Plasmonic Nanoantenna Optimization Using Characteristic Mode Analysis

Dey, Sumitra; Chatterjee, Deb; Garboczi, Edward J.; Hassan, Ahmed M.

doi:10.1109/tap.2019.2938705

Cited by 21 publications

(15 citation statements)

References 43 publications

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“…Some authors have used an approximated method to simulate Fabry-Perot resonances in a single cylindrical wire; 30 FDTD simulations to study the near-eld optical behavior of Fabry-Perot resonances in thin metal nanowires referred to as quasi one-dimensional plasmonic nanoantennas; 31 characteristic mode analysis to optimize complex plasmonic nanoantenna; 32 integral equations formulation solved using the Method of Moments to study a plasmonic wire-grid array of nanorods; 33 the commercial nite-difference time-domain (FDTD) soware (Lumerical FDTD) to study the electric near-eld enhancement of cascaded plasmonic nanorod antenna; 34 and a circuit equivalent of a plasmonic nanoantenna. 35 However, to the best of our knowledge, until now no theoretical and experimental study has been reported about the geometrical effects on the EF when these nanorods are covered with gold.…”

Section: Introductionmentioning

confidence: 99%

Geometry-induced enhancement factor improvement in covered-gold-nanorod-dimer antennas

et al. 2021

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Section: Introductionmentioning

confidence: 99%

Geometry-induced enhancement factor improvement in covered-gold-nanorod-dimer antennas

et al. 2021

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“…At a much smaller scale than the previous application, in the order of nanometers, CMA has been recently used to analyze and optimize the absorption and scattering characteristics of a wide range of metallic nanoparticles (NPs) as shown in Fig. 6a [28]- [30], [63], [64]. Plasmonic NPs contain losses.…”

Section: Nanoparticles Scattering Analysis and Optimizationmentioning

confidence: 99%

“…Yet, the approach and tools of CMA, such as the MS spectrum and the current distribution of the modes, can be used to understand and optimize the response of plasmonic NPs. For example, the shape, size, and material of a splitring nanoantenna were optimized using CMA to yield more than 700% near-field intensity enhancement at the desired frequency [64].…”

Section: Nanoparticles Scattering Analysis and Optimizationmentioning

confidence: 99%

Characteristic Modes: Progress, overview, and emerging topics

Lau

Čapek

Hassan

2022

IEEE Antennas Propag. Mag.

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Over the past decade, characteristic mode analysis (CMA) research has grown from a niche topic to a mainstream topic, warranting a tutorial-style special issue to survey the significant progress that has been made in this field. In this introductory article (PAPER 1), the focus is on providing the big picture. We start with a simple description of characteristic modes. Next, we examine the trends in this field, followed by providing further insights into CMA's historical development. We will also address common myths surrounding the subject. Then, leaving the detailed coverage of major topics to the following papers, we summarize recent applications of CMA in scattering and other emerging topics. Finally, we conclude with some future perspectives on this field.

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“…The theory of characteristic modes (TCM), also termed characteristic mode analysis (CMA), is a computational technique that is used in a wide range of electromagnetic applications such as antenna design [1][2][3][4][5][6][7], electromagnetic compatibility [8][9][10][11][12][13], and nano-antenna analysis and design [14][15][16][17]. The numerical recipe of the CMA implementation involves the numerical analysis of the method of moments (MoM) impedance matrix using operations such as the singular value decomposition (SVD), multiplication, inverse, slicing, and matrix transpose [18].…”

Section: Introductionmentioning

confidence: 99%

Scalable and Fast Characteristic Mode Analysis using GPUs

et al. 2022

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Characteristic mode analysis (CMA) is used in the design and analysis of a wide range of electromagnetic devices such as antennas and nanostructures. The implementation of CMA involves the evaluation of a large method of moments (MoM) complex impedance matrix at every frequency. In this work, we use different open-source software for the GPU acceleration of the CMA. This open-source software comprises a wide range of computer science numerical and machine learning libraries not typically used for electromagnetic applications. Specifically, this paper shows how these different Python-based libraries can optimize the computational time of the matrix operations that compose the CMA algorithm. Based on our computational experiments and optimizations, we propose an approach using a GPU platform that is able to achieve up to 16×× and 26×× speedup for the CMA processing of a single 15k ×× 15k MoM matrix of a perfect electric conductor scatterer and a single 30k ×× 30k MoM matrix of a dielectric scatterer, respectively. In addition to improving the processing speed of CMA, our approach provided the same accuracy as independent CMA simulations. The speedup, efficiency, and accuracy of our CMA implementation will enable the analysis of electromagnetic systems much larger than what was previously possible at a fraction of the computational time.

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Plasmonic Nanoantenna Optimization Using Characteristic Mode Analysis

Cited by 21 publications

References 43 publications

Geometry-induced enhancement factor improvement in covered-gold-nanorod-dimer antennas

Geometry-induced enhancement factor improvement in covered-gold-nanorod-dimer antennas

Characteristic Modes: Progress, overview, and emerging topics

Scalable and Fast Characteristic Mode Analysis using GPUs

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