Full-wave Electromagnetic Model Research Articles

Fast EM-driven nature-inspired optimization of antenna input characteristics using response features and variable-resolution simulation models

Utilization of optimization technique is a must in the design of contemporary antenna systems. Often, global search methods are necessary, which are associated with high computational costs when conducted at the level of full-wave electromagnetic (EM) models. In this study, we introduce an innovative method for globally optimizing reflection responses of multi-band antennas. Our approach uses surrogates constructed based on response features, smoothing the objective function landscape processed by the algorithm. We begin with initial parameter space screening and surrogate model construction using coarse-discretization EM analysis. Subsequently, the surrogate evolves iteratively into a co-kriging model, refining itself using accumulated high-fidelity EM simulation results, with the infill criterion focusing on minimizing the predicted objective function. Employing a particle swarm optimizer (PSO) as the underlying search routine, extensive verification case studies showcase the efficiency and superiority of our procedure over benchmarks. The average optimization cost translates to just around ninety high-fidelity EM antenna analyses, showcasing excellent solution repeatability. Leveraging variable-resolution simulations achieves up to a seventy percent speedup compared to the single-fidelity algorithm.

Universal Chiral-Plasmon-Induced Upward and Downward Transfer of Circular Dichroism to Achiral Molecules.

Electromagnetic chirality transfer represents an effective means of the nanoscale manipulation of optical chirality. While most of the previous reports have exclusively focused on the circular dichroism (CD) transfer from UV-responsive chiral molecules toward visible-resonant achiral colloidal nanoparticles, here we demonstrate a reverse process in which plasmonic chirality can be transferred to achiral molecules, either upward from visible to UV or downward from visible to near infrared (NIR). By hybridizing achiral UV- or NIR-responsive dye molecules with chiral metal nanoparticles in solution, we observe a chiral-plasmon-induced CD (CPICD) signal at the intrinsically achiral molecular absorption bands. Full-wave electromagnetic modeling reveals that both near-field Coulomb interaction and far-field radiative coupling contribute to the observed CPICD, indicating that the mechanism considered here is universal for different material systems and types of optical resonances. Our study provides a set of design guidelines for broadband nanophotonic chiral sensing from the UV to NIR spectral regime.

System-Level Simulation Suite for the Design of mmWave-Over-Fiber-Based Distributed Antenna Systems

Although challenging propagation conditions reduce its reliability, the mmWave spectrum is considered a cornerstone of (beyond-)5G networks. Recently, distributed antenna systems (DASs) surrounding the mobile users with multiple remote antenna units (RAUs) interconnected by mmWave-over-fiber technology were identified as a prime candidate to unlock high throughput and reliable coverage. This article proposes a dedicated simulation suite facilitating the deployment of such a mmWave-over-fiber-based DAS by accurately predicting system-level performance and enabling time-efficient optimization of the hardware configuration, including the RAUs, the central office (CO), and the signal processing units, toward the target application. It incorporates accurate models for the mmWave-over-fiber link and the amplifiers, including nonlinear distortion and noise, full-wave electromagnetic models for the antenna front-ends, and analytical models for the wireless channel. The simulation suite is validated by a measurement campaign, not only focusing on a single mobile user served by a fixed-beam RAU but also considering a multibeam RAU serving two users simultaneously by means of two independent mmWave-over-fiber links. The model accurately predicts up-and downlink quality over a wide range of user positions, different system parameters, and also accurately captures inter-user interference.

High-efficacy global optimization of antenna structures by means of simplex-based predictors

