Modeling Of Microwave Devices Research Articles

Modeling and simulations of high-power microwave devices using the CHIPIC code

AbstractThe CHIPIC code, a fully electromagnetic particle-in-cell (PIC) code for modeling and simulations of high-power microwave (HPM) devices, is introduced in this paper. It consists of a two-dimensional (2D) code and a three-dimensional (3D) code. The 2D code can model and simulate HPM devices with symmetric structure on 2D Cartesian, cylindrical and polar grids, while the 3D code can model and simulate HPM devices on 3D Cartesian and cylindrical grids. The fields are calculated using the finite-difference time-domain scheme, and the particles are described by the PIC scheme. Various types of boundary conditions have also been implemented for different kinds of applications. In addition, the 3D code is specifically designed for high-performance modeling and computing. It uses the message passing interface and the open specifications for multiprocessing (OpenMP) for parallelization. Its parallel design ensures that it is capable of efficiently executing on a variety of architectures. In order to allow efficient use of parallel architectures, it provides automated partitioning and dynamic load balancing. Even though this code is still in development, it has successfully simulated various real-world HPM experimental devices. Simulation results on some typical HPM devices by using the CHIPIC code are given, which agree well with those obtained from some well-known PIC codes. Direction for future work is also presented.

Bayesian Support Vector Regression With Automatic Relevance Determination Kernel for Modeling of Antenna Input Characteristics

The modeling of microwave antennas and devices typically requires that non-linear input-output mappings be determined between a set of variable parameters (such as geometry dimensions and frequency), and the corresponding scattering parameter(s). Support vector regression (SVR) employing an isotropic Gaussian kernel has been widely used for such tasks; this kernel has one tunable hyperparameter that can be optimized (along with the penalty constant C) using a standard procedure that involves a parameter grid search combined with cross-validation. The isotropic kernel however suffers from limited expressiveness, and might provide inadequate predictive accuracy for nonlinear mappings that involve multiple tunable input variables. The present study shows that Bayesian support vector regression using the inherently more flexible Gaussian kernel with automatic relevance determination (ARD) is eminently suitable for highly non-linear modeling tasks, such as the input reflection coefficient magnitude |S11| of broadband and ultrawideband antennas. The Bayesian framework enables efficient training of the multiple kernel ARD hyperparameters-a task that would be computationally infeasible for the grid search/cross-validation approach of standard SVR.

Accurate modeling and optimization of microwave circuits and devices using adaptive neuro-fuzzy inference system

In this paper, an accurate neuro-fuzzy-based model is proposed for efficient computer-aided design (CAD) modeling and optimization of microwave circuits and devices. The adaptive neuro-fuzzy inference system (ANFIS) approach is used to determine the scattering parameters of a microstrip filter and is applied to the optimization design of this microstrip filter. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of artificial neural networks. The neuro-fuzzy model has been trained and tested with different sets of input/output data. Finally, different results, which confirm the validity of the proposed model, are reported.

Accurate modeling of microwave devices using kriging‐corrected space mapping surrogates

SUMMARYWe present an accurate modeling technique that exploits standard space mapping (SM) as a trend function and the correction layer implemented with kriging. The kriging process allows us to efficiently utilize all available fine model data (not possible in the standard SM approach) and to obtain accuracy better than both the SM and recently published combinations of SM and radial basis function interpolation and fuzzy systems. We present application examples of design optimization and statistical analysis of several microwave filters. Copyright © 2011 John Wiley & Sons, Ltd.

Evolutionary Neuro-Space Mapping Technique for Modeling of Nonlinear Microwave Devices

This paper presents a new advance in Neuro-space mapping (Neuro-SM) techniques for modeling nonlinear microwave devices. Suppose that existing device models (namely, coarse models) cannot match the behavior of a new device (referred to as the fine model). By neural network mapping of the voltage and current signals from the coarse to the fine models, Neuro-SM can modify the behavior of the coarse model to match that of the fine model. However, the efficiency of mapping depends on both the mapping structure and the coarse model. In this paper, a structural optimization technique is presented to achieve optimal combinations of mapping structure and coarse model. An aggressive optimization formulation exploring detailed structural variations in both the mapping and the coarse model is proposed, where the internal branches of coarse models and separate mappings for the voltage and current at gate and drain are used as basic topology variables. The formulation of such a structural optimization by an evolutionary optimization algorithm is proposed. Numerical examples of metal-semiconductor field-effect transistor and high electron-mobility transistor modeling demonstrate that, by using the proposed algorithm, optimal combinations of space mapping and coarse model structures can be achieved leading to the best modeling accuracy with the simplest mapping function.

