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Accurate performance evaluation of microwave components can be carried out using full‐wave electromagnetic (EM) simulation tools, routinely employed for circuit verification but also in the design process itself. Unfortunately, the computational cost of EM‐driven design may be high. This is especially pertinent to tasks entailing considerable number of simulations (eg, parametric optimization, statistical analysis). A possible way of alleviating these difficulties is utilization of fast replacement models, also referred to as surrogates. Notwithstanding, conventional modeling methods exhibit serious limitations when it comes to handling microwave components. The principal challenges include large number of geometry and material parameters, highly nonlinear characteristics, as well as the necessity of covering wide ranges of operating conditions. The latter is mandatory from the point of view of the surrogate model utility. This article presents a novel modeling approach that incorporates variable‐fidelity EM simulations into the recently reported nested kriging framework. A combination of domain confinement due to nested kriging, and low‐/high‐fidelity EM data blending through cokriging, enables the construction of reliable surrogates at a fraction of cost required by single‐fidelity nested kriging. Our technique is validated using a three‐section miniaturized impedance matching transformer with its surrogate model rendered over wide range of operating frequencies. Comprehensive benchmarking demonstrates superiority of the proposed method over both conventional models and nested kriging. [ABSTRACT FROM AUTHOR]

Full-wave electromagnetic (EM) analysis is a versatile tool for evaluating the performance of high-frequency components. Its potential drawback is its high computational cost, inhibiting the execution of EM-driven tasks requiring massive simulations. The applicability of equivalent network models is limited owing to the topological complexity of compact microstrip components because of EM cross-coupling effects. Development of alternative representations (surrogate models) is therefore necessary. This article proposes a two-level methodology for reliable modelling of compact microstrip components. The keystone is to define the surrogate domain using the first-level model approximating the set of pre-existing reference designs. This limits the volume of the parameter space region that needs to be sampled when constructing the second-level model. The presented approach provides far greater accuracy than conventional methods and is capable of establishing surrogates covering wide ranges of geometric parameters and operating conditions of a particular structure. Applications for parametric optimization are also provided. [ABSTRACT FROM AUTHOR]

Design of microwave components is an inherently multiobjective task. Often, the objectives are at least partially conflicting and the designer has to work out a suitable compromise. In practice, generating the best possible trade‐off designs requires multiobjective optimization, which is a computationally demanding task. If the structure of interest is evaluated through full‐wave electromagnetic (EM) analysis, the employment of widely used population‐based metaheuristics algorithms may become prohibitive in computational terms. This is a common situation for miniaturized components, where considerable cross‐coupling effects make traditional representations (eg, network equivalents) grossly inaccurate. This article presents a framework for accelerated EM‐driven multiobjective design of compact microwave devices. It adopts a recently reported nested kriging methodology to identify the parameter space region containing the Pareto front and to render a fast surrogate, subsequently used to find the first approximation of the Pareto set. The final trade‐off designs are produced in a separate, surrogate‐assisted refinement process. Our approach is demonstrated using a three‐section impedance matching transformer designed for the best matching and the minimum footprint area. The Pareto set is generated at the cost of only a few hundred of high‐fidelity EM simulations of the transformer circuit despite a large number of geometry parameters involved. [ABSTRACT FROM AUTHOR]

In the paper, a recently reported nested kriging methodology is employed for modeling of miniaturized microwave components. The approach is based on identifying the parameter space region that contains high-quality designs, and, subsequently, rendering the surrogate in this subset. The results obtained for a miniaturized unequal-power-split rat-race coupler and a compact three-section impedance transformer demonstrate reliability of the method even for highly-dimensional parameter spaces, as well as its superiority over conventional modeling methods. [ABSTRACT FROM AUTHOR]

Utilization of electromagnetic (EM) simulation tools has become indispensable for reliable evaluation of microwave components. As the cost of an individual analysis may already be considerable, the computational overhead associated with EM-driven tasks that require massive simulations (e.g., optimization) may turn prohibitive. One of mitigation methods is the employment of equivalent network models. Yet, they are incapable of accounting for cross-coupling effects that occur in devices of complex geometries. Another option are fast replacement models (surrogates), especially the data-driven ones: readily available, generic and problem independent. Unfortunately, due to the curse of dimensionality, their applicability is limited to low-dimensional parameter spaces and narrow parameter ranges. From the utility perspective, however, the surrogate has to be valid over broad ranges of parameters and operating conditions. The recently reported performance-modeling techniques (especially nested-kriging) allow for rendering such surrogates even for complex devices. Key concept is to carry out the modeling process within a confined domain, being a subset of the parameter space that encompasses the designs of high-quality regarding the performance figures of choice. The goal of this work is to reduce the cost of reference design acquisition, which adds up to the total cost of constructing the surrogate. Toward this end, gradient-enhanced kriging is incorporated into the performance-driven modeling framework. The predictive power of the surrogates rendered using our approach by far exceeds that of the conventional methods and is comparable to the original nested kriging technique while requiring a significantly smaller number of reference designs (thus, the CPU cost). These features are demonstrated using a three-section transformer and a rat-race coupler. [ABSTRACT FROM AUTHOR]

Electromagnetic (EM) analysis has become ubiquitous in the design of microwave components and systems. One of the reasons is the increasing topological complexity of the circuits. Their reliable evaluation—at least at the design closure stage—can no longer be carried out using analytical or equivalent network representations. This is especially pertinent to miniaturized structures, where considerable EM cross-coupling effects occurring in densely arranged layouts affect the performance in a non-negligible manner. Although mandatory, EM-driven design is normally associated with significant computational expenses. Consequently, expediting the procedures that require massive simulations, such as parametric optimization, is a practical necessity. In this paper, a framework for accelerated parameter tuning is proposed. The keystones of our methodology are a set of pre-existing designs optimized for various design objectives, as well as kriging interpolation surrogates. The latter are constructed to yield—for a given set of performance specifications—a reasonably good starting point and to enable rapid optimization by providing the initial approximation of the Jacobian matrix of the circuit outputs. The proposed approach is validated using two compact impedance matching transformers designed within the objective spaces defined by wide ranges of operating bandwidths. As demonstrated, the average tuning cost corresponds to a few EM simulations of the respective circuit despite large numbers of adjustable parameters. [ABSTRACT FROM AUTHOR]