Optimization of photonic crystal nanocavities based on deep learning.

Abstract

An approach to optimizing the Q factors of two-dimensional photonic crystal (2D-PC) nanocavities based on deep learning is hereby proposed and demonstrated. We prepare a data set consisting of 1000 nanocavities generated by randomly displacing the positions of many air holes in a base nanocavity and calculate their Q factors using a first-principles method. We train a four-layer neural network including a convolutional layer to recognize the relationship between the air holes' displacements and the Q factors using the prepared data set. After the training, the neural network is able to estimate the Q factors from the air holes' displacements with an error of 13% in standard deviation. Crucially, the trained neural network can estimate the gradient of the Q factor with respect to the air holes' displacements very quickly using back-propagation. A nanocavity structure with an extremely high Q factor of 1.58 × 109 was successfully obtained by optimizing the positions of 50 holes over ~106 iterations, taking advantage of the very fast evaluation of the gradient in high-dimensional parameter spaces. The obtained Q factor is more than one order of magnitude higher than that of the base cavity and more than twice that of the highest Q factors reported so far for cavities with similar modal volumes. This approach can optimize 2D-PC structures over a parameter space of a size unfeasibly large for previous optimization methods that were based solely on direct calculations. We believe that this approach is also useful for improving other optical characteristics.