A time domain method for quantitative analysis of geometrical uncertainty is introduced here by combining polynomial chaos expansion (PCE) with the radial point interpolation method (RPIM). The shape function matrix of RPIM is represented in a stochastic framework. The proposed method is validated in a two dimensional problem and its accuracy is compared with Monte-Carlo (MC) simulation using Kolmogorov Smirnov (KS) test. Time complexity of the proposed method is remarkably better than MC. Since RPIM is an unstructured node based approach, this method can be well adapted for complex curved geometries.
In this paper, a transceiver architecture suitable for future reconfigurable and programmable wireless communication and sensing systems is proposed and demonstrated. The two-dimensional (2D) distribution of both receiver (Rx) and transmitter (Tx) unit-cells enables the integration of multiple functions in a single transceiver topology, named Virtual Transceiver Matrix (VTM). The reason behind calling it "virtual" is rooted in a dynamic allocation of both modulating and demodulating unit-cells in this architecture. This allocation is dependent on the incoming signal properties and desired output radiations. Furthermore, the number of Rx and Tx channels is significantly increased compared to conventional fixed architectures. Mathematical modeling of VTM unit-cells is presented and simulations are performed to confirm the desired operation of the proposed topological transceiver.
In this contribution, we present a new approach to fully characterize interconnects composed out of arbitrary polygonal cross-sections and containing piecewise homogeneous material parameters. The complex per-unit-of-length inductance and capacitance matrices are obtained through the application of pertinent Dirichlet-to-Neumann operators, which are computed by means of an extended Fokas method, that are integrated in a boundary integral equation approach. As the complete RLGC-data of the structures under study is computed, we are able to assess relevant properties such as signal attenuation and cross-talk while the support for polygonal shapes allows for the inclusion of manufacturing effects such as etching.
In this paper, an ANN model development approach is outlined and studied for the development of tunable band-pass filter (tBPF). Circuit models are fast and efficient for design and optimization while physical models based on electromagnetic full-wave fields are accurate but computationally expensive and slow. In this work, an equivalent circuit model is developed from a full-wave tBPF structure. Then, equivalent circuit parameters are generated and mapped to tunable geometric parameters of the physical field model of the tBPF. The optimization is thus carried out on the equivalent circuit model. Finally, the geometric parameters are obtained by the ANN model from the optimized equivalent circuit parameters. A practical example of tBPF is used to demonstrate the proof of concept.
Numerous experimental parameters affect the accuracy of impedance measurements in scanning microwave micro-copy (SMM). Investigating their effects on the measured values is particularly challenging. Here, we present the development of a fully-numerical FEM-based environment as a digital-twin to the actual measurements in SMM. We demonstrate the application of a self-calibration procedure for the simulated measurements with a maximal deviation of ± 6 % relative to reference capacitances determined via electrostatic calculations. Furthermore, we show the possibility to simulate the effects of the tip apex geometry on the simulated SMM measurements.
In this paper, the analysis method by hybrid the method of moments (MoM) and finite element method (FEM) is presented for a circularly-polarized circular- and hexagonal-slot element on a parallel-plate waveguide with perpendicular corporate feed. The method uses analytical and numerical entire domain basis functions depending on the slot shape. The analysis results are compared with commercial software and showed agreement.
Simulation-inserted optimization (SIO) is a novel optimization method in electromagnetic (EM) field recently. Quasi-Newton has been proposed to replace decomposition of Upper/Lower matrices in Newton-based SIO with approximate inverse of Hessian matrix. This paper aims to apply the SIO using combined quasi-Newton method with Lagrangian method to optimize a four-order waveguide filter.
The resonance frequencies of the system of dielectric objects are investigated with a previously constructed rigorous algorithm based on the Analytical Regularization Method (ARM) which is widely used to construct well-conditioned algebraic equation systems of the second kind by using some problem-dependent left and right hand- side regularizers. In this paper the previous algorithm is extended to handle arbitrary boundaries and the integral equation system is constructed in a way that eliminates the inner resonances of the perfectly conducting object of the same shape. The algebraic equation system resulting from the discretization of the boundary integral equation system is a first-kind one and does not allow to search of the eigenvalues of the matrix numerically. That is why through the operators of ARM, this algebraic equation system is transformed to a second kind one for which the matrix entries are convenient for the search of eigenvalues numerically. The numerical results show that the ARM-based algorithm allows finding the eigenvalues of the system of a dielectric object accurately whereas the first kind of system does not because of numerical overflow during the root search algorithm.
