Full-wave Electromagnetic Simulation Research Articles

Reliability of VO textsubscript 2-Based mmWave Switches Under 100 Million Thermal Cycles

We report the lifetime investigations of the reversible phase transition vanadium dioxide (VO2) thin film switches integrated with coplanar waveguide (CPW) under fast and direct thermal cycling. We observe that after 100 million thermal cycles, there is no degradation in the performance of VO2 shunt switches operating at 35 – 45 GHz. S11 reflection coefficient as well as S21 transmission measurements (verified independently at both activated and inactivated states) show no statistical difference in port-to-port coupling of the VO2 switch as a function of a cycle number. This study is enabled by the integration of Joule heaters on chip at a close proximity to a shunt VO2 switch and a metallic CPW transmission line. Full-wave electromagnetic and multiphysics electrothermal finite element simulations are conducted to analyze the feasibility of the CPW design and the required time and power for thermal cycling. On-wafer mmWave measurements are carried out on the test vehicle after each thermal cycling phase.

Optimal Design of Antennas based on CNN and BLS

Surrogate can greatly reduce the computing cost of full-wave electromagnetic simulation software in antenna design, and neural networks as a kind of surrogate have become very popular in recent years, among which the Convolutional Neural Network (CNN) is a very hot topic due to its better feature extraction and generalization capability. In this paper, Broad Learning System (BLS) is introduced to address the problems that CNN may fall into with local optimization and overfitting when dealing with antenna data. It provides a learning method to increase nodes in width for regression prediction, which has good generalization ability. Specifically, in antenna design, feature extraction is first applied using the convolution and pooling of CNN. The original dataset is combined with the extracted feature data to realize data augmentation, and then the BLS is used for regression training. Compared with the traditional deep CNN, this model uses lightweight convolutional layers, which is promising in solving the problem of the network falling into local optimum or overfitting. The simulation results show that the accuracy reaches 93.91% and 92.34% respectively on the Abalone benchmark and antenna data, proving the effectiveness of the proposed model in this paper.

Swarm Intelligence-Homotopy Hybrid Optimization-Based ANN Model for Tunable Bandpass Filter

High-performance tunable radio frequency (RF)/ microwave and millimeter-wave filter design is a challenging task due to the lack of a basic theory. The filtering characteristics are highly sensitive to the variation of tuning elements that are commonly modeled and achieved by optimization algorithms. However, those optimizations only provide satisfactory results with a good set of initial parameters. Such range-limited optimization algorithms generally have issues of falling into local optima, slow convergence, and cumbersome implementation. To mitigate this problem, for the first time, a topology-based local optimizer is integrated with metaheuristic global optimization algorithms in this work. We have hybridized the homotopy method with an improved whale optimization algorithm (WOA) and a gray wolf optimization (GWO) algorithm. In this work, an artificial neural network (ANN) is formulated and studied, which has twofold applications. First, ANN is used as a surrogate model to represent the time-consuming electromagnetic (EM) model in expediting the hybrid optimization process of tunable filters. Second, an ANN model is developed on data generated by the proposed optimization algorithm for predicting tunable circuit parameters at different tuning stages. The proposed ANN model-based algorithm is then applied to a fifth-order lumped-element tunable circuit and two fourth-order full-wave EM simulation models of two tunable bandpass filters (tBPFs). The calculated results out of the ANN model demonstrate a good agreement with simulation and measurement counterparts.

