arXiv subjects

Flavio Abreu Araujo

Publications and source records attributed to Flavio Abreu Araujo.

At least 19 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 form keeps the gyrovector and damping tensor fixed at their rigid-vortex values and becomes inaccurate at large gyration amplitudes. We introduce a data-driven Thiele equation approach 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↗

arXiv2026

Solution-Based Synthesis of Fe-Co-Ni Prussian Blue Analogue Powders: A Comparative Structural, Spectroscopic and Thermal Study

Prussian Blue (Fe-PBA) and its Co and Ni analogues (Co-PBA and Ni-PBA) were synthesized by an additive-free aqueous co-precipitation route using K$_4$[Fe(CN)$_6$] and FeCl$_3$, CoCl$_2$, or NiCl$_2$, respectively. The three powders were systematically compared by X-ray diffraction, ATR-FTIR and Raman spectroscopy, FEG-SEM/EDS, and TG/SDTA. All compositions exhibit the characteristic cubic cyanide-bridged PBA framework, with apparent lattice parameters of 10.10 +/- 0.02, 10.01 +/- 0.02, and 10.10 +/- 0.03 Angstrom for Fe-PBA, Co-PBA, and Ni-PBA, respectively. The dominant C$\equiv$N stretching band in ATR-FTIR shifts from 2062 to 2071 and 2087 cm$^{-1}$ across the Fe-Co-Ni series, indicating composition-dependent changes in the local cyanide environment. Fe-PBA contains less potassium than the Co- and Ni-containing powders and exhibits a substantially larger low-temperature mass loss. Sharp reflections assigned to crystalline KCl are observed in all three diffraction patterns, while additional unassigned reflections in Ni-PBA indicate the presence of at least one further crystalline phase. These results show that differences among Fe-, Co-, and Ni-based PBAs cannot be attributed solely to transition-metal identity, as precursor oxidation state and washing efficiency also influence the composition and thermal response of the resulting powders.

cond-mat.mtrl-sci↗

arXiv2026

Beyond binary at submicron dimensions: crossed-ellipse MTJ free layers as multi-state cells for spintronic crossbars

Spintronic crossbars are promising hardware platforms for energy-efficient neuromorphic computing, but conventional magnetic tunnel junctions (MTJs) are binary, limiting the information density and synaptic precision of each crosspoint. Here, MuMax3 micromagnetic simulations are used to investigate how size and aspect ratio control the switching and remanent-state landscape of crossed-ellipse MTJ free layers, with permalloy as the main model system and CoFeB checks for transferability. At fixed 8:1 aspect ratio, shrinking the device from 16 micron x 2 micron to 80 nm x 10 nm lowers the absolute switching current but raises the switching field from about 11 Oe to about 301 Oe and increases the SOT current density. At fixed major axis 1.6 micron, aspect-ratio tuning produces a low-field four-state regime, while lower aspect ratios stabilize additional remanent states with lower switching fields and current densities. A 1.6 micron x 0.8 micron device exhibits twelve accessible remanent plateaus in its angle-resolved planar Hall response, with the resolved state count depending on both aspect ratio and absolute size. Projected MTJ readout gives multiple electrical levels, while minimum-energy-path calculations show that the twelve configurations are not all thermally independent. Independent MuMax+ calculations reproduce the multistate topology and reveal lower-barrier multistep escape pathways between nominally distant states. These results define a geometry-dependent design window for scalable multistate spintronic crossbar cells.

cond-mat.mtrl-sci↗

arXiv2026

The Deformed Image Vortex Ansatz: A Perturbation-Aware Description of Magnetic Vortices in In-Plane Fields

