Miguel Ángel Cascales-Sandoval

High-resolution imaging of magnetic nanostructures is essential for understanding fundamental spin phenomena and designing advanced devices. Recent developments in three-dimensional (3D) nanomagnetism have highlighted the growing need for imaging techniques that can capture magnetic structures across curved or tilted surfaces with high sensitivity. Though widely used as a powerful technique for imaging magnetization states, conventional magneto-optical Kerr effect (MOKE) microscopy faces inherent limitations in measuring nonplanar systems due to its shallow depth of field (DOF). Here, we present an extended depth-of-field MOKE imaging approach, combining through-focus scanning with image-stitching-based reconstruction, to obtain sharp and well-resolved magnetic domain images across nonplanar sample geometries. The method is validated on both perpendicularly and in-plane magnetized films tilted on purpose for this study, enabling quantitative analysis of domain morphology and detection sensitivity. This laboratory-accessible technique provides a fast and versatile route for 3D magnetic imaging, complementing large-scale x-ray-based methods and offering a practical tool for investigating nonplanar magnetic samples with tilted or curved 3D geometries.

Double-helix (DH) nanowires provide a platform to study the influence of geometric chirality on spin chirality. Their three-dimensional (3D) helical architecture and tunable inter-strand coupling enable control of spin chirality, including the stabilization of topological 3D magnetic states such as helical domains and domain walls, topological stray fields, and extended helical vortex/skyrmion tubes. So far, the study of these and other 3D nanostructures is usually confined to a limited number of magnetic microscopy experiments in large facilities. Here, we investigate the reversal mechanism of a single DH nanowire using dark-field magneto-optical Kerr effect (DF-MOKE) magnetometry under external 3D magnetic fields. By analyzing the angular dependence of the DF-MOKE signal, we fit the reversal process using established models for domain-wall nucleation and propagation, finding a characteristic behavior similar to that reported for cylindrical nanowires. Micromagnetic simulations indicate that the reversal process goes through nucleation of the helical vortex tube in a curling manner, while ptychographic x-ray magnetic circular dichroism data reveal that this helical vortex tube state forms through a mixed nucleation-propagation process. These observations provide a consistent microscopic picture of reversal mediated by a helical vortex tube extending along the nanowire. Our work provides a comprehensive characterization of magnetization reversal in DH nanowires and demonstrates that DF-MOKE magnetometry is effective for probing reversal mechanisms in single 3D nanostructures. This lab-based approach expands the range of accessible experiments beyond large-scale facilities, enabling extensive exploration of the rich spin states supported by 3D nano-geometries.

Chiral magnetic textures are key for the development of modern spintronic devices. In multilayered thin films, these are typically stabilized via the interfacial intralayer Dzyaloshinskii-Moriya interaction (DMI). Additionally, it has been recently observed that DMI may also promote vector spin chirality along the third dimension, coupling spins in different magnetic layers via nonmagnetic spacer layers, an effect referred to as interlayer DMI (IL-DMI). This interaction holds promise for 3D nanomagnetism, from the creation of 3D spin structures such as hopfions to new forms of magnetic functionality in the vertical direction via remote control of chiral spin states. In this work, we provide direct experimental evidence that the in-plane vector spin chirality of magnetic textures within a ferromagnetic layer can be controlled via the net out-of-plane spin configuration of an adjacent one. Both ferromagnetic layers form part of a synthetic antiferromagnet, previously shown to have IL-DMI. This in-plane vector chirality dependence on the out-of-plane configuration of another layer implies that the overall scalar spin chirality is preserved when computed across layers. We draw these conclusions from a combination of magnetic x-ray scattering and imaging experiments, complemented by various simulations. These findings thus uncover a type of chiral interlayer interactions, which can lead to the formation of intricate spin states across a metallic interface, demonstrating other ways to control magnetic chirality in three dimensions.

In operando techniques enable real-time measurement of intricate physical properties at the micro- and nano-scale under external stimuli, allowing the study of a wide range of materials and functionalities. In nanomagnetism, in operando techniques greatly benefit from precise three-dimensional (3D) magnetic field control, enabling access to complex magnetic states forming in systems where multiple energies are set to compete with each other. However, achieving such precision is challenging and uncommon, as specific applications impose constraints on the type and geometry of magnetic field sources, limiting their capabilities. Here, we introduce an approach that leverages machine learning algorithms to achieve precise 3D magnetic field control using a hexapole electromagnet that is composed of three independent, non-collinear dipole electromagnets. In our experimental setup, magnetic field sensors are placed at a distance from the sample position due to inherent constraints, leading to indirect field measurements that differ from the magnetic field experienced by the sample. We find that the existing relationship between the remote and sample frames of reference is non-linear, thus requiring a more complex calibration method. To address this, we employ a multi-layer perceptron neural network that processes multiple inputs from a dynamic magnetic field sequence, effectively capturing the time-dependent non-linear field response. The network achieves high calibration accuracy and demonstrates exceptional generalization to unseen magnetic field sequences. This study highlights the significant potential of machine learning in achieving high-precision control and calibration, crucial for in operando experiments where direct measurement at the point of interest is not possible.

The interlayer Dzyaloshinskii–Moriya interaction (IL-DMI) chirally couples spins in different ferromagnetic layers of multilayer heterostructures. So far, samples with IL-DMI have been investigated utilizing magnetometry and magnetotransport techniques, where the interaction manifests as a tunable chiral exchange bias field. Here, we investigate the nanoscale configuration of the magnetization vector in a synthetic anti-ferromagnet (SAF) with IL-DMI, after applying demagnetizing field sequences. We add different global magnetic field offsets to the demagnetizing sequence in order to investigate the states that form when the IL-DMI exchange bias field is fully or partially compensated. For magnetic imaging and vector reconstruction of the remanent magnetic states, we utilize x-ray magnetic circular dichroism photoemission electron microscopy, evidencing the formation of 360° domain wall rings of typically 0.5–3.0 μm in diameter. These spin textures are only observed when the exchange bias field due to the IL-DMI is not perfectly compensated by the magnetic field offset. From a combination of micromagnetic simulations, magnetic charge distribution, and topology arguments, we conclude that a non-zero remanent effective field with components both parallel and perpendicular to the anisotropy axis of the SAF is necessary to observe the rings. This work shows how the exchange bias field due to IL-DMI can lead to complex metastable spin states during reversal, important for the development of future spintronic devices.