Adaptively Weighted Training of Space-Mapping Surrogates for Accurate Yield Estimation of Microwave Components
Electromagnetic (EM)-based yield estimation plays an important role in microwave design due to the presence of uncertainties in manufacturing processes. In this paper, we propose a novel training approach with adaptive weighting factors to increase the yield estimation accuracy of microwave components using space mapping (SM) surrogates. In this approach, an adaptive weighting factor is set up for each frequency point of interest based on the sensitivity degree of the EM response relative to the design specification. A novel error function incorporating the adaptive weighting factors is proposed specifically for EM-based yield estimation. Using the proposed error function to train the SM surrogate enhances the model accuracy at the key frequency points where the EM response is sensitive w.r.t. to statistical variables while preserving the model accuracy at other ordinary frequency points over the whole frequency range of interest. Compared with the existing training method, the proposed approach achieves higher yield estimation accuracy especially for microwave circuits with high sensitivities. The proposed approach is illustrated by a microwave filter example.