Abstract

Using machine learning, we optimized an ultrasmall photonic crystal nanocavity to attain a high $Q$. Training data were collected via finite-difference time-domain simulation for models with randomly shifted holes, and a fully connected neural network (NN) was trained, resulting in a coefficient of determination between predicted and calculated values of 0.977. By repeating NN training and optimization of the $Q$ value on the trained NN, the $Q$ was roughly improved by a factor of 10–20 for various situations. Assuming a 180-nm-thick semiconductor slab at a wavelength approximately 1550 nm, we obtained $Q={1},\!{011},\!{400}$ in air; 283,200 in a solution, which was suitable for biosensing; and 44,600 with a nanoslot for high sensitivity. Important hole positions were also identified using the linear Lasso regression algorithm.

© 2020 Optical Society of America

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