Optimization of an H0 photonic crystal nanocavity using machine learning
Ryotaro Abe, Taichi Takeda, Ryo Shiratori, Shinichi Shirakawa, Shota Saito, Toshihiko Baba · Optics Letters · 2019
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.