Design of modern antenna systems has become highly dependent on computational tools, especially full-wave electromagnetic (EM) simulation models. EM analysis is capable of yielding accurate representation of antenna characteristics at the expense of considerable evaluation time. Consequently, execution of simulation-driven design procedures (optimization, statistical analysis, multi-criterial design) is severely hindered by the accumulated cost of multiple antenna evaluations. This problem is especially pronounced in the case of global search, frequently performed using nature-inspired algorithms, known for poor computational efficiency. At the same time, global optimization is often required, either due to multimodality of the design task or the lack of sufficiently good starting point. A workaround is to combine metaheuristics with surrogate modeling methods, yet a construction of reliable metamodels over broad ranges of antenna parameters is challenging. This work introduces a novel procedure for global optimization of antenna structures. Our methodology involves a simplex-based automated search performed at the level of approximated operating and performance figures of the structure at hand. The presented approach capitalizes on weakly-nonlinear dependence between the operating figures and antenna geometry parameters, as well as computationally cheap design updates, only requiring a single EM analysis per iteration. Formal convergence of the algorithm is guaranteed by implementing the automated decision-making procedure for reducing the simplex size upon detecting the lack of objective function improvement. The global optimization stage is succeeded by gradient-based parameter refinement. The proposed procedure has been validated using four microstrip antenna structures. Multiple independent runs and statistical analysis of the results have been carried out in order to corroborate global search capability. Satisfactory outcome obtained for all instances, and low average computational cost of only 120 EM antenna simulations, demonstrate superior efficacy of our algorithm, also in comparison with both local optimizers and nature-inspired procedures.

Optimal design of transmitarray antennas via low-cost surrogate modelling

Over the recent years, reflectarrays and transmitarrays have been drawing a considerable attention due to their attractive features, including a possibility of realizing high gain and pencil-like radiation patterns without the employment of complex feeding networks. Among the two, transmitarrays seem to be superior over reflectarrays in terms of achieving high radiation efficiency without the feed blockage. Notwithstanding, the design process of transmitarrays is more intricate due to the necessity of manipulating both the transmission phase and magnitude of its unit elements. For reliability, the design process has to be conducted at the level of full-wave electromagnetic models, which makes direct optimization prohibitive. The most widely used workaround is to employ surrogate modeling techniques to construct fast representations of the unit elements, yet the initial model setup cost is typically high and includes acquisition of thousands of training data points. In this paper, we propose a novel approach to cost-efficient design of transmitarrays. It is based on artificial-intelligence-enabled data-driven surrogates, which can be constructed using only a few hundreds of training data samples, while exhibiting the predictive power sufficient for reliable design. Our methodology is demonstrated by re-using the presented surrogate for the design of high-performance transmitarrays operating at various frequency ranges of 8–14 GHz, 22–28 GHz, and 28–36 GHz.

RadarSense: Accurate Recognition of Mid-air Hand Gestures with Radar Sensing and Few Training Examples

Microwave radars bring many benefits to mid-air gesture sensing due to their large field of view and independence from environmental conditions, such as ambient light and occlusion. However, radar signals are highly dimensional and usually require complex deep learning approaches. To understand this landscape, we report results from a systematic literature review of ( N =118) scientific papers on radar sensing, unveiling a large variety of radar technology of different operating frequencies and bandwidths and antenna configurations but also various gesture recognition techniques. Although highly accurate, these techniques require a large amount of training data that depend on the type of radar. Therefore, the training results cannot be easily transferred to other radars. To address this aspect, we introduce a new gesture recognition pipeline that implements advanced full-wave electromagnetic modeling and inversion to retrieve physical characteristics of gestures that are radar independent, i.e., independent of the source, antennas, and radar-hand interactions. Inversion of radar signals further reduces the size of the dataset by several orders of magnitude, while preserving the essential information. This approach is compatible with conventional gesture recognizers, such as those based on template matching, which only need a few training examples to deliver high recognition accuracy rates. To evaluate our gesture recognition pipeline, we conducted user-dependent and user-independent evaluations on a dataset of 16 gesture types collected with the Walabot, a low-cost off-the-shelf array radar. We contrast these results with those obtained for the same gesture types collected with an ultra-wideband radar made of a vector network analyzer with a single horn antenna and with a computer vision sensor, respectively. Based on our findings, we suggest some design implications to support future development in radar-based gesture recognition.

