Predicting and synthesizing terahertz spoof surface plasmon polariton devices with a convolutional neural network model.

With the increasing global attention to deep learning and the advancements made in applying convolutional neural networks in electromagnetics, we have recently witnessed the utilization of deep learning-based networks for predicting the spectrum and electromagnetic properties of structures instead of traditional tools like fully numerical-based methods. In this study, a Convolutional Neural Network (CNN is proposed for predicting spoof surface plasmon polaritons, enabling the examination of the absorption spectrum of metallic multilevel-grating structures (MMGS) and designing various sensor devices and absorbers in the shortest time possible. To expedite the training process of this network, a semi-analytical method of rigorous coupled-wave analysis (RCWA) enhanced with the fast Fourier factorization (FFF) technique has been employed, significantly reducing the data generation time for training.

A semi-analytical method for characterization of fractal spoof surface plasmon polaritons with a transfer matrix and bloch theory.

In this paper, a semi-analytical approach is introduced to analyze a spoof plasmonic structure, with an arbitrary geometry. This approach is based on a combination of techniques that employ a full-wave simulator and the Bloch theorem. By applying periodic boundary conditions, the real and imaginary parts of the equation obtained from the equivalent network have been calculated.