A Novel Deep Neural Network Topology for Parametric Modeling of Passive Microwave Components

Abstract

Artificial neural network technique has gained recognition as a powerful technique in microwave modeling and design. This paper proposes a novel deep neural network topology for parametric modeling of microwave components. In the proposed deep neural network, the outputs are S-parameters. The inputs of the proposed model include geometrical variables and the frequency. We divide the hidden layers in the proposed deep neural network topology into two parts. Hidden layers in Part I handle both the geometrical inputs and the frequency inputs while hidden layers in Part II only handle the geometrical inputs. In this way, more training parameters are utilized to specifically learn the relationship between the S-parameters and the geometrical variables, which are more complicated than that between the S-parameters and the frequency. The purpose is to reduce the total number of training parameters in the deep neural network model. New formulations are derived to calculate the derivatives of the error function with respect to training parameters in the deep neural network. Taking advantage of the calculated derivatives, we propose an advanced two-stage training algorithm for the deep neural network. The two-stage training algorithm can determine the number of hidden layers in both parts during the training process and guarantee that the proposed deep neural network model can achieve the required model accuracy. The proposed deep neural network can achieve similar model accuracy with less training parameters compared to the commonly used fully connected neural network. The proposed technique is demonstrated by two microwave parametric modeling examples.