Fast EM-Driven Parameter Tuning of Microwave Circuits with Sparse Sensitivity Updates via Principal Directions - Publication - Bridge of Knowledge

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Fast EM-Driven Parameter Tuning of Microwave Circuits with Sparse Sensitivity Updates via Principal Directions

Abstract

Numerical optimization has become more important than ever in the design of microwave components and systems, primarily as a consequence of increasing performance demands and growing complexity of the circuits. As the parameter tuning is more and more often executed using full-wave electromagnetic (EM) models, the CPU cost of the overall process tends to be excessive even for local optimization. Some ways of alleviating these issues exist, yet, they are limited either by their accessibility or applicability range. This work presents a novel algorithmic approach to accelerated gradient-based parameter tuning of microwave components with numerical derivatives. In our methodology, computational savings are achieved by exploiting the problem-specific knowledge, specifically, by restricting the gradient updates to an orthonormal basis of essential directions corresponding to the maximum variability of the circuit responses within the frequency bands of interest. The said directions are selected through an automated decision-making process involving the analysis of the circuit response variability. Our approach is demonstrated using two multi-parameter microwave devices. Comprehensive comparison with the benchmark methods, including the standard trust-region algorithm and the three accelerated versions, indicate savings of up to fifty percent associated only with minor reduction of the design quality.

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Category:
Articles
Type:
artykuły w czasopismach
Published in:
KNOWLEDGE-BASED SYSTEMS no. 252,
ISSN: 0950-7051
Language:
English
Publication year:
2022
Bibliographic description:
Pietrenko-Dąbrowska A., Kozieł M.: Fast EM-Driven Parameter Tuning of Microwave Circuits with Sparse Sensitivity Updates via Principal Directions// KNOWLEDGE-BASED SYSTEMS -Vol. 252, (2022), s.109388-
DOI:
Digital Object Identifier (open in new tab) 10.1016/j.knosys.2022.109388
Sources of funding:
  • Free publication
Verified by:
Gdańsk University of Technology

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