{"id":"https://openalex.org/W3090617142","doi":"https://doi.org/10.3390/s20195650","title":"Equipment Anomaly Detection for Semiconductor Manufacturing by Exploiting Unsupervised Learning from Sensory Data","display_name":"Equipment Anomaly Detection for Semiconductor Manufacturing by Exploiting Unsupervised Learning from Sensory Data","publication_year":2020,"publication_date":"2020-10-02","ids":{"openalex":"https://openalex.org/W3090617142","doi":"https://doi.org/10.3390/s20195650","mag":"3090617142","pmid":"https://pubmed.ncbi.nlm.nih.gov/33023191"},"language":"en","primary_location":{"id":"doi:10.3390/s20195650","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20195650","pdf_url":"https://www.mdpi.com/1424-8220/20/19/5650/pdf?version=1602238512","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/20/19/5650/pdf?version=1602238512","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020590612","display_name":"Chieh-Yu Chen","orcid":"https://orcid.org/0000-0002-5612-5166"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chieh-Yu Chen","raw_affiliation_strings":["Department of Electrical Engineering, National Taiwan University, Taipei 10617, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-5612-5166","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Taiwan University, Taipei 10617, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089159973","display_name":"Shi\u2010Chung Chang","orcid":"https://orcid.org/0000-0001-7595-2485"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Shi-Chung Chang","raw_affiliation_strings":["Department of Electrical Engineering, National Taiwan University, Taipei 10617, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Taiwan University, Taipei 10617, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036148239","display_name":"Da-Yin Liao","orcid":"https://orcid.org/0000-0003-2059-6035"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Da-Yin Liao","raw_affiliation_strings":["Straight &amp; Up Intelligent Innovations Group Co., San Jose, CA 95113, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Straight &amp; Up Intelligent Innovations Group Co., San Jose, CA 95113, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5089159973"],"corresponding_institution_ids":["https://openalex.org/I16733864"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2673},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2673},"fwci":1.4745,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.86302666,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"20","issue":"19","first_page":"5650","last_page":"5650"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9983999729156494,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7995254993438721},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7504559755325317},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5424100160598755},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.48623815178871155},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4813978672027588},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4272025525569916},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.375589519739151},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.3550683259963989},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.28664618730545044},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.28312748670578003}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7995254993438721},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7504559755325317},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5424100160598755},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.48623815178871155},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4813978672027588},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4272025525569916},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.375589519739151},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3550683259963989},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.28664618730545044},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28312748670578003}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/s20195650","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20195650","pdf_url":"https://www.mdpi.com/1424-8220/20/19/5650/pdf?version=1602238512","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:33023191","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33023191","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:7c805ba7a65d429480d0d2099b4b98f9","is_oa":true,"landing_page_url":"https://doaj.org/article/7c805ba7a65d429480d0d2099b4b98f9","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 20, Iss 19, p 5650 