Rapid multi-criterial design of microwave components with robustness analysis by means of knowledge-based surrogates

Slawomir Koziel, Anna Pietrenko-Dabrowska*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Manufacturing tolerances and uncertainties concerning material parameters, e.g., operating conditions or substrate permittivity are detrimental to characteristics of microwave components. The knowledge of relations between acceptable parameter deviations (not leading to violation of design specifications) and the nominal performance (not considering uncertainties), and is therefore indispensable. This paper proposes a multi-objective optimization technique of microwave components with tolerance analysis. The goal is to identify a set of trade-off designs: nominal performance versus robustness (quantified by the maximum input tolerance values that allow for achieving 100-percent fabrication yield). Our approach exploits knowledge-driven regression predictors rendered using characteristic points (features) of the component’s response for a rapid evaluation of statistical performance figures, along with trust-region algorithm to enable low execution cost as well as convergence. The proposed methodology is verified with the use of three microstrip circuits, a broadband filter, and two branch-line couplers (a single- and a dual-band one). It is demonstrated that a Pareto set w.r.t. nominal performance and robustness objectives can be produced using only 40 to 60 EM simulations of the respective structure (per design). Reliability of the proposed algorithm is corroborated with the use of EM-based Monte Carlo simulation.

Original languageEnglish
Article number5953
JournalScientific Reports
Volume13
Issue number1
DOIs
Publication statusPublished - 12 Apr 2023

Bibliographical note

Funding Information:
The authors would like to thank Dassault Systemes, France, for making CST Microwave Studio available. This work is partially supported by the Icelandic Centre for Research (RANNIS) Grant 206606 and by National Science Centre of Poland Grant 2020/37/B/ST7/01448.

Publisher Copyright:
© 2023, The Author(s).

© 2023. The Author(s).

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