arXiv subjects

Bartel Van Waeyenberge

Publications and source records attributed to Bartel Van Waeyenberge.

At least 19 recordsLinked to original sources

arXiv2026

Simulating altermagnets using mumax+

In this paper, we demonstrate how altermagnets can be simulated in the recently released micromagnetic simulation package mumax+. We have added a new magnet class for d-wave altermagnets and demonstrate how mumax+ is able to reproduce the analytical solutions for line profiles of the Néel vector and net magnetization for a Bloch domain wall. Next, we show simulation results of the magnon dispersion relation and its dependence on the anisotropic nature of the exchange interaction. Finally, we study the motion of a Néel skyrmion by applying a spin transfer torque. This new feature was implemented by extending the pre-existing code base for antiferromagnetic simulations. The object-oriented design of mumax+ allows for a correct calculation of the magnetostatic field in multi-sublattice systems, a feature that many other micromagnetic simulators lack.

cond-mat.mes-hall↗

arXiv2026

Strain-Driven Domain Walls in Antiferromagnets

We derive an equation describing domain wall motion in antiferromagnets under the influence of normal strain. From this equation, we find that the domain wall moves towards positions where $\varepsilon_{xx}$ is high and $\varepsilon_{zz}$ is low. Furthermore, each strain component leads to a different terminal velocity for the same strain profile. This difference arises because both strains affect the domain wall width in opposite ways: $\varepsilon_{xx}$ reduces the width, whereas $\varepsilon_{zz}$ increases it. The model is then compared with mumax$^+$ simulations for various strain profiles, including a strain gradient, an oscillating strain, and a Rayleigh wave. The comparison shows good agreement between the analytical and numerical results. Finally, we demonstrate the potential of standing surface acoustic waves as an error correction method in racetrack memory.

cond-mat.mtrl-sci↗

arXiv2025

mumax+: extensible GPU-accelerated micromagnetics and beyond

We present mumax+, an extensible GPU-accelerated micromagnetic simulator with a Python user interface, to address the challenges posed by current magnetism research into systems with complex magnetic ordering and interfaces. It is a general solver for the space- and time-dependent evolution of the magnetization and related vector quantities, using finite difference discretization. Here, we present its application and design and discuss features not available in \mumaxthree{}, such as the modeling of antiferromagnets with magnetoelastic coupling. As an illustration of its capabilities, we use \mumaxp{} to simulate state of the art magnetic systems. Specifically, we demonstrate the current induced domain wall motion in a polycrystalline antiferromagnet, we simulate the working principle of a strain-driven antiferromagnetic racetrack memory and we reproduce experimentally observed domain structures in a non-collinear antiferromagnet.

cond-mat.mes-hall↗

arXiv2025

The design, verification, and applications of Hotspice: a Monte Carlo simulator for artificial spin ice

We present Hotspice, a Monte Carlo simulation software designed to capture the dynamics and equilibrium states of Artificial Spin Ice (ASI) systems with both in-plane (IP) and out-of-plane (OOP) geometries. An Ising-like model is used where each nanomagnet is represented as a macrospin, with switching events driven by thermal fluctuations, magnetostatic interactions, and external fields. To improve simulation accuracy, we explore the impact of several corrections to this model, concerning for example the calculation of the dipole interaction in IP and OOP ASI, as well as the impact of allowing asymmetric rather than symmetric energy barriers between stable states. We validate these enhancements by comparing simulation results with experimental data for pinwheel and kagome ASI lattices, demonstrating how these corrections enable a more accurate simulation of the behavior of these systems. We finish with a demonstration of `clocking' in pinwheel and OOP square ASI as an example of reservoir computing.

physics.comp-ph↗

arXiv2024

Perpendicular-anisotropy artificial spin ice with spontaneous ordering: a platform for neuromorphic computing with flexible timescales

