Novel Dominant-Order-Based Pole/Zero Matching Technique for MOR-Based Neuro-DTF Parametric Modeling of Microwave Components

The model order reduction (MOR)-based modeling method combining neural networks and the transfer function (neuro-TF) is widely used for efficient parametric modeling of electromagnetic (EM) behaviors in microwave components. However, the order of MOR is usually required to be much higher than the actual order of the microwave components to achieve accurate EM solutions. This extra order by MOR results in a high possibility of uncertainty and discontinuity of the extracted extra poles/zeros with respect to geometrical variations for neuro-TF model development. To address the above issue, this article proposes a novel dominant-order-based pole/zero matching technique for parametric modeling, incorporating dominant-order-based transfer functions and neural networks (short for neuro-DTF). The proposed dominant-order-based pole/zero matching technique is to identify dominant poles and zeros through a novel adaptive variable-order reduction process. Dominant poles and zeros are screened and retained by matching successive high- and low-order reduced systems, and nondominant poles and zeros are transferred into a complex exponential function. By combining the dominant poles and zeros with the complex exponential function, the dominant-order-based transfer function is obtained. The core objective is to retain poles and zeros with dominant influence on key frequency bands while simplifying nondominant dynamics. The proposed technique effectively eliminates pole/zero mismatch issues caused by the uncertainty and discontinuity of the extracted extra poles/zeros with respect to geometrical variations. Compared to conventional MOR-based neuro-TF parametric modeling, the proposed neuro-DTF method, incorporating the dominant-order-based pole/zero matching technique, achieves higher accuracy and robustness, especially under large geometrical parameter variations. Its effectiveness is verified through two microwave examples.