PREDICTION AND OPTIMIZATION OF PARAMETERS OF PHOTONIC SUPERLATTICES, FORMED BY METAMATERIALS, THROUGH MACHINE LEARNING
Photonic structures. Photonic crystals. Metamaterials. Machine Learning.
In this work, we study one-dimensional photonic superlattices, composed of layers of air and metamaterials, with the aim of better understanding these structures. Using the transfer matrices method to simulate the system, we verified through the photonic dispersion that for the angle of incidence different from zero, the magnetic component of the field applied to the superlattice, couples to the matter, generating what we call plasmon-polariton coupling. However, the normal incidence of light in the medium does not generate the appearance of thegap plasmon-polariton, since this component is totally parallel to the propagation direction of the wave. Now, in order to make predictions and optimizations about our photonic systems, we intend to use machine learning as a tool. This may enable the improvement of these systems, bringing possible advances to the research area.