Space-mapping modelling of microwave devices using multi-fidelity electromagnetic simulations

The authors present efficient space mapping (SM) modelling of microwave devices where they assume a tight limit on the number of high-fidelity electromagnetic (EM) simulations available to set up a suitable model. The authors’ method exploits a two-stage procedure. In the first stage, the standard SM surrogate is enhanced by a functional approximation layer using coarse-mesh EM simulation data. In the second stage, this surrogate is updated through space-mapping-based matching to high-fidelity EM simulation responses at a limited number of base locations (designs). The first stage normally uses a physically based coarse model, but in some cases direct approximation of the coarse-mesh EM simulation data is sufficient. The authors demonstrate that their proposed approach improves modelling accuracy in comparison with standard SM, and no extra fine model evaluations are required to set up the model. Applications to parametric design optimisation are discussed.

The recent developments in microwave design

Artificial neural networks have been used as an important technique in microwave modelling and optimisation. This paper gives an overview and recent developments on the knowledge-based neural modelling techniques in microwave modelling and design. The knowledge-based artificial neural networks are constructed by incorporating the existing knowledge such as empirical formulas, equivalent circuit models and semi-analytical equations in neural network structures. When one of the knowledge-based methods can not provide sufficient accuracy, two of them can be used in the same modelling process. This combination of methods is named hybrid technique. Using knowledge-based techniques requires less training data and has better extrapolation performance than classical neural networks. The advantages of using knowledge-based neural network modelling are demonstrated with microwave device modelling applications.

Smart Modeling of Microwave Devices

Modeling and computer-aided design (CAD) techniques are essential for microwave design, especially with the drive towards first-pass design success. We have described neural networks for microwave modeling and design. Neural networks are suitable when modeling a required relationship for which analytical formulas are hard to derive, or for which the computational effort is too high. This relationship can be either of the IO relationship of the overall model (straight neural network model), the output-input relationship (inverse model), a relationship between existing model and desired data (neuro-SM), or relationship of a subpart of the overall model (knowledge based neural network). Neural networks are fast to evaluate, and the neural network formulas are easy to implement into microwave CAD. The simplicity of adding input neurons or hidden neurons makes neural network flexible in handling functions of different dimensions and of different degree of nonlinearity. We have also demonstrated that neural networks are helpful in developing parametric or scalable models for passive and active microwave devices.

Recent advances in space‐mapping‐based modeling of microwave devices

AbstractWe review the latest developments in space‐mapping‐based modeling techniques with applications in microwave engineering. We discuss the two techniques that utilize a combination of standard space mapping and function approximation methodologies, in particular fuzzy systems and support vector regression (SVR). In both cases, the initial space‐mapping model is enhanced by an additional term that approximates the differences between the fine model and the initial space‐mapping surrogate. We compare the standard and enhanced space‐mapping models, as well as the fuzzy systems and SVR directly used for modeling fine model data. A discussion of the advantages and disadvantages of the presented methods is also given. Copyright © 2010 John Wiley & Sons, Ltd.

Invited Paper: Applications of vector non-linear microwave measurements

This study discusses the present day capabilities of vector non-linear microwave measurements. It starts off with a historical review on the development of such instrumentation. Next, the author elaborates on how vector non-linear measurements changed the way in which models for non-linear microwave devices are validated and constructed, how they broadened physical understanding, and how they have impacted microwave circuit design.

