole, lacking single-particle selectivity. Here, we propose an "acoustic tweezer" that deterministically traps and routes individual skyrmions using spatially extended acoustic beams. We reveal that spatially confined longitudinal waves carry nontrivial phonon spin, inducing a magnetoelastic field whose chirality is locked to the acoustic spin texture. This generates polarity-selective radiation forces, distinct from conservative gradient forces, that attract skyrmions to local phonon spin maxima. Intersecting orthogonal beams create reconfigurable attractive points for adiabatic, deterministic manipulation. Our global-field-local-interaction paradigm establishes a non-destructive, on-chip route for high-precision topological spintronics.

cond-mat.mes-hall↗

arXiv2026

A micromagnetic model with bidirectional magneto-thermal coupling

Most conventional micromagnetic frameworks in spin caloritronics rely on a unidirectional coupling approximation, wherein thermal fluctuations drive magnetization dynamics while the feedback of magnetic dissipation onto the thermal reservoir is neglected. Here, we establish a rigorously self-consistent bidirectional magneto-thermal coupling model by integrating the stochastic Landau-Lifshitz-Gilbert (sLLG) equation with a generalized heat transfer equation. In this closed-loop framework, the local temperature acts as a dynamical variable, and the damping-induced dissipation alongside stochastic work dynamically feeds back into the thermal bath as localized heat sources. Utilizing Ito stochastic calculus, we analytically prove that this coupled system strictly obeys the first law of thermodynamics and spontaneously recovers the correct Boltzmann statistics at equilibrium. Spatially resolved micromagnetic simulations further validate the energy exchange mechanism, capturing the finite-bath temperature reduction induced by spatial variation of magnetic moments and the modified density of states under exchange interactions. This bidirectional framework provides a robust microscopic foundation for investigating complex nonequilibrium magneto-thermal dynamics, such as the unidirectional spin-wave heat conveyer effect, paving the way for advanced spin-caloritronic applications.

cond-mat.mes-hall↗

arXiv2026

Controllable highly oriented skyrmion track array in Fe3GaTe2

Magnetic skyrmions are emerging as promising candidates for next-generation information technologies, while the realization of scalable skyrmion lattices with tailored configurations is essential for advancing fundamental skyrmion physics and developing future applications. Here we achieved the controllable generation and regulation of a large-area, highly oriented skyrmion track array (STA) in ferromagnetic Fe3GaTe2 using a vector magnetic field manipulation technique. The orientation and ordering of STA, along with the types and density of skyrmions, are precisely controlled by modulating parameters during the manipulation. The critical roles of in-plane magnetic fields and Dzyaloshinskii-Moriya interaction in STA generation is further confirmed by micromagnetic simulation. Our findings develop a strategy for engineering large-area and highly-oriented skyrmion configurations, offering a new pathway for the future application of next-generation spintronic and information technologies.

cond-mat.mtrl-sci↗

arXiv2025

Restricted Boltzmann machine as a probabilistic Enigma

We theoretically propose a symmetric encryption scheme based on Restricted Boltzmann Machines that functions as a probabilistic Enigma device, encoding information in the marginal distributions of visible states while utilizing bias permutations as cryptographic keys. Theoretical analysis reveals significant advantages including factorial key space growth through permutation matrices, excellent diffusion properties, and computational complexity rooted in sharp P-complete problems that resist quantum attacks. Compatible with emerging probabilistic computing hardware, the scheme establishes an asymmetric computational barrier where legitimate users decrypt efficiently while adversaries face exponential costs. This framework unlocks probabilistic computers' potential for cryptographic systems, offering an emerging encryption paradigm between classical and quantum regimes for post-quantum security.

cond-mat.stat-mech↗

arXiv2025

Superior probabilistic computing using operationally stable probabilistic-bit constructed by manganite nanowire

Probabilistic computing has emerged as a viable approach to treat optimization problems. To achieve superior computing performance, the key aspect during computation is massive sampling and tuning on the probability states of each probabilistic bit (p-bit), demanding its high stability under extensive operations. Here, we demonstrate a p-bit constructed by manganite nanowire that shows exceptionally high stability. The p-bit contains an electronic domain that fluctuates between metallic (low resistance) and insulating (high resistance) states near its transition temperature. The probability for the two states can be directly controlled by nano-ampere electrical current. Under extensive operations, the standard error of its probability values is less than 1.3%. Simulations show that our operationally stable p-bit plays the key role to achieve correct inference in Bayesian network by strongly suppressing the relative error, displaying the potential for superior computing performance. Our p-bit also serves as high quality random number generator without extra data-processing, beneficial for cryptographic applications.

physics.app-ph↗

arXiv2025

A self-learning magnetic Hopfield neural network with intrinsic gradient descent adaption