Arrays of coupled nanomagnets have wide-ranging fundamental and practical applications in artificial spin ices, neuromorphic computing and spintronics. However, lacking in these fields are nanomagnets with perpendicular magnetic anisotropy with sufficient magnetostatic interaction. This would not only open up new possibilities for artificial spin ice geometries but also enable novel coupling methods for applications. Here we demonstrate a method to engineer the energy landscape of artificial spin lattices with perpendicular magnetic anisotropy. With this, we are able to realize for the first time magnetostatically-coupled 2D lattices of out-of-plane Ising spins that spontaneously order at room temperature on timescales that can be precisely engineered. We show how this property, together with straightforward electrical interfacing, make this system a promising platform for reservoir computing. Our results open the way to investigate the thermodynamics of out-of-plane magnetostatically coupled nanomagnet arrays with novel spin ice geometries, as well as to exploit such nanomagnet arrays in unconventional computing, taking advantage of the adjustable temporal dynamics and strong coupling between nanomagnets.

cond-mat.mes-hall↗

arXiv2024

Addressing Class Imbalance and Data Limitations in Advanced Node Semiconductor Defect Inspection: A Generative Approach for SEM Images

Precision in identifying nanometer-scale device-killer defects is crucial in both semiconductor research and development as well as in production processes. The effectiveness of existing ML-based approaches in this context is largely limited by the scarcity of data, as the production of real semiconductor wafer data for training these models involves high financial and time costs. Moreover, the existing simulation methods fall short of replicating images with identical noise characteristics, surface roughness and stochastic variations at advanced nodes. We propose a method for generating synthetic semiconductor SEM images using a diffusion model within a limited data regime. In contrast to images generated through conventional simulation methods, SEM images generated through our proposed DL method closely resemble real SEM images, replicating their noise characteristics and surface roughness adaptively. Our main contributions, which are validated on three different real semiconductor datasets, are: i) proposing a patch-based generative framework utilizing DDPM to create SEM images with intended defect classes, addressing challenges related to class-imbalance and data insufficiency, ii) demonstrating generated synthetic images closely resemble real SEM images acquired from the tool, preserving all imaging conditions and metrology characteristics without any metadata supervision, iii) demonstrating a defect detector trained on generated defect dataset, either independently or combined with a limited real dataset, can achieve similar or improved performance on real wafer SEM images during validation/testing compared to exclusive training on a real defect dataset, iv) demonstrating the ability of the proposed approach to transfer defect types, critical dimensions, and imaging conditions from one specified CD/Pitch and metrology specifications to another, thereby highlighting its versatility.

cs.CV↗

arXiv2023

Improved Defect Detection and Classification Method for Advanced IC Nodes by Using Slicing Aided Hyper Inference with Refinement Strategy

In semiconductor manufacturing, lithography has often been the manufacturing step defining the smallest possible pattern dimensions. In recent years, progress has been made towards high-NA (Numerical Aperture) EUVL (Extreme-Ultraviolet-Lithography) paradigm, which promises to advance pattern shrinking (2 nm node and beyond). However, a significant increase in stochastic defects and the complexity of defect detection becomes more pronounced with high-NA. Present defect inspection techniques (both non-machine learning and machine learning based), fail to achieve satisfactory performance at high-NA dimensions. In this work, we investigate the use of the Slicing Aided Hyper Inference (SAHI) framework for improving upon current techniques. Using SAHI, inference is performed on size-increased slices of the SEM images. This leads to the object detector's receptive field being more effective in capturing small defect instances. First, the performance on previously investigated semiconductor datasets is benchmarked across various configurations, and the SAHI approach is demonstrated to substantially enhance the detection of small defects, by approx. 2x. Afterwards, we also demonstrated application of SAHI leads to flawless detection rates on a new test dataset, with scenarios not encountered during training, whereas previous trained models failed. Finally, we formulate an extension of SAHI that does not significantly reduce true-positive predictions while eliminating false-positive predictions.

cs.CV↗

arXiv2023

SEMI-DiffusionInst: A Diffusion Model Based Approach for Semiconductor Defect Classification and Segmentation