Electromagnetic modeling of axis-symmetric microwave devices

This paper presents calculations of electromagnetic field distributions with different microwave-driven devices. Results are obtained by using a two-dimensional numerical simulation tool that solves Maxwell's equations for an axis-symmetric TM mode, by considering a harmonic time description. The tool is applied to a coaxial waveguide, a circular waveguide with a shortening plate at the top, and a plasma reactor operated by an axial injection torch. This reactor consists of a circular cavity powered by a coaxial waveguide assembled to a nozzle, and creates a micro-plasma that affects only very locally the electric field distribution. We compare the solutions obtained for the three devices. We also carry out a simulation benchmarking in terms of accuracy, by using the analytical solution to the problem (in cases where it is available), or in terms of precision, by using the numerical calculation of the average power absorbed (in the presence of plasma).

Modeling and Characterization of Smart LSMO Ferromagnetic Thin-Film Tunable Resistance

This paper addresses the potential use of the (La0.67, Sr0.33)MnO3 (LSMO) ferromagnetic thin-film materials as microwave tunable enabling technology. LSMO thin-film materials have a strong interaction between their electrical and magnetic properties that could be translated into innovative tunable microwave components. The knowledge of the electroactive and magnetoactive properties of these materials is essential for modeling and design of novel microwave devices. The activity of these materials can be described by their electro/magnetoresistance and magnetocapacitance. The 400-nm-thick LSMO thin film is formed by the chemical solution deposition. Interdigital capacitor is chosen due to its high sensitivity for tunability and the fact that the metallization and patterning by photolithography are key issues in the fabrication of such structures which are formed by the deposition of metal on a single side of the LSMO thin film. The I-V relation, measured by tuning the bias voltage, is nonlinear and symmetric with respect to the polarity of the applied field. The switching to high resistive state is definite and sharp. Under electrostatic bias field, it is assumed that electric current flowing through narrow low-resistance path induces intense local magnetic field, resulting in the decrease of film resistance. This paper takes a close look in modeling the change of the LSMO resistance in terms of both working frequency and applied bias.

Broadband Modeling of High-Frequency Microwave Devices

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Circuit modeling of high-frequency devices described by tabulated multiport parameters has generated immense interest during recent years. In most cases, equivalent circuit models (ECMs) are available to the designers, which correlate well with the measured parameters at lower frequencies, however, deviate at higher frequencies. Traditional efforts to improve ECMs are device specific, laborious, and <emphasis emphasistype="boldital">ad-hoc</emphasis> in nature. In order to address these difficulties, this paper presents an efficient and automated algorithm to identify appropriate frequency-dependent elements for adding to the ECM at arbitrary locations so as to correct for high-frequency errors. The new method enables the designers to retain their existing physical models while providing a means to capture the high-frequency effects accurately. </para>

Statistical Neuro-Space Mapping Technique for Large-Signal Modeling of Nonlinear Devices

A new technique, called statistical neuro-space mapping, is proposed for large-signal statistical modeling of nonlinear microwave devices. The proposed technique is an advance over a recent linear statistical mapping technique. It uses nonlinear mapping to overcome the accuracy limitations of the linear mapping in modeling large statistical variations among different devices. For a given population of device samples, the nominal device model is determined from dc, small-, and large-signal data. The behavior of a random device in the population is obtained by a nonlinear mapping from that of the nominal device. The unknown mapping function is represented by neural networks trained using dc and small-signal data of various devices in the population. A novel statistical mapping is formulated by introducing a compact set of statistical variables to control the mapping to map from the nominal device toward different devices in the population. A new training method is proposed for simultaneous statistical parameter extraction and neural-network training. The proposed technique is confirmed by statistical modeling of microwave transistor examples, and use of the models in statistical analyses of a two-stage amplifier. It is demonstrated that, for small or large statistical variations, the proposed technique outperforms the existing methods, using a minimum amount of expensive large-signal data to provide the most accurate large-signal statistical model.

