Abstrakt

The effects of uncertainties, primarily manufacturing tolerances but also incomplete information about operating conditions or material parameters, can be detrimental to the performance of microwave components. Quantification of such effects is essential to ensure a meaningful evaluation of the structure, in particular, its reliability under imperfect fabrication procedures. The improvement of the circuit robustness can be achieved by reducing sensitivity to geometry/material parameter deviations, which requires optimization of suitably chosen statistical performance metrics such as the yield. The prerequisite for the latter is statistical analysis. In the case of compact circuits, it is executed through full‐wave electromagnetic (EM) analysis. The fundamental difficulty, that is, the high CPU cost, can be alleviated by the employment of fast surrogate models, which is the method of choice for the majority of contemporary approaches. Despite its advantages, a practical challenge of surrogate‐assisted design is the initial computational overhead related to metamodel construction. As a workaround, this work proposes the employment of a recently introduced concept of constrained modelling, where the surrogate domain is confined only to contain the essential subsets of the parameter space. In the context of yield optimization, the domain needs to correspond to directions featuring maximum variability of the circuit responses (particularly the parts thereof that affect the yield value in the most significant way) with respect to its geometry parameters. The small volume of the domain spanned by such directions permits setting up an accurate model using a fraction of training data samples required by conventional methods. The proposed technique is demonstrated using a miniaturized microstrip rat‐race coupler with its yield optimized at the cost of just a few dozen of EM simulations of the circuit. EM‐based Monte Carlo simulations corroborate the reliability of the approach.

Cytowania

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