Statistical evaluation of Q factors of fabricated photonic crystal nanocavities designed by using a deep neural network
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- 仲代, 匡宏
- Department of Electronic Science and Engineering, Kyoto University
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- 浅野, 卓
- Department of Electronic Science and Engineering, Kyoto University
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- 野田, 進
- Department of Electronic Science and Engineering, Kyoto University
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- Takahashi, Yasushi
- Department of Physics and Electronics, Osaka Prefecture University
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- Noda, Susumu
- Department of Electronic Science and Engineering, Kyoto University・Photonics and Electronics Science and Engineering Center, Kyoto University
抄録
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.
収録刊行物
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- Applied Physics Express
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Applied Physics Express 13 (1), 012002-, 2020-01-01
IOP Publishing
