Advances in space mapping optimization of microwave circuits Theses

This thesis concerns itself with advances in Space Mapping optimization of microwave circuits and with the developments in Parameter Extraction. Space Mapping (SM) optimization aims at efficiently optimizing microwave circuits using the accurate and time-intensive electromagnetic simulators. Such simulators represent "fine" models of the circuit under consideration. SM exploits the existence of a less accurate but fast "coarse" model, e.g., an empirical model. A mapping is established between the parameter spaces of the coarse and fine models. The fine model design is the inverse mapping of the optimal coarse model design. A crucial step for any SM-based optimization algorithm is Parameter Extraction (PE). In this step a coarse model point that corresponds to a given fine model response is obtained through an optimization process. The nonuniqueness of PE can lead to divergence or oscillation of the optimization iterates. We introduced the Trust Region Aggressive Space Mapping (TRASM) algorithm. This algorithm integrates a trust region methodology with SM optimization. The iterate is confined to a trust region in which the utilized linearization can be trusted. TRASM also exploits a recursive multi-point parameter extraction step to enhance the uniqueness of PE. The Aggressive Parameter Extraction (APE) algorithm addresses the optimal selection of parameter perturbations used to increase trust in PE uniqueness. We establish an appropriate criterion for the generation of these perturbations. The APE algorithm classifies possible solutions for the PE problem. Two different approaches for obtaining subsequent perturbations are utilized based on a classification of the extracted parameters. The algorithm is demonstrated through parameter extraction of microwave filters and transformers. The Hybrid Aggressive Space Mapping (HASM) algorithm addresses the case of a poor coarse model. HASM utilizes SM optimization as long as it is converging. Otherwise, it switches to a direct optimization phase. We developed a relationship that relates the established mapping and the first order derivatives of the coarse and fine models. This relationship is utilized in switching between the SM phase and the direct optimization phase. We also present a Surrogate Model-based Space Mapping (SMSM) optimization algorithm. SMSM integrates two approaches for efficient optimization: SM optimization and surrogate model optimization. It exploits a surrogate model in predicting new iterates. This model is a convex combination between a mapped coarse model and a linearized fine model. The mapped coarse model exploits a frequency-sensitive mapping. During the optimization iterates, the coarse and fine models are simulated at two different sets of frequencies. Utilizing a frequency sensitive mapping is shown to enhance the uniqueness of PE. It also overcomes severe frequency misalignments between the responses of both models.