Sub-6 GHz MIMO antenna design for 5G smartphones: A deep learning approach

Traditional optimization methods for antenna design are time-consuming, whereby full-wave electromagnetic (EM) simulations are performed repeatedly to find the optimum design parameters for acceptable performance. However, deep learning (DL) is an effective method for determining the optimum physical parameters based on their complex, nonlinear relationship with the design characteristics. This paper proposes a time and resource-efficient DL approach for designing MIMO antenna arrays. A wideband, eight-element MIMO antenna array for fifth-generation (5G) cellphones is designed to operate at n77/n78/n79 sub-6 GHz new radio (NR) bands and optimized using the proposed approach. The proposed method utilizes the feature reduction method to reduce the design space and generate an effective dataset. A novel dual-channel deep neural network (DC-DNN) is developed to predict the scattering parameters. A direct optimization approach is used by utilizing the defined figure of merit (FOM) for multiple design objectives instead of using an optimization algorithm with the proposed DL model. The optimized MIMO antenna is fabricated and tested for performance evaluation. The optimized design achieved −6 dB impendence bandwidth of 45.5 % (3.28–5.21 GHz), isolation < –12.5 dB, TARC < –6 dB, ECC < 0.1, MEG < −6 dB, and CCL < 0.35 bps/Hz.

Tunable and ultrasensitive sensor covering terahertz to telecommunication range based on a Fabry–Perot interference of graphene plasmonic waves

We present an ultrasensitive tunable terahertz (THz) sensor that comprises monolayer graphene-based gratings integrated with a Fabry-Perot (FP) cavity through precisely numerical modeling. Due to the vertical polarization of the incident light on the graphene gratings, they act as an electric dipole, which causes interference in the propagation of plasmonic waves, and provides an excellent foundation for a wide range of ultrasensitive refractive index (RI) sensing with an ultra-high-quality factor (Q). The main advantage of the proposed structure is not only sensing a wide range of refractive indices but due to its unique physical mechanism, it covers electromagnetic waves from a few terahertz to 300 THz, including the telecommunication bands. The primary physical mechanism in the proposed sensor is the FP interference in its active area. Also, due to the extreme sensitivity of FP interference (FPi) to the (RI) of the medium and the active area, the proposed structure owns an ultra-high sensitivity and Q. Our full-wave electromagnetic simulations demonstrate a frequency sensitivity of up to 83 THz per RI unit (RIU) and a high value of Q near 275. In addition, the proposed structure for index sensing has the functionality to be tuned in any desired frequency of interest from THz to the Mid-IR range and telecommunication band, which entirely covers 1–300 THz. The chemical potential of graphene can be electrically controlled to tailor the sensing capability and incident frequency. According to the applied gate voltage, the proposed structure can detect ammonia concentration in water with a high sensitivity of 70–85 THz/RIU and a quality factor of more than 275. The error of the ammonia concentration sensing is smaller than 0.5%. In addition, the proposed structure can detect different types of matter, including various microfluidics, air, gasoline, liquid paraffin, ethanol, glycerin, and glucose. The graphene plasmonic grating method suggested here is a good contender for on-chip integrated THz to mid-IR, including telecommunication bio-sensors.

A distributed small signal model extraction method based on FW-EM simulation for InP HEMT

In this paper, a method for 0.1 µm InP HEMT parameters extraction using Full-wave Electromagnetic(FW-EM) simulation is proposed. The parasitic capacitances are divided into pad and electrode capacitances to describe the distributed effects. Based on the extrinsic equivalent circuit, five standards are proposed to determine the parasitic capacitances and inductances using FW-EM simulation. Additionally, as ohmic contact resistance cannot be acquired using FW-EM simulation, parasitic resistances are determined using a Cold-Fet method without forward gate bias. Multi-bias extraction for various device sizes has been used to validate the accuracy and robustness of the suggested extraction approach, and the results indicate good agreement from 0.1 to 40 GHz.

