Fabrication and characterization of an L3 nanocavity designed by an iterative machine-learning method

This is the authors' abstract. We don't add key points for this paper.

Optical nanocavities formed by defects in a two-dimensional photonic crystal (PC) slab can simultaneously realize a very small modal volume and an ultrahigh quality factor (Q). Therefore, such nanocavities are expected to be useful for the enhancement of light–matter interaction and slowdown of light in devices. In the past, it was difficult to design a PC hole pattern that makes sufficient use of the high degree of structural freedom of this type of optical nanocavity, but very recently, an iterative optimization method based on machine learning was proposed that efficiently explores a wide parameter space. Here, we fabricate and characterize an L3 nanocavity that was designed by using this method and has a theoretical Q value of 29 × 106 and a modal volume of 0.7 cubic wavelength in the material. The highest unloaded Q value of the fabricated cavities is 4.3 × 106; this value significantly exceeds those reported previously for an L3 cavity, i.e., ≈2.1 × 106. The experimental result shows that the iterative optimization method based on machine learning is effective in improving cavity Q values.