Uncertainty quantification of additive manufacturing post-fabrication tuning of resonator-based microwave sensors

Reconfigurability, especially in terms of the ability of adjusting the operating frequency, has become an important prerequisite in the design of modern microwave components and systems. It is also pertinent to microwave sensors developed for a variety of applications such as characterization of material properties of solids or liquids. This paper discusses uncertainty quantification of additive-manufacturing-based post-fabrication tuning of resonator-based sensors implemented using a microstrip technology. Therein, the operating frequency is altered by adding metallic patches of a specific size determined by the full-wave electromagnetic (EM) model of the system. The reliability of setting up the center frequency depends on both the accuracy of the patch size (manually cut out of the copper tape), and its allocation with respect to the resonator. A rigorous statistical analysis of the patch size and its allocation errors is carried out, including a quantification of their joint effects on the sensor operating frequency. Furthermore, the analysis of a possibility of compensating the patch size inaccuracies through its appropriate positioning is conducted. The details of the proposed approach are explained using a complementary symmetric split ring resonator (CSSRR)-based sensor designed to operate in X and Ku bands with the tuning range between 10 GHz and 20 GHz. The optimized sensor's fundamental resonant frequency is 9.4 GHz, its exterior size is 25 × 30 mm2, the quality factor of the fabricated sensor is 29, and the sensitivity of the considered design is 1.1 GHz/mm with the measurement error is 0.1 percent. The obtained measurement data are indicative of a practical utility of the additive-manufacturing-based tuning technique, in particular, a possibility of reliable center frequency tuning under mild assumptions on the accuracy of manual preparation of the tuning patches. Furthermore, a practical tuning scheme has been developed and experimentally validated, which allows for a precise allocation of the operating frequency with the error not exceeding 0.01 GHz (or 0.1% in relative terms), all under assumptions of a manual preparation and placement of the tuning patch.

Electromagnetic Modeling Using an FDTD-Equivalent Recurrent Convolution Neural Network: Accurate computing on a deep learning framework

In this study, a recurrent convolutional neural network (RCNN) is designed for full-wave electromagnetic (EM) modeling. This network is equivalent to the finite difference time domain (FDTD) method. The convolutional kernel can describe the finite difference operator, and the recurrent neural network (RNN) provides a framework for the time-marching scheme in FDTD. The network weights are derived from the FDTD formulation, and the training process is not needed. Therefore, this FDTD-RCNN can rigorously solve a given EM modeling problem as an FDTD solver does.

Rapid Design of 3D Reflectarray Antennas by Inverse Surrogate Modeling and Regularization

Reflectarrays (RAs) exhibit important advantages over conventional antenna arrays, especially in terms of realizing pencil-beam patterns without the employment of the feeding networks. Unfortunately, microstrip RA implementations feature narrow bandwidths, and are severely affected by losses. A considerably improved performance can be achieved for RAs involving grounded dielectric layers, which are also easy to manufacture using 3D printing technology. Regardless of the implementation details, a practical bottleneck of RA design is the necessity of independent adjustment of a large number of unit cells, which has to be carried out using full-wave electromagnetic (EM) simulation models to ensure reliability. The associated computational costs are extraordinary. A practical workaround is the incorporation of surrogate modeling methods; however, a construction of accurate metamodel requires a large number of training data samples. This letter introduces an alternative RA design approach, where the unit cells are adjusted using an inverse surrogate model established with a small number of anchor points, pre-optimized for the reference reflection phases. To ensure solution uniqueness, the anchor point optimization involves regularization, here, based on the minimum-volume condition for the unit cell. The presented approach reduces the computational cost of RA design to a few dozens of EM analyses of the cell. Several demonstration examples are provided, along with an experimental validation of the selected RA realization.

Nanoscale Characterization of Graphene Oxide-Based Epoxy Nanocomposite Using Inverted Scanning Microwave Microscopy.

Scanning microwave microscopy (SMM) is a novel metrological tool that advances the quantitative, nanometric, high-frequency, electrical characterization of a broad range of materials of technological importance. In this work, we report an inverted near-field scanning microwave microscopy (iSMM) investigation of a graphene oxide-based epoxy nanocomposite material at a nanoscopic level. The high-resolution spatial mapping of local conductance provides a quantitative analysis of the sample's electrical properties. In particular, the electrical conductivity in the order of ∼10-1 S/m as well as the mapping of the dielectric constant with a value of ∼4.7 ± 0.2 are reported and validated by the full-wave electromagnetic modeling of the tip-sample interaction.

