From Vertices to Vortices in magnetic nanoislands

Recent studies in magnetic nanolithography show that a variety of complex magnetic states emerge as a function of a single magnetic island's aspect ratio. We propose a model which, in addition to fitting experiments, predicts magnetic states with continuous symmetry at particular aspect ratios and reveals a duality between vortex and vertex states. Our model then opens new means of engineering novel types of artificial spin systems, and their application to complex magnetic textures in devices and computing.Comment: 3 pages + epsilon + 18 supplementary materia

Spectral fingerprinting: microstate readout via remanence ferromagnetic resonance in artificial spin ice

Artificial spin ices (ASIs) are magnetic metamaterials comprising geometrically tiled strongly-interacting nanomagnets. There is significant interest in these systems spanning the fundamental physics of many-body systems to potential applications in neuromorphic computation, logic, and recently reconfigurable magnonics. Magnonics focused studies on ASI have to date have focused on the in-field GHz spin-wave response, convoluting effects from applied field, nanofabrication imperfections (‘quenched disorder’) and microstate-dependent dipolar field landscapes. Here, we investigate zero-field measurements of the spin-wave response and demonstrate its ability to provide a ‘spectral fingerprint’ of the system microstate. Removing applied field allows deconvolution of distinct contributions to reversal dynamics from the spin-wave spectra, directly measuring dipolar field strength and quenched disorder as well as net magnetisation. We demonstrate the efficacy and sensitivity of this approach by measuring ASI in three microstates with identical (zero) magnetisation, indistinguishable via magnetometry. The zero-field spin-wave response provides distinct spectral fingerprints of each state, allowing rapid, scaleable microstate readout. As artificial spin systems progress toward device implementation, zero-field functionality is crucial to minimize the power consumption associated with electromagnets. Several proposed hardware neuromorphic computation schemes hinge on leveraging dynamic measurement of ASI microstates to perform computation for which spectral fingerprinting provides a potential solution

Reconfigurable Training and Reservoir Computing in an Artificial Spin-Vortex Ice via Spin-Wave Fingerprinting

Strongly-interacting artificial spin systems are moving beyond mimicking naturally-occurring materials to emerge as versatile functional platforms, from reconfigurable magnonics to neuromorphic computing. Typically artificial spin systems comprise nanomagnets with a single magnetisation texture: collinear macrospins or chiral vortices. By tuning nanoarray dimensions we achieve macrospin/vortex bistability and demonstrate a four-state metamaterial spin-system 'Artificial Spin-Vortex Ice' (ASVI). ASVI can host Ising-like macrospins with strong ice-like vertex interactions, and weakly-coupled vortices with low stray dipolar-field. Vortices and macrospins exhibit starkly-differing spin-wave spectra with analogue-style mode-amplitude control and mode-frequency shifts of df = 3.8 GHz. The enhanced bi-textural microstate space gives rise to emergent physical memory phenomena, with ratchet-like vortex training and history-dependent nonlinear fading memory when driven through global field cycles. We employ spin-wave microstate fingerprinting for rapid, scaleable readout of vortex and macrospin populations and leverage this for spin-wave reservoir computation. ASVI performs linear and non-linear mapping transformations of diverse input signals as well as chaotic time-series forecasting. Energy costs of machine learning are spiralling unsustainably, developing low-energy neuromorphic computation hardware such as ASVI is crucial to achieving a zero-carbon computational future

Optimising network interactions through device agnostic models

Physically implemented neural networks hold the potential to achieve the performance of deep learning models by exploiting the innate physical properties of devices as computational tools. This exploration of physical processes for computation requires to also consider their intrinsic dynamics, which can serve as valuable resources to process information. However, existing computational methods are unable to extend the success of deep learning techniques to parameters influencing device dynamics, which often lack a precise mathematical description. In this work, we formulate a universal framework to optimise interactions with dynamic physical systems in a fully data-driven fashion. The framework adopts neural stochastic differential equations as differentiable digital twins, effectively capturing both deterministic and stochastic behaviours of devices. Employing differentiation through the trained models provides the essential mathematical estimates for optimizing a physical neural network, harnessing the intrinsic temporal computation abilities of its physical nodes. To accurately model real devices' behaviours, we formulated neural-SDE variants that can operate under a variety of experimental settings. Our work demonstrates the framework's applicability through simulations and physical implementations of interacting dynamic devices, while highlighting the importance of accurately capturing system stochasticity for the successful deployment of a physically defined neural network

Task-adaptive physical reservoir computing

Reservoir computing is a neuromorphic architecture that may offer viable solutions to the growing energy costs of machine learning. In software-based machine learning, computing performance can be readily reconfigured to suit different computational tasks by tuning hyperparameters. This critical functionality is missing in 'physical' reservoir computing schemes that exploit nonlinear and history-dependent responses of physical systems for data processing. Here we overcome this issue with a 'task-adaptive' approach to physical reservoir computing. By leveraging a thermodynamical phase space to reconfigure key reservoir properties, we optimize computational performance across a diverse task set. We use the spin-wave spectra of the chiral magnet Cu2OSeO3 that hosts skyrmion, conical and helical magnetic phases, providing on-demand access to different computational reservoir responses. The task-adaptive approach is applicable to a wide variety of physical systems, which we show in other chiral magnets via above (and near) room-temperature demonstrations in Co8.5Zn8.5Mn3 (and FeGe)

