Statistical evaluation of Q factors of fabricated photonic crystal nanocavities designed by using a deep neural network. (3rd December 2019)

• Record Type: Journal Article
• Authors: Nakadai, Masahiro; Tanaka, Kengo; Asano, Takashi; Takahashi, Yasushi; Noda, Susumu
• Is Part Of: Applied physics express. Volume 13:Number 1(2020)
• Publication Date: 2019-12-03
• Subjects: Physics -- Periodicals; Technology -- Periodicals; 621.05
• DOI: 10.7567/1882-0786/ab5978
• Languages: English
• ISSNs: 1882-0778

Abstract

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.