Precise Modeling of Magnetically Biased Graphene Through a Recursive Convolutional FDTD Method

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.