Rapid Variable-Resolution Parameter Tuning of Antenna Structures Using Frequency-Based Regularization and Sparse Sensitivity Updates

Geometry parameter tuning is an inherent part of the antenna design process. While most often performed in a local sense, it still entails considerable computational expenses when carried out at the level of full-wave electromagnetic (EM) simulation models. Moreover, the optimization outcome may be impaired if a good initial design is not available. This article proposes a novel approach to fast and improved-reliability gradient-based optimization of antenna structures. Our approach employs a frequency-based regularization to facilitate the relocation of antenna operating parameters to their target values, which increases the chances of identifying a satisfactory design under challenging conditions (e.g., poor-quality starting point). At the same time, the computational efficiency of the tuning process is enhanced through the involvement of variable-resolution EM models, and restricting the finite-differentiation (FD) sensitivity updates to selected parameters only. The latter is decided upon based on the analysis of the design relocation between the subsequent iterations of the optimization algorithm. The presented technique is validated using three examples of microstrip antennas optimized under different scenarios (matching improvement, gain enhancement, and size reduction). The results demonstrate superior performance in terms of reliability and design quality as compared to conventional gradient-based and derivative-free search procedures. At the same time, a significant speedup is achieved over the frequency-regularization-based procedure not using the acceleration mechanisms.

Tolerance Optimization of Antenna Structures by Means of Response Feature Surrogates

Fabrication tolerances and other types of uncertainties, e.g., the lack of precise knowledge of material parameters, have detrimental effects on the electrical and field performance of antenna systems. In the case of input characteristics, these are particularly noticeable for narrowband and multiband antennas where deviations of geometry parameters from their nominal values lead to frequency shifts of the operating frequency bands. Improving design robustness is therefore important yet challenging. On the one hand, it is numerically demanding as it involves uncertainty quantification (UQ), in particular, estimation and improvement of appropriately defined statistical performance metrics. On the other hand, it has to be carried out at the level of full-wave electromagnetic (EM) simulation models, which incurs considerable computational expenses. Executing UQ tasks at practically acceptable costs can be realized using surrogate modeling methods; however, the construction of reliable metamodels is hindered by the curse of dimensionality. This article proposes a novel approach to the robust design of antenna structures, where the task is formulated to increase the maximum values of parameter deviations for which 100% fabrication yield is ensured. Low cost of the optimization process is enabled by incorporating feature-based regression models for rapid yield estimation, as well as the employment of the trust-region framework for adaptive adjustment of design relocation but also as a convergence safeguard. Our methodology is validated using three microstrip antennas, including two dual-band and a triple-band structure.

A D-Band Manifold Triplexer With High Isolation Utilizing Novel Waveguide Dual-Mode Filters

This letter presents our design of a wideband manifold-coupled triplexer with high isolation in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</i> -band. The basic structure of channel filters is the novel TM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">120</sub> and TE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">101</sub> dual-mode cavity, which provide two reflection zeros and one transmission zero. To achieve good computational efficiency and design accuracy, the space-mapping technique, consists of a coarse distribute model and a fine full-wave electromagnetic model, is adopted in the development of the triplexer. The proposed triplexer is fabricated by high-precision computer numerical control milling technology. The measured results are all in good agreement with the simulated ones. The measured lowest in-band insertion loss is 0.54/0.62/0.55 dB for three channels, respectively. The return loss of each channel is better than 14 dB and the isolation between each channel is better than 45 dB.

