Multiphysics Parametric Modeling of Microwave Components Using Combined Neural Networks and Transfer Function

Abstract

This paper proposes a new technique to develop an accurate multiphysics parametric model for microwave components to speed up the multiphysics modeling process. In the proposed technique, the artificial neural networks (ANNs) and pole/residue based transfer function are incorporated to represent the highly non-linear relationships between electromagnetic centric (EM-centric) multiphysics behaviors and multiphysics geometrical/non-geometrical design parameters. Vector fitting technique is utilized to obtain the poles/residues of the transfer function for each multiphysics sample. Since the relationship between multiphysics design parameters and the pole/residues of the transfer function is non-linear and unknown, two mapping functions are proposed to establish the mathematical links between the multiphysics design parameters and poles/residues. Parallel multiphysics data generation is proposed to generate the training and testing data for establishing the proposed multiphysics parametric model. A two stage training algorithm is proposed to guide the multiphysics training process. Once an accurate overall model is developed, it can be used to provide accurate and fast prediction of the multiphysics behavior of microwave components with geometrical and non-geometrical parameters as variables, and further can be used in the high level design. Compared with the existing multiphysics modeling methods, the proposed technique can achieve better model accuracy with high efficiency. The proposed technique provides an accurate and efficient methodology even when the coarse model or empirical model is unavailable. Two microwave examples are used to illustrate the validity of the proposed multiphysics parametric modeling technique.