The (inverse) magnetostrictive effect in ferromagnets couples the magnetic properties to the mechanical stress, allowing for an interaction between the magnetic and mechanical degrees of freedom. In this work, we present a time-integration scheme for the self-consistent simulation of coupled magnetoelastic dynamics within the framework of finite-difference micromagnetism. The proposed implementation extends the Landau-Lifshitz-Gilbert equation by a strain-induced effective field and concurrently solves the elastic equation of motion, while correctly incorporating stress and strain discontinuities at material interfaces. We then present a comprehensive set of examples, ranging from static stress configurations over material boundaries to simulations of surface-acoustic-wave attenuation in magnetically structured thin and thick films. These computational experiments both validate the implementation and underscore the significance of properly handling jump and boundary conditions in magnon-phonon interaction studies.

ABSTRACT A three‐dimensional magnetic field sensing concept based on the anomalous Hall effect (AHE) in chiral multilayers driven by spin–orbit torques (SOTs) is presented. In W/CoFeB/MgO stacks that host homochiral stripe and skyrmion states, SOT‐induced domain reorientation and stripe‐to‐bubble transitions are exploited to detect both in‐plane and out‐of‐plane magnetic fields. Finite‐temperature micromagnetic simulations reveal the reversible evolution from stripe domains to skyrmions under applied fields and high current densities. The symmetry of the SOT enables offset‐free in‐plane sensing, while Joule heating‐assisted domain ordering ensures linear out‐of‐plane operation. The device achieves linear ranges of (in‐plane) and (out‐of‐plane), sensitivities up to , and zero‐field offsets below for in‐plane fields. These results introduce SOT‐driven chiral multilayers as a promising platform for vector magnetic field sensing and establish a route toward offset‐free, scalable three‐dimensional Hall sensors based on chiral spin textures.

Inverse design - specifying a desired functionality and letting a computational algorithm find the optimal structure - has emerged as a powerful paradigm for magnonic device engineering. In this article, we survey the rapidly growing field of inverse-design magnonics, organising it along two axes: the design variables (topology, material parameters, and magnetic field landscape) and the algorithmic toolbox (gradient-free, gradient-based, and neural-network-based methods) together with the differentiable micromagnetic solvers that enable them. We then identify open frontiers that we consider most promising for the next phase of the field: sensitivity analysis and robust design to bridge the gap between simulation and experiment; input shaping and transducer optimisation; the incorporation of nonlinear spin-wave effects as an explicit design resource; spatially structured amplification; self-adapting media and machine-learning-based design; and the long-term vision of a universal, reconfigurable magnonic platform. We argue that magnonics and artificial intelligence are converging from two directions - machine-learning tools for designing magnonic devices, and magnonic devices as hardware for neuromorphic computation - and propose the term AI magnonics to describe this emerging paradigm.

Controlling wave-vector-selective coupling between microwave antennas and spin waves is a central challenge in magnonic transducer design. In all-electrical propagating spin-wave spectroscopy, the launched -spectrum is set by Fourier transform of the perpendicular near-field component of the antenna, but realistic sub-micron antennas exhibit skin and proximity effects, impedance mismatch, and electromagnetic leakage from feed tapers that uniform-current models cannot capture. We introduce a coupled finite-element-finite-difference (FE-FD) framework linking impedance-matched nanoantenna geometries to propagating spin-wave dynamics. Finite-element simulations yield the full complex vector RF near field, projected onto the precession-driving perpendicular component, discretised onto a grid, and injected into micromagnetic Landau-Lifshitz-Gilbert simulations. The antenna wave-vector weighting and excitation intensity are extracted by Fourier analysis and compared with experiment. Applying this framework to coplanar-waveguide and stripline nanoantennas on a yttrium-iron-garnet film, we achieve quantitative agreement in dispersion ridges, group velocities, and wave-vector peak positions. The simulations resolve how antenna width, ground-return symmetry, taper geometry, and leakage shape the launched -spectrum, providing design rules for wave-vector-selective spin-wave excitation in classical and quantum magnonic devices. We further quantify leakage from feed tapers, identify dominant loss channels, and compare CPW and stripline performance, providing routes to efficiency optimization.