Integrated optical beam steering device using switchable nanoantennas and a reflective metalens

In this paper, an integrated optical device is proposed in which a reflective meta-lens and five switchable nano-antennas are combined to provide optical beam steering at the standard telecommunication wavelength of 1550 nm. For this purpose, a graphene-based switchable power divider is designed and integrated with nano-antennas to control the flow of the light entering the device. To achieve a higher angular accuracy in the radiated beams, a new algorithm is proposed and utilized to optimize the location of feeding nano-antennas in accordance with the reflective meta-lens. In order to achieve a minimum fluctuation in the light intensity when the beams are rotated in the space, an algorithm is developed to select optimum unit cells for the engineered meta-lens. The whole device is numerically analyzed using Electromagnetic full-wave simulations illustrating an optical beam steering with high accuracy (better than 1 degree) in the beam direction, and a low variation (less than 1 dB) in the radiated light intensity. The proposed integrated device can be used for many applications such as inter- and intra-chip optical interconnects, optical wireless communication systems, and advanced integrated LIDARs.

3 bit terahertz coder based on a graphene composite metasurface

Terahertz (THz) frequency coders are indispensable components of broadband wireless communication systems of the future. However, the coding of THz wave at multiple frequencies is still a challenge. In this work, a 3 bit THz coder based on a graphene composite metasurface is proposed and simulated using a full-wave electromagnetic simulation software. The unit cell of the device consists of three graphene-embedded split-ring-resonators (SRRs), i.e., SRR1, SRR2, and SRR3. The device has three transmission peaks modulated by external voltages of V1, V2, and V3. The central frequency of the peak generated by SRR1 locates at 0.864 THz and its maximum modulation depth is 79.6%. The second peak generated by SRR2 locates at 1.161 THz and has a maximum modulation depth of 87%. The third peak at 1.541 THz is produced by SRR3 and has a maximum modulation depth of 85.3%. More importantly, the device can be used as a 3 bit coder, and the codes of 111, 110, 101, 011, 100, 010, 001, and 000 are successfully realized by controlling the voltages of V1, V2 and V3. The proposed device has potential applications in next-generation broadband wireless communications.

Transmission line characterization and modeling for electronic circuits and systems design

Channel bandwidth-limited high-speed links or interfaces make circuit solutions not efficient. Both recent and subsequent links (SerDes-Serializer/Deserializer) design demand efficient and effective coupling between future circuit design, communication, and optimization. The challenges vary and new solutions are needed. In this article, an analytical wireline model is presented to predict electronic path loss towards adequate designs of electronic circuits and systems. An open loop system analysis is adapted in this paper. Our model was tested against different channels: a legacy channel with via stub discontinuity and FR4 dielectric, and a more recent microwave-engineered channel without stub and NELCO 6,000 dielectric, a very good matching attained. Good agreement was observed between our model and electromagnetic full-wave simulation data, as a result showed high level of applicability to thin-film microstrip line for adequate circuit design. The model is recommended for electronic engineers for adequate and faster interfaces and high-speed links designs.

Highly Accurate Radar Cross-Section and Transfer Function Measurement of a Digital Calibration Transponder without Known Reference—Part II: Uncertainty Estimation and Validation

Active radar calibrators (ARCs), also known as calibration transponders, are often used as reference targets for the absolute radiometric calibration of radar systems due to their large radar cross-sections (RCSs). Before such a transponder can be used as a reference target, the hardware itself has to be calibrated. A novel method, called the three-transponder method, was proposed some years ago to allow for RCS calibration of digital transponders without using any known RCS targets as reference. The first part of this paper refines the technique and presents the measurement setup as well as the results of a comprehensive measurement campaign performed to calibrate a single digital transponder. In this part of the paper, the results are validated and a comprehensive uncertainty analysis is performed to estimate the total RCS uncertainty associated with the presented measurement data. This uncertainty analysis follows the international standard “Guide to the expression of uncertainty in measurement” (GUM) and will derive expressions for all major sources of uncertainty. For the validation, the measurement results will be compared with full-wave electromagnetic simulations of trihedral corner reflectors; there is excellent agreement between the simulation and measurements.

