4.6 Article

Tuning Ginzburg-Landau theory to quantitatively study thin ferromagnetic materials

Journal

Publisher

IOP Publishing Ltd
DOI: 10.1088/1742-5468/abe40a

Keywords

interfaces in random media; defects; dynamical processes; numerical simulations

Funding

  1. Federal Commission for Scholarships for Foreign Students for the Swiss Government Excellence Scholarship (ESKAS) [2018.0636]
  2. Swiss National Science Foundation (FNS/SNF)
  3. Agencia Nacional de Promocion Cientifica y Tecnologica [PICT 2016-0069, PICT 2017-0906]
  4. Universidad Nacional de Cuyo [06/C561, M083]
  5. IDMAG

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A tuned Ginzburg-Landau model is presented to study the dynamics of domain walls in thin ferromagnetic systems with perpendicular magnetic anisotropy. The model quantitatively reproduces different dynamical regimes of domain wall motion in experimental velocity-field data, while also providing detailed nano-scale information.
Along with experiments, numerical simulations are key to gaining insight into the underlying mechanisms governing domain wall motion in thin ferromagnetic systems. However, a direct comparison between numerical simulation of model systems and experimental results still represents a great challenge. Here, we present a tuned Ginzburg-Landau model to quantitatively study the dynamics of domain walls in quasi two-dimensional ferromagnetic systems with perpendicular magnetic anisotropy. This model incorporates material and experimental parameters and the micromagnetic prescription for thermal fluctuations, allowing us to perform material-specific simulations and at the same time recover universal features. We show that our model quantitatively reproduces previous experimental velocity-field data in the archetypal perpendicular magnetic anisotropy Pt/Co/Pt ultra-thin films in the three dynamical regimes of domain wall motion (creep, depinning and flow). In addition, we present a statistical analysis of the domain wall width parameter, showing that our model can provide detailed nano-scale information while retaining the complex behavior of a statistical disordered model.

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