Full-wave Electromagnetic Simulation Research Articles

Broadband Permittivity Characterization of a Substrate Material Using Deep Neural Network Trained With Full-Wave Simulations

It is crucial to know the permittivity of dielectric materials used in radio frequency (RF) components and devices because their operation frequency and loss characteristics are significantly affected by the permittivity. In this study, we propose a permittivity characterization technique based on a deep neural network (DNN). The latter was trained using data obtained from full-wave electromagnetic simulation software. With the DNN trained with more than 95% testing accuracy, the measured complex transmission coefficient of the material under test (MUT) was assigned as an input to the DNN model, and the complex permittivity of the MUT was retrieved at the output. The proposed technique was validated by measuring FR-4 epoxy resin substrates of different thicknesses. The results obtained with the DNN model showed good agreement with each other, with an error of less than 1.2% for the relative permittivity value over a broad frequency range of 1 – 10 GHz. We also compared the results with those obtained from a conventional permittivity characterization technique based on analytical solutions to highlight the effectiveness of the proposed method.

On-demand terahertz surface wave generation with microelectromechanical-system-based metasurface

During the past decade, metasurfaces have shown great potential to complement standard optics, providing novel pathways to control the phase, amplitude, and polarization of electromagnetic waves utilizing arrays of subwavelength resonators. We present dynamic surface wave (SW) switching at terahertz frequencies utilizing a mechanically reconfigurable metasurface. Our metasurface is based on a microelectromechanical system (MEMS) consisting of an array of micro-cantilever structures, enabling dynamic tuning between a plane wave (PW) and a SW for normal incidence terahertz radiation. This is realized using line-by-line voltage control of the cantilever displacements to achieve full-span ( 2 π ) phase control. Full-wave electromagnetic simulations and terahertz time-domain spectroscopy agree with coupled mode theory, which was employed to design the metasurface device. A conversion efficiency of nearly 60% has been achieved upon switching between the PW and SW configurations. Moreover, a nearly 100 GHz working bandwidth is demonstrated. The MEMS-based control modality we demonstrate can be used for numerous applications, including but not limited to terahertz multifunctional metasurface devices for spatial light modulation, dynamic beam steering, focusing, and beam combining, which are crucial for future “beyond 5G” communication systems.

Electrical Conductivity Measurement on Metallic Materials With a Cylindrical Resonator

This article presents a novel method for measuring the electrical conductivity of metallic materials using a cylindrical resonator. The theory and the process of measuring the electrical conductivity are explained. The cylindrical resonator is designed and proved using full-wave electromagnetic simulations. A prototype operating at 10.5 GHz is fabricated, and the scattering parameters are measured. The quality factor of the cylindrical resonator is calculated at the first step from the measured scattering parameters, and the electrical conductivity of the metallic material is derived using Maxwell's equations. By replacing the top cover of the cylindrical resonator with different materials or metallic sheets, the electrical conductivities can be measured easily. For validating the method, three top covers and six metallic sheets are fabricated and tested. The measured electrical conductivities are compared with other published results and typical values. Besides, by adjusting the operating frequency of the cylindrical resonator, the proposed method can be applied to determine the electrical conductivity at specific frequencies.

Globalized parametric optimization of microwave components by means of response features and inverse metamodels

Simulation-based optimization of geometry parameters is an inherent and important stage of microwave design process. To ensure reliability, the optimization process is normally carried out using full-wave electromagnetic (EM) simulation tools, which entails significant computational overhead. This becomes a serious bottleneck especially if global search is required (e.g., design of miniaturized structures, dimension scaling over broad ranges of operating frequencies, multi-modal problems, etc.). In pursuit of mitigating the high-cost issue, this paper proposes a novel algorithmic approach to rapid EM-driven global optimization of microwave components. Our methodology incorporates a response feature technology and inverse regression metamodels to enable fast identification of the promising parameter space regions, as well as to yield a good quality initial design, which only needs to be tuned using local routines. The presented technique is illustrated using three microstrip circuits optimized under challenging scenarios, and demonstrated to exhibit global search capability while maintaining low computational cost of the optimization process of only about one hundred of EM simulations of the structure at hand on the average. The performance is shown to be superior in terms of efficacy over both local algorithms and nature-inspired global methods.

