Neural space mapping methods for modeling and design of microwave circuits - Rayas-Sánchez, José Ernesto (My University, 2001)
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.
Keywords: Electrical and Computer Engineering; Electrical and Computer Engineering