A Novel Convolutional-Autoencoder Based Surrogate Model for Fast S-Parameter Calculation of Planar BPFs
Recently, surrogate models based on deep learning are introduced to speed up electromagnetic (EM) analysis. For instance, a surrogate model, of which the input is image of geometry under analysis and the output is its characteristics, has been constructed by convolutional neural network (CNN). However, a large amount of EM simulation results is required to build such surrogate models. To solve this problem, this paper proposes a novel convolutional-autoencoder (CAE) based surrogate model, which consists of an encoder in the CAE and dense layers, to calculate S-parameters from circuit-pattern images of planar bandpass filters (BPFs). As an example, the surrogate model for a typical third-order BPF is constructed through unsupervised and transfer learnings. The generalization performance of the CAE-based surrogate model is evaluated by comparing with that of conventional CNN-based one.