Thiele-based descriptions of magnetic vortex dynamics in thin ferromagnetic nanodots rely on magnetization ansätze that describe the equilibrium texture but cannot represent perturbation-induced deformations. We introduce the Deformed Image Vortex Ansatz (DIVA): a perturbation-aware ansatz in which the response to an external perturbation is built into the magnetization profile itself, rather than appended to the dynamics as a correction. Here, we demonstrate the concept for a uniform, stationary in-plane field applied to a Permalloy nanodot, for which the deformation is analytically tractable. A symmetry-based perturbative expansion identifies the leading deformation as a single $m = 1$ harmonic around the disk, while energy minimization and a dominant-balance analysis yield a closed-form interpolant for the radial profile. Benchmarked against micromagnetic simulations on Permalloy disks of aspect ratio $t/R = 0.1$ and $0.0125$, this realization reduces the disk-averaged angular deviation by a factor of 3 to 6 relative to the two-vortex ansatz, depending on geometry and field, and reduces the total-energy deviation by about a factor of six in the thicker disk.

cond-mat.mes-hall↗

arXiv2026

Multibit neural inference in a N-ary crossbar architecture

In-memory computing (IMC) is a paradigm that enables neural network inference by computing analog matrix-vector multiplications (MVM) directly in memory crossbar arrays, with the potential for energy efficiency gains over conventional von Neumann architectures. In this work we present a simulation framework for N-ary crossbar architectures that retrieves MVM results with minimal implementation assumptions. The XOR and MNIST classification tasks were successfully inferred using a simulated crossbar array of (4x4) 4-states magnetic tunnel junctions (MTJ). MNIST accuracy reached 93.56% (vs. 97.56% software baseline). PCA dimensionality reduction was shown to drastically lower the number of required operations and improve the software baseline, for only a modest reduction in crossbar inference accuracy. We identified weight quantization as the primary error source, and studied its impact alongside systematic non-idealities and random noise. We find that cell-specific random noise is less detrimental than systematic errors due to averaging across the array. Finally, we demonstrate an optimal number of states per cell that balances quantization error against resistance state resolution to minimize total MVM error.

cs.AR↗

arXiv2026

Spintronics for image recognition: performance benchmarking via data-driven simulations

We present a demonstration of image classification using an extreme learning machine (ELM) based on a unique simulated magnetic tunnel junction (MTJ) delayed in time. As the ground state of the MTJ is a magnetic vortex, we refer to it as a vortex-based spin-torque oscillator (STVO). The dynamics of the magnetic vortex is simulated with a model called the data-driven Thiele equation approach (DD-TEA). This allows to avoid the constraints associated with repeated experimental manipulation for hyperparameters search and benchmarking. We showcase the versatility of our implementation by using it successfully for classification tasks on the MNIST, EMNIST-letters and Fashion MNIST datasets. Through simulations, we show that within an ELM with a sufficient number of parameters, the performance reached using the STVO dynamics as a source of nonlinearity is equivalent to the ones obtained with classical software activation functions such as the reLU and the sigmoid. While achieving state-of-the-art accuracy levels on the MNIST dataset, our model's performance on EMNIST-letters and Fashion MNIST is lower due to the simplicity of the network architecture and the increased complexity of the data. We expect that the DD-TEA framework will enable the exploration of deeper and more complex STVO-based architectures, ultimately leading to improved classification accuracy.

cs.CV↗

arXiv2025

Towards a general diffusion-based information quality assessment model

The rapid and unregulated dissemination of information in the digital era has amplified the global "infodemic," complicating the identification of high quality information. We present a lightweight, interpretable and non-invasive framework for assessing information quality based solely on diffusion dynamics, demonstrated here in the context of academic publications. Using a heterogeneous dataset of 29,264 sciences, technology, engineering, mathematics (STEM) and social science papers from ArnetMiner and OpenAlex, we model the diffusion network of each paper as a set of three theoretically motivated features: diversity, timeliness, and salience. A Generalized Additive Model (GAM) trained on these features achieved Pearson correlations of 0.834 for next-year citation gain and up to 95.62% accuracy in predicting high-impact papers. Feature relevance studies reveal timeliness and salience as the most robust predictors, while diversity offers less stable benefits in the academic setting but may be more informative in social media contexts. The framework's transparency, domain-agnostic design, and minimal feature requirements position it as a scalable tool for global information quality assessment, opening new avenues for moving beyond binary credibility labels toward richer, diffusion-informed evaluation metrics.

