A Novel Training Approach for Parametric Modeling of Microwave Passive Components Using Padé via Lanczos and EM Sensitivities

• Published in: IEEE transactions on microwave theory and techniques Vol. 68; no. 6; pp. 2215 - 2233
• Main Authors: Zhang, Jianan, Feng, Feng, Zhang, Wei, Jin, Jing, Ma, Jianguo, Zhang, Qi-Jun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks (ANNs); electromagnetic (EM) modeling; EM sensitivities; finite element method (FEM); Geometric accuracy; Matching; Mathematical models; Microwave circuits; Model accuracy; Modelling; Neural networks; Padé via Lanczos; Parameter sensitivity; parametric modeling; Parametric statistics; Passive components; Poles; Sensitivity; Sensitivity analysis; Training; Transfer functions
• ISSN: 0018-9480; 1557-9670
• DOI: 10.1109/TMTT.2020.2979445

This article proposes a novel training approach for parametric modeling of microwave passive components with respect to changes in geometrical parameters using matrix Padé via Lanczos (MPVL) and electromagnetic (EM) sensitivities. In the proposed approach, the EM responses of passive components versus frequency are represented by pole-zero-gain transfer functions. The relationships between the poles/zeros/gain in the transfer function and geometrical variables are learned by neural networks. To generate training data, we apply the MPVL algorithm to compute (or recompute) the poles/zeros each time we change the geometrical parameters. However, the indices of the poles/zeros after the recomputation may not have clear correspondences with those before the recomputation, posing additional challenges to predict the poles/zeros reliably for a new change of geometrical parameters. We propose a novel sensitivity-analysis-based pole-/zero-matching algorithm to obtain the correct correspondences between the poles/zeros at different geometrical parameter values. The proposed algorithm exploits the EM sensitivities, which provide useful information for the direction of movement of the poles/zeros, to predict the new positions of the poles/zeros for each change of geometrical parameters in the multidimensional parameter space. The predicted new positions are then used to guide the matching process of poles/zeros between different geometrical parameter values. Using the matched poles/zeros to train the neural networks allows us to have fast and reliable predictions for the poles/zeros subject to large geometrical variations, consequently increasing the accuracy and robustness of the overall model. Compared with the existing methods, the proposed approach can obtain better accuracy in challenging applications involving large geometrical variations. Three microwave examples are used to illustrate the proposed approach.