Modeling of microwave devices with space mapping and radial basis functions

AbstractWe review recent developments in space mapping techniques for modeling of microwave devices. We present a surrogate modeling methodology that utilizes space mapping combined with radial basis function interpolation. The method has advantages both over the standard space mapping modeling methodology and the recently published space mapping modeling with variable weight coefficients. In particular, it provides accuracy comparable or better than the latter method and computational efficiency as good as the standard space mapping modeling procedure. A comparison between the space mapping modeling methodologies as well as application examples of optimization and statistical analysis of microwave structures is presented. Copyright © 2007 John Wiley &amp; Sons, Ltd.

Microwave Devices and Antennas Modelling by Support Vector Regression Machines

Development of fast and accurate models of microwave devices and antennas is of paramount importance in computer-aided design and circuit analysis. At this purpose, artificial neural networks (ANNs) have been extensively exploited in technical literature. However, in the last years support vector machines (SVMs) developed by Vapnik are gaining popularity due to many attractive features capable to overcome the limitations connected to ANNs. In this work, support vector regression machines (SVRMs) modelling performances are investigated and compared with ANNs performances by means of several cases of study

Space‐mapped neuro‐fuzzy optimization for microwave device modeling

AbstractFour modeling methods of microwave devices using multiple neuro‐fuzzy inference systems (MANFIS) based on space‐mapping (SM) approach are presented. In contrast with previous works using conventional mathematical tools and artificial neural networks, the proposed SM‐based neuro‐fuzzy modeling methods can construct nonlinear multidimensional mapping functions based on both human knowledge in the form of fuzzy if–then rules easy to understand by human beings and stipulated input–output data pairs, without employing precise quantitative analyzes. Optimization by microgenetic algorithm is used to find nonlinear multidimensional mapping functions for the modeling methods that present a singular system. These methods are applied to a shielded microstrip line and the obtained models are discussed. These space‐mapped neuro‐fuzzy models could be integrated in a toolbox of any commercially available computer‐aided design tools for radio frequency/microwave circuits. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 1328–1334, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22460

An evolution algorithm for noise modeling of HEMT’s down to cryogenic temperatures

The implementation of an Evolution Algorithm (EA) for the cryogenic noise modeling of microwave devices has been the object of the present work. Several approaches exist to treat the modeling issue as far as advanced microwave transistors are concerned. The aim of such procedures is mostly oriented to extract a linear equivalent circuit including noise performance that allows to reproduce the global device behavior in a small signal regime. In the present paper, the authors show how EA’s can be employed to solve the noise modeling problem according to a black-box approach. The application refers to the simulation of the Noise Parameters of High Electron Mobility Transistors (HEMT) in the 6-18 GHz frequency range and down to cryogenic temperatures (90K) compared with experimental data. The quality of results indicates that EA techniques represent a truly alternative way to determine the microwave noise performance of HEMT devices, thus furnishing a flexible tool to support CAD of high sensitivity – ultra high speed circuits.

On the use of adaptive rational interpolation for the calculation of resonator characteristics from EM analysis

Interpolation models are used in computational electromagnetic (CEM) analysis techniques to provide highly accurate models for microwave devices. In this article the use of a Thiele-type adaptive rational interpolation formulation is exploited to accurately predict the natural frequencies and Q-factors of a microstrip ring resonator. © 2006 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2006.

Simplified space-mapping approach to enhancement of microwave device models

In this article, we present advances in microwave and RF device modeling exploiting the space mapping (SM) technology. New SM-based modeling techniques are proposed that are easy to implement entirely in the Agilent ADS framework. A simplified SM-based model description is discussed. Using a two-section transformer example, we show how the modeling accuracy is affected by the model flexibility. Tables, diagrams, and flowcharts are developed to help in understanding the concepts. This makes the SM modeling concept available to engineers through widely used commercial software. Our approach permits the creation of library models that can be used for model enhancement of microwave elements. Frequency-interpolation techniques are discussed and implemented. A set of four different SM-based models is presented along with corresponding implementations in the ADS schematic for a microstrip right-angle bend and a microstrip shaped T-junction. We use a three-section transformer to illustrate the implementation procedure in full details. We apply the technique to a more complicated HTS filter modeling problem. Fine-model data is obtained from Sonnet's em. We discuss the relation between the model complexity and accuracy as well as further improvement of the model. © 2006 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2006.