With continuous progression of Moore's Law, integrated circuit (IC) device complexity is also increasing. Scanning Electron Microscope (SEM) image based extensive defect inspection and accurate metrology extraction are two main challenges in advanced node (2 nm and beyond) technology. Deep learning (DL) algorithm based computer vision approaches gained popularity in semiconductor defect inspection over last few years. In this research work, a new semiconductor defect inspection framework "SEMI-DiffusionInst" is investigated and compared to previous frameworks. To the best of the authors' knowledge, this work is the first demonstration to accurately detect and precisely segment semiconductor defect patterns by using a diffusion model. Different feature extractor networks as backbones and data sampling strategies are investigated towards achieving a balanced trade-off between precision and computing efficiency. Our proposed approach outperforms previous work on overall mAP and performs comparatively better or as per for almost all defect classes (per class APs). The bounding box and segmentation mAPs achieved by the proposed SEMI-DiffusionInst model are improved by 3.83% and 2.10%, respectively. Among individual defect types, precision on line collapse and thin bridge defects are improved approximately 15\% on detection task for both defect types. It has also been shown that by tuning inference hyperparameters, inference time can be improved significantly without compromising model precision. Finally, certain limitations and future work strategy to overcome them are discussed.

cs.CV↗

arXiv2023

SEMI-CenterNet: A Machine Learning Facilitated Approach for Semiconductor Defect Inspection

Continual shrinking of pattern dimensions in the semiconductor domain is making it increasingly difficult to inspect defects due to factors such as the presence of stochastic noise and the dynamic behavior of defect patterns and types. Conventional rule-based methods and non-parametric supervised machine learning algorithms like KNN mostly fail at the requirements of semiconductor defect inspection at these advanced nodes. Deep Learning (DL)-based methods have gained popularity in the semiconductor defect inspection domain because they have been proven robust towards these challenging scenarios. In this research work, we have presented an automated DL-based approach for efficient localization and classification of defects in SEM images. We have proposed SEMI-CenterNet (SEMI-CN), a customized CN architecture trained on SEM images of semiconductor wafer defects. The use of the proposed CN approach allows improved computational efficiency compared to previously studied DL models. SEMI-CN gets trained to output the center, class, size, and offset of a defect instance. This is different from the approach of most object detection models that use anchors for bounding box prediction. Previous methods predict redundant bounding boxes, most of which are discarded in postprocessing. CN mitigates this by only predicting boxes for likely defect center points. We train SEMI-CN on two datasets and benchmark two ResNet backbones for the framework. Initially, ResNet models pretrained on the COCO dataset undergo training using two datasets separately. Primarily, SEMI-CN shows significant improvement in inference time against previous research works. Finally, transfer learning (using weights of custom SEM dataset) is applied from ADI dataset to AEI dataset and vice-versa, which reduces the required training time for both backbones to reach the best mAP against conventional training method.

cs.CV↗

arXiv2023

Towards Magnonic Logic and Neuromorphic Computing: Controlling Spin-Waves by Spin-Polarized Current

Spin-waves (magnons) are among the prime candidates for building fast yet energy-efficient platforms for information transport and computing. We here demonstrate theoretically and in state-of-the-art micromagnetic simulation the effects that strategically-injected spin-polarized current can have on controlling magnonic transport. We reveal analytically that the Zhang-Li spin-transfer-torque induced by applied current is analogous to the Dzyaloshinskii-Moriya interaction for scattering the magnons in the linear regime, to then provide a generalized Snell's law that describes the spin-wave propagation across regions with different current densities. We validate the latter in numerical simulations of realistic systems, and exemplify how these findings may help advance the design of spin-wave logic and neuromorphic computing devices.

cond-mat.mtrl-sci↗

arXiv2022

A tabletop Optically Pumped Magnetometer setup for the monitoring of magnetic nanoparticle clustering and immobilization using Thermal Noise Magnetometry