cs.SI↗

arXiv2024

Thermoelectric and magneto-transport characteristics of interconnected networks of ferromagnetic nanowires and nanotubes

Macroscopic-scale nanostructures, situated at the interface of nanostructures and bulk materials, hold significant promise in the realm of thermoelectric materials. Nanostructuring presents a compelling avenue for enhancing material thermoelectric performance as well as unlocking intriguing nanoscale phenomena, including spin-dependent thermoelectric effects. This is achieved while preserving high power output capabilities and ease of measurements related to the overall macroscopic dimensions. Within this framework, the recently developed three-dimensional interconnected nanowire and nanotube networks, integrated into a flexible polymer membrane, emerge as promising candidates for macroscopic nanostructures. The flexibility of these composites also paves the way for advances in the burgeoning field of flexible thermoelectrics. In this study, we demonstrate that the three-dimensional nanowire networks made of ferromagnetic metals maintain the intrinsic bulk thermoelectric power of their bulk constituent even for a diameter reduced to approximately 23 nm. Furthermore, we showcase the pioneering magneto-thermoelectric measurements of three-dimensional interconnected nickel nanotube networks. These macroscopic materials, comprising interconnected nanotubes, enable the development of large-area devices that exhibit efficient thermoelectric performance, while their nanoscale tubular structures provide distinctive magneto-transport properties. This research represents a significant step toward harnessing the potential of macroscopic nanostructured materials in the field of thermoelectrics.

cond-mat.mes-hall↗

arXiv2024

Geometrical properties of 3D crossed nanowire networks

Three-dimensional interconnected nanowire networks have recently attracted notable attention for the fabrication of new devices for energy harvesting/storage, sensing, catalysis, magnetic and spintronic applications and for the design of new hardware neuromorphic computing architectures. However, the complex branching of these nanowire networks makes it challenging to investigate these 3D nanostructured systems theoretically. Here, we present a theoretical description and simulations of the geometric properties of these 3D interconnected nanowire networks with selected characteristics. Our analysis reveals that the nanowire segment length between two crossing zones follows an exponential distribution. This suggests that shorter nanowire segments have a more pronounced influence on the nanowire network properties compared to their longer counterparts. Moreover, our observations reveal a homogeneous distribution in the smallest distance between the cores of two crossing nanowires. The results are highly reproducible and unaffected by changes in the nanowire network characteristics. The density of crossing zones and interconnected nanowire segments are found to vary as the square of the nanowire density multiplied by their diameter, further multiplied by a factor dependent on the packing factor. Finally, densities of interconnected segments up to 10$^{13}$ cm$^{-2}$ can be achieved for 22-$μ$m-thick nanowire networks with high packing factors. This has important implications for neuromorphic computing applications, suggesting that the realization of 10$^{14}$ interconnections, which corresponds to the approximate number of synaptic connections in the human brain, is achievable with a nanowire network of about 10 cm$^{2}$.

physics.app-ph↗

arXiv2023

Current-controlled periodic double-polarity reversals in a spin-torque vortex oscillator

Micromagnetic simulations are used to study a spin-torque vortex oscillator excited by an out-of-plane dc current. The vortex core gyration amplitude is confined between two orbits due to periodical vortex core polarity reversals. The upper limit corresponds to the orbit where the vortex core reaches its critical velocity triggering the first polarity reversal which is immediately followed by a second one. After this double polarity reversal, the vortex core is on a smaller orbit that defines the lower limit of the vortex core gyration amplitude. This double reversal process is a periodic phenomenon and its frequency as well as the upper and lower limits of the vortex core gyration are controlled by the input current density while the vortex chirality determines the onset of this confinement regime. In this non-linear regime, the vortex core never reaches a stable orbit and thus, it may be of interest for neuromorphic application, for example as a leaky integrate-and-fire neuron.