We introduce jaxFMM, an open-source, adaptive, highly parallel point-charge Fast Multipole Method implementation for the Laplace kernel written in JAX. It is based on a non-uniform refinement strategy with on-the-fly rotation-based transforms tailored around JAX’s just-in-time compiler, which results in extremely concise and simple code. Benchmarks show that the algorithm performs well at moderate accuracies, even for highly non-uniform charge distributions. JaxFMM already massively speeds up stray-field computations in micromagnetics and with JAX features like autodiff, novel applications such as inverse-design problems and machine-learning tasks can be tackled with ease in the future.

Reconstructing complex magnetization textures from nitrogen-vacancy (NV) magnetometry stray-field measurements presents a challenging inverse problem. In this work, we introduce a physics-informed method that addresses this by incorporating the full micromagnetic energy directly into the variational formulation. Built on a PyTorch backend, our forward model integrates an auto-differentiable finite-differences micromagnetic framework with FFT-based stray-field calculations and Fourier-space upward continuation. This enables efficient gradient-based optimization via the adjoint method and allows the sensor-sample distance to be treated as an optimization parameter. By doing so, we eliminate the experimental uncertainty arising from unknown NV implantation depths and surface oxidation layers. Validation on synthetic data demonstrates high-fidelity reconstruction of spin textures and precise sensor height estimation. Furthermore, when applied to NV measurements of the van der Waals ferromagnet Fe_3-xGaTe_2, the method reconstructs the previously unknown NV-sample distance and physically plausible magnetization textures, which accurately reproduce the experimental observations.

We investigate the excitation modes of confined synthetic-antiferromagnetic (SAF) skyrmions using micromagnetic eigenvalue and ringdown simulations. Starting from a single skyrmion in a ferromagnetic layer, where the lowest-frequency modes are a gyrotropic and a breathing mode, we study how antiferromagnetic interlayer coupling modifies the dynamics in SAF bilayers. We consider several geometries: single SAF skyrmions in square and rectangular confinement, unequal layer thicknesses, and strips containing multiple skyrmions. The antiferromagnetic coupling strongly modifies the low-frequency dynamics. The square geometry exhibits two nearly degenerate gyrotropic modes, where in each both layers have the same rotation sense. In rectangular geometries, we instead find nearly linear SAF skyrmion translation emerging from opposite gyration sense in the two layers. These translational modes become the characteristic low-frequency excitations of SAF skyrmion chains. For skyrmion chains, we identify collective translational and breathing modes with standing-wave-like spatial profiles. Beyond ferromagnetic-like breathing modes, the SAF geometry supports breathing oscillations in which the two layers oscillate out of phase. We further demonstrate signal propagation along extended SAF skyrmion chains with propagation velocities comparable to ferromagnetic skyrmion chains. These results provide a systematic description of the collective dynamics of SAF skyrmions arising from the interplay of geometric confinement, intralayer, and interlayer coupling.

We present framework for extracting internal magnetic structures and intrinsic magnetic material parameters from stray field measurements. The approach introduces a tunable bias field into Landau-Lifshitz-Gilbert dynamics and identifies optimal parameters by minimizing the mismatch between simulated and target stray fields or magnetic force microscopy (MFM) frequency shift contrast. Using synthetic data, we demonstrate recovery of global parameters including the uniaxial anisotropy constant [Formula: see text], saturation magnetization [Formula: see text], exchange stiffness [Formula: see text], and Dzyaloshinskii-Moriya interaction constant [Formula: see text]. A sensitivity analysis reveals that [Formula: see text] has the strongest influence on the optimization loss, while [Formula: see text] and [Formula: see text] exhibit relatively shallow minima. We further assess robustness to noise in the input stray field and find that accurate parameter estimation remains feasible at moderate noise levels. We further analyze the inverse reconstruction of magnetization textures and show that, while the strong stray-field side of Néel skyrmions allows for reliable reconstructions, the weak stray-field side poses significant challenges. These difficulties can be mitigated by employing a convolutional neural network (U-Net) trained on synthetic micromagnetic data to learn the mapping from stray-field slices to magnetization textures. The network provides a robust initialization for the subsequent physics-based relaxation, thereby improving convergence and reconstruction accuracy in challenging scenarios such as the weak stray-field side of Néel skyrmions. The framework is implemented using automatic differentiation in PyTorch, enabling gradient-based optimization and suggesting future extensions toward spatially resolved parameter reconstruction. This hybrid learning-and-physics method offers a flexible and robust strategy for material characterization based on micromagnetic forward models and experimental magnetic imaging data.

