Statistical evaluation of Q factors of fabricated photonic crystal nanocavities designed by using a deep neural network
-
- 仲代, 匡宏
- Department of Electronic Science and Engineering, Kyoto University
-
- 浅野, 卓
- Department of Electronic Science and Engineering, Kyoto University
-
- 野田, 進
- Department of Electronic Science and Engineering, Kyoto University
-
- Takahashi, Yasushi
- Department of Physics and Electronics, Osaka Prefecture University
-
- Noda, Susumu
- Department of Electronic Science and Engineering, Kyoto University・Photonics and Electronics Science and Engineering Center, Kyoto University
この論文をさがす
説明
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
-
- Applied Physics Express
-
Applied Physics Express 13 (1), 012002-, 2020-01-01
IOP Publishing
- Tweet
詳細情報 詳細情報について
-
- CRID
- 1050002213396382208
-
- NII論文ID
- 120006784461
- 210000157742
-
- ISSN
- 18820778
- 18820786
-
- HANDLE
- 2433/245624
-
- 本文言語コード
- en
-
- 資料種別
- journal article
-
- データソース種別
-
- IRDB
- Crossref
- CiNii Articles
- KAKEN
- OpenAIRE