Optimization-based parameter adjustment involving full-wave electromagnetic (EM) simulation models is a crucial stage of present-day microwave design process. In fact, rigorous optimization is the only reliable mean permitting to simultaneously handle multiple geometry/material parameters, objectives, and constraints. Unfortunately, EM-driven design is a computationally intensive endeavor. While local tuning is usually manageable, direct global search is most often prohibitively costly. At the same time, global optimization is often required: design of compact microwave passives, multi-modal problems, circuit re-design across broad range of operating conditions, are a few examples. A popular mitigation method is to employ surrogate modeling approach. Still, constructing reliable models is impeded by the curse of dimensionality. This paper presents a novel algorithm for globalized design of microwave devices, which employs simplex-based regression models build at the level of operating parameters and performance figures of the circuit of interest. Computational efficiency of our technique stems from topological simplicity of the surrogate and its updating rules, as well as nearly-linear dependence between the circuit dimensions and its operational parameters. Furthermore, the updating rules of the regression model guarantee convergence of the optimization process. The global search capability of our technique is validated through repetitive optimization of three microstrip circuits. A perfect success rate is demonstrated along with a low (average) computational cost of just about eighty EM analyses of the circuit at hand. Numerical results are supported by experimental validation of selected designs.
Authors
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1109/tmtt.2023.3318702
- Category
- Publikacja w czasopiśmie
- Type
- artykuły w czasopismach
- Language
- angielski
- Publication year
- 2024