Silver particles embedded in silicon: The fabrication process and their application in surface enhanced Raman scattering (SERS)

The formation of hybrid structures for creating sensitive sensors is a difficult task. In this study, we developed a facile technique of semiconductor technology to fabricate hybrid structures based on SiO2/embeddedAg/Si and embeddedAg/Si for a surface-enhanced Raman scattering. Herein, our hybrid structures were created in one technological cycle, which consists of the reduction of silver nitrate salt from a solution on the silicon surface and subsequent high-temperature annealing at 1000 °C. In addition, it allows not only to preserve the plasmonic properties of Ag particles on the Si surface but also to protect them from further external influences by coating them with a SiO2 layer. Also, the basis for creating such hybrid structures is the performed numerical calculations of the position of a localized surface plasmon resonance as a function of the geometry of structures. Furthermore, the theoretical full-wave electromagnetic modeling of Raman scattering in an embedded Ag/Si structure showed the presence of enhancement regions “hot spots” at sharp corners with a maximum enhancement factor of 106. Finally, surface-enhanced Raman scattering spectroscopy of such structures showed reliable detection of the methyl orange from an aqueous solution at a concentration of 10-5M.

Multiobjective Optimization for Heat Transfer Performance of Micro-Fins and Signal Integrity of Key Interconnect Technologies in 3-D Integrated Chips

This article is devoted to the synergistic design of the thermal management and signal integrity for 3-D integrated chips. The structure parameters of the micro-fin and interconnected components are optimized to obtain a balanced performance of chip heat dissipation and signal integrity. The 3-D integrated chip flows heat transfer model under the action of electrothermal coupling is developed. A full-wave electromagnetic simulation model of the signal interconnection components is proposed with the same structural parameters. The effects of various structural factors on the heat transfer performance and signal integrity of 3-D integrated chips are investigated. A multiobjective optimization algorithm is carried out to simultaneously reduce the total thermal resistance of the 3-D integrated chip and the signal transmission losses of the critical interconnect components. The results show that the total thermal resistance of the 3-D IC becomes lower with a larger through-silicon via (TSV) diameter, micro-fins height and micro-fin radius, and smaller oxide thickness. When the TSV diameter and oxide thickness increase, the height and radius of the micro-fins are reduced to facilitate the signal transmission of the chip’s interconnected components. The response surface analysis method was used to design the experimental combination and thus obtain the fit function for the performance of the two objectives. Hundred sets of Pareto optimal solutions are obtained by multiobjective optimization. The technique for order preference by similarity to ideal solution (TOPSIS) method is used to select the weights of the two optimization objectives to determine the optimal solution. When thermal resistance and signal transmission are given equal weight in the chip design, thermal resistance is reduced by 13.8%, and signal transmission efficiency is improved by 9.74%. The study has important implications for chip thermal management and signal integrity.

Fast EM-driven parameter tuning of microwave circuits with sparse sensitivity updates via principal directions

Numerical optimization has become more important than ever in the design of microwave components and systems, primarily as a consequence of increasing performance demands and growing complexity of the circuits. As the parameter tuning is more and more often executed using full-wave electromagnetic (EM) models, the CPU cost of the overall process tends to be excessive even for local optimization. Some ways of alleviating these issues exist, yet, they are limited either by their accessibility or applicability range. This work presents a novel algorithmic approach to accelerated gradient-based parameter tuning of microwave components with numerical derivatives. In our methodology, computational savings are achieved by exploiting the problem-specific knowledge, specifically, by restricting the gradient updates to an orthonormal basis of essential directions corresponding to the maximum variability of the circuit responses within the frequency bands of interest. The said directions are selected through an automated decision-making process involving the analysis of the circuit response variability. Our approach is demonstrated using two multi-parameter microwave devices. Comprehensive comparison with the benchmark methods, including the standard trust-region algorithm and the three accelerated versions, indicate savings of up to fifty percent associated only with minor reduction of the design quality.

Design specification management with automated decision-making for reliable optimization of miniaturized microwave components

The employment of numerical optimization techniques for parameter tuning of microwave components has nowadays become a commonplace. In pursuit of reliability, it is most often carried out at the level of full-wave electromagnetic (EM) simulation models, incurring considerable computational expenses. In the case of miniaturized microstrip circuits, densely arranged layouts with strong cross-coupling effects make EM-driven tuning imperative to achieve the optimum performance. The process is even more challenging due to a typically large number of geometry parameters, and the lack of reasonable initial designs. The latter often encourages the use of global search procedures, which may be prohibitively expensive. In this paper, a novel automated framework for reliable optimization of miniaturized microwave components is proposed. Our methodology is based on design specification management, where the performance requirements imposed on the system are temporarily relaxed if the current design is unlikely to be improved (e.g., due to being away from the target operating frequency). The specifications are re-adjusted at each iteration of the algorithm, and eventually converge to their original values. Using two examples of compact microstrip couplers and a power divider, the presented technique is demonstrated to significantly improve the efficacy of local search routines under challenging design scenarios.