Accelerated Gradient-Based Optimization of Antenna Structures Using Multifidelity Simulations and Convergence-Based Model Management Scheme

The importance of numerical optimization has been steadily growing in the design of contemporary antenna structures. The primary reason is the increasing complexity of antenna topologies, a typically large number of adjustable parameters that have to be simultaneously tuned. Design closure is no longer possible using traditional methods, including theoretical models or supervised parameter sweeping. To ensure reliability, optimization is normally carried out at the level of full-wave electromagnetic (EM) simulations, which incurs major computational expenses. The issue can be alleviated using a variety of methods such as the incorporation of adjoint sensitivities, sparse sensitivity updates (for local optimization), or the employment of surrogate modeling methods (in the context of global search). Another possibility is utilization of variable-fidelity simulation models, which, in practice, is most often restricted to two levels (coarse/fine or low-/high-fidelity models), and accompanied by appropriate low-fidelity model correction (e.g., space mapping). This article proposes an accelerated version of a trust-region (TR) gradient-based procedure, which involves simulation model management by continuous adjustment of EM analysis fidelity throughout the optimization process. Decision-making process is based on the convergence status of the algorithm. The initial stages of the optimization run are executed using the coarsest discretization of the structure at hand with the model being gradually refined toward the end of the process. This enables considerable computational savings without degrading the quality of the final design. The presented approach has been comprehensively validated using a benchmark set of four broadband antennas and compared with the reference TR procedure and two state-of-the-art accelerated algorithms. The average computational savings are almost 60% as compared with the reference.

On geometry parameterization for simulation-driven design closure of antenna structures

Full-wave electromagnetic (EM) simulation tools have become ubiquitous in antenna design, especially final tuning of geometry parameters. From the reliability standpoint, the recommended realization of EM-driven design is through rigorous numerical optimization. It is a challenging endeavor with the major issues related to the high computational cost of the process, but also the necessity of handling several objectives and constraints over often highly-dimensional parameter spaces. From the numerical perspective, making decisions about the formulation of the optimization problem, the approach to handling the design constraints, but also parameterization of the antenna geometry, are all non-trivial. At the same time, these issues are interleaved, and may play an important role in the performance and reliability of the simulation-based design closure process. This paper demonstrates that the approach to arranging the structure parameterization (e.g., the use of absolute or relative parameters) may have a major effect of the optimization outcome. Our investigations are carried out using three broadband monopole antennas optimized under different scenarios and using different parameterizations. In particular, the results indicate that relative parameterization is preferred for optimization of input characteristics, whereas absolute parameterization is more suitable for size reduction.

The Development of Frequency Tripler Based on Six-Anode Schottky Varactors.

The development of a millimeter-wave unbalanced frequency tripler based on the nonlinear characteristics of planar Schottky varactors is presented. The entire module is designed by hybrid integration. A frequency multiplier circuit model was established to reflect the influence of diode parameters and the impedance matching on the multiplier in different frequency bands. The effect of junction imbalance on the output power of the frequency multiplier was investigated and the multiplier was improved based on the basic design. The addition of a cut microstrip stub in the improved diode unit reduced the impact of a power imbalance on frequency multiplier performance. The characteristics of the multiplier circuit were analyzed by the full-wave electromagnetic simulation of the three-dimensional structure and the harmonic balance simulation of the circuit. Test results showed that the peak output power of the improved frequency tripler was 12.6 mW at 277 GHz with an input power of 200 mW, an effective 12% improvement over the basic design.

