Parametric Modeling of a Four-Pole Waveguide Filter Using Derivatives-Analysis-Assisted Adjoint Neural-Network Model

This paper introduces an advanced model for microwave components using the Derivatives-Analysis-Assisted Adjoint Neural-Network (DAANN) technique. The proposed technique simultaneously trains input-output behavior of the microwave component and electromagnetic (EM) simulation derivatives to obtain a robust parametric model. Exact first and second-order derivatives of general multilayer neural-network structures are calculated to adjust the weights of DAANN, ensuring accurate output results. New formulations have been derived for the computation of second-order derivatives. The DAANN structure provides more accurate and generalized parametric models with less training data compared to existing artificial neural network (ANN) structures. This technique is demonstrated to be valid through an example of a four-pole waveguide filter.