{"created":"2023-06-20T15:13:23.659523+00:00","id":10383,"links":{},"metadata":{"_buckets":{"deposit":"2f818379-a7d9-4664-8f67-24c23ea07413"},"_deposit":{"created_by":3,"id":"10383","owners":[3],"pid":{"revision_id":0,"type":"depid","value":"10383"},"status":"published"},"_oai":{"id":"oai:ynu.repo.nii.ac.jp:00010383","sets":["495:496"]},"author_link":["36769","35840","36770","36767","36768","36771"],"item_2_biblio_info_8":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2020-01-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicPageEnd":"322","bibliographicPageStart":"319","bibliographicVolumeNumber":"45","bibliographic_titles":[{"bibliographic_title":"Optics Letters"}]}]},"item_2_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Using machine learning, we optimized an ultrasmall photonic crystal nanocavity to attain a high Q. Training data were collected via finite-difference time-domain simulation for models with randomly shifted holes, and a fully connected neural network (NN) was trained, resulting in a coefficient of determination between predicted and calculated values of 0.977. By repeating NN training and optimization of the Q value on the trained NN, the Q was roughly improved by a factor of 10–20 for various situations. Assuming a 180-nm-thick semiconductor slab at a wavelength approximately 1550 nm, we obtained Q = 1,011,400 in air; 283,200 in a solution, which was suitable for biosensing; and 44,600 with a nanoslot for high sensitivity. Important hole positions were also identified using the linear Lasso regression algorithm.","subitem_description_type":"Abstract"}]},"item_2_publisher_35":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"OSA"}]},"item_2_relation_13":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isVersionOf","subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1364/OL.381616","subitem_relation_type_select":"DOI"}}]},"item_2_rights_14":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"© 2020 Optical Society of America. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibited."}]},"item_2_source_id_11":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11868198","subitem_source_identifier_type":"NCID"}]},"item_2_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"15394794","subitem_source_identifier_type":"ISSN"}]},"item_2_text_4":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Department of Electrical and Computer Engineering, Yokohama National University, 79-5 Tokiwadai, Hodogayaku, Yokohama 240-8501, Japan"}]},"item_2_version_type_18":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_ab4af688f83e57aa","subitem_version_type":"AM"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Abe, Ryotaro"}],"nameIdentifiers":[{"nameIdentifier":"36767","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Takeda, Taichi"}],"nameIdentifiers":[{"nameIdentifier":"36768","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Shiratori, Ryo"}],"nameIdentifiers":[{"nameIdentifier":"36769","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Shirakawa, Shinichi"}],"nameIdentifiers":[{"nameIdentifier":"36770","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"90633272","nameIdentifierScheme":"e-Rad","nameIdentifierURI":"https://kaken.nii.ac.jp/ja/search/?qm=90633272"}]},{"creatorNames":[{"creatorName":"Saito, Shota"}],"nameIdentifiers":[{"nameIdentifier":"36771","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Baba, Toshihiko"}],"nameIdentifiers":[{"nameIdentifier":"35840","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"50202271","nameIdentifierScheme":"e-Rad","nameIdentifierURI":"https://kaken.nii.ac.jp/ja/search/?qm=50202271"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2021-01-16"}],"displaytype":"detail","filename":"Abe submission final.pdf","filesize":[{"value":"672.1 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"Abe submission final.pdf","url":"https://ynu.repo.nii.ac.jp/record/10383/files/Abe submission final.pdf"},"version_id":"579bc579-06d6-4f0d-aec4-e7b2fe42757c"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Photonic Crystals and Devices","subitem_subject_scheme":"Other"},{"subitem_subject":"Cavity quantum electrodynamics","subitem_subject_scheme":"Other"},{"subitem_subject":"Laser operation","subitem_subject_scheme":"Other"},{"subitem_subject":"Neural networks","subitem_subject_scheme":"Other"},{"subitem_subject":"Photonic crystal cavities","subitem_subject_scheme":"Other"},{"subitem_subject":"Photonic crystals","subitem_subject_scheme":"Other"},{"subitem_subject":"Stochastic gradient descent","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Optimization of an H0 photonic crystal nanocavity using machine learning","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Optimization of an H0 photonic crystal nanocavity using machine learning"}]},"item_type_id":"2","owner":"3","path":["496"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-03-16"},"publish_date":"2020-03-16","publish_status":"0","recid":"10383","relation_version_is_last":true,"title":["Optimization of an H0 photonic crystal nanocavity using machine learning"],"weko_creator_id":"3","weko_shared_id":3},"updated":"2023-06-20T18:30:33.897503+00:00"}