Small Triple-Band Meandered PIFA for Brain-Implantable Biotelemetric Systems: Development and Testing in a Liquid Phantom

We present a meandered triple-band planar-inverted-F antenna (PIFA) for integration into brain-implantable biotelemetric systems. The target applications are wireless data communication, far-field wireless power transfer, and switching control between sleep/wake-up mode at the Medical Device Radiocommunication Service (MedRadio) band (401–406 MHz) and Industrial, Scientific and Medical (ISM) bands (902–928 MHz and 2400–2483.5 MHz), respectively. By embedding meandered slots into the radiator and shorting it to the ground, we downsized the antenna to the volume of 11 × 20.5 × 1.8 mm3. We optimized the antenna using a 7-layer numerical human head model using full-wave electromagnetic field simulation. In the simulation, we placed the implant in the cerebrospinal fluid (CSF) at a depth of 13.25 mm from the body surface, which is deeper than in most works on implantable antennas. We manufactured and tested the antenna in a liquid phantom which we replicated in the simulator for further comparison. The measured gain of the antenna reached the state-of-the-art values of −43.6 dBi, −25.8 dBi, and −20.1 dBi at 402 MHz, 902 MHz, and 2400 MHz, respectively.

Knowledge-based performance-driven modeling of antenna structures

The importance of surrogate modeling techniques in the design of modern antenna systems has been continuously growing over the recent years. This phenomenon is a matter of practical necessity rather than simply a fashion. On the one hand, antenna design procedures rely on full-wave electromagnetic (EM) simulation tools. On the other hand, the computational costs incurred by repetitive EM analyses involved in solving common tasks (parameter tuning, uncertainty quantification, multi-criterial design, etc.), are often prohibitive. The replacement of full-wave simulations by fast surrogates may mitigate these issues; as a matter of fact, it is the only viable option for carrying out EM-driven design in many cases. Among available modeling approaches, data-driven surrogates are by far the most popular due to their accessibility and versatility. At the same time, a construction of reliable models is hindered by the curse of dimensionality, high nonlinearity of antenna characteristics, as well as broad ranges of parameters and operating conditions that the model has to cover to ensure its design utility. Recently proposed performance-driven modeling frameworks offer a workaround these issues by restricting the model domain to the parameter space regions that contain high-quality designs (w.r.t. the assumed performance metrics). However, the domain determination requires acquisition of a set of pre-optimized reference designs, which adds to the overall computational cost of the surrogate model setup in a significant manner. This work proposes a novel two-stage knowledge-based approach, where the confined domain is defined without using any reference designs. Instead, a preselected set of random observables is employed to establish an inverse regression model being a basis for domain determination of the final surrogate. Comprehensive numerical validation involving three antenna structures indicates that our methodology offers the computational benefits similar to those of the previous performance-driven methods while considerably reducing the initial setup cost, by a factor of sixty percent on the average, which has been achieved by exploiting the problem-specific knowledge.

Surface-Enhanced Raman Scattering-Active Gold-Decorated Silicon Nanowire Substrates for Label-Free Detection of Bilirubin.

Bilirubin (BR) is a product of hemoglobin breakdown, and its increasing levels in the blood may indicate liver disorders and lead to jaundice. Kernicterus is most dangerous in newborns when the unconjugated BR concentration can quickly rise to toxic levels, causing neurological damage and even death. The development of an accurate, fast, and sensitive sensor for BR detection will help reduce diagnostic time and ensure successful treatment. In this study, we propose a new method for creating a surface-enhanced Raman scattering (SERS)-active substrate based on gold-decorated silicon nanowires (Au@SiNWs) for sensitive label-free BR detection. Gold-assisted chemical etching of crystalline silicon wafers was used to synthesize SiNWs, the tops of which were then additionally decorated with gold nanoparticles. The low detection limit of model analyte 4-mercaptopyridine down to the concentration of 10-8 M demonstrated the excellent sensitivity of the obtained substrates for SERS application. The theoretical full-wave electromagnetic simulations of Raman scattering in the Au@SiNW substrates showed that the major contribution to the total SERS signal comes from the analyte molecules located on the SiNW surface near the gold nanoparticles. Therefore, for efficient BR adsorption and SERS detection, the surface of the SiNWs was modified with amino groups. Label-free detection of BR using amino modified Au@SiNWs with high point-to-point, scan-to-scan, and batch-to-batch reproducibility with a detection limit of 10-6 M has been demonstrated. Artificial urine, mimicking human urine samples, was used as the matrix to get insights into the influence of different parameters such as matrix complexity on the overall BR SERS signal. The signal stability was demonstrated for 7 days after adsorption of BR with a concentration of 5 × 10-5 M, which is the required sensitivity for clinical applications.

