{"id":"https://openalex.org/W2981182119","doi":"https://doi.org/10.1109/etfa.2019.8869311","title":"A Novel Visual Fault Detection and Classification System for Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural Networks","display_name":"A Novel Visual Fault Detection and Classification System for Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural Networks","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2981182119","doi":"https://doi.org/10.1109/etfa.2019.8869311","mag":"2981182119"},"language":"en","primary_location":{"id":"doi:10.1109/etfa.2019.8869311","is_oa":false,"landing_page_url":"https://doi.org/10.1109/etfa.2019.8869311","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1911.11250","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083970552","display_name":"Tobias Schlosser","orcid":"https://orcid.org/0000-0002-0682-4284"},"institutions":[{"id":"https://openalex.org/I2610724","display_name":"Chemnitz University of Technology","ror":"https://ror.org/00a208s56","country_code":"DE","type":"education","lineage":["https://openalex.org/I2610724"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Tobias Schlosser","raw_affiliation_strings":["Junior Professorship of Media Computing, Chemnitz University of Technology, 09107 Chemnitz, Germany"],"affiliations":[{"raw_affiliation_string":"Junior Professorship of Media Computing, Chemnitz University of Technology, 09107 Chemnitz, Germany","institution_ids":["https://openalex.org/I2610724"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052562698","display_name":"Frederik Beuth","orcid":"https://orcid.org/0000-0001-5482-9787"},"institutions":[{"id":"https://openalex.org/I2610724","display_name":"Chemnitz University of Technology","ror":"https://ror.org/00a208s56","country_code":"DE","type":"education","lineage":["https://openalex.org/I2610724"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Frederik Beuth","raw_affiliation_strings":["Junior Professorship of Media Computing, Chemnitz University of Technology, 09107 Chemnitz, Germany"],"affiliations":[{"raw_affiliation_string":"Junior Professorship of Media Computing, Chemnitz University of Technology, 09107 Chemnitz, Germany","institution_ids":["https://openalex.org/I2610724"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006134131","display_name":"Michael Friedrich","orcid":"https://orcid.org/0000-0001-6326-4749"},"institutions":[{"id":"https://openalex.org/I2610724","display_name":"Chemnitz University of Technology","ror":"https://ror.org/00a208s56","country_code":"DE","type":"education","lineage":["https://openalex.org/I2610724"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Michael Friedrich","raw_affiliation_strings":["Junior Professorship of Media Computing, Chemnitz University of Technology, 09107 Chemnitz, Germany"],"affiliations":[{"raw_affiliation_string":"Junior Professorship of Media Computing, Chemnitz University of Technology, 09107 Chemnitz, Germany","institution_ids":["https://openalex.org/I2610724"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038259339","display_name":"Danny Kowerko","orcid":"https://orcid.org/0000-0002-4538-7814"},"institutions":[{"id":"https://openalex.org/I2610724","display_name":"Chemnitz University of Technology","ror":"https://ror.org/00a208s56","country_code":"DE","type":"education","lineage":["https://openalex.org/I2610724"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Danny Kowerko","raw_affiliation_strings":["Junior Professorship of Media Computing, Chemnitz University of Technology, 09107 Chemnitz, Germany"],"affiliations":[{"raw_affiliation_string":"Junior Professorship of Media Computing, Chemnitz University of Technology, 09107 Chemnitz, Germany","institution_ids":["https://openalex.org/I2610724"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5083970552"],"corresponding_institution_ids":["https://openalex.org/I2610724"],"apc_list":null,"apc_paid":null,"fwci":2.6479,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.91498991,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1511","last_page":"1514"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9943000078201294,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12549","display_name":"Image and Object Detection Techniques","score":0.9828000068664551,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7852556705474854},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7584352493286133},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7113288640975952},{"id":"https://openalex.org/keywords/visual-inspection","display_name":"Visual inspection","score":0.6163602471351624},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6144409775733948},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5983692407608032},{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.5522543787956238},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.516409695148468},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.5115159749984741},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5081131458282471},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45041438937187195},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4484168291091919},{"id":"https://openalex.org/keywords/semiconductor-device-fabrication","display_name":"Semiconductor device