Optimization of photonic crystal nanocavities based on deep learning

Understand

An approach to optimizing the Q factors of two-dimensional photonic crystal (2D-PC) nanocavities based on deep learning is proposed and demonstrated.

• We prepare a dataset consisting of 1000 nanocavities generated by randomly displacing the positions of many air holes of a base nanocavity and their Q factors calculated by a first-principle 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 dataset.
• After the training, the neural network becomes able to estimate the Q factors from the air holes' displacements with an error of 13% in standard deviation.