Physical neural networks using physical materials and devices to mimic synapses and neurons offer an energy-efficient way to implement artificial neural networks. Yet, training physical neural networks are difficult and heavily relies on external computing resources. An emerging concept to solve this issue is called physical self-learning that uses intrinsic physical parameters as trainable weights. Under external inputs (i.e. training data), training is achieved by the natural evolution of physical parameters that intrinsically adapt modern learning rules via autonomous physical process, eliminating the requirements on external computation resources.Here, we demonstrate a real spintronic system that mimics Hopfield neural networks (HNN) and unsupervised learning is intrinsically performed via the evolution of physical process. Using magnetic texture defined conductance matrix as trainable weights, we illustrate that under external voltage inputs, the conductance matrix naturally evolves and adapts Oja's learning algorithm in a gradient descent manner. The self-learning HNN is scalable and can achieve associative memories on patterns with high similarities. The fast spin dynamics and reconfigurability of magnetic textures offer an advantageous platform towards efficient autonomous training directly in materials.

cond-mat.dis-nn↗

arXiv2024

Probing disorder-induced time-reversal symmetry breaking in Josephson junctions

The relation between superconductivity and time-reversal symmetry (TRS) is one of the most fascinating problems in condensed matter physics. Although most superconductors inherently possess TRS, nonmagnetic disorder can induce states that demonstrate the breaking of this symmetry. Yet, the identification of experimental signatures of superconductivity with broken TRS remains a challenge. Here, we fabricate vertical Josephson junctions using metallic superconductor (Al) and ion bombarded Sr2RuO4 to study disorder-driven TRS breaking effects. We observe persistent magnetoresistive hysteresis behavior dependent on the disorder deposition time that provides evidence of TRS breaking below the superconducting transition temperature. Field and temperature dependent measurements suggest that the observed effects arise from disorder-induced anomalous flux in Sr2RuO4 which can be sensitively detected by superconducting Al. Our experimental results can be accounted within a physical framework of disorder-induced reconstruction of the superconducting order parameter as described within a multiband Ginzburg-Landau approach.

cond-mat.supr-con↗

arXiv2024

Magnetostatic effect on spin dynamics properties in antiferromagnetic Van der Waals material CrSBr

Van der Waals (vdW) antiferromagnets are exceptional platforms for exploring the spin dynamics of antiferromagnetic materials owing to their weak interlayer exchange coupling. In this study, we examined the antiferromagnetic resonance spectra of anisotropic Van der Waals antiferromagnet CrSBr. In addition to the ordinary resonance modes, we observed a dipolar spin wave mode when the microwave field was oriented perpendicular to the in-plane easy axis of CrSBr. Furthermore, our results uncovered a pronounced dependency of various resonant modes on the orientation of the microwave field, which is pivotal for the accurate determination of exchange coupling constants. Numerical simulations have elucidated this orientation dependence of spin dynamics arises from the magnetostatic effect. This discovery underscores the previously underappreciated significance of dipolar interactions in shaping the dynamical properties of two-dimensional AFM materials, thereby enhancing our understanding of the intrinsic dynamic properties of vdW magnets.

cond-mat.mtrl-sci↗

arXiv2024

Frequency modulation on magnons in synthetic dimensions

Magnons are promising candidates for next-generation computing architectures, offering the ability to manipulate their amplitude and phase for information encoding. However, the frequency degree of freedom remains largely unexploited due to the complexity of nonlinear process. In this work, we introduce the concept of synthetic frequency dimension into magnonics, treating the eigenfrequency of inherent modes as an additional degree of freedom. This approach enables the effective description of the temporal evolution of a magnon state using an effective tight-binding model, analogous to a charged particle hopping in a modulated lattice. A magnonic ring resonator is investigated as an example, and several intriguing phenomena are predicted, including Bloch oscillations and a leverage effect during unidirectional frequency shifts, all of which are verified through micromagnetic simulations. Notably, our strategy operates in the linear spin-wave regime, excluding the involvement of multi-magnon scattering and high-power generation. This work expands the toolkit for designing magnonic devices based on frequency modulation and paves the way for a new paradigm called magnonics in synthetic dimensions.

cond-mat.mes-hall↗

arXiv2024

Physical Neural Networks with Self-Learning Capabilities

Physical neural networks are artificial neural networks that mimic synapses and neurons using physical systems or materials. These networks harness the distinctive characteristics of physical systems to carry out computations effectively, potentially surpassing the constraints of conventional digital neural networks. A recent advancement known as ``physical self-learning'' aims to achieve learning through intrinsic physical processes rather than relying on external computations. This article offers a comprehensive review of the progress made in implementing physical self-learning across various physical systems. Prevailing learning strategies are discussed that contribute to the realization of physical self-learning. Despite challenges in understanding fundamental mechanism of learning, this work highlights the progress towards constructing intelligent hardware from the ground up, incorporating embedded self-organizing and self-adaptive dynamics in physical systems.

physics.app-ph↗

arXiv2023

Dynamic Exchange Coupling between Magnets Mediated by Attenuating Elastic Waves

Coupling between spatially separated magnets can be mediated by excitations such as photons and phonons, which can be characterized as coherent coupling and dissipative coupling with real and imaginary coupling rate. We theoretically predict the existence of dynamic exchange coupling in a closed magneto-elastic system mediated by attenuating elastic waves and whose coupling rate is complex in general, leading to alternating repulsive or attractive spectrum depending on thickness of the elastic media. The presence of dynamic exchange coupling and its competition with coherent coupling are numerically verified according to the generalized Hooke's law in magneto-elastic systems. The predicted mechanism provides a new strategy to synchronize precessing magnets as well as other excitations over long distance and pave the way for non-Hermitian engineering of collective modes in hybrid magnonics, phononics and photonics.

