Electromagnetic Solver Research Articles

SQuADDS: A validated design database and simulation workflow for superconducting qubit design

We present an open-source database of superconducting quantum device designs that may be used as the starting point for customized devices. Each design can be generated programmatically using the open-source Qiskit Metal package, and simulated using finite-element electromagnetic solvers. We present a robust workflow for achieving high accuracy on design simulations. Many designs in the database are experimentally validated, showing excellent agreement between simulated and measured parameters. Our database includes a front-end interface that allows users to generate ``best-guess'' designs based on desired circuit parameters. This project lowers the barrier to entry for research groups seeking to make a new class of devices by providing them a well-characterized starting point from which to refine their designs.

RCWA4D: Electromagnetic solver for layered structures with incommensurate periodicities

We describe RCWA4D, an electromagnetic solver for layered structures with incommensurate periodicities. Our method is based on an extension of the rigorous coupled wave analysis. We illustrate our method on the example of twisted bilayer photonic crystal and show that various properties of such structures can be reliably simulated. The method can be generalized to multi-layer structures in general in which each layer is periodic or quasi-periodic.

Analysis of Near-Field Characteristics on Improved Structures of Double-Slot Antipodal Vivaldi Antenna.

A characterization of near-field impulse responses based on electromagnetic (EM) near-field data from an EM solver to explore features of the propagation process on a well-known wideband traveling wave antenna-double-slot Vivaldi antenna-is presented in this article. The intensity, propagating time and partitional response characteristics facilitate interpretation of the propagation process and impacts of the antenna partitions on the process. The EM energy flows guided, reoriented and scattered along a sequence of antennas transmitting and radiating segments were recognized. The geometric features of near-field wavefront surfaces supported evaluation of the EM flow proportions and antenna directivity. Impact of the structural section on radiation was also assessed by the partitional far-field response characteristic in frequency and time domains. Supported by many complementary characteristics in the analyses, inherent features of the propagation process were emphasized and false flags were minimized. By this approach, the simplification for the near-field propagation model contributed to enhancing the insight of near-field propagation processes on the double-slot antipodal Vivaldi antennas and enabled optimizing the antenna structure details.

Optimum design of a novel Ku-band rasorber for RADAR warfare systems using ML neural network

The design, fabrication, and testing of a conformal Frequency Selective Rasorber (FSR) operating at A-T-A mode is designed for RADAR warfare systems. In the design of a miniaturized single-layer FSR element, multiple parameters influencing the absorption and transmission characteristics need to be optimized. This simulation using conventional methods consumes more simulation time. Thus, the geometrical parameters are optimized and predicted using supervised machine learning (ML) techniques to expedite the process. The multiple output regression neural network (MORNN) is used to generate multiple input and output features from the dataset. The ML algorithm is trained using the datasets generated from the electromagnetic solver using which a scalable FSR is synthesized. The reflection coefficient, (|S11|), and transmission coefficient (|S21|) are used as input data, and the dimension of the FSR unit cell for the user input frequency requirements are derived as output data. The extracted dimensions of the FSR offered a small mean square error (MSE) of 0.02 between the desired and observed results. The designed FSR offers absorption at 11.5 GHz and 18.9 GHz while the transmission window extends from 15.02 GHz to 16.09 GHz. The neural network results are endorsed using the EM simulation tool and validated by experimental measurements.

Legume: A free implementation of the guided-mode expansion method for photonic crystal slabs

We describe legume, a free electromagnetic solver that implements the guided-mode expansion method for patterned multilayer waveguides, or photonic crystal slabs. legume has a built-in tool for automatic differentiation, which makes it suitable for the inverse design of photonic crystal structures with desired physical properties. Compared to a previous version of the method (M. Minkov et al., 2020 [12]), here we introduce several new features of the code, we discuss additional technical aspects of the method and its numerical implementation. The novel features that are treated in this paper include: (i) the separation of modes according to their mirror symmetry with respect to a vertical symmetry plane of the photonic structure, (ii) the problem of polarization mixing in coupling to far-field radiation modes, and (iii) the description of active two-dimensional layers through a suitably formulated radiation-matter coupling Hamiltonian, allowing to describe the physics of both weakly and strongly coupled exciton-photon modes, the latter leading to photonic crystal polariton eigenmodes. Detailed and direct comparisons with rigorous coupled-wave analysis simulations are used to test the accuracy of the method and the numerical efficiency of the code. These newly added features of the legume code significantly increase the prospective applications of guided-mode expansion, making it a very practical and versatile tool enabling the design of advanced photonic structures and the description of radiation-matter interaction. Program summaryProgram Title:legumeCPC Library link to program files:https://doi.org/10.17632/kf3cwknx4d.1Developer's repository link:https://github.com/fancompute/legumeLicensing provisions: MITProgramming language: PythonNature of problem: Dispersion and radiative losses of photonic eigenmodes in patterned multilayer waveguides/photonic crystal slabs/periodic metasurfaces. Interaction of photonic modes with exciton resonances leading to exciton-polaritons. Inverse design by optimization of the parameters.Solution method: Finite-basis expansion using a basis of guided modes of an effective homogeneous waveguide, perturbation theory to describe coupling with far-field radiation. Quantum theory of excitons, photons and their interaction to describe the occurrence of exciton-polaritons. Automatic differentiation via Autograd to implement inverse design. In this upgraded version of the legume code we implement symmetrization with respect to a vertical mirror plane and light-matter interaction for exciton-polaritons. Inverse design has been described previously, here we focus on the new features and applications of the code.

