Beyond the quasi-particle: Stochastic domain wall dynamics in soft ferromagnetic nanowires

We study the physical origins of stochastic domain wall pinning in soft ferromagnetic nanowires using focused magneto-optic Kerr effect measurements and dynamic micromagnetic simulations. Our results illustrate the ubiquitous nature of these effects in Ni80Fe20 nanowires, and show that they are not only a result of the magnetisation history of the system (i.e. the magnetisation structure of the injected domain walls), and the onset of non-linear propagation dynamics above the Walker breakdown field, but also a complex interplay between the two. We show that this means that, while micromagnetics can be used to make qualitative predictions of the behaviour of domain walls at defect sites, making quantitative predictions is much more challenging. Together, our results reinforce the view that even in these simple pseudo-one dimensional nanomagnets, domain walls must be considered as complex, dynamically evolving objects rather than simple quasi-particles

Influence of geometry on the giant magnetoimpedance of high-aspect ratio amorphous magnetic ribbons

We study the influence of ribbon geometry on the giant magnetoimpedance (GMI) behavior of both low- and high-aspect ratio [length (l)/width (w) = 2–150] ribbons made from commercially available amorphous magnetic materials. Our results indicate that the variation of the ribbons’ GMI with geometry is due to the combination of edge effects (due to damage created by the ribbon cutting process) and global shape anisotropy. In high-aspect ratio ribbons [length (l)/width (w)≥ 20], we find that the GMI decreases with width, which we suggest is due to the cutting process creating induced stresses that suppress the transverse susceptibility at the edge of the material. In lower aspect ratio ribbons [length (l)/width (w) ≤ 20], shape anisotropy results in a relatively rapid increase in GMI with increasing length. We conclude that, with suitable optimization, high-aspect ratio ribbons prepared from commercially available materials are suitable for use as macro-scale sensors that detect small magnetic fields/strains over a large sensing area

Intrinsic Nature of Stochastic Domain Wall Pinning Phenomena in Magnetic Nanowire Devices

Finite temperature micromagnetic simulations are used to probe stochastic domain wall pinning behaviours in magnetic nanowire devices. By exploring field-induced propagation both below and above the Walker breakdown field it is shown that all experimentally observed phenomena can be comprehensively explained by the influence of thermal perturbations on the domain walls’ magnetisation dynamics. Nanowires with finite edge roughness are also investigated, and these demonstrate how this additional form of disorder couples with thermal perturbations to significantly enhance stochasticity. Cumulatively, these results indicate that stochastic pinning is an intrinsic feature of DW behaviour at finite temperatures, and would not be suppressed even in hypothetical systems where initial DW states and experimental parameters were perfectly defined

Direct imaging of domain-wall interactions in Ni80Fe20 planar nanowires

We have investigated magnetostatic interactions between domain walls in Ni80Fe20 planar nanowires using magnetic soft x-ray microscopy and micromagnetic simulations. In addition to significant monopole-like attraction and repulsion effects we observe that there is coupling of the magnetization configurations of the walls. This is explained in terms of an interaction energy that depends not only on the distance between the walls, but also upon their internal magnetization structure

Magnetic domain walls : types, processes and applications

Domain walls (DWs) in magnetic nanowires are promising candidates for a variety of applications including Boolean/unconventional logic, memories, in-memory computing as well as magnetic sensors and biomagnetic implementations. They show rich physical behaviour and are controllable using a number of methods including magnetic fields, charge and spin currents and spin-orbit torques. In this review, we detail types of DWs in ferromagnetic nanowires and describe processes of manipulating their state. We look at the state of the art of DW applications and give our take on the their current status, technological feasibility and challenges

Dynamics of high-velocity domain wall motion and spin wave excitation in trilayer structures