A Simulation Study of Triband Low SAR Wearable Antenna

The proposed paper presents a flexible antenna that is capable of operating in several frequency bands, namely 2.45 GHz, 5.8 GHz, and 8 GHz. The first two frequency bands are frequently utilized in industrial, scientific, and medical (ISM) as well as wireless local area network (WLAN) applications, whereas the third frequency band is associated with X-band applications. The antenna, with dimensions of 52 mm × 40 mm (0.79 λ × 0.61 λ), was designed using a 1.8 mm thick flexible kapton polyimide substrate with a permittivity of 3.5. Using CST Studio Suite, full-wave electromagnetic simulations were conducted, and the proposed design achieved a reflection coefficient below −10 dB for the intended frequency bands. Additionally, the proposed antenna achieves an efficiency value of up to 83% and appropriate values of gain in the desired frequency bands. In order to quantify the specific absorption rate (SAR), simulations were conducted by mounting the proposed antenna on a three-layered phantom. The SAR1g values recorded for the frequency bands of 2.45 GHz, 5.8 GHz, and 8 GHz were 0.34, 1.45, and 1.57 W/Kg respectively. These SAR values were observed to be significantly lower than the 1.6 W/Kg threshold set by the Federal Communication Commission (FCC). Moreover, the performance of the antenna was evaluated by simulating various deformation tests.

EFFICIENCY ENHANCEMENT OF PHOTOVOLTAIC SOLAR CELLS USING METAMATERIALS ABSORBING SCREEN

This paper proposes a novel technique for the efficiency enhancement of photovoltaic (PV) solar cells using metamaterials absorbing screens. This kind of engineered material comprises resonant metallic rings that are printed on a host low-loss dielectric substance and made periodic in a two-dimensional lattice. The absorbing screen has been carefully designed, and its retrieved effective constitutive parameters, effective electric permittivity ϵeff and effective magnetic permeability µeff, are integrated within a numerically modelled amorphous-Silicon-based PV solar cell structure as an impedance matching layer. Such arrangement will greatly achieve matching between the effective impedance of the composite solar cell structure and free-space impedance and will result in higher photons absorption through the metamaterials anti-reflective screen. Numerical full-wave electromagnetic simulations are carried out using CST Microwave Studio for the design of a metamaterial absorbing screen. Due to the large computational resources required, COMSOL Multiphysics was adopted in the design and analysis of the composite structure comprising a two-dimensional PV solar cells layer. Based on the numerical results, both optical and electric characteristics of the PV solar cell structure were enhanced with the use of a metamaterial layer. Moreover, efficiency enhancement by 5% was permissible, in which efficiency reached 12% with the use of metamaterials as compared to the efficiency of the classical PV cells of 7%. The obtained results are very promising, and the potential integration of metamaterials in commercial PV solar cells will show significant advancement in efficiency enhancement of PV cells and realization of smart PV solar cells with the consideration of additional features from metamaterials.

A Multisurrogate-Assisted Optimization Framework for SSPP-Based mmWave Array Antenna

Spoof surface plasmon polariton (SSPP) transmission lines are frequently used to excite millimeter-wave (mmWave) antenna arrays due to high field confinement and easy integration with planar structures. However, the design of the SSPP-based array antennas requires significant efforts because of multiple specifications, the high cost of electromagnetic (EM) evaluations, and a large number of design parameters. This article proposes a multisurrogate-assisted optimization framework for the efficient design of SSPP-based mmWave array antennas. The framework has three stages. In each stage, a surrogate is proposed to solve a specific task. The first stage is to find the initial values of four key design parameters using artificial neural network (ANN)-based surrogate model optimization. The second stage focuses on the optimization of sidelobe level based on another ANN surrogate model. The third stage is a beamforming-focused optimization procedure using the space mapping technique with an improved array factor formula as a surrogate. An mmWave SSPP-based array antenna with 16 circular patches at a central frequency of 28 GHz is successfully designed using the proposed framework. The antenna is also redesigned for five sets of main lobe directions and null locations at 24 GHz. All antennas are verified using full-wave EM simulations.

