IEEE transactions on magnetics, 2018-03, Vol.54 (3), p.1-4
2018
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Details
- Autor(en) / Beteiligte
- Titel
- Precise Modeling of Magnetically Biased Graphene Through a Recursive Convolutional FDTD Method
- Ist Teil von
- IEEE transactions on magnetics, 2018-03, Vol.54 (3), p.1-4
- Ort / Verlag
- New York: IEEE
- Erscheinungsjahr
- 2018
- Quelle
- IEEE Xplore
- Beschreibungen/Notizen
- An efficient and consistent technique to implement numerically a magnetically biased graphene layer is introduced in this paper. Through the novel scheme and after applying a magnetic bias perpendicular to graphene, its surface conductivity presents anisotropic behavior and this effect is systematically modeled and incorporated in terms of the recursive convolution formulation in the finite-difference time-domain algorithm. The extracted numerical results are comprehensively compared with the corresponding analytical expressions to validate the significant performance of the proposed method over a wide frequency range.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 0018-9464eISSN: 1941-0069DOI: 10.1109/TMAG.2017.2749558Titel-ID: cdi_ieee_primary_8026176
- Format
- –
- Schlagworte
- Anisotropic conductivity, Conductivity, Convolution, Finite difference method, Finite difference methods, Finite difference time domain method, finite-difference time-domain (FDTD) methods, Graphene, magnetic bias, Magnetism, Mathematical analysis, Mathematical models, Perpendicular magnetic anisotropy, recursive convolution technique, Recursive methods, Surface waves, Time-domain analysis