A Lossy Transmission Line Model for the Scan Impedance of the Infinite Dipole Array

In this paper, an analytical formulation of the infinite dipole array scan impedance is presented. The primary contribution of this work is the introduction of a lossy transmission line analogue which simultaneously models the Floquet modal impedances as well as the reactive properties of the dipolefs physical geometry. A scan dependent line inductance is implemented, resulting in accurate impedance predictions at extreme scan angles. The model accurately predicts the input scan impedance of tightly-coupled arrays and connected arrays for a variety of element geometries. Ground planes, substrates, superstrates, and feed structures are also incorporated in the model. Calculated impedances are compared to full-wave electromagnetic models, demonstrating high accuracy predictions from broadside to near 90. in each scan plane. Finally, an array which scans to 60. in each scan plane is rapidly optimized using only the transmission line model and compared to full-wave simulations, achieving a 3:1 VSWR over a 6.1:1 bandwidth, demonstrating the value of the model as a starting point for array design.

Design Centering of Compact Microwave Components Using Response Features and Trust Regions

Fabrication tolerances, as well as uncertainties of other kinds, e.g., concerning material parameters or operating conditions, are detrimental to the performance of microwave circuits. Mitigating their impact requires accounting for possible parameter deviations already at the design stage. This involves optimization of appropriately defined statistical figures of merit such as yield. Although important, robust (or tolerance-aware) design is an intricate endeavor because manufacturing inaccuracies are normally described using probability distributions, and their quantification has to be based on statistical analysis. The major bottleneck here is high computational cost: for reliability reasons, miniaturized microwave components are evaluated using full-wave electromagnetic (EM) models, whereas conventionally utilized analysis methods (e.g., Monte Carlo simulation) are associated with massive circuit evaluations. A practical approach that allows for circumventing the aforementioned obstacles offers surrogate modeling techniques, which have been a dominant trend over the recent years. Notwithstanding, a construction of accurate metamodels may require considerable computational investments, especially for higher-dimensional cases. This paper brings in a novel design-centering approach, which assembles forward surrogates founded at the level of response features and trust-region framework for direct optimization of the system yield. Formulating the problem with the use of characteristic points of the system response alleviates the issues related to response nonlinearities. At the same time, as the surrogate is a linear regression model, a rapid yield estimation is possible through numerical integration of the input probability distributions. As a result, expenditures related to design centering equal merely few dozens of EM analyses. The introduced technique is demonstrated using three microstrip couplers. It is compared to recently reported techniques, and its reliability is corroborated using EM-based Monte Carlo analysis.

A 200–225-GHz Manifold-Coupled Multiplexer Utilizing Metal Waveguides

This article presents a 220-GHz four-channel, noncontiguous, and manifold-coupled waveguide multiplexer for future terahertz (THz) multichannel communication application. The multiplexer is composed of four Chebyshev bandpass filters based on metal waveguide technology. Through a unique design in which the tuning dimensional variables are reduced to 14 and a co-design of low-order electromagnetic (EM) distributed models and full-wave EM models, the design optimization is achieved with a good computational efficiency and design accuracy. The proposed multiplexer is fabricated by high-precision computer numerical control (CNC) milling technology, in which the fabrication errors are evaluated to be within <inline-formula> <tex-math notation="LaTeX">$\pm 3~\mu \text{m}$ </tex-math></inline-formula>. The measured results exhibit 1.7 dB of in-band insertion loss and better than 15 dB of average common-port return loss for each of the channel filter. The measured results are all in good agreement with the simulated ones, thereby validating the complete design procedure.