PACOSYT: A Passive Component Synthesis Tool Based on Machine Learning and Tailored Modeling Strategies Towards Optimal RF and mm-Wave Circuit Designs

In this paper, the application of regression-based supervised machine learning (ML) methods to the modeling of integrated inductors and transformers is examined. Different ML techniques are used and compared to improve accuracy. However, it is demonstrated that none of the ML techniques considered provided good results unless a smart modeling strategy, tailored to the specific design problem, is used. Taking advantage of these modeling strategies, high accuracy can be obtained when compared to full-wave electromagnetic (EM) simulations (less than 2% error) and experimental measurements (less than 5% error). The most accurate model, obtained by the appropriate combination of an ML technique and modeling strategy, has been integrated into a tool called PACOSYT. The tool uses optimization algorithms to allow the designer to obtain an inductor/transformer with optimal performances in just seconds while keeping the accuracy of EM simulations. Furthermore, the tool provides the passive component S parameter description file for seamless use in circuit simulations. The tool can be used standalone or integrated with design frameworks, like Cadence Virtuoso or AIDASoft, a framework for circuit optimization. To illustrate the different usages of the tool, several passive devices are synthesized, and hundreds of millimeter-wave power amplifiers are synthesized using AIDASoft together with PACOSYT. The tool has been developed using open-source Python frameworks and does not use any closed-source licenses. PACOSYT, which also allows other designers to create their models for different technologies, is made publicly available.

Electromagnetic Imaging of Passive Intermodulation Sources Based on Virtual Array Expansion Synchronous Imaging Compressed Sensing

Interference caused by passive intermodulation (PIM) has seriously affected system performance. Compressed sensing (CS) can be used to locate radial PIMs. In CS-based electromagnetic imaging methods, the ability to locate the number of targets is usually limited by the number of receiving antennas, and there are flooding problems in the imaging results. To solve these problems, traditional methods are used to increase the number of receiving antennas. To reduce the number of receiving antennas and improve the imaging effect, this paper proposes a virtual array expansion synchronous imaging compressed sensing (SI-CS) method. With the full-wave electromagnetic simulation demonstrations, the proposed method exhibits significant improvement in imaging performance compared to the conventional methods. In addition, the proposed method can discriminate between targets in the presence of the flooding problem in which the paraflap is too large and the location of the PIM source cannot be determined by the amplitude value.

Equivalent circuit model for a graphene-based high efficiency tunable broadband terahertz polarizer.

An equivalent circuit model for a graphene-based high-efficiency tunable broadband THz polarizer is presented. The conditions for linear-to-circular polarization conversion in the transmission mode are utilized to derive a set of closed-form design formulas. Given a set of target specifications, the key structural parameters of the polarizer are directly calculated using this model. The proposed model is rigorously validated by comparing the circuit model and full-wave electromagnetic simulation results, from which it is found that the model is accurate and effective, accelerating the analysis and design processes. This offers a further step in developing a high-performance and controllable polarization converter with potential applications in imaging, sensing, and communications.

Rapid antenna optimization with restricted sensitivity updates by automated dominant direction identification

Meticulous tuning of geometry parameters turns pivotal in improving performance of antenna systems. It is more and more often realized using formal optimization methods, which is demonstrably the most efficient way of handling multiple design variables, objectives, and constraints. Although in some cases a need for launching global search arises, a typical design scenario only requires local optimization, especially when a decent initial design can be rendered using engineering experience or parametric studies. At the same time, antenna optimization is typically conducted using full-wave electromagnetic (EM) simulations, which entails considerable computational expenses. In this paper, we introduce a novel procedure for expedited antenna tuning. Its fundamental mechanism is to restrict the antenna response sensitivity updates to the selected dominant directions within the parameter space, determined based on the problem-specific knowledge, in particular, the estimated changes of antenna characteristics when moving across one-dimensional affine subspaces spanned by these directions. Thus, the said dominant directions affect the most the responses of the antenna structure under design as assessed using the introduced metrics. The decision making process concerning the number of directions to be used relies on quantification of the aggregated system response variability metrics. The proposed approach is demonstrated by means of several antenna structures and benchmarked against conventional trust-region algorithm, but also its accelerated versions. The results indicate considerable (up to over 60%) speedup over the reference procedure without noticeable quality degradation.