Inverse design and optimization of a one-dimensional metagrating beam deflector by smart pattern search

This work introduces a fast semi-analytical algorithm for the inverse design and optimization of a one-dimensional beam deflector metagrating, utilizing Smart Pattern Search (SPS), an enhanced pattern search algorithm from MATLAB’s Global Optimization Toolbox. This algorithm demonstrates a significantly shorter processing time compared to machine learning based approaches for the same metagrating structure setup, parameters, and electromagnetic solver while achieving highly competitive efficiencies. At a wavelength of 1100 nm with angles of 60° and 70°, SPS even outperforms these methods. The SPS algorithm needs no state-of-the-art computers and completes the process in less than 27 min, while for counterpart methods at least several hours are needed on an Intel Core i7-3632QM CPU at 2.2 GHz, with 8 GB of DDR3 RAM.

Large-scale photonic inverse design: computational challenges and breakthroughs

Abstract Recent advancements in inverse design approaches, exemplified by their large-scale optimization of all geometrical degrees of freedom, have provided a significant paradigm shift in photonic design. However, these innovative strategies still require full-wave Maxwell solutions to compute the gradients concerning the desired figure of merit, imposing, prohibitive computational demands on conventional computing platforms. This review analyzes the computational challenges associated with the design of large-scale photonic structures. It delves into the adequacy of various electromagnetic solvers for large-scale designs, from conventional to neural network-based solvers, and discusses their suitability and limitations. Furthermore, this review evaluates the research on optimization techniques, analyzes their advantages and disadvantages in large-scale applications, and sheds light on cutting-edge studies that combine neural networks with inverse design for large-scale applications. Through this comprehensive examination, this review aims to provide insights into navigating the landscape of large-scale design and advocate for strategic advancements in optimization methods, solver selection, and the integration of neural networks to overcome computational barriers, thereby guiding future advancements in large-scale photonic design.

Deep Learning Electromagnetic Inversion Solver Based on a Two-Step Framework for High-Contrast and Heterogeneous Scatterers

Deep Learning Electromagnetic Inversion Solver Based on a Two-Step Framework for High-Contrast and Heterogeneous Scatterers

Anisotropic and Optimized FFT-Based Iterative Electromagnetic Solver for the PEEC Method

Anisotropic and Optimized FFT-Based Iterative Electromagnetic Solver for the PEEC Method

Accelerating the shooting and bouncing ray based electromagnetic scattering calculations via CPU and GPU Implementations: Application to PREDICS tool

In this paper, we present the parallel computation of physical electromagnetic (EM) solvers, namely physical optics (PO), shooting and bouncing rays (SBR), and the physical theory of diffraction (PTD), which are used for EM scattering/diffraction calculations. The accelerated solvers have been tested on CPU and GPU kernels for significantly faster calculation times. As an application, the developed implementation is employed to recently developed tool of PREDICS that uses combined techniques PO, SBR, and PTD for the calculation of EM scattering/diffraction and radar cross section (RCS) from electrically large, complex objects. First, the CPU parallelization of these three combined techniques on the PREDICS code is explained. The parallelization implementation is specially tailored for the nature of SBR technique such that the CPU iteration loop is accomplished over triangular facets of the CAD file by employing a read/write spin lock task dispatching technique. Next, a lock-free implementation of the PO, SBR, and PTD techniques on the GPU threads is presented. Thanks to availability of many GPU cores, the parallelization procedure is widened such that triangular facets, observation angles of elevation and azimuth are used for a much faster calculation set-up. Numerical examples for the EM/RCS calculation of simple canonical and electrically big, complex objects are shared to demonstrate the effectiveness, accuracy and the computational acceleration based on the multi-CPU and multi-GPU implementations. The key metric of the contribution of the study is the speed-up value of the implementations. The acceleration gained via multi-CPU realization yielded directly in parallel with the number of available CPU cores. On the other hand, the acceleration factor can reach to hundreds when multi-GPUs are used. In fact, the acceleration factor has reached to 4.7 for a machine with 4 Core 8 Thread CPU processor, and 141.3 for a 2560 CUDA GPU Core system.

