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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2503.23783 |
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| _version_ | 1866908291737583616 |
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| author | Chen, Tianqi Huang, Wei Wu, Qiang Yang, Li Gómez-García, Roberto Zhu, Xi |
| author_facet | Chen, Tianqi Huang, Wei Wu, Qiang Yang, Li Gómez-García, Roberto Zhu, Xi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23783 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ANNs-SaDE: A Machine-Learning-Based Design Automation Framework for Microwave Branch-Line Couplers Chen, Tianqi Huang, Wei Wu, Qiang Yang, Li Gómez-García, Roberto Zhu, Xi Signal Processing 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. |
| title | ANNs-SaDE: A Machine-Learning-Based Design Automation Framework for Microwave Branch-Line Couplers |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2503.23783 |