http://ab-initio.mit.edu/~oskooi/papers/Oskooi10.pdf

Meep: A flexible free-software package for electromagnetic simulations by the FDTD method

Coinage metals, such as Au, Ag, and Cu, have been important materials throughout history.1 While in ancient cultures they were admired primarily for their ability to reflect light, their applications have become far more sophisticated with our increased understanding and control of the atomic world. Today, these metals are widely used in electronics, catalysis, and as structural materials, but when they are fashioned into structures with nanometer-sized dimensions, they also become enablers for a completely different set of applications that involve light. These new applications go far beyond merely reflecting light, and have renewed our interest in maneuvering the interactions between metals and light in a field known as plasmonics.2–6

In plasmonics, the metal nanostructures can serve as antennas to convert light into localized electric fields (E-fields) or as waveguides to route light to desired locations with nanometer precision. These applications are made possible through a strong interaction between incident light and free electrons in the nanostructures. With a tight control over the nanostructures in terms of size and shape, light can be effectively manipulated and controlled with unprecedented accuracy.3,7 While many new technologies stand to be realized from plasmonics, with notable examples including superlenses,8 invisible cloaks,9 and quantum computing,10,11 conventional technologies like microprocessors and photovoltaic devices could also be made significantly faster and more efficient with the integration of plasmonic nanostructures.12–15 Of the metals, Ag has probably played the most important role in the development of plasmonics, and its unique properties make it well-suited for most of the next-generation plasmonic technologies.16–18

1.1. What is Plasmonics?
Plasmonics is related to the localization, guiding, and manipulation of electromagnetic waves beyond the diffraction limit and down to the nanometer length scale.4,6 The key component of plasmonics is a metal, because it supports surface plasmon polariton modes (indicated as surface plasmons or SPs throughout this review), which are electromagnetic waves coupled to the collective oscillations of free electrons in the metal.

While there are a rich variety of plasmonic metal nanostructures, they can be differentiated based on the plasmonic modes they support: localized surface plasmons (LSPs) or propagating surface plasmons (PSPs).5,19 In LSPs, the time-varying electric field associated with the light (Eo) exerts a force on the gas of negatively charged electrons in the conduction band of the metal and drives them to oscillate collectively. At a certain excitation frequency (w), this oscillation will be in resonance with the incident light, resulting in a strong oscillation of the surface electrons, commonly known as a localized surface plasmon resonance (LSPR) mode.20 This phenomenon is illustrated in Figure 1A. Structures that support LSPRs experience a uniform Eo when excited by light as their dimensions are much smaller than the wavelength of the light.



Figure 1

Schematic illustration of the two types of plasmonic nanostructures discussed in this article as excited by the electric field (Eo) of incident light with wavevector (k). In (A) the nanostructure is smaller than the wavelength of light and the free electrons ...



In contrast, PSPs are supported by structures that have at least one dimension that approaches the excitation wavelength, as shown in Figure 1B.4 In this case, the Eo is not uniform across the structure and other effects must be considered. In such a structure, like a nanowire for example, SPs propagate back and forth between the ends of the structure. This can be described as a Fabry-Perot resonator with resonance condition l=nλsp, where l is the length of the nanowire, n is an integer, and λsp is the wavelength of the PSP mode.21,22 Reflection from the ends of the structure must also be considered, which can change the phase and resonant length. Propagation lengths can be in the tens of micrometers (for nanowires) and the PSP waves can be manipulated by controlling the geometrical parameters of the structure.23

/pdf/controlling-the-synthesis-and-assembly-of-silver-1ktlgomyt6.pdf

Controlling the synthesis and assembly of silver nanostructures for plasmonic applications

This book is a tutorial written by researchers and developers behind the FEniCS Project and explores an advanced, expressive approach to the development of mathematical software. The presentation spans mathematical background, software design and the use of FEniCS in applications. Theoretical aspects are complemented with computer code which is available as free/open source software. The book begins with a special introductory tutorial for beginners. Followingare chapters in Part I addressing fundamental aspects of the approach to automating the creation of finite element solvers. Chapters in Part II address the design and implementation of the FEnicS software. Chapters in Part III present the application of FEniCS to a wide range of applications, including fluid flow, solid mechanics, electromagnetics and geophysics.

Automated Solution of Differential Equations by the Finite Element Method: The FEniCS Book

