Multiphysics Transfer Function Parameters Extraction Based on FEM and MOR for Parametric Modeling of Microwave Waveguide Filters

When the performances of microwave waveguide filters are obviously influenced by thermal effects, designers have to consider and analyze the multiphysics coupling effect in devices. Most existing parametric modeling techniques based on pure electromagnetic (EM) are not suitable for multiphysics scenarios. Among the available approaches, vector fitting-based neuro-transfer functions (neuro-TFs) represent one of the techniques compatible with multiphysics parametric modeling due to their data-driven characteristics. However, vector fitting-based neuro-TF inherently suffers from discontinuities in the transfer function parameters caused by order changing. For pure EM problems, physics-driven transfer functions can be derived using finite element methods (FEMs) and model order reduction (MOR), which significantly improves the continuity of the extracted transfer function parameters and thereby enhances modeling accuracy. However, in the case of multiphysics problems, it is currently infeasible to extract physics-driven transfer function parameters. In this article, we propose an implicit iterative formulation for extracting multiphysics transfer function parameters based on FEM and MOR in the EM-thermal-mechanical multiphysics. The continuity of the multiphysics transfer function parameters is ensured through mesh deformation, and an iterative convergence criterion is established. Based on the extracted multiphysics transfer function parameters, a pole-zero neuro-TF method for multiphysics parametric modeling is developed. The proposed method is validated through two high-power filter examples. Compared with existing conventional neuro-TF and multiphysics modeling methods, the proposed multiphysics parametric modeling approach achieves significantly higher model accuracy.