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 simulated using an unconventional data-driven Thiele equation approach

In this study, we developed a quantitative description of the dynamics of spin-torque vortex nano-oscillators (STVOs) through an unconventional model based on the combination of the Thiele equation approach (TEA) and data from micromagnetic simulations (MMS). Solving the STVO dynamics with our analytical model allows to accelerate the simulations by 9 orders of magnitude compared to MMS while reaching the same level of accuracy. Here, we showcase our model by simulating a STVO-based neural network for solving a classification task. We assess its performance with respect to the input signal current intensity and the level of noise that might affect such a system. Our approach is promising for accelerating the design of STVO-based neuromorphic computing devices while decreasing drastically its computational cost.

cs.CV↗

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↗

arXiv2023

A semi-analytical model to simulate the spin-diode effect and accelerate its use in neuromorphic computing

The spin-diode effect is studied both experimentally and with our original semi-analytical method. The latter is based on an improved version of the Thiele equation approach (TEA) that we combine to micromagnetic simulation data to accurately model the non-linear dynamics of spin-torque vortex oscillator (STVO). This original method, called data-driven Thiele equation approach (DD-TEA), absorbs the difference between the analytical model and micromagnetic simulations to provide a both ultra-fast and quantitative model. The DD-TEA model predictions also agree very well with the experimental data. The reversal of the spin-diode effect with the chirality of the vortex, the impact of the input current and the origin of a variation at half of the STVO frequency are presented as well as the ability of the model to reproduce the experimental behavior. Finally, the spin-diode effect and its simulation using the DD-TEA model are discussed as a promising perspective in the framework of neuromorphic computing.

cond-mat.mes-hall↗

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↗

arXiv2021

Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations

Deep learning has an increasing impact to assist research, allowing, for example, the discovery of novel materials. Until now, however, these artificial intelligence techniques have fallen short of discovering the full differential equation of an experimental physical system. Here we show that a dynamical neural network, trained on a minimal amount of data, can predict the behavior of spintronic devices with high accuracy and an extremely efficient simulation time, compared to the micromagnetic simulations that are usually employed to model them. For this purpose, we re-frame the formalism of Neural Ordinary Differential Equations (ODEs) to the constraints of spintronics: few measured outputs, multiple inputs and internal parameters. We demonstrate with Spin-Neural ODEs an acceleration factor over 200 compared to micromagnetic simulations for a complex problem -- the simulation of a reservoir computer made of magnetic skyrmions (20 minutes compared to three days). In a second realization, we show that we can predict the noisy response of experimental spintronic nano-oscillators to varying inputs after training Spin-Neural ODEs on five milliseconds of their measured response to different excitations. Spin-Neural ODE is a disruptive tool for developing spintronic applications in complement to micromagnetic simulations, which are time-consuming and cannot fit experiments when noise or imperfections are present. Spin-Neural ODE can also be generalized to other electronic devices involving dynamics.

cs.LG↗

arXiv2019

Role of non-linear data processing on speech recognition task in the framework of reservoir computing

The reservoir computing neural network architecture is widely used to test hardware systems for neuromorphic computing. One of the preferred tasks for bench-marking such devices is automatic speech recognition. However, this task requires acoustic transformations from sound waveforms with varying amplitudes to frequency domain maps that can be seen as feature extraction techniques. Depending on the conversion method, these may obscure the contribution of the neuromorphic hardware to the overall speech recognition performance. Here, we quantify and separate the contributions of the acoustic transformations and the neuromorphic hardware to the speech recognition success rate. We show that the non-linearity in the acoustic transformation plays a critical role in feature extraction. We compute the gain in word success rate provided by a reservoir computing device compared to the acoustic transformation only, and show that it is an appropriate benchmark for comparing different hardware. Finally, we experimentally and numerically quantify the impact of the different acoustic transformations for neuromorphic hardware based on magnetic nano-oscillators.

eess.AS↗