For the simultaneous solution of sequences of linear systems with multiple right-hand sides that result from the discretization of boundary integral equations using the Method of Moments, we show experiments using a robust variant of the block GMRES method. To address memory concerns and improve the convergence of block GMRES, the method combines an initial deflation strategy of the set of right-hand sides with an eigenvalue recycling technique. Experiments are presented to prove the method's potential for solving many right-hand side linear systems efficiently, which can sometimes be the computational bottleneck in integral equation-based engineering applications.
Conventional metasurface design methods require a large number of full-wave electromagnetic(EM) simulations to obtain the optimal geometric parameter values, resulting in a low optimization efficiency. Recently, coupled mode theory (CMT) and neural networks have been combined (i.e., neuro-CMT) to rapidly predict the EM response of a metasurface and thus accelerate its design optimization process, in which gradient-based optimization methods (i.e., Quasi-Newton) are used to find the optimal geometric parameter values. However, gradient-based optimization methods may not achieve the optimal design when the initial design is far away from the optimal solution. In this paper, we investigate the performance of four optimization algorithms (i.e., quasi-Newton, genetic algorithm, patternsearch, and surrogateopt) in neuro-CMT-based design optimization of metasurfaces, aiming to further improve the optimization efficiency of the neuro-CMT method.
This research proposes a microwave filter design method using convolutional neural network (CNN) based models trained on small data sets for parameter extraction in microwave filter optimization. The proposed method and CNN models are described with examples for 3-pole and 5-pole parallel coupled line microstrip filters. The proposed method uses CNN models trained on a pair of small data sets to design microwave filters with various center frequencies and bandwidths. As a result, the proposed method saves on computation time during parameter extraction at each space mapping iteration. Furthermore, the models provide fast, reliable, and robust parameter extraction across varying filter requirements.
In this paper, we present a microfluidic flow cytometer for simultaneous imaging and dielectric characterization of individual biological cells within a flow. Utilizing a combination of dielectrophoresis (DEP) and high-speed imaging, this system offers a dual-modality approach to analyze both cell morphology and dielectric properties, enhancing the ability to analyze, characterize, and discriminate cells in a heterogeneous population. A high-speed camera is used to capture images of and track multiple cells in real-time as they flow through a microfluidic channel. A wide channel is used, enabling analysis of many cells in parallel. A coplanar electrode array perpendicular to cell flow is incorporated at the bottom of the channel to perform dielectrophoresis-based dielectric characterization. A frequency-dependent voltage applied to the array produces a non-uniform electric field, translating cells to higher or lower velocity depending on their dielectric polarizability. In this paper, we demonstrate how cell size, obtained by optical imaging, and DEP response, obtained by particle tracking, can be used to discriminate viable and non-viable Chinese hamster ovary cells in a heterogeneous cell culture. Multiphysics electrostatic-fluid dynamics simulation is used to develop a relationship between cell incoming velocity, differential velocity, size, and the cell's polarizability, which can subsequently be used to evaluate its physiological state. Measurement of a mixture of polystyrene microspheres is used to evaluate the accuracy of the cytometer.
The 101–190 Impedance Standard Substrate (ISS) from FormFactor® has been designed and simulated in CST® Suite Studio. Electrical properties of a $50\ \Omega$ load from the ISS were measured in the direct current (DC) and the radiofrequency (RF) ranges using a voltmeter and a vector network analyser (VNA) respectively. This works come as a preliminary study to improve the on-wafer calibration procedure for precise GSG probing.
