Yield-Constrained Optimization Design Using Polynomial Chaos for Microwave Filters

TLDR: In this paper, an efficient yield-constrained optimization using polynomial chaos surrogates (YCOPCS) is employed for microwave filters considering multiple performance objectives, such as low-cost and high-accuracy of Polynomial Chaos surrogates.

Abstract: Yield optimization aims at finding microwave filter designs with high yield under fabrication tolerance The electromagnetic (EM) simulation-based yield optimization methods are computationally expensive because a large number of EM simulations is required Moreover, the microwave filter design usually requires several performance objectives to be met, which is not considered by the current yield optimization methods for microwave filters In this paper, an efficient yield-constrained optimization using polynomial chaos surrogates (YCOPCS) is employed for microwave filters considering multiple objectives In the YCOPCS method, the low-cost and high-accuracy of polynomial chaos is used as a surrogate An efficient yield-constrained design framework is implemented to obtain the optimal design solution Two numerical examples demonstrate the performance of the YCOPCS method, including a coupling matrix model of a fourth-order filter with cascaded quadruplet topology and an EM simulation model of a microwave waveguide bandpass filter The numerical results show that the YCOPCS method can obtain the filter designs with higher yield and reduce EM simulations by 80% compared to Monte Carlo-based yield optimization in all testing examples