Interpolation of Dispersion Relations and Group Index in Photonic Crystal Waveguides using Artificial Neural Network
Designing dispersion free photonic crystal structures requires lot of computational time. Often, these structures are designed by solving the conventional Maxwell’s equations. Different computational techniques are available for solving such coupled equations and obtaining the eigen frequencies of the system. However, these methods are time consuming and limit the designing of dispersion free structures. On the other side, Machine learning and deep learning are providing the assistance to the researchers in finding the solutions for various complex, time consuming and tedious problems. In the field of dispersion free structure, researchers are choosing machine learning for designing, developing and analyzing various structures. Here, in this paper, we made an attempt to adopt machine learning for predicting the dispersion values and group indices of a lattice shifted photonic crystal waveguide. Conventional methods are used to generate the eigen frequencies, group indices and wave vector points of the structure. These data sets are used for training the machine by artificial neural network. The model is trained and optimized to predict the dispersion parameters and group indices with applied lattice shift. Repeated random sub-sampling cross validation is used for testing the performance of the model. To the best of our knowledge, we made a first attempt to study a lattice shifted photonic crystal waveguide using machine learning. This work will be exciting and may lead to develop a connection between tailored waveguide structures and machine learning.