The Influence of Geometric Parameters for Training an Artificial Neural Network to Predict the Band Structure of 1-D Fishbone Photonic Crystal

Abstract

With the rising demand for the transmission of large amounts of information over long distances, the development of integrated light circuits is the key to improving this technology, and silicon photonics have been developed with low absorption in the near-infrared range and with sophisticated fabrication techniques. To build devices that work in different functionalities, photonic crystals are one of the most used structures due to their ability to manipulate light. The investigation of photonic crystals requires the calculation of photonic band structures and is usually time-consuming work. To reduce the time spent on calculations, a trained ANN is introduced in this study to directly predict the band structures using only a minimal amount of pre-calculated band structure data. A well-used 1-D fishbone-like photonic crystal in the form of a nanobeam is used as the training target, and the influence of adjusting the geometric parameters is discussed, especially the lattice constant and the thickness of the nanobeam. To train the ANN with very few band structures, each of the mode points in the band structure is considered as a single datapoint to increase the amount of training data. The datasets are composed of various raw band structure data. The optimized ANN is introduced at the end of this manuscript.