{"created":"2025-01-19T00:58:17.506504+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00192590","sets":["1164:2036:9343:9613"]},"path":["9613"],"owner":"44499","recid":"192590","title":["ニューラルネットワークを用いたランダムキャプチャセーフテストベクトル生成について"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-11-28"},"_buckets":{"deposit":"bdc4d8bf-8ffd-4a7f-9083-e51040830943"},"_deposit":{"id":"192590","pid":{"type":"depid","value":"192590","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"ニューラルネットワークを用いたランダムキャプチャセーフテストベクトル生成について","author_link":["449754","449755","449761","449756","449760","449757","449762","449759","449763","449758"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ニューラルネットワークを用いたランダムキャプチャセーフテストベクトル生成について"},{"subitem_title":"On the Generation of Random Capture Safe Test Vectors Using Neural Networks","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"高信頼化・セーフテスト","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2018-11-28","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"日本大学大学院生産工学研究科"},{"subitem_text_value":"日本大学大学院生産工学研究科"},{"subitem_text_value":"日本大学生産工学部"},{"subitem_text_value":"日本大学生産工学部"},{"subitem_text_value":"日本大学生産工学部"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Industrial Technology, Nihon University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Industrial Technology, Nihon University","subitem_text_language":"en"},{"subitem_text_value":"College of Industrial Technology, Nihon University","subitem_text_language":"en"},{"subitem_text_value":"College of Industrial Technology, Nihon University","subitem_text_language":"en"},{"subitem_text_value":"College of Industrial Technology, Nihon University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/192590/files/IPSJ-SLDM18185018.pdf","label":"IPSJ-SLDM18185018.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SLDM18185018.pdf","filesize":[{"value":"533.5 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"10"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"f14fb569-2abf-44e5-b700-6bfaa1aa8af9","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2018 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"越智, 小百合"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"三澤, 健一郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"細川, 利典"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山内, ゆかり"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"新井, 雅之"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Sayuri, Ochi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kenichirou, Misawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Toshirori, Hosokawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yukari, Yamauchi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masayuki, Arai","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11451459","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8639","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"実速度スキャンテストにおいて，過度のキャプチヤ消費電力は IR ドロップを引き起こし，誤テストにより歩留り損失が発生する．キャプチャ消費電力を削減するためのテスト生成法として，キャプチャセーフテストベクトルの故障伝搬経路を模倣し低消費電力テストベクトルを生成する手法が提案されている．しかしながら，キャプチャセーフテストベクトル数が少ない場合，故障によっては模倣するベクトルが存在しない場合がある．本論文ではテストベクトルとフリップフロップの状態遷移情報を入力層，回路構造を中間層，キャプチャセーフの判定を出力層に構築したニューラルネットワークを用いてランダムテストベクトルの消費電力特性を学習し，効率的にランダムキャプチャセーフテストベクトルを生成する手法を検討する．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Excessive capture power consumption at scan testing causes the excessive IR drop and it might cause test-induced yield loss. A low-capture-power test generation method for transition faults based on LOC using fault simulation was proposed to resolve the problem. The method mimics fault propagation path information for capture-safe test vectors which have low launch switching activity in the initial test sets. However, when the number of capture-safe test vectors is smaller, there exists faults which do not have any mimicked capture-safe test vectors. In this paper, we construct neural networks which are constituted from a test vector and state transition information of flip-flops as an input layer, circuit structure information as a middle layer, and capture-safe decision as an output layer. We learn low power properties of random test vectors using the neural network and consider an effective method of random capture-safe test vector generation using the neural network.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告システムとLSIの設計技術（SLDM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2018-11-28","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"18","bibliographicVolumeNumber":"2018-SLDM-185"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":192590,"updated":"2025-01-20T00:05:05.139585+00:00","links":{}}