Neural space mapping methods for modeling and design of microwave circuits

This thesis contributes to the development of novel methods and techniques for computer-aided electromagnetics (EM)-based modeling and design of microwave circuits exploiting two previously unrelated technologies: space mapping (SM) and artificial neural networks (ANNs).; The conventional approach to EM-based modeling of microwave circuits is reviewed, as well as other state-of-the-art neuromodeling techniques. The fundamental space mapping concept is also reviewed. Developing neuromodels based on space mapping technology is addressed. Several SM-based neuromodeling techniques are described. Contrast with other neuromodeling approaches is realized.; An algorithmic procedure to design, called Neural Space Mapping (NSM) optimization, is described. NSM enhances an SM-based neuromodel at each iteration. Other techniques for optimization of microwave circuits using artificial neural networks are reviewed.; Efficient EM-based statistical analysis and yield optimization of microwave components using SM-based neuromodels is described. Other yield-driven EM optimization strategies are briefly reviewed. An innovative strategy to avoid extra EM simulations when asymmetric variations in the physical parameters are assumed is described.; Neural Inverse Space Mapping (NISM) optimization for EM-based microwave design is described. A neural network approximates the inverse mapping at each iteration. The NISM step simply consists of evaluating this neural network at the optimal empirical solution. NISM step is proved to be a quasi-Newton step when the amount of nonlinearity in the inverse neuromapping is small. NISM optimization is compared with other SM-based optimization algorithms.; The theoretical developments are implemented using available software on several advanced and industrially relevant microwave circuits.; Suggestions for further research are provided.