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/20/19/5650/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s20195650","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 20; Issue 19; Pages: 5650","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:7582566","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7582566","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s20195650","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20195650","pdf_url":"https://www.mdpi.com/1424-8220/20/19/5650/pdf?version=1602238512","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.5199999809265137,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1504182636","https://openalex.org/W1572851314","https://openalex.org/W1576462183","https://openalex.org/W1974140104","https://openalex.org/W1979336685","https://openalex.org/W2112841480","https://openalex.org/W2122646361","https://openalex.org/W2134128809","https://openalex.org/W2145094598","https://openalex.org/W2265219777","https://openalex.org/W2545810962","https://openalex.org/W2583645756","https://openalex.org/W2620661538","https://openalex.org/W2755997547","https://openalex.org/W2799741169","https://openalex.org/W2800288036","https://openalex.org/W2888173334","https://openalex.org/W2896255741","https://openalex.org/W2901773810","https://openalex.org/W2908923193","https://openalex.org/W2911727487","https://openalex.org/W2918377632","https://openalex.org/W2918485831","https://openalex.org/W2947207002","https://openalex.org/W2947797412","https://openalex.org/W2953306898","https://openalex.org/W2967151852","https://openalex.org/W2997574889","https://openalex.org/W2999892561","https://openalex.org/W3004424032","https://openalex.org/W3017264588","https://openalex.org/W3027507763","https://openalex.org/W6634571909","https://openalex.org/W6681096077","https://openalex.org/W6757076993"],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W3017266184","https://openalex.org/W2918377632","https://openalex.org/W3194885736","https://openalex.org/W3046391934","https://openalex.org/W4363671829","https://openalex.org/W4285233543","https://openalex.org/W2997921738","https://openalex.org/W2965146396","https://openalex.org/W2770818364"],"abstract_inverted_index":{"In-line":[0],"anomaly":[1,191],"detection":[2,188],"(AD)":[3],"not":[4],"only":[5],"identifies":[6],"the":[7,47,113,116,121,174],"needs":[8],"for":[9,60,73,109,169,182],"semiconductor":[10],"equipment":[11,25,190],"maintenance":[12],"but":[13],"also":[14],"indicates":[15],"potential":[16],"line":[17],"yield":[18,31],"problems.":[19],"Prompt":[20],"AD":[21,110],"based":[22,111],"on":[23,112],"available":[24],"sensory":[26],"data":[27,119,157],"(ESD)":[28],"facilitates":[29],"proactive":[30],"and":[32,41,69,89,120,131,134,164,167,192],"operations":[33],"management.":[34],"However,":[35],"ESD":[36,58,101,138],"items":[37,59],"are":[38],"highly":[39],"diversified":[40],"drastically":[42],"scale":[43],"up":[44],"along":[45],"with":[46],"increased":[48],"use":[49],"of":[50,81,86,100,139,177,189,194],"sensors.":[51],"Even":[52],"veteran":[53],"engineers":[54],"lack":[55],"knowledge":[56],"about":[57],"automated":[61],"AD.":[62],"This":[63],"paper":[64],"presents":[65],"a":[66],"novel":[67],"Spectral":[68],"Time":[70],"Autoencoder":[71],"Learning":[72],"Anomaly":[74],"Detection":[75],"(STALAD)":[76],"framework.":[77],"The":[78],"design":[79],"consists":[80],"four":[82],"innovations:":[83],"(1)":[84],"identification":[85],"cycle":[87],"series":[88],"spectral":[90],"transformation":[91],"(CSST)":[92],"from":[93,98],"ESD,":[94],"(2)":[95],"unsupervised":[96],"learning":[97,133],"CSST":[99],"by":[102],"exploiting":[103],"Stacked":[104],"AutoEncoders,":[105],"(3)":[106],"hypothesis":[107],"test":[108],"difference":[114],"between":[115],"learned":[117],"normal":[118],"tested":[122],"sample":[123],"data,":[124],"(4)":[125],"dynamic":[126],"procedure":[127],"control":[128,179],"enabling":[129],"periodic":[130],"parallel":[132],"testing.":[135],"Applications":[136],"to":[137,154,173,196],"an":[140],"HDP-CVD":[141],"tool":[142],"demonstrate":[143],"that":[144],"STALAD":[145,184],"learns":[146],"normality":[147],"without":[148],"engineers'":[149],"prior":[150],"knowledge,":[151],"is":[152,165],"tolerant":[153],"some":[155],"abnormal":[156],"in":[158],"training":[159],"input,":[160],"performs":[161],"correct":[162],"AD,":[163,183],"efficient":[166],"adaptive":[168],"fab":[170],"applications.":[171],"Complementary":[172],"current":[175],"practice":[176],"using":[178],"wafer":[180],"monitoring":[181],"may":[185],"facilitate":[186],"early":[187],"assessment":[193],"impacts":[195],"process":[197],"quality.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