On the accuracy of Tier 4 simulations to predict RF heating of wire implants during magnetic resonance imaging at 1.5 T.

Magnetic Resonance Imaging (MRI) access remains conditional to patients with conductive medical implants, as RF heating generated around the implant during scanning may cause tissue burns. Experiments have been traditionally used to assess this heating, but they are time-consuming and expensive, and in many cases cannot faithfully replicate the in-vivo scenario. Alternatively, ISO TS 10974 outlines a four-tier RF heating assessment approach based on a combination of experiments and full-wave electromagnetic (EM) simulations with varying degrees of complexity. From these, Tier 4 approach relies entirely on EM simulations. There are, however, very few studies validating such numerical models against direct thermal measurements. In this work, we evaluated the agreement between simulated and measured RF heating around wire implants during RF exposure at 63.6 MHz (proton imaging at 1.5 T). Heating was assessed around wire implants with 25 unique trajectories within an ASTM phantom. The root mean square percentage error (RMSPE) of simulated vs. measured RF heating remained <1.6% despite the wide range of observed heating (0.2 °C-53 °C). Our results suggest that good agreement can be achieved between experiments and simulations as long as important experimental features such as characteristics of the MRI RF coil, implant's geometry, position, and trajectory, as well as electric and thermal properties of gel are closely mimicked in simulations.Clinical Relevance- This work validates the application of full-wave EM simulations for modeling and predicting RF heating of conductive wires in an MRI environment, providing researchers with a validated tool to assess MRI safety in patients with implants.

Predicting RF Heating of Conductive Leads During Magnetic Resonance Imaging at 1.5 T: A Machine Learning Approach.

The number of patients with active implantable medical devices continues to rise in the United States and around the world. It is estimated that 50-75% of patients with conductive implants will need magnetic resonance imaging (MRI) in their lifetime. A major risk of performing MRI in patients with elongated conductive implants is the radiofrequency (RF) heating of the tissue surrounding the implant's tip due to the antenna effect. Currently, applying full-wave electromagnetic simulation is the standard way to predict the interaction of MRI RF fields with the human body in the presence of conductive implants; however, these simulations are notoriously extensive in terms of memory requirement and computational time. Here we present a proof-of-concept simulation study to demonstrate the feasibility of applying machine learning to predict MRI-induced power deposition in the tissue surrounding conductive wires. We generated 600 clinically relevant trajectories of leads as observed in patients with cardiac conductive implants and trained a feedforward neural network to predict the 1g-averaged SAR at the lead tips knowing only the background field of MRI RF coil and coordinates of points along the lead trajectory. Training of the network was completed in 11.54 seconds and predictions were made within a second with R2 = 0.87 and Root Mean Squared Error (RMSE) = 14.5 W/kg. Our results suggest that machine learning could provide a promising approach for safety assessment of MRI in patients with conductive leads.Clinical Relevance- Machine learning can potentially allow real-time assessment of MRI RF safety in patients with conductive leads when only the knowledge of lead's trajectory (image-based) and MRI RF coil features (vendor-specific) are in hand.

Polarization and Incidence Angle Independent Low-Profile Wideband Metamaterial Electromagnetic Absorber Using Indium Tin Oxide (ITO) Film

We use indium tin oxide (ITO), one of the representative resistive materials, for the implementation of a metamaterial electromagnetic (EM) absorber with a high absorbance in a wide frequency range. Highly symmetrical split ring resonators made of ITO film are deposited on the polyethylene terephthalate with transparent and flexible features, and such a configuration causes the proposed absorber to be insensitive to polarization and incidence angles. The proposed absorber, with a profile of only 0.171λ, exhibits a wideband absorbance of 7.2 GHz to 27 GHz, with a 90% absorption criterion. A prototype is built, and all the computed expectations from the full-wave EM simulations in this work are verified experimentally.

