Space mapping frameworks for modeling and design of microwave circuits

This thesis contributes to computer-aided design and modeling of microwave circuits exploiting space mapping technology. Comprehensive frameworks for enhancing available empirical models or creating new ones are presented. A novel technique for microwave circuit design is also presented.; A comprehensive framework to engineering device modeling which we call Generalized Space Mapping (GSM) is introduced. GSM aims at significantly enhancing the accuracy of available empirical models of microwave devices by utilizing a few relevant full-wave EM simulations. Three fundamental illustrations are presented: a basic Space Mapping Super Model (SMSM), Frequency-Space Mapping Super Model (FSMSM) and Multiple Space Mapping (MSM). Two variations of MSM are presented: MSM for Device Responses (MSMDR) and MSM for Frequency Intervals (MSMFI). A novel criterion to discriminate between coarse models of the same device is also presented.; A new computer-aided modeling methodology to develop broadband physics-based models for passive components is presented. Full-wave EM simulators, artificial neural networks, multivariable rational functions, dimensional analysis and frequency mapping are coherently integrated to establish broadband models. Frequency mapping is used to develop the frequency-dependent empirical models. Useful properties of the frequency mapping are utilized in the modeling process. Transformations from frequency-dependent models to frequency-independent ones are also considered. The passivity conditions of the frequency-dependent empirical model are also considered.; We present a novel design framework for microwave circuits. We expand the original space mapping technique by allowing preassigned parameters (which are not used in optimization) to change in some components of the coarse model. We refer to those components as “relevant” components and we present a method based on sensitivity analysis to identify them. As a result, the coarse model can be calibrated to align with the fine model. Our algorithm establishes a mapping from some of the optimizable parameters to the preassigned parameters of the relevant components. This mapping is updated iteratively until we reach the optimal solution.