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The design of miniaturized microwave components is largely based on computational models, primarily, full-wave electromagnetic (EM) simulations. The EM analysis is capable of giving an accurate account for cross-coupling effects, substrate and radiation losses, or interactions with environmental components (e.g., connectors). Unfortunately, direct execution of EM-based design tasks, such as parametric optimization or uncertainty quantification (UQ), may turn prohibitively expensive in computational terms. A workaround has been offered by surrogate-assisted procedures that capitalize on replacing expensive EM simulations by fast metamodels, notably data-driven ones. However, the construction of general-purpose metamodels is impeded by the curse of dimensionality as well as a limited capability of approximation techniques to represent highly nonlinear responses of microwave devices. This article proposes a novel technique that integrates the performance-driven modeling paradigm as well as variable-resolution EM simulations. The former focuses on the construction of the surrogate in the parameter space subset encompassing high-quality designs, which effectively addresses the dimensionality issues. The latter—realized through co-kriging—contributes to further computational savings by executing the majority of circuit evaluations at the level of coarse-discretization EM analysis. Verification experiments conducted for three microstrip components demonstrate the superiority of the proposed approach over existing performance-driven techniques, let alone conventional modeling procedures, both with respect to accuracy and computational cost of the surrogate construction. [ABSTRACT FROM AUTHOR]

Full-wave electromagnetic (EM) analysis is one of the most important tools in the design of modern microwave components and systems. EM simulation permits reliable evaluation of circuits at the presence of cross-coupling effects or substrate anisotropy, as well as for accounting for interactions with the immediate environment. However, repetitive analyses required by EM-driven procedures, such as parametric optimization or statistical analysis, may entail considerable computational expenditures, often prohibitive. Tackling the high-cost issue fostered the shift toward the incorporation of fast replacement models, including both physics-based surrogates and data-driven ones. While the latter is more popular and versatile, the construction of reliable approximation metamodels for microwave components is hindered by the curse of dimensionality and nonlinearity of system responses. The recent performance-driven modeling methodologies are capable of alleviating these difficulties by confining the surrogate domain to a vicinity of the optimum design manifold (i.e., the region that contains high-quality designs rather than the entire parameter space). Although setting up the model in a constrained domain requires small amounts of training data, domain definition itself requires a set of preoptimized reference designs, acquisition of which is an expensive endeavor. This work proposes a novel approach, which replaces the reference designs with a small set of random observables, thereby considerably reducing the overall cost of the model setup. Comprehensive verification involving several miniaturized microstrip structures demonstrates that our methodology is competitive to performance-driven frameworks both in terms of modeling accuracy and computational efficiency with an average savings of around 80%. [ABSTRACT FROM AUTHOR]