{"created":"2025-08-04T07:05:43.804520+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:02003411","sets":["6164:6165:7651:1750311113378"]},"path":["1750311113378"],"owner":"80578","recid":"2003411","title":["EUVリソグラフィシミュレーション高速化のためのCNN学習用カーブリニアマスクパターン生成"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2025-08-20"},"_buckets":{"deposit":"5461f4d3-9ba5-4f74-ba10-f78ea293f2c4"},"_deposit":{"id":"2003411","pid":{"type":"depid","value":"2003411","revision_id":0},"owners":[80578],"status":"published","created_by":80578},"item_title":"EUVリソグラフィシミュレーション高速化のためのCNN学習用カーブリニアマスクパターン生成","author_link":[],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"EUVリソグラフィシミュレーション高速化のためのCNN学習用カーブリニアマスクパターン生成","subitem_title_language":"ja"},{"subitem_title":"Curvilinear Mask Pattern Generation for CNN Training to Accelerate EUV Lithography Simulation","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"製造プロセス","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2025-08-20","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京科学大学"},{"subitem_text_value":"東京科学大学"},{"subitem_text_value":"東京科学大学"},{"subitem_text_value":"東京科学大学"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Institute of Science Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Institute of Science Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Institute of Science Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Institute of Science Tokyo","subitem_text_language":"en"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/2003411/files/IPSJ-DAS2025017.pdf","label":"IPSJ-DAS2025017.pdf"},"date":[{"dateType":"Available","dateValue":"2027-08-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DAS2025017.pdf","filesize":[{"value":"1.8 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"10"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"74348580-796d-47d5-976d-2dcf49cc00a1","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2025 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"杉山,萌"}]},{"creatorNames":[{"creatorName":"田辺,容由"}]},{"creatorNames":[{"creatorName":"下田,将之"}]},{"creatorNames":[{"creatorName":"高橋,篤司"}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Moe Sugiyama","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Hiroyoshi Tanabe","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Masayuki Shimoda","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Atsushi Takahashi","creatorNameLang":"en"}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"光リソグラフィでは，回路パターンの微細化に伴い，マスクパターンがそのまま回路パターンとしてウェハ上に転写されず，ウェハ上に形成される回路パターンを目標パターンに近づけるために，マスクパターンを補正する光近接効果補正が必要となっている．EUV露光においては，光近接効果補正のために，マスク3D効果の考慮が必要である．しかし，マスク3D効果を考慮できる電磁場計算は非常に時間がかかる．そのため先行研究では，マスク3D効果を高速に推定するCNNを用いる手法が提案された．その手法では，マンハッタンマスクパターンを教師データとする学習モデルが構築され，マンハッタンマスクパターンのマスク3D効果を精度良く推定できることが確認された．しかし今後利用が拡大すると考えられるカーブリニアマスクパターンについては，教師データの不足により精度が低いという課題があった．本研究では，実際的なカーブリニアマスクパターンをILTを用いて生成法を提案する．提案手法で生成するカーブリニアマスクパターンを教師データとして用いてCNNを学習することで，実際的なカーブリニアマスクパターンのマスク3D効果を精度良く推定できることが期待される．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Lithography simulation simulates the circuit pattern to be transferred onto a wafer. In EUV exposure, it is necessary to improve the accuracy of optical proximity effect correction by considering the mask 3D effect. Electromagnetic field calculations have been used to obtain the transferred circuit pattern with high accuracy, but there is an issue that electromagnetic field calculations take a very long time. In a previous research, a method using CNN was proposed to obtain the transferred circuit pattern with high accuracy and speed. In the method, a learning model was constructed using Manhattan patterns as training data, but there was an issue that the accuracy was low for curvilinear patterns due to a lack of training data. In this paper, generation of training data for curvilinear mask patterns is discussed. By training a CNN using generated training data, we aim to achieve faster lithography simulations for curvilinear mask patterns than conventional electromagnetic field calculations and higher accuracy than existing methods.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"112","bibliographic_titles":[{"bibliographic_title":"DAシンポジウム2025論文集"}],"bibliographicPageStart":"106","bibliographicIssueDates":{"bibliographicIssueDate":"2025-08-20","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2025"}]},"relation_version_is_last":true,"weko_creator_id":"80578"},"links":{},"id":2003411,"updated":"2025-08-04T07:05:47.675633+00:00"}