Foreword. Series Editor's Foreword. Preface. 1. Liquid crystal physics.* Introduction.* Thermodynamics and statistic physics.* Orientational order.* Elastic properties of liquid crystals.* Response of liquid crystals to electro-magnetic fields.* Anchoring effects of nematic liquid crystal at surfaces. 2. Propagation of light in anisotropic optical medium.* Electromagnetic wave.* Polarization.* Propagation of light in uniform anisotropic optical media.* Propagation of light in cholesteric liquid crystals. 3. Optical modeling methods.* Jones matrix method.* Mueller matrix method.* Berreman 4x4 method. 4. Effects of Electric field on Liquid Crystals.* Dielectric interaction.* Flexoelectric Effect.* Ferroelectricity in liquid crystals. 5. Freedericksz transition.* Calculus of variation.* The Fredeericksz transition: statics.* The Freedericksz transition: dynamics. 6. Liquid Crystal Materials.* Introduction.* Refractive indices.* Dielectric constants.* Rotational Viscosity.* Elastic constant.* Figure-of-merits.* Refractive index matching between liquid crystals and polymers. 7. Modeling of liquid crystal director configuration.* Electric energy of liquid crystals.* Modeling electric field.* Simulation of liquid crystal director configuration. 8. Transmissive liquid crystal display.* Introduction.* Twisted nematic cells.* In plane switching (IPS) mode.* Vertical alignment (VA) mode.* Multi-domain Vertical Alignment (MVA) Cells.* Optically compensated bend (OCB) cell. 9. Reflective and Trasreflective display.* Introduction.* Reflective liquid crystal displays.* Transflector.* Classification of Transflective LCDs.* Dual-cell-gap Transflective LCDs.* Single-cell-gap Transflective LCDs.* Performance of transflective LCDs. 10. Liquid crystal display matrices, drive schemes and bistable displays.* Segmented displays.* Passive matrix displays and drive scheme.* Active Matrix Displays.* Bistable ferroelectric liquid crystal displays and drive scheme.* Bistable nematic displays.* Bistable cholesteric reflective display. 11. Liquid crystal/polymer composites. * Introduction.* Phase separation.* Scattering properties of liquid crystal/polymer composites.* Polymer dispersed liquid crystals.* Polymer stabilization liquid crystals.* Displays from liquid crystal/polymer composites. 12. Tunable liquid crystal photonic devices. * Introduction.* Laser beam steering.* Variable Optical Attenuators.* Tunable-Focus Lens.* Polarization-Independent LC Devices. Index.

Fundamentals of Liquid Crystal Devices

https://web.stanford.edu/group/fan/publication/Liu_ComputerPhysicsCommunications_183_2233_2012.pdf

S4 : A free electromagnetic solver for layered periodic structures

This Key Note presents a summary of the development of the Finite Element Method in the field of Electromagnet ic Engineering, together with a description of several contributions of the authors to the Finite Element Method and its application to the solution of electromagnetic problems. First, a self-adaptive mesh scheme is presented in the context of the quasi-static and full-wave analysis of general anisotropic multiconductor arbitrary shaped waveguiding structures. A comparison between two a posteriori error estimates is done. The first one is based on the complete residual of the differential equations defining the problem. The second one is based on a recovery or smoothing technique of the electromagnetic field. Next, an implementation of the first family of Nedelec's curl-conforming elements done by the authors is outlined. Its features are highlighted and compared with other curl-conforming elements. A presentation of an iterative procedure using a numerically exact radiation condition for the analysis of open (scattering and radiation) problems follows. Other contributions of the authors, like the use of wavelet like basis functions and an implementation of a Time Domain Finite Element Method in the context of two-dimensional scattering problems are only mentioned due to the lack of space.

The finite element method in electromagnetics

3D magnetotelluric modeling using high-order tetrahedral Nédélec elements on massively parallel computing platforms

A novel feeding method for linear DRA arrays is presented, illuminating the use of the power divider, transitions, and launchers, and keeping uniform excitation to array elements. This results in a high-gain DRA array with low losses with a design that is simple, compact and inexpensive. The proposed feeding method is based on exciting standing waves using discrete metallic patches in a simple design procedure. Two arrays with two and four DRA elements are presented as a proof of concept, which provide high gains of 12 and 15dBi, respectively, which are close to the theoretical limit based on array theory. The radiation efficiency for both arrays is about 93%, which is equal to the array element efficiency, confirming that the feeding method does not add losses as in the case of standard methods. To facilitate the fabrication process, the entire array structure is 3D-printed, which significantly decreases the complexity of fabrication and alignment. Compared to state-of-the-art feeding techniques, the proposed method provides higher gain and higher efficiency with a smaller electrical size.

https://www.mdpi.com/1424-8220/22/8/3089/pdf?version=1650275797

Standing-Wave Feeding for High-Gain Linear Dielectric Resonator Antenna (DRA) Array

/pdf/tailored-meshing-for-parallel-3d-electromagnetic-modeling-2cybtc5i.pdf

Tailored meshing for parallel 3D electromagnetic modeling using high-order edge elements

Abstract Achieving maximum parallel performance on multi-core CPUs and many-core GPUs is a challenging task depending on multiple factors. These include, for example, the number and granularity of the computations or the use of the memories of the devices. In this paper, we assess those factors by evaluating and comparing different parallelizations of the same problem on a multiprocessor containing a CPU with 40 cores and four P100 GPUs with Pascal architecture. We use, as study case, the convolutional operation behind a non-standard finite element mesh truncation technique in the context of open region electromagnetic wave propagation problems. A total of six parallel algorithms implemented using OpenMP and CUDA have been used to carry out the comparison by leveraging the same levels of parallelism on both types of platforms. Three of the algorithms are presented for the first time in this paper, including a multi-GPU method, and two others are improved versions of algorithms previously developed by some of the authors. This paper presents a thorough experimental evaluation of the parallel algorithms on a radar cross-sectional prediction problem. Results show that performance obtained on the GPU clearly overcomes those obtained in the CPU, much more so if we use multiple GPUs to distribute both data and computations. Accelerations close to 30 have been obtained on the CPU, while with the multi-GPU version accelerations larger than 250 have been achieved. 

/pdf/strategies-to-parallelize-a-finite-element-mesh-truncation-1sc4dnkp.pdf

Luis-Emilio García-Castillo

Papers

The finite element method in electromagnetics

3D magnetotelluric modeling using high-order tetrahedral Nédélec elements on massively parallel computing platforms

Standing-Wave Feeding for High-Gain Linear Dielectric Resonator Antenna (DRA) Array

Tailored meshing for parallel 3D electromagnetic modeling using high-order edge elements

Strategies to parallelize a finite element mesh truncation technique on multi-core and many-core architectures