Shape Modeling of Microstrip Filters Based on Convolutional Neural Network

Abstract

An effective convolutional neural network (CNN) with the transfer function (TF) is proposed for shape modeling of electromagnetic (EM) behaviors of microstrip filters. The input of CNN is the images of metallic strips instead of the geometric parameters. To define the training samples, a one-to-one relation between the strip contour and the knot positions is built with a shape-changing technique based on cubic spline interpolation. The proposed model is confirmed with an example of a microstrip/coplanar waveguide (CPW) ultrawideband (UWB) filter. Compared with the parametric artificial neural network (ANN) and the shape ANN, the proposed model shows the improvement of design flexibility and the expansion of the solution domain.