Deep learning-based design of broadband GHz complex and random metasurfaces

We are interested in exploring the limit in using deep learning (DL) to study the electromagnetic (EM) response for complex and random metasurfaces, without any specific applications in mind. For simplicity, we focus on a simple pure reflection problem of a broadband EM plane wave incident normally on such complex metasurfaces in the frequency regime of 2–12 GHz. In doing so, we create a DL-based framework called the metasurface design deep convolutional neural network (MSDCNN) for both forward and inverse designs of three different classes of complex metasurfaces: (a) arbitrary connecting polygons, (b) basic pattern combination, and (c) fully random binary patterns. The performance of each metasurface is evaluated and cross-benchmarked. Dependent on the type of complex metasurfaces, sample size, and DL algorithms used, the MSDCNN is able to provide good agreement and can be a faster design tool for complex metasurfaces than the traditional full-wave EM simulation methods. However, no single universal deep convolutional neural network model can work well for all metasurface classes based on detailed statistical analysis (such as mean, variance, kurtosis, and mean-squared error). Our findings report important information on the advantages and limitations of current DL models in designing these ultimately complex metasurfaces.

Extrinsic equivalent circuit modeling of InP HEMTs based on full-wave electromagnetic simulation

With the widespread utilization of indium-phosphide-based high-electron-mobility transistors (InP HEMTs) in the millimeter-wave (mmW) band, the distributed and high-frequency parasitic coupling behavior of the device is particularly prominent. We present an InP HEMT extrinsic parasitic equivalent circuit, in which the conductance between the device electrodes and a new gate–drain mutual inductance term L mgd are taken into account for the high-frequency magnetic field coupling between device electrodes. Based on the suggested parasitic equivalent circuit, through HFSS and advanced design system (ADS) co-simulation, the equivalent circuit parameters are directly extracted in the multi-step system. The HFSS simulation prediction, measurement data, and modeled frequency response are compared with each other to verify the feasibility of the extraction method and the accuracy of the equivalent circuit. The proposed model demonstrates the distributed and radio-frequency behavior of the device and solves the problem that the equivalent circuit parameters of the conventional InP HEMTs device are limited by the device model and inaccurate at high frequencies when being extracted.

A novel 3D printed curved monopole microstrip antenna design for biomedical applications

This paper proposes a novel and compact monopole microstrip antenna design with a three-dimensional (3D) printed curved substrate for biomedical applications. A curved substrate was formed by inserting a semi-cylinder structure in the middle of the planar substrate consisting of polylactic acid. The antenna was fed with a microstrip line, and a partial ground plane was formed at the bottom side of the substrate. The copper plane with two triangular slots is arranged on the curved semi-cylinder structure of the substrate. The physical dimensions of the radiating plane and ground plane were optimally determined with the use of the sparrow search algorithm to provide a wide-10dB bandwidth between 3 and 12GHz. A total of six microstrip antennas having different parameters related to physical dimensions were designed and simulated to compare the performance of the proposed antenna with the help of full-wave electromagnetic simulation software called CST Microwave Studio. The proposed curved antenna was fabricated, and a PNA network analyzer was used to measure the S11 of the proposed antenna. It was demonstrated that the measured S11 covers the desired frequency range.

Analysis of C-shaped Compact Microstrip Antennas Using Deep Neural Networks Optimized by Manta Ray Foraging Optimization with Lévy-Flight Mechanism

In recent years, microstrip antennas have become a popular research subject with the increasing use of mobile technologies. With the development of neural networks, the design and analysis of microstrip antennas are carried out quickly with high accuracy. However, optimizing the weight matrices and bias vectors of deep neural learning models is an important challenge for engineering problems. This study presents a deep neural network-based (DNN-based) neural model to estimate the gain and scattering parameter (S11) of C-shaped compact microstrip antennas (CCMAs). For this purpose, the S11 and gain values of 324 CCMAs with different physical and electrical properties were obtained using full-wave electromagnetic simulation software based on the finite integration technique (FIT). The data related to 324 CCMAs were used for the training and testing process. The improved manta ray foraging optimization (MRFO) algorithm based on the Lévy-flight (LF) mechanism was used to optimize the connection weights matrices and bias vectors. The MRFO-optimized model has estimation success for training and testing data as 0.925 and 0.922, in terms of R2 score, respectively. The estimated resonant frequencies using the trained model are compared with the studies in the literature, and an average percentage error (APE) of 0.933% is obtained.

