arXiv subjects

Simon De Wergifosse

Publications and source records attributed to Simon De Wergifosse.

5 recordsLinked to original sources

arXiv2026

Data-Driven Thiele Equation Approach for State-Dependent Coefficients in the Nonlinear Dynamics of Vortex-based Nano-Oscillators

Spin-torque vortex oscillators provide a model system for the nonlinear dynamics of a confined magnetic texture. Their motion is commonly described by the Thiele equation, but its standard constant-coefficient form relies on a rigid-texture approximation and becomes inaccurate at large gyration amplitudes. We introduce a data-driven Thiele equation approach (DD-TEA) that extracts effective, position-dependent Thiele coefficients from a single current-ramp micromagnetic simulation by interpolating the magnetization in vortex-core-position space. The extracted maps reveal a weak increase of the gyrovector magnitude and a pronounced separation of the radial and azimuthal dissipation as the orbit expands, providing quantitative signatures of confinement- and motion-induced vortex deformation. Because the gyrotropic, dissipative, conservative, and spin-transfer contributions retain their usual Thiele structure, the resulting description remains physically interpretable rather than acting as a black-box surrogate. Incorporating these state-dependent coefficients into the equation reproduces the nonlinear stable-orbit dynamics and accurately predicts the response to time-varying currents, whereas a conventional constant-coefficient description predicts vortex expulsion. The framework therefore provides a systematic route for deriving effective collective-coordinate dynamics from full micromagnetic states with high computational efficiency

cond-mat.mes-hall↗

arXiv2023

Quantitative and realistic description of the magnetic potential energy of spin-torque vortex oscillators

Understanding the dynamics of magnetic vortices has emerged as an important challenge regarding the recent development of spin-torque vortex oscillators. Either micromagnetic simulations or the analytical Thiele equation approach are typically used to study such systems theoretically. This work focuses on the precise description of the restoring forces exerted on the vortex when it is displaced from equilibrium. In particular, the stiffness parameters related to a modification of the magnetic potential energy terms are investigated. A method is proposed to extract exchange, magnetostatic and Zeeman stiffness expressions from micromagnetic simulations. These expressions are then compared to state-of-the-art analytical derivations. Furthermore, it is shown that the stiffness parameters depend not only on the vortex core position but also on the injected current density. This phenomenon is not predicted by commonly used analytical ansätze. We show that these findings result from a deformation of the theoretical magnetic texture caused by the current induced Ampère-Oersted field.

cond-mat.mes-hall↗

arXiv2023

Neuromorphic spintronics accelerated by an unconventional data-driven Thiele equation approach

We design a neural network based on a single spin-torque vortex nano-oscillator (STVO) multiplexed in time. The behavior of the STVO is simulated with an improved ultra-fast and quantitative model based on the Thiele equation approach. Different mathematical and numerical adaptations are brought to the model in order to increase the accuracy and the speed of the simulations. We demonstrate the high added value and adaptability of such a neural network through the resolution of three standard machine learning tasks in the framework of reservoir computing. The first one is a task of waveform (sines and squares) classification. We show the ability of the system to effectively classify waveforms with high accuracy and low root-mean-square error thanks to the intrinsic short-term memory of the device. Given the high throughput of the simulations, two innovative parametric studies on the intensity of the input signal and the level of noise in the system are performed to demonstrate the value of our new models. The efficiency of our system is then tested during a speech recognition task on the TI-46 dataset and shows the agreement between the new models and the corresponding experimental measurements. Finally, we use our STVO-based neural network to perform image recognition on the MNIST dataset. State-of-the-art performances are demonstrated, and the interest of using the STVO dynamics as an activation function is highlighted. These results support and facilitate the future development of neuromorphic STVO-based hardware for energy-efficient machine learning.

cs.ET↗

arXiv2022

Memristive and tunneling effects in 3D interconnected silver nanowires

Due to their memristive properties nanowire networks are very promising for neuromorphic computing applications. Indeed, the resistance of such systems can evolve with the input voltage or current as it confers a synaptic behaviour to the device. Here, we propose a network of silver nanowires (Ag-NWs) which are grown in a nanopourous membrane with interconnected nanopores by electrodeposition. This bottom-up approach fabrication method gives a conducting network with a 3D architecture and a high density of Ag-NWs. The resulting 3D interconnected Ag-NW network exhibits a high initial resistance as well as a memristive behavior. It is expected to arise from the creation and the destruction of conducting silver filaments inside the Ag-NW network. Moreover, after several cycles of measurement, the resistance of the network switches from a high resistance regime, in the GOhm range, with a tunnel conduction to a low resistance regime, in the kOhm range.

cond-mat.mes-hall↗

arXiv2022

Data-driven Thiele equation approach for solving the full nonlinear spin-torque vortex oscillator dynamics

The dynamics of vortex based spin-torque nano-oscillators is investigated theoretically. Starting from a fully analytical model based on the Thiele equation approach, fine-tuned data-driven corrections are carried out to the gyrotropic and damping terms. These adjustments, based on micromagnetic simulation results, allow to quantitatively model the response of such oscillators to any dc current within the range of the vortex stability. Both, the transient and the steady-state regimes are accurately predicted under the proposed data-driven Thiele equation approach. Furthermore, the computation time required to solve the dynamics of such system is reduced by about six orders of magnitude compared to the most powerful micromagnetic simulations. This major breakthrough opens the path for unprecedented high-throughput simulations of spin-torque vortex oscillators submitted to long-duration input signals, for example in neuromorphic computing applications.

cond-mat.mes-hall↗