Statistical evaluation of Q factors of fabricated photonic crystal nanocavities designed by using a deep neural network

Abstract

Photonic crystal (PC) nanocavities with ultra-high quality (Q) factors and small modal volumes enable advanced photon manipulations, such as photon trapping. In order to improve the Q factors of such nanocavities, we have recently proposed a cavity design method based on machine learning. Here, we experimentally compare nanocavities designed by using a deep neural network with those designed by the manual approach that enabled a record value. Thirty air-bridge-type two-dimensional PC nanocavities are fabricated on silicon-on-insulator substrates, and their photon lifetimes are measured. The realized median Q factor increases by about one million by adopting the machine-learning-based design approach.