The importance of multiphysics analysis in the design of high-performance RF/microwave components and systems cannot be overstated. Grasping the interplay between various physical domains is crucial for a precise system analysis. In this paper, we present a comprehensive overview of the recent breakthroughs in multiphysics parametric modeling through the utilization of space mapping and in multiphysics optimization through the application of a pole-residue-based transfer function surrogate model. To demonstrate these cutting-edge methods, two examples of microwave components are provided.
In order to achieve the large signal modeling for the microwave power devices, the double hidden layer back propagation neural network (BPNN) and general regression neural network (GRNN) are utilized for X-parameter modeling of a GaN transistor here. Then, the harmonic balance experiments have been carried out to verify the validity of both models. As a result, the three harmonic and modulus value of predicted data and expected data can be obtained. In detail, the three harmonic errors of the double hidden layer BPNN are 5.287 dBm, 3.320 dBm and 4.483 dBm, respectively, and the three harmonic errors of the GRNN are 0.130 dBm, 0.001dBm and 1.235 dBm, respectively. In addition, the three harmonic modulus errors of the double hidden layer BPNN are 0.003, 0.521e-4 and 0.683e-6, respectively, and the three harmonic modulus errors of the GRNN are 0.001, 0.235e-4 and 0.304e-6, respectively. In conclusion, the proposedGRNN model is effective to model the large signals for GaN HEMT transistors..
The use of the theory of characteristic modes (TCM) or characteristic mode (CM) theory in fast modeling of electromagnetic (EM) scattering from complex objects has great potential for various real-world applications. This paper presents a concise review of how CM theory can be employed to compute EM scattering from objects described with different CM formulations. Furthermore, it provides an in-depth examination of EM scattering from complex objects from the CM perspective. Additionally, this paper provides comparisons between TCM and other EM simulation techniques, and it discusses some unresolved issues in the field.
In this paper, we propose using the diffuse optical breast scanning (DOB-Scan) probe, which employs an ensemble learning method to enable earlier detection of breast cancer. For this, we utilized an ensemble of nine models with various regression algorithms as base estimators to predict optical properties for liquid breast-mimicking phantoms. These regression models included Polynomial Regression, Support Vector, Random Forest, K-Nearest Neighbors, Decision Tree, Multi-layer Perceptron, XGBoost, CatBoost, and Extra Trees Regressors. We evaluated the performance of our models based on accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). Our analysis revealed that the Extra Trees model had the highest accuracy of 93%, making it the best regression model. Additionally, the Bagging with the KNN model achieved 100% accuracy in classifying the optical properties into healthy and unhealthy categories. These results suggest that the DOB-Scan probe, utilizing an ensemble learning approach, has the potential to detect breast cancer at an earlier stage.
The port tuning methodology starts by inserting strategically placed internal ports into a microwave circuit layout followed by a precise EM analysis including full and numerically exact de-embedding of the internal ports. Then circuit theory components are attached to the internal ports and used to rapidly optimize, or tune the circuit. This technique is now widely used when success on first fabrication and fast design turn-around are required. This invited paper describes the origin of the methodology and provides a short tutorial that will enable any microwave designer to immediately start optimizing microwave structures with full EM accuracy and at circuit theory speed.
Owing to their increased carrier velocities, Dirac materials have become a promising option for the integration into nanoelectronics. However, without the aid of simulation software that is able to accurately describe the behavior of these materials, the fabrication of novel devices is extremely challenging. In this work, we present a second-order accurate, multiphysics solution method for the pertinent time-dependent Maxwell-Dirac equations. The numerical stencils of the separate equations are presented, leading to a novel stability criterion for the minimally coupled Dirac equation. Afterwards, the second-order accuracy is demonstrated via a numerical example, in which a Dirac particle is represented as a wave packet.
To efficiently assess the impact of field-to-wire coupling problems in a multiscale structure, a hybrid discontinuous Galerkin time domain (DGTD) and finite difference time domain with transmission line (FDTD-TL) method is proposed in this article. Specifically, the EB scheme DGTD method is utilized to calculate electromagnetic fields in the multiscale region, while the FDTD-TL method is employed to establish connections between the electromagnetic fields and wires based on the Agrawal model.
