Abstract

Fabrication tolerances, as well as uncertainties of other kinds, e.g., concerning material parameters or operating conditions, are detrimental to the performance of microwave circuits. Mitigating their impact requires accounting for possible parameter deviations already at the design stage. This involves optimization of appropriately defined statistical figures of merit such as yield. Alt-hough important, robust (or tolerance-aware) design is an intricate endeavor because manufac-turing inaccuracies are normally described using probability distributions, and their quantification has to be based on statistical analysis. The major bottleneck here is high computational cost: for re-liability reasons, miniaturized microwave components are evaluated using full-wave electromag-netic (EM) models, whereas conventionally utilized analysis methods (e.g., Monte Carlo simula-tion) are associated with massive circuit evaluations. A practical approach that allows for cir-cumventing aforementioned obstacles offer surrogate modeling techniques, which has been a dominant trend over the recent years. Notwithstanding, a construction of accurate metamodels may require considerable computational investments, especially for higher-dimensional cases. This paper brings in a novel design centering approach, which assembles forward surrogates founded at the level of response features and trust-region framework for direct optimization of the system yield. Formulating the problem with the use of characteristic points of the system response allevi-ates the issues related to response nonlinearities. At the same time, as the surrogate is a linear re-gression model, a rapid yield estimation is possible through numerical integration of the input probability distributions. As a result, expenditures related to design centering are as low as a few dozens of EM analyses. The introduced technique is demonstrated using three microstrip couplers. It is compared to recently reported techniques, and its reliability is corroborated using EM-based Monte Carlo analysis.

Citations

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