Effective Constitutive Parameters Extraction of Bianisotropic Metamaterials Using Bloch Modes

In this article, a method is proposed to extract the effective constitutive parameters of bianisotropic metamaterials using Bloch modes. We consider 3-D structures that are realized by stacking identical unit layers, where each unit layer is a 2-D periodic array of metallic or dielectric elements of arbitrary shape embedded in a background dielectric medium. Bianisotropic metamaterials do not satisfy the condition of reflection symmetry of unit layers considered in previous studies. The eigenvectors and eigenvalues of the generalized transfer matrix of a unit layer are related to the Bloch modes of the structure. When only two dominant Bloch modes exist, closed-form expressions are found for effective permittivity, permeability, and magnetoelectric tensors of the medium. The effects of nondominant Bloch modes are taken into account as interface surface impedance matrices. The calculated scattering parameters of a slab of bianisotropic metamaterial are shown to be in good agreement with the results obtained from full-wave electromagnetic simulations using commercial solvers.

Reliable Computationally Efficient Behavioral Modeling of Microwave Passives Using Deep Learning Surrogates in Confined Domains

The importance of surrogate modeling techniques has been steadily growing over the recent years in high-frequency electronics, including microwave engineering. Fast metamodels are employed to speed up design processes, especially those conducted at the level of full-wave electromagnetic (EM) simulations. The surrogates enable massive system evaluations at nearly EM accuracy and negligible costs, which is invaluable in parameter tuning, multiobjective optimization, or uncertainty quantification. Nevertheless, modeling of electrical characteristics of microwave components is impeded by nonlinearity of their electrical characteristics, the need for covering broad parameter ranges, as well as dimensionality issues. Recently, a two-stage modeling approach has been proposed, which addresses some of these issues by constraining the surrogate model domain to only include high-quality designs, thereby reducing the cardinality of the dataset required to establish an accurate metamodel. In this article, a novel technique is proposed, which combines the two-stage modeling concept with multihead deep regression network (MHDRN) surrogates customized to handle responses of microwave passives over wide ranges of operating frequencies and geometry parameters. Using three microstrip circuits, a superior performance of the proposed modeling framework is demonstrated with respect to multiple state-of-the-art benchmark methods. In particular, the relative rms error is shown to reach the level of less than 3% for the datasets consisting of just a few hundred samples.

WR-3.4 Band Waveguide and Bandpass Filters Using Copper Additive Manufacturing

This article presents a waveguide with a pair of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${E}$ </tex-math></inline-formula> -plane bends and two waveguide bandpass filters (BPFs) which operate in WR-3.4 band (220–330 GHz). These devices are fabricated by a micro metal additive manufacturing (M-MAM) technology on copper in one piece. Both filters are fifth order and operate at a center frequency of 280 GHz with a bandwidth of 8 GHz. Cylindrical resonators are employed for filter design. The standard feed waveguides are designed in bend structures, facilitating the measurements. The M-MAM technology is a layer-by-layer additive manufacturing process constructing the waveguide devices in five layers. Since the layout of the devices has a strong impact on the electrostatic field distributions and hence the electroforming quality, both full-wave electromagnetic (EM) simulations and electrostatic analysis are simultaneously considered in the device design. It eases the subsequent electroforming and polishing processes, effectively improving the performance of the devices. The insertion losses and return losses of the measured filters are lower than 1.65 dB and better than 15 dB. The center frequency shifts are less than 0.3%. The excellent results attribute to the delicate co-design method between EM and electrostatic analyses encountered both in the filter design and in the additive manufacturing process.