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

Amanatiadis, Stamatios; Kantartzis, Nikolaos V.; Ohtani, Tadao; Kanai, Y.

doi:10.1109/tmag.2017.2749558

Cited by 15 publications

(10 citation statements)

References 11 publications

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“…Along with this research process, new methods are much in demand to model-based electronic devices based on the magnetized graphene [10][11][12][13][14][15]. In most of the numerical methods, magnetized graphene is usually incorporated in the algorithm by transforming the surface conductivity to a volumetric conductivity by dividing the thickness of the graphene, which might introduce a multiscale computational problem in simulation, require massive CPU time and memory, and make the optimization process tiresome.…”

Section: Introductionmentioning

confidence: 99%

Non-Reciprocal Antenna Array Based on Magnetized Graphene for THZ Applications Using the Iterative Method

Hlali¹,

Houaneb²,

Zairi³

2020

PIER M

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An effective and precise approach to the Wave Concept Iterative Process method modeling of magnetized graphene sheet as an anisotropic conductive surface is used to analyze the anisotropy of magnetostatically biased graphene and for studying an electrically doped magnetically biased graphene non-reciprocal antenna array for THz applications. The tuning of the performance of the array antenna is possible by varying the magnetic field and the chemical potential of graphene material. The return loss value decreases by increasing the magnetostatic bias and increases when the chemical potential increases.

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Section: Introductionmentioning

confidence: 99%

Non-Reciprocal Antenna Array Based on Magnetized Graphene for THZ Applications Using the Iterative Method

Hlali¹,

Houaneb²,

Zairi³

2020

PIER M

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“…So far, two main methods have been reported to establish the electromagnetic model for investigating the interaction between the electromagnetic field and the graphene. In one method, graphene is regarded as a two-dimensional (2-D) conductive sheet [3][4][5][6][7][8]; in the other method, graphene is treated as a bulk material [9][10][11][12][13][14][15]. Based on the first method with graphene as a 2-D sheet, Sounas et al successfully obtained the analytic expressions of the reflection and transmission coefficients when a plane wave obliquely incidents on a magnetostatic biased graphene sheet in free space [5].…”

Section: Introductionmentioning

confidence: 99%

“…This significantly enhances the difficulty of analytic modeling, and thus leads to a fact that the numerical algorithms (e.g. the finite-difference time-domain (FDTD) algorithm) are mainly adopted when graphene is regarded as a bulk material [9][10][11][12]. However, the ultra-thin nature of the graphene would inevitably increases the numbers of grids (and thus degrading the simulation efficiency) for the conventional FDTD algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Analytical Method to Investigate Propagation Properties of Magnetostatic Biased Graphene Layers

Shi

Wang

et al. 2020

IEEE Access

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The transmission and reflection properties of magnetostatic biased graphene layers are investigated based on analytical method, where graphene is treated as a bulk material. Considering the anisotropic nature of the magnetostatic biased graphene (magnetized graphene), the generalized reflection and transmission coefficients of a plane wave incidence on anisotropic multilayer structures are first derived. The accuracy of this method is validated by a numerical example of a plane wave incidence on a magnetized monolayer graphene in free space. Based on this model, the transmission coefficient decreases with increasing the incident angle for the TE (transverse electric) polarized wave incidence, while an opposite phenomenon occurs for the TM (transverse magnetic) polarized wave incidence. The transmission properties can be also modulated by changing the external static magnetic field. The electromagnetic propagation properties of composites consisting of magnetized graphene and dielectric layers are also analyzed based on this method. The transmission properties degrade with increasing the number of magnetized graphene layers.

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“…Magnetized graphene is an infinitesimally thin sheet biased by a magnetostatic field and behaves as an anisotropic conducting sheet characterized by a conductivity tensor [1,2]. For the modeling of magnetized graphene, a number of numerical methods have already been developed to quantify the anisotropic properties of graphene such the method of moments (MOM) [3], finite difference time domain (FDTD) [4][5][6] method, and partial element equivalent circuit (PEEC) method [7].…”

Section: Introductionmentioning

confidence: 99%

“…However, each method has its advantages and drawbacks [8]. FDTD modeling of magnetized graphene has been addressed in earlier works [4][5][6]. In [6], an FDTD approach is developed by transforming the surface conductivity of graphene to a volumetric conductivity by dividing the thickness of graphene and implementing it by using the auxiliary differential equation (ADE) and matrix exponential method.…”

Section: Introductionmentioning

confidence: 99%

Effective Modeling of Magnetized Graphene by the Wave Concept Iterative Process Method Using Boundary Conditions

Hlali¹,

Houaneb²,

Zairi³

2019

PIER C

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Due to static magnetic field, the conductivity of graphene becomes an anisotropic tensor, which complicates most modeling methodologies. A practical approach to the Wave Concept Iterative Process method (WCIP) modeling of magnetized graphene sheets as an anisotropic conductive surface from the microwave to terahertz frequencies is proposed. We first introduce a brief description of modeling magnetized graphene as an infinitesimally thin conductive sheet. Then, we present a novel manner for the implementation of the anisotropic boundary conditions using the wave concept in the WCIP method. This proposed method is benchmarked with numerical examples to demonstrate its applicability and accuracy. The proposed approach is used to compare the anisotropic model, isotropic model, and the metal for a strip waveguide. We show that the anisotropic model gives more efficient results.

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

Cited by 15 publications

References 11 publications

Non-Reciprocal Antenna Array Based on Magnetized Graphene for THZ Applications Using the Iterative Method

Non-Reciprocal Antenna Array Based on Magnetized Graphene for THZ Applications Using the Iterative Method

An Analytical Method to Investigate Propagation Properties of Magnetostatic Biased Graphene Layers

Effective Modeling of Magnetized Graphene by the Wave Concept Iterative Process Method Using Boundary Conditions

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