Filter Model Extraction with Convolutional Neural Network Based on Magnitude Information

In this paper, a new model extraction method for band-pass filters with convolutional neural network (CNN) based on magnitude information only is introduced. A data augmentation approach and a data decentralization process to improve the CNN model performance by statistical analysis are proposed. A correction process utilizing real test data is introduced to improve the model performance. Compared to the existing model extraction method, this CNN model approach needs no phase information that is highly dependent on the loading circuit of a filter. Merely using the magnitude information, a good fitness for the response of high-order filters with complicated topology is achieved with this method. CNN model demonstrates a faster convergence compared to the traditional fully connected neural model.