Numerical Methods for Electromagnetic Modeling of Graphene: A Review

Niu, Kaikun; Li, Ping; Huang, Zhixiang; Jiang, Li Jun; Bağcı, Hakan

doi:10.1109/jmmct.2020.2983336

Cited by 22 publications

(9 citation statements)

References 111 publications

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“…The coating of antenna at the sub‐MMW band shows better performance improvement that due to the skin effect, where the current is mainly concentrated on the surface of conducting layer. The skin depth is inversely proportional with frequency, where at high frequencies, most of the current are concentrated in the top layer (coated layer), which has a better conductivity compared to the inner (copper) layer in this frequency band 38,39 . The difference between directivity and realized gain is caused by the loss of dissipated power in the parts of antenna elements (patch, ground, feedline and substrate), where the metal loss represents a main part of it.…”

Section: Resultsmentioning

confidence: 99%

Modeling and performance evaluation of antennas coated using monolayer graphene in the millimeter and sub‐millimeter wave bands

Gatte

Soh

Kadhim

et al. 2021

Int J Numerical Modelling

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In the applications of millimeter and sub‐millimeter wave, the conductivity of metal parts in electronic devices can easily degrade when conventional metals like copper are employed. Furthermore, oxidation may arise when such devices are utilized in severe environmental conditions. To avoid this, conventional conductors such as copper can be coated with other non‐active materials to inhibit this problem. Monolayer graphene is used in this study as a coating layers for copper in millimeter‐wave antennas. Two types of graphene coatings are investigated: non‐doped and doped monolayer graphene. These coatings can either be used as the patch, ground or both conducting layers of a microstrip patch antenna. Results showed that coating using doped graphene improves the performance of antenna in terms of gain, radiated power and radiation efficiency by 11.81%, 8.48%, and 11.48%, respectively, compared to antennas made using copper and coated using gold and non‐doped graphene at millimeter‐wave frequencies. Meanwhile, at sub‐millimeter wave frequencies, the metal (copper and gold)‐based antenna showed worse performance compared to millimeter waves. Furthermore, coating of the conducting elements for the sub‐millimeter wave antenna using doped and non‐doped graphene improved gain, radiated power and radiation efficiency by 33.94%, 32.73%, and 32.01%, respectively, for the coating with doped graphene, and about 14.87%, 16.56%, and 15.72% for the coating with non‐doped graphene. This indicates the suitability of graphene‐based antennas in both frequency bands and the expected levels of improvements for different parameters when these antenna elements are coated with doped and non‐doped graphene.

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Section: Resultsmentioning

confidence: 99%

Modeling and performance evaluation of antennas coated using monolayer graphene in the millimeter and sub‐millimeter wave bands

Gatte

Soh

Kadhim

et al. 2021

Int J Numerical Modelling

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“…Assume that the graphene sheet is located on an interface between two subdomains. Then, RTCs in (2) are modified to account for RBCs in (1). This modification yields RRTCs that are expressed as In the each subdomain, RTCs (2) and RRTCs (3) are used to "connect" the electric and magnetic field SIEs that are used as the subdomain's governing equations [7], [8].…”

Section: Formulationmentioning

confidence: 99%

“…To efficiently simulate electromagnetic field interactions on these devices and systems, often the graphene sheet is modeled as an infinitesimally thin surface and resistive boundary conditions (RBCs) are enforced this surface to relate the electromagnetic fields on its two sides [2]. Indeed, this approach has been successfully incorporated within finite-difference time-domain method [3] and discontinuous Galerkin time-domain scheme [4].…”

Section: Introductionmentioning

confidence: 99%

A Multi-trace Surface Integral Equation Solver to Analyze Electromagnetic Field Interactions on Graphene-based Devices

Zhao

Chen

et al. 2022

2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI)

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A multi-trace surface integral equation (MT-SIE) solver is proposed to analyze electromagnetic field interactions on graphene-based devices. This solver decomposes the computation domain into an exterior subdomain (the background medium where the device resides in) and an interior subdomain that represents the device's dielectric substrate. The infinitesimally thin graphene sheet partially covers the interface between these two subdomains. On the graphene surface and the rest of this interface, resistive Robin transmission conditions (formulated for the first time in this work) and the traditional Robin transmission conditions are used to "connect" these two subdomains, respectively.In each subdomain, the electric and magnetic field equations are used as the governing equations. Various numerical examples are presented to demonstrate the accuracy and the applicability of the proposed MT-SIE solver.

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“…), only the intra-band contribution can be considered, so the surface conductivity can be approximately expressed as [18], [20], [21]:…”

Section: Characteristics Of Graphene Reconfigurable Reflectarray Antennamentioning

confidence: 99%

Efficient Graphene Reconfigurable Reflectarray Antenna Electromagnetic Response Prediction Using Deep Learning

Shi

Zhang

et al. 2021

IEEE Access

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Aiming at the time-consuming problem of the full-wave (FW) simulation of the scattering characteristics of the traditional graphene reconfigurable reflectarray antenna, a fast prediction method of electromagnetic (EM) response based on deep learning is proposed. The convolutional neural network (CNN) method in deep learning is effectively used in the research of this paper. This method first discretizes the input vector (patch geometry, chemical potential, frequency, incident angle, etc.) of the graphene reflectarray antenna, and then preprocesses the data into a two-dimensional image suitable for CNN training, and finally uses CNN to train the model instead of extensive FW simulation calculations, the EM response of the reflectarray antenna is calculated. The training results of three algorithms of support vector regression (SVR), radial basis function network (RBFN) and CNN are comprehensively compared. The experimental results show that CNN method has good performance and accuracy in the EM response prediction of the graphene reconfigurable reflectarray antenna, with an accuracy of over 99%, and can also save at least 99% of time.INDEX TERMS Convolutional neural network (CNN), electromagnetic (EM) response, graphene, reconfigurable reflectarray antenna.

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Numerical Methods for Electromagnetic Modeling of Graphene: A Review

Cited by 22 publications

References 111 publications

Modeling and performance evaluation of antennas coated using monolayer graphene in the millimeter and sub‐millimeter wave bands

Modeling and performance evaluation of antennas coated using monolayer graphene in the millimeter and sub‐millimeter wave bands

A Multi-trace Surface Integral Equation Solver to Analyze Electromagnetic Field Interactions on Graphene-based Devices

Efficient Graphene Reconfigurable Reflectarray Antenna Electromagnetic Response Prediction Using Deep Learning

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