Parametric Modeling of Microwave Components Using Adjoint Neural Networks and Pole-Residue Transfer Functions With EM Sensitivity Analysis
This paper proposes a pole-residue-based adjoint neuro-transfer function (neuro-TF) technique with electromagnetic (EM) sensitivity analysis for parametric modeling of EM behavior of microwave components with respect to changes in geometrical parameters. The purpose is to increase model accuracy by utilizing EM sensitivity information and to speed up model development by reducing the number of training data required for developing the model. The proposed parametric model consists of original and adjoint pole-residue based neuro- TF models. New formulations are derived for calculating the second-order derivatives for training the adjoint pole-residuebased neuro-TF model. An advanced pole-residue tracking technique is proposed to exploit the sensitivity information to track the splitting of poles as geometrical parameters change. This pole-residue tracking technique allows the model to bridge the differences of the orders of transfer function over different regions of the geometrical parameters, and ultimately form smooth and continuous functions between the pole/residues and the geometrical variables. The proposed technique addresses the challenges of tracking pole splitting when training data are limited. By exploiting the sensitivity information, the proposed technique can speed up the model development process over the existing pole-residue parametric modeling method which does not use sensitivity analysis.
Electromagnetic EM, neural network, parametric modeling, sensitivity analysis, transfer function.