• 文献标题:   Precise Modeling of Magnetically Biased Graphene Through a Recursive Convolutional FDTD Method
  • 文献类型:   Article
  • 作  者:   AMANATIADIS SA, KANTARTZIS NV, OHTANI T, KANAI Y
  • 作者关键词:   anisotropic conductivity, finitedifference timedomain fdtd method, graphene, magnetic bia, recursive convolution technique
  • 出版物名称:   IEEE TRANSACTIONS ON MAGNETICS
  • ISSN:   0018-9464 EI 1941-0069
  • 通讯作者地址:   Aristotle Univ Thessaloniki
  • 被引频次:   3
  • DOI:   10.1109/TMAG.2017.2749558
  • 出版年:   2018

▎ 摘  要

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.