Abstract

Uncertainty quantification is an important aspect of engineering design, also pertaining to the development and performance evaluation of high-frequency structures systems. Manufacturing tolerances as well as other types of uncertainties, related to material parameters (e.g., substrate permittivity) or operating conditions (e.g., bending) may affect the characteristics of antennas or microwave devices. For example, in the case of narrow- or multi-band antennas, this usually leads to frequency shifts of the operating bands. Quantifying these effects is imperative to adequately assess the design quality, either in terms of the statistical moments of the performance parameters or the yield. Reducing the system sensitivity to parameter deviations is even more essential when increasing the probability of the system satisfying the prescribed requirements is of concern. The prerequisite of such procedures is statistical analysis, normally carried out at the level of full-wave electromagnetic (EM) analysis. Although necessary to ensure reliability, it entails considerable computational expenses, often prohibitive. Following the recently fostered concept of constrained modeling, this chapter discusses a simple technique for rapid surrogate-assisted yield optimization of high-frequency structures. The keystone of the approach is an appropriate definition of the optimization domain. This is realized by considering a few pre-optimized designs that represent the 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. Due to a small volume of such a domain, an accurate replacement model can be established therein using a small number of training samples, and employed to improve the yield. The implementation details are tailored to a particular type of device. Verification results obtained for several antenna structures and a miniaturized rat-race coupler indicate that the optimization process can be accomplished at low cost of a few dozen of EM simulations. The result reliability is validated through comparisons with EM-based Monte Carlo simulations.

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