Fast Electromagnetic Inversion Solver Based on Conditional Generative Adversarial Network for High-Contrast and Heterogeneous Scatterers

Fast Electromagnetic Inversion Solver Based on Conditional Generative Adversarial Network for High-Contrast and Heterogeneous Scatterers

Evaluation of methods and metrics for identifying scattering regime of dielectric particulate medium

The norm for analyzing radiative transfer in randomly dispersed particulate medium is to use the independent scattering assumption, where the radiative characteristics of the system are derived from the superposition of the individual particles' radiative characteristics. This assumption is valid when the particles are sparsely distributed. There are established regime maps that are used to identify if the independent scattering assumption is valid. However, these maps which are based on experimental data have been recently questioned and updates are proposed by studies relying on direct solution of Maxwell's equations, in which scattering coefficients, cross-sections and/or efficiencies of particle ensembles are considered to identify presence of dependent scattering effects. Notably, both directionality of the scattering phenomenon and the presence of coherent scattering occurring on the free-space - particle cloud boundary are overlooked while identifying transition between independent and dependent scattering regimes. This study provides a critical overview of recent numerical approaches and shows that a regime map cannot be drawn relying solely on scattering cross section estimated by electromagnetic field solvers. The dependent scattering effects for dielectric particulate medium can be well represented using static structure factor including the directionality of scattering. Transport scattering coefficient that accounts for both magnitude and directionality of the scattering can be used to represent the radiative behavior adequately. Therefore, it can be used as the single intrinsic characteristic for identifying the transition between dependent and independent scattering, rather than the scattering coefficient and the asymmetry parameter alone.

A Digital Twin Development Framework for an Electrical Submersible Pump (ESP)

Premature failure of a sub-system can be critical for an industrial Cyber-Physical System (CPS). A digital twin (DT) assisted predictive maintenance procedure can reduce the risk of costly unplanned maintenance. This study presents a generalized DT development framework for an Electrical Submersible Pump (ESP) that can assist in predictive maintenance. The framework is applied on a single-phase ESP as a proof of concept. The maximum winding temperature of the selected ESP is simulated using a multiphysics simulation tool with transient electromagnetic and transient heat transfer solvers. The simulation parameters were refined using data captured through an ESP free-run experiment. Simulating the total energy loss in the ESP stator and rotor and the transfer of heat from the outer fluid domain facilitates a relationship between the measurable external temperature and the maximum temperature in the stator winding. Following a design of experiments (DOE) approach, a series of simulations were run to establish a statistical model for the winding temperature in terms of the fluid temperature, the time duration a particular temperature was persistent, and the initial maximum stator winding temperature. As the instantaneous maximum stator winding temperature is related to the remaining useful lifetime, it was shown using a case study that the proposed framework can prognosticate the ESP failure, assisting effective decision-making for predictive maintenance of a CPS.

Novel printed passive ultra high frequency radio frequency identification antenna using meander technique

<p>In this paper, a novel radio frequency identification (RFID) antenna using a meander technique associated with a slotted patch is studied for RFID applications in the ultra high frequency (UHF) band [867.5-868 MHz]. The proposed RFID antenna is designed on a Kapton substrate with dielectric constant 3.5 and loss of 0.0027. It consists of two opposite meander line antennas of 8 turns each one and interconnected to ALIEH H3 microchip associated to two slotted patch’s with a global size 105×25×0.1 mm<sup>3</sup>. The proposed RFID antenna is designed and simulated using CST MWS as an electromagnetic solver. The results of the simulation show a return loss of -22.64 dB at 868 MHz, a reading distance of around 5 m, and a simulated input impedance of the antenna are 31.72+j109.68 Ω at the operating frequency 868 MHz.</p>

High‐gain W‐band‐printed antenna on flexible substrate

SummaryThis article demonstrates a novel type of series‐fed planar antenna array for a W‐band (70 GHz) application. The proposed architecture is a 5‐element antenna array with 10 numbers of gap‐coupled parasitic patches in microstrip configuration over a thin, flexible and bio‐compatible substrate named LCP (liquid crystal polymer). A detailed design methodology with all fabrication constraints has been elaborated on here. Full wave analysis of the whole structure has been carried out in the FEM‐based 3D electromagnetic (EM) solver ANSYS HFSS Suite V.19.2. Parametric simulations were studied to achieve the optimized values of all design parameters. Further, an empirical electrical equivalent circuit model is proposed for the antenna array, and it was validated with the simulation results obtained from the FEM solver. Two prototypes have been fabricated, and measurements were carried out to fetch all the designed antenna parameters. The proto version of the antenna offers peak directive gain of about 19 dBi with better than 22 dB of return loss and 80% radiation efficiency for the frequency range of 67–85 GHz. Experimental and simulated results closely match each other. Small deviations are attributed to practical imperfections incurred by fabrication tolerances, measurement inaccuracies, testing, assembly‐related issues and so forth. Finally, the current research work is compared with the recently reported literature.

