Computationally Efficient Performance-Driven Surrogate Modeling of Microwave Components Using Principal Component Analysis

This paper addresses cost-efficient surrogate modeling of miniaturized microwave components. Following the recently proposed performance-driven modeling paradigm, the set of reference designs pre-optimized with respect to the selected operating conditions are employed to determine the model domain and to focus the surrogate build-up process only on the promising regions of the parameter space. Here, the principal component analysis (PCA) is applied to reduce the domain dimensionality. This is achieved by spanning it using a limited number of principal directions which are the most relevant from the point of view of the reference design correlations. The fundamental advantage of the proposed methodology is an improved predictive power of the surrogate and better scalability (as a function of the training data set size), as compared to the previous performance-driven modeling attempts. The procedure is illustrated using a compact impedance matching transformer.