scholarly journals Exploiting symmetries in skyrmionic micromagnetic simulations: Cylindrical and radial meshes

2022 ◽  
pp. 168972
Author(s):  
Josep Castell-Queralt ◽  
Leonardo González-Gómez ◽  
Nuria Del-Valle ◽  
Carles Navau
Keyword(s):  
Micromagnetic Simulations

scholarly journals Electrical characterisation of higher order spin wave modes in vortex-based magnetic tunnel junctions

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Alex. S. Jenkins ◽  
Lara San Emeterio Alvarez ◽  
Samh Memshawy ◽  
Paolo Bortolotti ◽  
Vincent Cros ◽  
...  
Keyword(s):  
Spin Wave ◽  
Tunnel Junctions ◽  
Magnetic Tunnel Junctions ◽  
Higher Order ◽  
Spin Torque ◽  
Micromagnetic Simulations ◽  
Higher Order Modes ◽  
Spin Diode ◽  
Super High Frequency ◽  
Wave Modes

AbstractNiFe-based vortex spin-torque nano-oscillators (STNO) have been shown to be rich dynamic systems which can operate as efficient frequency generators and detectors, but with a limitation in frequency determined by the gyrotropic frequency, typically sub-GHz. In this report, we present a detailed analysis of the nature of the higher order spin wave modes which exist in the Super High Frequency range (3–30 GHz). This is achieved via micromagnetic simulations and electrical characterisation in magnetic tunnel junctions, both directly via the spin-diode effect and indirectly via the measurement of the coupling with the gyrotropic critical current. The excitation mechanism and spatial profile of the modes are shown to have a complex dependence on the vortex core position. Additionally, the inter-mode coupling between the fundamental gyrotropic mode and the higher order modes is shown to reduce or enhance the effective damping depending upon the sense of propagation of the confined spin wave.

scholarly journals Neuro-Inspired Signal Processing in Ferromagnetic Nanofibers

Biomimetics ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 32
Author(s):  
Tomasz Blachowicz ◽  
Jacek Grzybowski ◽  
Pawel Steblinski ◽  
Andrea Ehrmann
Keyword(s):  
Data Processing ◽  
Data Storage ◽  
Domain Walls ◽  
Data Transfer ◽  
Synaptic Activity ◽  
Neuromorphic Computing ◽  
Rotating Magnetic Fields ◽  
Micromagnetic Simulations ◽  
Energy Consuming ◽  
Stochastic Data

Computers nowadays have different components for data storage and data processing, making data transfer between these units a bottleneck for computing speed. Therefore, so-called cognitive (or neuromorphic) computing approaches try combining both these tasks, as is done in the human brain, to make computing faster and less energy-consuming. One possible method to prepare new hardware solutions for neuromorphic computing is given by nanofiber networks as they can be prepared by diverse methods, from lithography to electrospinning. Here, we show results of micromagnetic simulations of three coupled semicircle fibers in which domain walls are excited by rotating magnetic fields (inputs), leading to different output signals that can be used for stochastic data processing, mimicking biological synaptic activity and thus being suitable as artificial synapses in artificial neural networks.

scholarly journals Micromagnetic Simulations of Fe and Ni Nanodot Arrays Surrounded by Magnetic or Non-Magnetic Matrices

Nanomaterials ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 349
Author(s):  
Devika Sudsom ◽  
Andrea Ehrmann
Keyword(s):  
Magnetic Materials ◽  
Data Storage ◽  
Magnetization Reversal ◽  
Hysteresis Loops ◽  
Object Oriented ◽  
Basic Research ◽  
Memory Systems ◽  
The Other ◽  
Micromagnetic Simulations ◽  
Nanodot Arrays

Combining clusters of magnetic materials with a matrix of other magnetic materials is very interesting for basic research because new, possibly technologically applicable magnetic properties or magnetization reversal processes may be found. Here we report on different arrays combining iron and nickel, for example, by surrounding circular nanodots of one material with a matrix of the other or by combining iron and nickel nanodots in air. Micromagnetic simulations were performed using the OOMMF (Object Oriented MicroMagnetic Framework). Our results show that magnetization reversal processes are strongly influenced by neighboring nanodots and the magnetic matrix by which the nanodots are surrounded, respectively, which becomes macroscopically visible by several steps along the slopes of the hysteresis loops. Such material combinations allow for preparing quaternary memory systems, and are thus highly relevant for applications in data storage and processing.

scholarly journals High-density Néel-type magnetic skyrmion phase stabilized at high temperature

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Hee Young Kwon ◽  
Kyung Mee Song ◽  
Juyoung Jeong ◽  
Ah-Yeon Lee ◽  
Seung-Young Park ◽  
...  
Keyword(s):  
High Temperature ◽  
High Density ◽  
Low Density ◽  
Memory Devices ◽  
Magnetic Multilayer ◽  
Multilayer Systems ◽  
Micromagnetic Simulations ◽  
Transmission Electron ◽  
Ultrahigh Density ◽  
Lorentz Transmission Electron Microscopy

AbstractThe discovery of a thermally stable, high-density magnetic skyrmion phase is a key prerequisite for realizing practical skyrmionic memory devices. In contrast to the typical low-density Néel-type skyrmions observed in technologically viable multilayer systems, with Lorentz transmission electron microscopy, we report the discovery of a high-density homochiral Néel-type skyrmion phase in magnetic multilayer structures that is stable at high temperatures up to 733 K (≈460 °C). Micromagnetic simulations reveal that a high-density skyrmion phase can be stabilized at high temperature by deliberately tuning the magnetic anisotropy, magnetic field, and temperature. The existence of the high-density skyrmion phase in a magnetic multilayer system raises the possibility of incorporating chiral Néel-type skyrmions in ultrahigh-density spin memory devices. Moreover, the existence of this phase at high temperature shows its thermal stability, demonstrating the potential for skyrmion devices operating in thermally challenging modern electronic chips.