Magnonic logic gates represent a crucial step toward realizing fully magnonic data processing systems without reliance on conventional electronic or photonic elements. Recently, a universal and reconfigurable inverse-design device has been developed, featuring a 7 by 7 array of independent current loops that create local inhomogeneous magnetic fields to scatter spin waves in an yttrium-iron-garnet film. Although initially used for linear radio frequency components, we now demonstrate key nonlinear logic gates, NOT, OR, NOR, AND, NAND, and a half-adder, sufficient for building a full processor. In this system, binary data (“0” and “1”) are encoded in the spin-wave amplitude. The contrast ratio, representing the difference between logic states, achieved values of 34, 53.9, 11.8, 19.7, 17, and 9.8 decibels for these gates, respectively.

This work provides a complete numeric framework, with which soft magnetic composite materials can be numerically analyzed through a combination of two distinct numerical methods. Key soft magnetic properties connected to energy efficiency, namely the permeability and the energy-loss can be predicted. The latter consists of different contributions. These contributions are treated separately in our work and therefore 2 decoupled numerical models are required to fully encompass these materials. Experimentally established concepts with the aim to improve magnetic properties for soft magnetic materials have been simulated, resulting in trends that agree well with the experimentally measured improvements. Moreover, we analyze the distinct loss contributions-an uncommon practice in experimental physics-providing valuable insights into the working principles of these materials, and therefore understanding which contributions become dominant under which conditions. Furthermore measurements of iron powders were conducted, in order to compare the numerical simulations with experimental data.

The inverse design approach in magnonics exploits the wave nature of magnons and machine learning to develop logical devices with functionalities that exceed the capabilities of analytical methods. While promising for analog, Boolean, and neuromorphic computing, current implementations face memory limitations that hinder the design of complex systems. This study presents a level-set parameterization method for topology optimization, combined with an adjoint-state approach for memory-efficient simulation of magnetization dynamics. The framework is implemented in NeuralMag, a GPU-accelerated micromagnetic solver featuring a nodal finite-difference scheme and automatic differentiation tools. To validate the method, we optimized the shape of a magnetic nanoparticle by applying constraints to the objective function, and designed a 300 nm-wide yttrium iron garnet demultiplexer achieving frequency-selective spin-wave separation. These results highlight the algorithm’s efficiency in exploring local minima across various initial configurations, establishing its utility as a versatile tool for the inverse design of magnonic logic devices.

In this work we demonstrate a spin-orbit torque (SOT) magnetic field sensor, designed as a Ta/CoFeB/MgO structure, with high sensitivity and capable of active offset compensation in all three spatial directions. This is described and verified in both experiment and simulation. The measurements of magnetic fields showed an offset of 36, 50, and 37μT for x-, y-, and z-fields. Furthermore, the sensitivities of these measurements had values of 590, 580, and 490V A^-1 T^-1 in the x-, y-, and z-direction. In addition, the robustness to bias fields is demonstrated via experiments and single spin simulations by applying bias fields in y-direction. Cross sensitivities were further analyzed via single spin simulations performing a parameter sweep of different bias fields in the y- and z-direction up to ±1mT. Finally, the extraction of the SOT parameters η_DL and η_FL is shown via optimization of a single-spin curve to the experimental measurements.