Many characterization techniques for magnetic nanoparticles depend on the usage of external fields. This is not the case in Thermal Noise Magnetometry (TNM), where thermal fluctuations in the magnetic signal of magnetic nanoparticle ensembles are measured without any external excitation. This can provide valuable information about the fundamental dynamical properties of the particles, due to the purely observative experiments of this relatively new technique. Until now, TNM signals have been detected only by a superconducting quantum interference device (SQUID) sensor. We present a tabletop setup using Optically Pumped Magnetometers (OPMs) in a small magnetic shield, offering a flexible and accessible alternative and show the agreement between both measurement systems for two different commercially available nanoparticle samples. We argue that the OPM setup with high accessibility complements the SQUID setup with high sensitivity and bandwidth. Furthermore, because of its excellent sensitivity in the lower frequencies, the OPM tabletop setup is well suited to monitor aggregation processes where the magnetization dynamics of the particles tend to slow down, e.g. in biological processes. As a proof of concept, we show for three different immobilization and clustering processes the changes in the noise spectrum measured in the tabletop setup: 1) the aggregation of particles due to the addition of ethanol, 2) the formation of polymer structures in the sample due to UV exposure, and 3) the cellular uptake of the particles by THP-1 cells. From our results we conclude that the tabletop setup offers a flexible and widely adoptable sensor measurement unit to monitor the immobilization and clustering of magnetic nanoparticles over time for different applications.

cond-mat.mes-hall↗

arXiv2021

Commensurate vortex core switching in magnetic nanodisks at Gigahertz frequencies

The development of future spintronic applications requires a thorough and fundamental understanding of the magnetisation dynamics. Of particular interest are magnetic nanodisks, in which the vortex state emerges as a stable spin configuration. Here, we focus on how the vortex core polarisation can be reversed periodically by an oscillating magnetic field, applied perpendicularly to the disk's surface. By means of micromagnetic simulations, we demonstrate the presence of several subharmonic switching modes, i.e., the commensurate ratio between the switching frequency of the core and the driving frequency. The underlying mechanism of this periodic behaviour depends on the disk thickness. For thin disks, the core switches periodically due to resonant excitation of radial spin wave modes, while it is due to the breathing mode in the case of thick disks. However, overlap of both modes impedes periodic vortex core switching. For thin disks, the threshold field amplitude required for periodic switching can be lowered to about 30~mT by increasing the disk diameter. For thick disks, in contrast, the minimal field is largely unaffected by the disk diameter, as only the energy density of a central region around the vortex core is relevant to excite the breathing mode. Our results contribute to the understanding of the switching mechanisms in magnetic nanodisks, which are of technological interest due to their potential in non-volatile memory devices.

cond-mat.mes-hall↗

arXiv2021

Thermal Noise Magnetometry of magnetic nanoparticles: simulations and experiments

Magnetic nanoparticles have proven to be valuable for biomedical applications. A prerequisite for the efficiency of these applications are precisely characterized particles. Thermal Noise Magnetrometry (TNM), unlike any other magnetic characterization method, allows to characterize the particles without the application of an external excitation. We present a theoretical framework to model the experiment in order to examine the magnetic noise power properties of particle ensembles in TNM and optimize the geometry of the nanoparticle sample. An optimized sample geometry is calculated to maximize the noise power measured by the detector. The theoretical framework and the newly designed sample holder are validated by measuring a sensitivity profile in the setup. The optimized sample holder increases the noise power with a factor of 3.5 compared to the regular sample holder, while its volume is decreased by more than half. Moreover, the experimental profiles are consistent with the simulated counterparts, confirming the relevance of the theoretical framework. These results contribute to the further establishment of TNM as magnetic nanoparticle characterization method.