fabrication","score":0.44302403926849365},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.41266608238220215},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3763970136642456},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23057833313941956},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16392457485198975}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7852556705474854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7584352493286133},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7113288640975952},{"id":"https://openalex.org/C168820333","wikidata":"https://www.wikidata.org/wiki/Q448889","display_name":"Visual inspection","level":2,"score":0.6163602471351624},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6144409775733948},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5983692407608032},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.5522543787956238},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.516409695148468},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.5115159749984741},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5081131458282471},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45041438937187195},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4484168291091919},{"id":"https://openalex.org/C66018809","wikidata":"https://www.wikidata.org/wiki/Q1570432","display_name":"Semiconductor device fabrication","level":3,"score":0.44302403926849365},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.41266608238220215},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3763970136642456},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23057833313941956},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16392457485198975},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C160671074","wikidata":"https://www.wikidata.org/wiki/Q267131","display_name":"Wafer","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C172707124","wikidata":"https://www.wikidata.org/wiki/Q423488","display_name":"Actuator","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/etfa.2019.8869311","is_oa":false,"landing_page_url":"https://doi.org/10.1109/etfa.2019.8869311","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1911.11250","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1911.11250","pdf_url":"https://arxiv.org/pdf/1911.11250","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1911.11250","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1911.11250","pdf_url":"https://arxiv.org/pdf/1911.11250","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6399999856948853}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1500514853","https://openalex.org/W1686810756","https://openalex.org/W1985204311","https://openalex.org/W2065429801","https://openalex.org/W2070415427","https://openalex.org/W2092243497","https://openalex.org/W2095848267","https://openalex.org/W2104550562","https://openalex.org/W2112845172","https://openalex.org/W2119888746","https://openalex.org/W2128272608","https://openalex.org/W2594332903","https://openalex.org/W2605344740","https://openalex.org/W2790607928","https://openalex.org/W2920311927","https://openalex.org/W2962835968","https://openalex.org/W4250819038","https://openalex.org/W6637373629"],"related_works":["https://openalex.org/W2781569684","https://openalex.org/W2519676117","https://openalex.org/W2478098815","https://openalex.org/W4290692565","https://openalex.org/W2218202131","https://openalex.org/W84108837","https://openalex.org/W2371486462","https://openalex.org/W2155740880","https://openalex.org/W2131713426","https://openalex.org/W1969801928"],"abstract_inverted_index":{"Automated":[0],"visual":[1,119,157],"inspection":[2],"in":[3,52,99,174],"the":[4,91,96,121,125,128,149,164,169,178],"semiconductor":[5],"industry":[6],"aims":[7],"to":[8,55,135],"detect":[9,56],"and":[10,30,41,60,93,113],"classify":[11],"manufacturing":[12,33,43,179],"defects":[13],"utilizing":[14],"modern":[15],"image":[16,47],"processing":[17,48],"techniques.":[18],"While":[19],"an":[20,38],"earliest":[21],"possible":[22],"detection":[23,92],"of":[24,32,69,95,106,118,130,139,143,154,166,171,177],"defect":[25,58,172],"patterns":[26,173],"allows":[27,90],"quality":[28],"control":[29],"automation":[31],"chains,":[34],"manufacturers":[35],"benefit":[36],"from":[37,132],"increased":[39],"yield":[40],"reduced":[42],"costs.":[44],"Since":[45],"classical":[46,83],"systems":[49],"are":[50],"limited":[51],"their":[53],"ability":[54],"novel":[57,76],"patterns,":[59],"machine":[61],"learning":[62],"approaches":[63,117,153],"often":[64],"involve":[65],"a":[66,75,87,160],"tremendous":[67],"amount":[68],"computational":[70],"effort,":[71],"this":[72],"contribution":[73],"introduces":[74],"deep":[77,84],"neural":[78,85,110],"network-based":[79],"hybrid":[80,108],"approach.":[81],"Unlike":[82],"networks,":[86],"multi-stage":[88],"system":[89,123],"classification":[94],"finest":[97],"structures":[98,134],"pixel":[100],"size":[101],"within":[102],"high-resolution":[103],"imagery.":[104],"Consisting":[105],"stacked":[107],"convolutional":[109],"networks":[111],"(SH-CNN)":[112],"inspired":[114],"by":[115],"current":[116,152],"attention,":[120],"realized":[122],"draws":[124],"focus":[126],"over":[127],"level":[129,165],"detail":[131,167],"its":[133],"more":[136],"task-relevant":[137],"areas":[138],"interest.":[140],"The":[141],"results":[142],"our":[144],"test":[145],"environment":[146],"show":[147],"that":[148],"SH-CNN":[150],"outperforms":[151],"learning-based":[155],"automated":[156],"inspection,":[158],"whereas":[159],"distinction":[161],"depending":[162],"on":[163],"enables":[168],"elimination":[170],"earlier":[175],"stages":[176],"process.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-28T08:17:26.163206","created_date":"2025-10-10T00:00:00"}