The increasing demand for higher data volume and faster transmission in modern wireless telecommunication systems has elevated requirements for 5G high-band RF hardware. Spin-Wave technology offers a promising solution, but its adoption is hindered by significant insertion loss stemming from the low efficiency of magnonic transducers. This work introduces a micromagnetic simulation method for directly computing the spin-wave resistance, the real part of spin-wave impedance, which is crucial for optimizing magnonic transducers. By integrating into finite-difference micromagnetic simulations, this approach extends analytical models to arbitrary transducer geometries. We demonstrate its effectiveness through parameter studies on transducer design and waveguide properties, identifying key strategies to enhance the overall transducer efficiency. Our studies show that by varying single parameters of the transducer geometry or the YIG thickness, the spin-wave efficiency, the parameter describing the efficiency of the transfer of electromagnetic energy to the spin wave, can reach values up to 0.75. The developed numerical model allows further fine-tuning of the transducers to achieve even higher efficiencies.

Delay lines (DL) are crucial components in communication systems, providing the required time delays for signal timing, synchronization, and processing. DLs providing nanosecond-scale delays are conventionally based on acoustic waves; however, they cannot operate conveniently in a high-frequency range (EU 5G high-band 24.25-27.5 GHz) required by a modern generation of 5G communication technologies to speed up data transfer. The proposed solution is to use DL based on spin-wave (SW) transmission, as SW devices allow for operation at high-frequency ranges and can be scaled down to a few mu m(2). In this study, we investigate SW-based DL at the microscale at the frequency ranges of 4, 9, and 25 GHz. The DL is based on SW transmission between a pair of 250 nm wide microwave coplanar waveguide transducers, each with a footprint of 2.25 x 100 mu m(2), and fabricated with varying mutual distances on a 97 nm thin yttrium iron garnet film. DLs are tested for in-plane SW modes (Damon-Eshbach and backward volume), and depending on the parameters, the extracted delay times are in the range of 6-165 ns. Furthermore, the insertion losses are extracted and compared to other DL concepts. Time-gating analysis of the measured transmission is performed, providing a detailed discussion of individual signal contributions to the measured spectra. Additionally, analytical theory is employed to compare the experimental delay times with analytical calculations and to predict how to adjust the device parameters to obtain variable time delays. (c) 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/). https://doi.org/10.1063/5.0286108

In this work we demonstrate a spin-orbit-torque (SOT) magnetic field sensor, designed as a Ta / Co Fe B / Mg O structure, with high sensitivity and capable of active offset compensation in all three spatial directions. This is described and verified in experiment and simulation. The measurements of magnetic fields showed an offset of 35, 42, and 3 μ T for x -, y -, and z -fields respectively. Furthermore, the sensitivities of these measurements had values of 590, 580, and 490 V A − 1 T − 1 in the x -, y -, and z -direction, respectively. In addition, the robustness to bias fields is demonstrated via experiments and single-spin simulations by applying bias fields in the y -direction. Cross sensitivities were further analyzed via single-spin simulations, performing a parameter sweep of different bias fields in the y - and z -direction up to ± 1 mT . The extraction of the SOT parameters η DL and η FL is shown via an optimization of a single-spin curve to the experimental measurements.

Power limiters are essential devices in modern rf communication systems to protect highly sensitive input channels from large incoming signals. Nowadays-used semiconductor limiters suffer from high electronic noise and switching delays when approaching the GHz range, which is crucial for the modern generation of 5G communication technologies aiming to operate at the EU 5G high band (24.25-27.5 GHz). The proposed solution is to use ferrite-based frequency selective limiters (FSLs), which maintain their efficiency at high GHz frequencies, although they have only been studied at the macroscale so far. In this study, we demonstrate a proof of concept of nanoscale FSLs. The devices are based on spin-wave transmission affected by four-magnon scattering phenomena in a 97-nm-thin yttrium iron garnet (YIG) film. Spin waves were excited and detected using coplanar waveguide (CPW) transducers of the smallest feature size of 250 nm. The FSLs are tested in the frequency range up to 25 GHz, and the key parameters are extracted (power threshold, power limiting level, insertion losses, bandwidth) for different spin-wave modes and transducer lengths. An analytical theory has been formulated to describe the fundamental physical processes, and a numerical model has been developed to quantitatively describe the insertion losses and power characteristics of the FSLs. Additionally, the perspective of the spin-wave devices is discussed, including the possibility of simultaneously integrating three devices into one: a frequency-selective limiter, an rf filter, and a delay line, allowing for more efficient use of space and energy.

