Abstract
Simulation-based optimization of geometry parameters is an inherent and important stage of microwave design process. To ensure reliability, the optimization process is normally carried out using full-wave electromagnetic (EM) simulation tools, which entails significant computational overhead. This becomes a serious bottleneck especially if global search is required (e.g., design of miniaturized structures, dimension scaling over broad ranges of operating frequencies, multi-modal problems, etc.). In pursuit of mitigating the high-cost issue, this paper proposes a novel algorithmic approach to rapid EM-driven global optimization of microwave components. Our methodology incorporates a response feature technology and inverse regression metamodels to enable fast identification of the promising parameter space regions, as well as to yield a good quality initial design, which only needs to be tuned using local routines. The presented technique is illustrated using three microstrip circuits optimized under challenging scenarios, and demonstrated to exhibit global search capability while maintaining low computational cost of the optimization process of only about one hundred of EM simulations of the structure at hand on the average. The performance is shown to be superior in terms of efficacy over both local algorithms and nature-inspired global methods.
Highlights
Simulation-based optimization of geometry parameters is an inherent and important stage of microwave design process
This is a consequence of growing performance demands[3], functionality requirements[4,5,6,7], and miniaturization trends[8]. Techniques such as transmission line (TL) folding[9] or slow-wave phenomenon[10], are often employed, leading to geometrically involved structures described by many p arameters[11,12]
In high-frequency design, practical applicability of the aforementioned global search algorithms is limited to cases in which the objective function is computationally cheap, EM simulation is relatively cheap, or a parallelization is possible
Summary
Simulation-based optimization of geometry parameters is an inherent and important stage of microwave design process. The optimization process is normally carried out using full-wave electromagnetic (EM) simulation tools, which entails significant computational overhead This becomes a serious bottleneck especially if global search is required (e.g., design of miniaturized structures, dimension scaling over broad ranges of operating frequencies, multi-modal problems, etc.). Numerical optimization allows proper handling of several objectives and constraints over multi-dimensional parameter spaces It is an expensive process as even local optimization involves a considerable number of system evaluations. The most popular global optimization methods today are population-based nature-inspired algorithms[19,20,21]. Their roots can be tracked back to late 1960s22, and eventually dominated global search practice since 2000s23–27. Surrogates can be used in combination with machine learning methods[53], or for parameter space pre-screening[54]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
