Tolerance Optimization of Antenna Structures by Means of Response Feature Surrogates

Fabrication tolerances and other types of uncertainties, e.g., the lack of precise knowledge of material parameters, have detrimental effects on the electrical and field performance of antenna systems. In the case of input characteristics, these are particularly noticeable for narrowband and multiband antennas where deviations of geometry parameters from their nominal values lead to frequency shifts of the operating frequency bands. Improving design robustness is therefore important yet challenging. On the one hand, it is numerically demanding as it involves uncertainty quantification (UQ), in particular, estimation and improvement of appropriately defined statistical performance metrics. On the other hand, it has to be carried out at the level of full-wave electromagnetic (EM) simulation models, which incurs considerable computational expenses. Executing UQ tasks at practically acceptable costs can be realized using surrogate modeling methods; however, the construction of reliable metamodels is hindered by the curse of dimensionality. This article proposes a novel approach to the robust design of antenna structures, where the task is formulated to increase the maximum values of parameter deviations for which 100% fabrication yield is ensured. Low cost of the optimization process is enabled by incorporating feature-based regression models for rapid yield estimation, as well as the employment of the trust-region framework for adaptive adjustment of design relocation but also as a convergence safeguard. Our methodology is validated using three microstrip antennas, including two dual-band and a triple-band structure.