Optimization of microwave components using machine learning and rapid sensitivity analysis

Recent years have witnessed a tremendous popularity growth of optimization methods in high-frequency electronics, including microwave design. With the increasing complexity of passive microwave components, meticulous tuning of their geometry parameters has become imperative to fulfill demands imposed by the diverse application areas. More and more often, achieving the best possible performance requires global optimization. Unfortunately, global search is an intricate undertaking. To begin with, reliable assessment of microwave components involves electromagnetic (EM) analysis entailing significant CPU expenses. On the other hand, the most widely used nature-inspired algorithms require large numbers of system simulations to yield a satisfactory design. The associated costs are impractically high if not prohibitive. The use of available mitigation methods, primarily surrogate-based approaches, is impeded by dimensionality-related problems and the complexity in microwave circuit characteristics. This research introduces a procedure for expedited globalized parameter adjustment of microwave passives. The search process is embedded in a surrogate-assisted machine learning framework that operates in a dimensionality-restricted domain, spanned by the parameter space directions being of importance in terms of their effects on the circuit characteristic variability. These directions are established using a fast global sensitivity analysis procedure developed for this purpose. Domain confinement reduces the cost of surrogate model establishment and improves its predictive power. The global optimization phase is complemented by local tuning. Verification experiments demonstrate the remarkable efficacy of the presented approach and its advantages over the benchmark methods that include machine learning in full-dimensionality space and population-based metaheuristics.