Magnetization reversal dynamics in epitaxial spin-valve structures

Lee, W. Y.; Samad, A.; Moore, T. A.; Bland, J. A. C.; Choi, B. C.

doi:10.1103/physrevb.61.6811

Cited by 15 publications

(20 citation statements)

References 29 publications

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“…Based on the analogy with Ising type models, experimental data are often analyzed in terms of universal scaling laws, such as the one relating the dynamical hysteresis loop area (A) to external parameters such temperature (T ), amplitude (H 0 ), and frequency (ω) of the applied magnetic field: A ∝ H α 0 ω β T −γ , where α, β, and γ are scaling exponents [1]. The experimental estimates of these exponents, often based on a very limited scaling regime, display a huge variability [4,5,6,7,8,9,10,11,12], so that the validity of a simple universal scaling law is still under question. Some authors also interpret the lack of good scaling of A(ω) as a cross-over between two distinct dynamical regimes, at low frequencies dominated by domain wall propagation, at high frequencies by the nucleation of new domains [2,10].…”

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confidence: 99%

Loss Separation for Dynamic Hysteresis in Ferromagnetic Thin Films

2006

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We develop a theory for dynamic hysteresis in ferromagnetic thin films, on the basis of the phenomenological principle of loss separation. We observe that, remarkably, the theory of loss separation, originally derived for bulk metallic materials, is applicable to disordered magnetic systems under fairly general conditions regardless of the particular damping mechanism. We confirm our theory both by numerical simulations of a driven random-field Ising model, and by re-examining several experimental data reported in the literature on dynamic hysteresis in thin films. All the experiments examined and the simulations find a natural interpretation in terms of loss separation. The power losses dependence on the driving field rate predicted by our theory fits satisfactorily all the data in the entire frequency range, thus reconciling the apparent lack of universality observed in different materials.

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confidence: 99%

Loss Separation for Dynamic Hysteresis in Ferromagnetic Thin Films

2006

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“…At lower thickness, the loops are roughly squared, as shown in the upper left corner of Fig. 1, and [4]: this behavior suggests the presence of two magnetic sub-systems having different coercive fields. We do not know the origin of this hysteresis, but we can confirm this is a common feature of Finemet samples having the same thickness, as we found similar loops for samples of slightly different composition.…”

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confidence: 87%

Investigation of scaling properties of hysteresis in Finemet thin films

Santi

Dorneles

Sommer

et al. 2004

Journal of Magnetism and Magnetic Materials

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We study the behavior of hysteresis loops in Finemet Fe$_{73.5}$Cu$_1$Nb$_3$Si$_{18.5}$B$_4$ thin films by using a fluxometric setup based on a couple of well compensated pickup coils. The presence of scaling laws of the hysteresis area is investigated as a function of the amplitude and frequency of the applied field, considering sample thickness from about 20 nm to 5 $\mu$m. We do not observe any scaling predicted by theoretical models, while dynamic loops show a logarithmic dependence on the frequency.Comment: 2 pages, 2 figure

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“…Previous experimental and theoretical investigations (e.g. [5][6][7][8][9][10][11][12][13]) have been previously performed. For instance, the hysteresis area A dependence on the external field parameters (i.e.…”

Section: Introductionmentioning

confidence: 99%

Concurrent Modeling of Magnetic Field Parameters, Crystalline Structures, and Ferromagnetic Dynamic Critical Behavior Relationships: Mean-Field and Artificial Neural Network Projections

Laosiritaworn¹,

Laosiritaworn²

2014

Journal of Magnetics

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In this work, Artificial Neural Network (ANN) was used to model the dynamic behavior of ferromagnetic hysteresis derived from performing the mean-field analysis on the Ising model. The effect of field parameters and system structure (via coordination number) on dynamic critical points was elucidated. The Ising magnetization equation was drawn from mean-field picture where the steady hysteresis loops were extracted, and series of the dynamic critical points for constructing dynamic phase-diagram were depicted. From the dynamic critical points, the field parameters and the coordination number were treated as inputs whereas the dynamic critical temperature was considered as the output of the ANN. The input-output datasets were divided into training, validating and testing datasets. The number of neurons in hidden layer was varied in structuring ANN network with highest accuracy. The network was then used to predict dynamic critical points of the untrained input. The predicted and the targeted outputs were found to match well over an extensive range even for systems with different structures and field parameters. This therefore confirms the ANN capabilities and indicates the ANN ability in modeling the ferromagnetic dynamic hysteresis behavior for establishing the dynamic-phase-diagram.

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Magnetization reversal dynamics in epitaxial spin-valve structures

Cited by 15 publications

References 29 publications

Loss Separation for Dynamic Hysteresis in Ferromagnetic Thin Films

Loss Separation for Dynamic Hysteresis in Ferromagnetic Thin Films

Investigation of scaling properties of hysteresis in Finemet thin films

Concurrent Modeling of Magnetic Field Parameters, Crystalline Structures, and Ferromagnetic Dynamic Critical Behavior Relationships: Mean-Field and Artificial Neural Network Projections

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