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PIER PIER B PIER C PIER M PIER Letters PIER Photonics
2018-08-20
Multi-Physics Parametric Modeling of Microwave Passive Components Using Artificial Neural Networks
By
Shuxia Yan,

    Professor Shuxia Yan

    School of Electronics and Information Engineering

    Tiangong University

    China

    Homepage

Yaoqian Zhang,

    Dr. Yaoqian Zhang

    School of Electronics and Information Engineering

    Tianjin Polytechnic University

    China

    Homepage

Xiaoyi Jin,

    Dr. Xiaoyi Jin

    School of Electronics and Information Engineering

    Tianjin Polytechnic University

    China

    Homepage

Wei Zhang,

    Dr. Wei Zhang

    School of Microelectronics

    Tianjin University

    China

    Homepage

Weiguang Shi

    Dr. Weiguang Shi

    School of Electronics and Information Engineering

    Tianjin Polytechnic University

    China

    Homepage

Progress In Electromagnetics Research M, Vol. 72, 79-88, 2018
doi:10.2528/PIERM18070403 | Google Scholar

Abstract
In this paper, a novel multi-physics parametric modeling approach using artificial neural networks (ANNs) for microwave passive components is proposed. In the proposed approach, the ANN is used to learn the nonlinear relationships between electromagnetic (EM) behaviors and multi-physics design variables. The trained model can accurately represent the EM responses of the passive components with respect to the multi-physics input parameters. Therefore, the proposed model can provide accurate and fast prediction of EM responses using low computational cost and little time for multi-physics design. The advantage of the proposed model is demonstrated by two microwave examples: the proposed model can save about 98% computational cost compared with the EM model, and the CPU time of the proposed model is less than 0.1 s while that of the EM model needs many minutes.
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Citation
Shuxia Yan, Yaoqian Zhang, Xiaoyi Jin, Wei Zhang, and Weiguang Shi, "Multi-Physics Parametric Modeling of Microwave Passive Components Using Artificial Neural Networks," Progress In Electromagnetics Research M, Vol. 72, 79-88, 2018.
doi:10.2528/PIERM18070403
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