Propagation of dipolar-coupled transverse domain walls in a permalloy/non-magnetic/permalloy trilayer was investigated using micromagnetic modeling. Circulating stray fields meant that the walls adopted a composite structure with behavior analogous to walls seen in nanotubes. Wall velocities were sensitive to the chirality of the stray field circulation, with velocities of the most favored chirality enhanced by 32% compared with velocities seen in the individual constituent layers just below their Walker breakdown field. Additionally, Walker breakdown was completely suppressed within the trilayer for both chiralities, despite occurring in the constituent layers when modelled in isolation, leading to a maximum of 317% velocity enhancement. Wall velocity saturated around 1100 m/s due to the Cherenkov-like emission of spin waves, comparable to the magnonic regime of nanotubes. By reproducing the advantageous domain wall dynamics of nanotubes within a planar system, we demonstrate that ultrafast magnetic switching may feasibly be realized within a lithographically produced system

Selective excitation of localized spin-wave modes by optically pumped surface acoustic waves

We explore the feasibility of exciting localized spin-wave modes in ferromagnetic nanostructures using surface acoustic waves. The time-resolved Faraday effect is used to probe the magnetization dynamics of an array of nickel nanowires. The optical-pump pulse excites both spin-wave modes of the nanowires and acoustic modes of the substrate and we observe that, when the frequencies of these modes coincide, the amplitude of magnetization dynamics is substantially enhanced due to magnetoelastic coupling between the two. Notably, by tuning the magnitude of an externally applied magnetic field, optically excited surface acoustic waves can selectively excite either the upper or lower branches of a splitting in the nanowire’s spin-wave spectrum, which micromagnetic simulations indicate is caused by localization of spin waves in different parts of the nanowire. Thus, our results indicate the feasibility of using acoustic waves to selectively excite spatially confined spin waves, an approach that may find utility in future magnonic devices where coherent structural deformations could be used as coherent sources of propagating spin waves

Machine learning using magnetic stochastic synapses

The impressive performance of artificial neural networks has come at the cost of high energy usage and CO2 emissions. Unconventional computing architectures, with magnetic systems as a candidate, have potential as alternative energy-efficient hardware, but, still face challenges, such as stochastic behaviour, in implementation. Here, we present a methodology for exploiting the traditionally detrimental stochastic effects in magnetic domain-wall motion in nanowires. We demonstrate functional binary stochastic synapses alongside a gradient learning rule that allows their training with applicability to a range of stochastic systems. The rule, utilising the mean and variance of the neuronal output distribution, finds a trade-off between synaptic stochasticity and energy efficiency depending on the number of measurements of each synapse. For single measurements, the rule results in binary synapses with minimal stochasticity, sacrificing potential performance for robustness. For multiple measurements, synaptic distributions are broad, approximating better-performing continuous synapses. This observation allows us to choose design principles depending on the desired performance and the device's operational speed and energy cost. We verify performance on physical hardware, showing it is comparable to a standard neural network

Neuromorphic computation with a single magnetic domain wall

Machine learning techniques are commonly used to model complex relationships but implementations on digital hardware are relatively inefficient due to poor matching between conventional computer architectures and the structures of the algorithms they are required to simulate. Neuromorphic devices, and in particular reservoir computing architectures, utilize the inherent properties of physical systems to implement machine learning algorithms and so have the potential to be much more efficient. In this work, we demonstrate that the dynamics of individual domain walls in magnetic nanowires are suitable for implementing the reservoir computing paradigm in hardware. We modelled the dynamics of a domain wall placed between two anti-notches in a nickel nanowire using both a 1D collective coordinates model and micromagnetic simulations. When driven by an oscillating magnetic field, the domain exhibits non-linear dynamics within the potential well created by the anti-notches that are analogous to those of the Duffing oscillator. We exploit the domain wall dynamics for reservoir computing by modulating the amplitude of the applied magnetic field to inject time-multiplexed input signals into the reservoir, and show how this allows us to perform machine learning tasks including: the classification of (1) sine and square waves; (2) spoken digits; and (3) non-temporal 2D toy data and hand written digits. Our work lays the foundation for the creation of nanoscale neuromorphic devices in which individual magnetic domain walls are used to perform complex data analysis tasks