Compact microstrip patch antenna loaded with Artificial Transmission Line for wireless applications

ABSTRACT This article presents an efficient yet simple spiral antenna design methodology for 2.44 GHz Wireless Local Area Network (WLAN) applications. In the suggested design, the antenna’s radiating section is redesigned with a particular Artificial Transmission Line (ATL) construction that allows for good downsizing. Circuit analysis and electromagnetic solver are used to verify the design, which demonstrate good agreement with the measured results. The electromagnetic simulation and passive component analysis reveal that the new design matches the traditional designs in terms of frequency tuning and size reduction. The overall footprint of the proposed ATL design is (33 × 25 × 1.6) mm3 which equals to 0.2684λ×0.2033λ×0.013λ, where λ is the distance travelled by the wave per cycle at the operating frequency of 2.44 GHz. The antenna has a gain of 3.8dBi and a 50Ω impedance. Modifying the interdigital capacitance and inductance used in the ATL structure to achieve a longer electrical length of the line can also improve the wave guide mode of excitation. As a result, the phase constant is regulated, allowing for good antenna size minimisation. The effect of interdigital inductance and capacitance on the operating bands is also analysed.

Optimization Assisted by Neural Network-Based Machine Learning in Electromagnetic Applications

We introduce an optimization assisted by a neural network (ONN) predictor to the electromagnetic community. ONN belongs to the class of the surrogate model-based optimization approaches, and approximates the objective function using a non-linear approximator – neural network. We provide a comprehensive description of the ONN algorithm and the details of its mathematical formulations. We apply ONN to optimize three popular benchmark functions and compare its performance with some commonly used optimization algorithms, namely particle swarm optimization, genetic algorithm, and Bayesian optimization. For the first time, we demonstrate ONN’s applicability and effectiveness in antenna design problems by optimizing a six-element Yagi-Uda antenna and by solving a challenging ten-dimensional dual-band slotted patch antenna constrained optimization problem. To achieve this, ONN is linked with a full-wave electromagnetic simulation solver through an application user interface. The optimized slotted patch is fabricated and measured to demonstrate how ONN can be part of the full antenna design process. Our empirical results indicate that ONN requires less objective function evaluations to reach the same qualitative point and reaches better optimal points for the same number of iterations for the studied benchmark functions and antenna optimization problems compared to the aforementioned baseline optimization algorithms.

Microwave Backscatter Phenomenology of Corn Fields at L-Band Using a Full-Wave Electromagnetic Solver

Microwave Backscatter Phenomenology of Corn Fields at L-Band Using a Full-Wave Electromagnetic Solver

Scaling Up Wave Calculations with a Scattering Network

Wave scattering is a challenging numerical problem, yet it is central to fields as diverse as seismology, fluid dynamics, acoustics, and photonics. Complex structures scatter waves in random yet deterministic ways. Advances in our understanding and control of scattering are key to applications such as deep-tissue microscopy. However, computing the internal fields on a scale relevant to microscopy remains excessively demanding for both conventional methods and physics-based neural networks. Here, we show how coherent scattering calculations can be scaled up to 21 × 10 6 cubic wavelengths by mapping the physics of multiple scattering onto a deterministic neural network that efficiently harnesses publicly available machine learning infrastructure. We refer to this as a scattering network. Memory usage, an important bottleneck to scaling beyond (10 μm)³, is kept to a minimum by the recurrent network topology and the convolutional derivatives it embodies. Tight integration with an open-source electromagnetic solver enables any researcher with an internet connection to compute complex light-wave scattering throughout volumes as large as (130 μm)³ or 25 mm 2 .

Utilizing Mixed Training and Multi-Head Attention to Address Data Shift in AI-Based Electromagnetic Solvers for Nano-Structured Metamaterials.

When designing nano-structured metamaterials with an iterative optimization method, a fast deep learning solver is desirable to replace a time-consuming numerical solver, and the related issue of data shift is a subtle yet easily overlooked challenge. In this work, we explore the data shift challenge in an AI-based electromagnetic solver and present innovative solutions. Using a one-dimensional grating coupler as a case study, we demonstrate the presence of data shift through the probability density method and principal component analysis, and show the degradation of neural network performance through experiments dealing with data affected by data shift. We propose three effective strategies to mitigate the effects of data shift: mixed training, adding multi-head attention, and a comprehensive approach that combines both. The experimental results validate the efficacy of these approaches in addressing data shift. Specifically, the combination of mixed training and multi-head attention significantly reduces the mean absolute error, by approximately 36%, when applied to data affected by data shift. Our work provides crucial insights and guidance for AI-based electromagnetic solvers in the optimal design of nano-structured metamaterials.