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.
Highlights
The ratio Q/V is a figure of merit for the strength of light–matter interactions and for the photon lifetime in devices with small footprints, which are important parameters for various applications including nonlinear optics,4–7) quantum optics,8–11) and photonic buffer memories.12,13) As a high Q is beneficial, there have been various efforts to increase the actual Q factors of 2D-PC slab nanocavities.14–26)
Factor (Qexp) was reported for a silicon (Si)-based heterostructure nanocavity.23) This cavity was designed by using the leaky-mode visualization method,22) which is a manual design process, and positions of eight air holes were optimized with respect to the theoretical Q factor
In 2018, a method based on machine learning for efficiently optimizing displacements of many air holes in nanocavities was proposed.26) By employing this method, which utilizes deep neural networks, a heterostructure cavity with an extremely high theoretical Q factor of
Summary
Masahiro Nakadai1*, Kengo Tanaka, Takashi Asano1*, Yasushi Takahashi, and Susumu Noda. 3(b) plot the correlations between the obtained λ and Qexp of the cavities employing the manual design (gray crosses) and the design developed with aid of the neural network (red dots), respectively. 3(a) and 3(b), we find that the average resonant wavelength of cavity type NN is only 8 nm larger than that of cavity type M This indicates that the average air hole radii of both PC slabs are almost the same. The standard deviations of the resonant wavelengths of both cavity types are almost the same
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