cond-mat.mes-hall↗

arXiv2022

Acoustic Frequency Multiplication and Pure Second Harmonic Generation of Phonons by Magnetic Transducers

We predict frequency multiplication of surface acoustic waves in dielectric substrates via the ferromagnetic resonance of adjacent magnetic transducers when driven by microwaves. We find pure second harmonic generation (SHG) without any linear and third harmonic components by a magnetic nanowire. The SHG and linear phonon pumping are switched by varying the saturated magnetization direction of the wire, or resolved directionally when pumped by magnetic nano-disc. We address the high efficiency of SHG with comparable magnitude to that of linear response, as well as unique non-reciprocal phonon transport that is remarkably distinct in different phonon harmonics. Such acoustic frequency comb driven by microwaves should bring unprecedented tunability for the miniaturized phononic and spintronic devices.

cond-mat.mes-hall↗

arXiv2022

Experimental generation of circulating cavity magnon polaritons

We experimentally realize circularly polarised unidirectional cavity magnon polaritons in a torus-shaped microwave cavity loaded by a small magnetic sphere. At special positions the clockwise and counterclockwise modes are circularly polarized, such that only one of them couples to the magnet, which breaks the mode degeneracy. We reveal the chiral nature of the spectral energy and angular momentum flow by observing and modelling non-reciprocities of the microwave scattering matrix.

cond-mat.mtrl-sci↗

arXiv2022

Magnetization dynamics affected by phonon pumping

"Pumping" of phonons by a dynamic magnetization promises to extend the range and functionality of magnonic devices. We explore the impact of phonon pumping on room-temperature ferromagnetic resonance (FMR) spectra of bilayers of thin yttrium iron garnet (YIG) films on thick gadolinium gallium garnet substrates over a wide frequency range. At low frequencies the Kittel mode hybridizes coherently with standing ultrasound waves of a bulk acoustic resonator to form magnon polarons that induce rapid oscillations of the magnetic susceptibility, as reported before. At higher frequencies, the phonon resonances overlap, merging into a conventional FMR line, but with an increased line width. The frequency dependence of the increased line broadening follows the predictions from phonon pumping theory in the thick substrate limit. In addition, we find substantial magnon-phonon coupling of a perpendicular standing spin wave (PSSW) mode. This evidences the importance of the mode overlap between the acoustic and magnetic modes, and provides a route towards engineering the magnetoelastic mode coupling.

cond-mat.mes-hall↗

arXiv2021

Dynamic Magnetoelastic Boundary Conditions and the Pumping of Phonons

We derive boundary conditions at the interfaces of magnetoelastic heterostructures under ferromagnetic resonance for arbitrary magnetization directions and interface shapes. We apply our formalism to magnet$\vert$nonmagnet bilayers and magnetic grains embedded in a nonmagnetic thin film, revealing a nontrivial magnetization angle dependence of acoustic phonon pumping.

cond-mat.mes-hall↗

arXiv2021

A Hopfield neural network in magnetic films with natural learning

Macroscopic spin ensembles possess brain-like features such as non-linearity, plasticity, stochasticity, selfoscillations, and memory effects, and therefore offer opportunities for neuromorphic computing by spintronics devices. Here we propose a physical realization of artificial neural networks based on magnetic textures, which can update their weights intrinsically via built-in physical feedback utilizing the plasticity and large number of degrees of freedom of the magnetic domain patterns and without resource-demanding external computations. We demonstrate the idea by simulating the operation of a 4-node Hopfield neural network for pattern recognition.

cond-mat.dis-nn↗

arXiv2020

Geometric magnonics with chiral magnetic domain walls

Spin wave, the collective excitation of magnetic order, is one of the fundamental angular momentum carriers in magnetic systems. Understanding the spin wave propagation in magnetic textures lies in the heart of developing pure magnetic information processing schemes. Here we show that the spin wave propagation across a chiral domain wall follows simple geometric trajectories, similar to the geometric optics. And the geometric behaviors are qualitatively different in normally magnetized film and tangentially magnetized film. We identify the lateral shift, refraction, and total reflection of spin wave across a ferromagnetic domain wall. Moreover, these geometric scattering phenomena become polarization-dependent in antiferromagnets, indicating the emergence of spin wave birefringence inside antiferromagnetic domain wall.

cond-mat.mtrl-sci↗

arXiv2020

Circulating cavity magnon polaritons

We predict magnon polariton states circulating unidirectionally in a microwave cavity when loaded by a number of magnets on special lines. Realistic finite-element numerical simulations, including dielectric, time-dependent and non-linear effects, confirm the validity of the approximations of a fully analytical input-output model. We find that a phased antenna array can focus all power into a coherent microwave beam with controlled direction and an intensity that scales with the number of magnets.

cond-mat.mes-hall↗