cond-mat.mes-hall↗

arXiv2020

Nanomagnetic Self-Organizing Logic Gates

The end of Moore's law for CMOS technology has prompted the search for low-power computing alternatives, resulting in several promising proposals based on magnetic logic[1-8]. One approach aims at tailoring arrays of nanomagnetic islands in which the magnetostatic interactions constrain the equilibrium orientation of the magnetization to embed logical functionalities[9-12]. Despite the realization of several proofs of concepts of such nanomagnetic logic[13-15], it is still unclear what the advantages are compared to the widespread CMOS designs, due to their need for clocking[16, 17] and/or thermal annealing [18,19] for which fast convergence to the ground state is not guaranteed. In fact, it seems increasingly evident that "beyond CMOS" technology will require a fundamental rethinking of our computing paradigm[20]. In this respect, a type of terminal-agnostic logic was suggested[21], where a given gate is able to "self-organize" into its correct logical states, regardless of whether the signal is applied to the traditional input terminals, or the output terminals. Here, we introduce nanomagnetic self-organizing balanced logic gates, that employ stray-field coupled nanomagnetic islands to perform terminal-agnostic logic. We illustrate their capabilities by implementing reversible Boolean circuitry to solve a two-bit factorization problem via numerical modelling. In view of their design and mode of operation, we expect these systems to improve significantly over those suggested in Ref.[21], thus offering an alternative path to explore memcomputing, whose usefulness has already been demonstrated by solving a variety of hard combinatorial optimization problems[22].

cond-mat.mes-hall↗

arXiv2020

Confined magnetoelastic waves in thin waveguides

The characteristics of confined magnetoelastic waves in nanoscale ferromagnetic magnetostrictive waveguides have been investigated by a combination of analytical and numerical calculations. The presence of both magnetostriction and inverse magnetostriction leads to the coupling between confined spin waves and elastic Lamb waves. Numerical simulations of the coupled system have been used to extract the dispersion relations of the magnetoelastic waves as well as their mode profiles.

cond-mat.mes-hall↗

arXiv2018

The effect of boundary-induced chirality on magnetic textures in thin films

In the quest for miniaturizing magnetic devices, the effects of boundaries and surfaces become increasingly important. Here we show how the recently predicted boundary-induced Dzyaloshinskii-Moriya interaction (DMI) affects the magnetization of ferromagnetic films with a $C_{\infty v}$ symmetry and a perpendicular magnetic anisotropy. For an otherwise uniformly magnetized film, we find a surface twist when the magnetization in the bulk is canted by an in-plane external field. This twist at the surfaces caused by the boundary-induced DMI differs from the common canting caused by internal DMI observed at the edges of a chiral magnet. Further, we find that the surface twist due to the boundary-induced DMI strongly affects the width of the domain wall at the surfaces. We also find that the skyrmion radius increases in the depth of the film, with the average size of the skyrmion increasing with boundary-induced DMI. This increase suggests that the boundary-induced DMI contributes to the stability of the skyrmion.

cond-mat.mes-hall↗

arXiv2018

Tunable Snell's law for spin waves in heterochiral magnetic films

Thin ferromagnetic films with an interfacially induced DMI exhibit nontrivial asymmetric dispersion relations that lead to unique and useful magnonic properties. Here we derive an analytical expression for the magnon propagation angle within the micromagnetic framework and show how the dispersion relation can be approximated with a comprehensible geometrical interpretation in the k-space of the propagation of spin waves. We further explore the refraction of spin waves at DMI interfaces in heterochiral magnetic films, after deriving a generalized Snell's law tunable by an in-plane magnetic field, that yields analytical expressions for critical incident angles. The found asymmetric Brewster angles at interfaces of regions with different DMI strengths, adjustable by magnetic field, support the conclusion that heterochiral ferromagnetic structures are an ideal platform for versatile spin-wave guides.

cond-mat.mtrl-sci↗

arXiv2017

Modelling compensated antiferromagnetic interfaces with MuMax3

We show how compensated antiferromagnetic spins can be implemented in the micromagnetic simulation program MuMax3. We demonstrate that we can model spin flop coupling as a uniaxial anisotropy for small canting angles and how we can take into account the exact energy terms for strong coupling between a ferromagnet and compensated antiferromagnet. We also investigate the training effect in biaxial antiferromagnets and reproduce the training effect in a polycrystalline IrMn/CoFe bilayer.

cond-mat.mes-hall↗