Fast and reliable knowledge-based design closure of antennas by means of iterative prediction-correction scheme

PurposeA novel framework for expedited antenna optimization with an iterative prediction-correction scheme is proposed. The methodology is comprehensively validated using three real-world antenna structures: narrow-band, dual-band and wideband, optimized under various design scenarios.Design/methodology/approachThe keystone of the proposed approach is to reuse designs pre-optimized for various sets of performance specifications and to encode them into metamodels that render good initial designs, as well as an initial estimate of the antenna response sensitivities. Subsequent design refinement is realized using an iterative prediction-correction loop accommodating the discrepancies between the actual and target design specifications.FindingsThe presented framework is capable of yielding optimized antenna designs at the cost of just a few full-wave electromagnetic simulations. The practical importance of the iterative correction procedure has been corroborated by benchmarking against gradient-only refinement. It has been found that the incorporation of problem-specific knowledge into the optimization framework greatly facilitates parameter adjustment and improves its reliability.Research limitations/implicationsThe proposed approach can be a viable tool for antenna optimization whenever a certain number of previously obtained designs are available or the designer finds the initial effort of their gathering justifiable by intended re-use of the procedure. The future work will incorporate response features technology for improving the accuracy of the initial approximation of antenna response sensitivities.Originality/valueThe proposed optimization framework has been proved to be a viable tool for cost-efficient and reliable antenna optimization. To the knowledge, this approach to antenna optimization goes beyond the capabilities of available methods, especially in terms of efficient utilization of the existing knowledge, thus enabling reliable parameter tuning over broad ranges of both operating conditions and material parameters of the structure of interest.

Constrained multi-objective optimization of compact microwave circuits by design triangulation and pareto front interpolation

Development of microwave components is an inherently multi-objective task. This is especially pertinent to the design closure stage, i.e., final adjustment of geometry and/or material parameters carried out to improve the electrical performance of the system. The design goals are often conflicting so that the improvement of one normally leads to a degradation of others. Compact microwave passives constitute a representative case: reduction of the circuit footprint area is detrimental to electrical figures of merit (e.g., the operating bandwidth). Identification of the best available trade-off designs requires multi-objective optimization (MO). This is a computationally expensive task, especially when executed at the level of full-wave electromagnetic (EM) simulation. The computational complexity issue can be mitigated through the employment of surrogate modeling methods, yet their application is limited by a typically high nonlinearity of system responses, and the curse of dimensionality. In this paper, a novel technique for fast MO of compact microwave components is proposed, which allows for sequential rendition of the trade-off designs using triangulation of the already available Pareto front as well as rapid refinement algorithms. Our methodology is purely deterministic; in particular, it does not rely on population-based nature-inspired procedures. The three major benefits are low computational cost, possibility of handling explicit design constraints, and a capability of producing a visually uniform representation of the Pareto front. The algorithm is demonstrated using a compact branch-line coupler and a three-section impedance matching transformer. In both cases, considerable savings are obtained over the benchmark, here, the state-of-the-art surrogate-assisted MO technique.

Full Explicit Numerical Modeling in Time-Domain for Nonlinear Electromagnetics Simulations in Ultrafast Laser Nanostructuring

The purpose of this paper is to present a new and accurate, fully explicit finite-difference time-domain method for modeling nonlinear electromagnetics. The approach relies on a stable algorithm based on a general vector auxiliary differential equation in order to solve the curl Maxwell’s equation in a frequency-dependent and nonlinear medium. The energy conservation and stability of the presented scheme are theoretically proved. The algorithms presented here can accurately describe laser pulse interaction with metals and nonlinear dielectric media interfaces where Kerr and Raman effects, as well as multiphoton ionization and metal dispersion, occur simultaneously. The approach is finally illustrated by simulating the nonlinear propagation of an ultrafast laser pulse through a dielectric medium transiently turning to inhomogeneous metal-like states by local free-electron plasma formation. This free carrier generation can also be localized in the dielectric region surrounding nanovoids and embedded metallic nanoparticles, and may trigger collective effects depending on the distance between them. The proposed numerical approach can also be applied to deal with full-wave electromagnetic simulations of optical guided systems where nonlinear effects play an important role and cannot be neglected.