We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTorch and JAX, to perform efficient tensor operations on various parallel hardware, including CPUs, GPUs, and TPUs. The library implements a novel nodal finite-difference discretization scheme that provides improved accuracy over traditional finite-difference methods without increasing computational complexity. NeuralMag is particularly well-suited for solving inverse problems, especially those with time-dependent objectives, thanks to its automatic differentiation capabilities. Performance benchmarks show that NeuralMag is competitive with state-of-the-art simulation codes while offering enhanced flexibility through its Python interface and integration with high-level computational backends.

The excitation and detection of propagating spin waves with lithographed nanoantennas underpin both classical magnonic circuits and emerging quantum technologies. Here, we establish a framework for all-electrical propagating spin-wave spectroscopy (AEPSWS) that links realistic electromagnetic drive fields to micromagnetic dynamics. Using finite-element (FE) simulations, we compute the full vector near-field of electrical impedance-matched, tapered coplanar and stripline antennas and import this distribution into finite-difference (FD) micromagnetic solvers. This approach captures the antenna-limited wave-vector spectrum and the component-selective driving fields (perpendicular to the static magnetisation) that simplified uniform-field models cannot. From this coupling, we derive how realistic current return paths and tapering shapes, k-weighting functions, for Damon-Eshbach surface spin waves in yttrium-iron-garnet (YIG) films are, for millimetre-scale matched CPWs and linear tapers down to nanometre-scale antennas. Validation against experimental AEPSWS on a 48 nm YIG film shows quantitative agreement in dispersion ridges, group velocities, and spectral peak positions, establishing that the antenna acts as a tunable k-space filter. These results provide actionable design rules for on-chip magnonic transducers, with immediate relevance for low-power operation regimes and prospective applications in quantum magnonics.

Speeding up computationally expensive problems, such as numerical simulations of large micromagnetic systems, requires efficient use of parallel computing infrastructures. While parallelism across space is commonly exploited in micromagnetics, this strategy performs poorly once a minimum number of degrees of freedom per core is reached. We use magnum.pi, a finite-element micromagnetic simulation software, to investigate the Parallel Full Approximation Scheme in Space and Time (PFASST) as a space- and time-parallel solver for the Landau-Lifshitz-Gilbert equation (LLG). Numerical experiments show that PFASST enables efficient parallel-in-time integration of the LLG, significantly improving the speedup gained from using a given number of cores as well as allowing the code to scale beyond spatial limits.

Femtosecond laser excitation of materials exhibiting magnetic spin textures promises advanced magnetic control via the generation of non-equilibrium spin dynamics. Ferrimagnetic [Fe(0.35 nm)/Gd(0.40 nm)]160 multilayers are used to explore this approach, as they host a rich diversity of magnetic textures from stripe domains at low magnetic fields, a dense bubble/skyrmion lattice at intermediate fields, and a single domain state for high magnetic fields. Using femtosecond magneto-optics, distinct coherent spin wave dynamics are observed in this material in response to a weak laser excitation, enabling an unambiguous identification of the different magnetic spin textures. Moreover, employing strong laser excitation, versatile control of the coherent spin dynamics via non-equilibrium transformation of magnetic spin textures becomes possible by both creating and annihilating bubbles/skyrmions. Micromagnetic simulations and Lorentz transmission electron microscopy with in situ optical excitation corroborate these findings. The coherent magneto-optical response of [Fe(0.35 nm)/Gd(0.40 nm)]160 multilayers to weak femtosecond laser excitation is shown to depend on the underlying magnetic spin texture (stripe domains, bubbles, and skyrmions, single domain state). Strong laser excitation can transform these spin textures and, in this way, the coherent response of the spin system can be controlled. image