scholarly journals Machine Learning for Statistical Modeling

2021 ◽  
Vol 26 (3) ◽  
pp. 1-17
Author(s):  
Urmimala Roy ◽  
Tanmoy Pramanik ◽  
Subhendu Roy ◽  
Avhishek Chatterjee ◽  
Leonard F. Register ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Distribution ◽  
Switching Time ◽  
A Priori ◽  
Random Access ◽  
Spin Transfer Torque ◽  
Support Vector ◽  
Micromagnetic Simulations ◽  
Physical Systems ◽  
Computational Resources

We propose a methodology to perform process variation-aware device and circuit design using fully physics-based simulations within limited computational resources, without developing a compact model. Machine learning (ML), specifically a support vector regression (SVR) model, has been used. The SVR model has been trained using a dataset of devices simulated a priori, and the accuracy of prediction by the trained SVR model has been demonstrated. To produce a switching time distribution from the trained ML model, we only had to generate the dataset to train and validate the model, which needed ∼500 hours of computation. On the other hand, if 10 6 samples were to be simulated using the same computation resources to generate a switching time distribution from micromagnetic simulations, it would have taken ∼250 days. Spin-transfer-torque random access memory (STTRAM) has been used to demonstrate the method. However, different physical systems may be considered, different ML models can be used for different physical systems and/or different device parameter sets, and similar ends could be achieved by training the ML model using measured device data.

scholarly journals Micromagnetic simulations of magnetization reversal of iron nanowire

2011 ◽  
Vol 266 ◽  
pp. 012022 ◽  
Author(s):  
H Xiang ◽  
D M Jiang ◽  
J C Yao ◽  
Y P Zheng ◽  
W Lu ◽  
...  
Keyword(s):  
Magnetization Reversal ◽  
Micromagnetic Simulations

Micromagnetic simulations on the grain shape effect in Nd-Fe-B magnets

2016 ◽  
Vol 120 (3) ◽  
pp. 033903 ◽  
Author(s):  
Min Yi ◽  
Oliver Gutfleisch ◽  
Bai-Xiang Xu
Keyword(s):  
Grain Shape ◽  
Shape Effect ◽  
Micromagnetic Simulations

scholarly journals Optimizing machine learning models for granular NdFeB magnets by very fast simulated annealing

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hyeon-Kyu Park ◽  
Jae-Hyeok Lee ◽  
Jehyun Lee ◽  
Sang-Koog Kim
Keyword(s):  
Machine Learning ◽  
Simulated Annealing ◽  
Permanent Magnets ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Micromagnetic Simulations ◽  
Ndfeb Magnets ◽  
Average Grain Size ◽  
Macroscopic Properties ◽  
Very Fast Simulated Annealing

AbstractThe macroscopic properties of permanent magnets and the resultant performance required for real implementations are determined by the magnets’ microscopic features. However, earlier micromagnetic simulations and experimental studies required relatively a lot of work to gain any complete and comprehensive understanding of the relationships between magnets’ macroscopic properties and their microstructures. Here, by means of supervised learning, we predict reliable values of coercivity (μ0Hc) and maximum magnetic energy product (BHmax) of granular NdFeB magnets according to their microstructural attributes (e.g. inter-grain decoupling, average grain size, and misalignment of easy axes) based on numerical datasets obtained from micromagnetic simulations. We conducted several tests of a variety of supervised machine learning (ML) models including kernel ridge regression (KRR), support vector regression (SVR), and artificial neural network (ANN) regression. The hyper-parameters of these models were optimized by a very fast simulated annealing (VFSA) algorithm with an adaptive cooling schedule. In our datasets of randomly generated 1,000 polycrystalline NdFeB cuboids with different microstructural attributes, all of the models yielded similar results in predicting both μ0Hc and BHmax. Furthermore, some outliers, which deteriorated the normality of residuals in the prediction of BHmax, were detected and further analyzed. Based on all of our results, we can conclude that our ML approach combined with micromagnetic simulations provides a robust framework for optimal design of microstructures for high-performance NdFeB magnets.

scholarly journals Physical Simulations of High Speed and Low Power NanoMagnet Logic Circuits

2018 ◽  
Vol 8 (4) ◽  
pp. 37 ◽  
Author(s):  
Giovanna Turvani ◽  
Laura D’Alessandro ◽  
Marco Vacca
Keyword(s):  
High Speed ◽  
Logic Circuits ◽  
Micromagnetic Simulations ◽  
Electrical Fields ◽  
Nanomagnet Logic ◽  
Nanomagnetic Logic ◽  
Elastic Effect ◽  
Physical Simulations ◽  
A Chain ◽  
Beyond Cmos

Among all “beyond CMOS” solutions currently under investigation, nanomagnetic logic (NML) technology is considered to be one of the most promising. In this technology, nanoscale magnets are rectangularly shaped and are characterized by the intrinsic capability of enabling logic and memory functions in the same device. The design of logic architectures is accomplished by the use of a clocking mechanism that is needed to properly propagate information. Previous works demonstrated that the magneto-elastic effect can be exploited to implement the clocking mechanism by altering the magnetization of magnets. With this paper, we present a novel clocking mechanism enabling the independent control of each single nanodevice exploiting the magneto-elastic effect and enabling high-speed NML circuits. We prove the effectiveness of this approach by performing several micromagnetic simulations. We characterized a chain of nanomagnets in different conditions (e.g., different distance among cells, different electrical fields, and different magnet geometries). This solution improves NML, the reliability of circuits, the fabrication process, and the operating frequency of circuits while keeping the energy consumption at an extremely low level.

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