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arXiv · 2503.23783

ANNs-SaDE: A Machine-Learning-Based Design Automation Framework for Microwave Branch-Line Couplers

Tianqi Chen·Wei Huang·Qiang Wu·Li Yang·Roberto Gómez-García·Xi Zhu
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Abstract

The traditional method for designing branch-line couplers involves a trial-and-error optimization process that requires multiple design iterations through electromagnetic (EM) simulations. Thus, it is extremely time consuming and labor intensive. In this paper, a novel machine-learning-based framework is proposed to tackle this issue. It integrates artificial neural networks with a self-adaptive differential evolution algorithm (ANNs-SaDE). This framework enables the self-adaptive design of various types of microwave branch-line couplers by precisely optimizing essential electrical properties, such as coupling factor, isolation, and phase difference between output ports. The effectiveness of the ANNs-SaDE framework is demonstrated by the designs of folded single-stage branch-line couplers and multi-stage wideband branch-line couplers.

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Tianqi Chen, Wei Huang, Qiang Wu, Li Yang, Roberto Gómez-García, Xi Zhu. 2025-03-31. ANNs-SaDE: A Machine-Learning-Based Design Automation Framework for Microwave Branch-Line Couplers. https://arxiv.org/abs/2503.23783

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