A microwave filter yield optimization method based on off-line surrogate model-assisted evolutionary algorithm

Zhang, Z., Liu, B. , Yu, Y. and Cheng, Q. S. (2022) A microwave filter yield optimization method based on off-line surrogate model-assisted evolutionary algorithm. IEEE Transactions on Microwave Theory and Techniques, 70(6), pp. 2925-2934. (doi: 10.1109/TMTT.2022.3163745)

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Most existing microwave filter yield optimization methods target a small number of sensitive design variables (e.g., around 5). However, for many real-world cases, more than ten sensitive design variables need to be considered. Due to the complexity, yield optimization quality and efficiency become challenges. Hence, a new method, called yield optimization for filters based on the surrogate model-assisted evolutionary algorithm (YSMA), is proposed. The fundamental idea of YSMA is to construct a single high-accuracy surrogate model offline, which fully replaces electromagnetic (EM) simulations in the entire yield optimization process. Global optimization is then enabled to find designs with substantial yield improvement efficiently using the surrogate model. To reduce the number of necessary samples (i.e., EM simulations) while obtaining the required prediction accuracy, a customized machine learning technique is proposed. The performance of YSMA is demonstrated by two real-world examples with 11 and 14 design variables, respectively. Experimental results show the advantages of YSMA compared to the current dominant sequential online surrogate model-based local optimization methods.

Item Type:Articles
Additional Information:This work was supported in part by the Scotland 5G Testbed Project under Grant 308106 and in part by the National Natural Science Foundation of China under Grant 62071211.
Glasgow Author(s) Enlighten ID:Liu, Dr Bo
Authors: Zhang, Z., Liu, B., Yu, Y., and Cheng, Q. S.
College/School:College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
Journal Name:IEEE Transactions on Microwave Theory and Techniques
ISSN (Online):1557-9670
Published Online:12 April 2022
Copyright Holders:Copyright © 2022 IEEE
First Published:First published in IEEE Transactions on Microwave Theory and Techniques 70(6): 2925-2934
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3081065G Centre of Excellence at GlasgowMuhammad ImranScottish Government (SCOTGOV)RKES 190720 (S5GC)ENG - Electronics & Nanoscale Engineering