Task-adaptive physical reservoir computing

Reservoir computing is a neuromorphic architecture that may offer viable solutions to the growing energy costs of machine learning. In software-based machine learning, computing performance can be readily reconfigured to suit different computational tasks by tuning hyperparameters. This critical functionality is missing in ‘physical’ reservoir computing schemes that exploit nonlinear and history-dependent responses of physical systems for data processing. Here we overcome this issue with a ‘task-adaptive’ approach to physical reservoir computing. By leveraging a thermodynamical phase space to reconfigure key reservoir properties, we optimize computational performance across a diverse task set. We use the spin-wave spectra of the chiral magnet Cu2OSeO3 that hosts skyrmion, conical and helical magnetic phases, providing on-demand access to different computational reservoir responses. The task-adaptive approach is applicable to a wide variety of physical systems, which we show in other chiral magnets via above (and near) room-temperature demonstrations in Co8.5Zn8.5Mn3 (and FeGe)

Neuromorphic Few-Shot Learning: Generalization in Multilayer Physical Neural Networks

Neuromorphic computing leverages the complex dynamics of physical systems for computation. The field has recently undergone an explosion in the range and sophistication of implementations, with rapidly improving performance. Neuromorphic schemes typically employ a single physical system, limiting the dimensionality and range of available dynamics - restricting strong performance to a few specific tasks. This is a critical roadblock facing the field, inhibiting the power and versatility of neuromorphic schemes. Here, we present a solution. We engineer a diverse suite of nanomagnetic arrays and show how tuning microstate space and geometry enables a broad range of dynamics and computing performance. We interconnect arrays in parallel, series and multilayered neural network architectures, where each network node is a distinct physical system. This networked approach grants extremely high dimensionality and enriched dynamics enabling meta-learning to be implemented on small training sets and exhibiting strong performance across a broad taskset. We showcase network performance via few-shot learning, rapidly adapting on-the-fly to previously unseen tasks

Task-adaptive physical reservoir computing

Reservoir computing is a neuromorphic architecture that potentially offers viable solutions to the growing energy costs of machine learning. In software-based machine learning, neural network properties and performance can be readily reconfigured to suit different computational tasks by changing hyperparameters. This critical functionality is missing in ``physical" reservoir computing schemes that exploit nonlinear and history-dependent memory responses of physical systems for data processing. Here, we experimentally present a `task-adaptive' approach to physical reservoir computing, capable of reconfiguring key reservoir properties (nonlinearity, memory-capacity and complexity) to optimise computational performance across a broad range of tasks. As a model case of this, we use the temperature and magnetic-field controlled spin-wave response of Cu$_2$OSeO$_3$ that hosts skyrmion, conical and helical magnetic phases, providing on-demand access to a host of different physical reservoir responses. We quantify phase-tunable reservoir performance, characterise their properties and discuss the correlation between these in physical reservoirs. This task-adaptive approach overcomes key prior limitations of physical reservoirs, opening opportunities to apply thermodynamically stable and metastable phase control across a wide variety of physical reservoir systems, as we show its transferable nature using above(near)-room-temperature demonstration with Co$_{8.5}$Zn$_{8.5}$Mn$_{3}$ (FeGe).Comment: Main manuscript: 14 pages, 5 figures. Supplementary materials: 13 pages, 10 figure

Ultrastrong Magnon-Magnon Coupling and Chiral Symmetry Breaking in a 3D Magnonic Metamaterial

Strongly-interacting nanomagnetic arrays are ideal systems for exploring the frontiers of magnonic control. They provide functional reconfigurable platforms and attractive technological solutions across storage, GHz communications and neuromorphic computing. Typically, these systems are primarily constrained by their range of accessible states and the strength of magnon coupling phenomena. Increasingly, magnetic nanostructures have explored the benefits of expanding into three dimensions. This has broadened the horizons of magnetic microstate spaces and functional behaviours, but precise control of 3D states and dynamics remains challenging. Here, we introduce a 3D magnonic metamaterial, compatible with widely-available fabrication and characterisation techniques. By combining independently-programmable artificial spin-systems strongly coupled in the z-plane, we construct a reconfigurable 3D metamaterial with an exceptionally high 16N microstate space and intense static and dynamic magnetic coupling. The system exhibits a broad range of emergent phenomena including ultrastrong magnon-magnon coupling with normalised coupling rates of $\frac{\Delta \omega}{\gamma} = 0.57$ and magnon-magnon cooperativity up to C = 126.4, GHz mode shifts in zero applied field and chirality-selective magneto-toroidal microstate programming and corresponding magnonic spectral control