{"id":"https://openalex.org/W2909041198","doi":"https://doi.org/10.1109/icmla.2018.00131","title":"A Comparison of Supervised Approaches for Process Pattern Recognition in Analog Semiconductor Wafer Test Data","display_name":"A Comparison of Supervised Approaches for Process Pattern Recognition in Analog Semiconductor Wafer Test Data","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2909041198","doi":"https://doi.org/10.1109/icmla.2018.00131","mag":"2909041198"},"language":"en","primary_location":{"id":"doi:10.1109/icmla.2018.00131","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla.2018.00131","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067345261","display_name":"Stefan Schrunner","orcid":"https://orcid.org/0000-0003-1327-4855"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Stefan Schrunner","raw_affiliation_strings":["KAI GmbH, Villach, Austria"],"affiliations":[{"raw_affiliation_string":"KAI GmbH, Villach, Austria","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112812254","display_name":"Olivia Bluder","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Olivia Bluder","raw_affiliation_strings":["KAI GmbH, Villach, Austria"],"affiliations":[{"raw_affiliation_string":"KAI GmbH, Villach, Austria","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029164463","display_name":"Anja Zernig","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anja Zernig","raw_affiliation_strings":["KAI GmbH, Villach, Austria"],"affiliations":[{"raw_affiliation_string":"KAI GmbH, Villach, Austria","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008907948","display_name":"Andre Kaestner","orcid":null},"institutions":[{"id":"https://openalex.org/I4210131793","display_name":"Infineon Technologies (Austria)","ror":"https://ror.org/03msng824","country_code":"AT","type":"company","lineage":["https://openalex.org/I137594350","https://openalex.org/I4210131793"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Andre Kaestner","raw_affiliation_strings":["Infineon Technologies Austria AG, Villach, Austria"],"affiliations":[{"raw_affiliation_string":"Infineon Technologies Austria AG, Villach, Austria","institution_ids":["https://openalex.org/I4210131793"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014398832","display_name":"Roman Kern","orcid":"https://orcid.org/0000-0003-0202-6100"},"institutions":[{"id":"https://openalex.org/I4210088621","display_name":"Know Center Research GmbH (Austria)","ror":"https://ror.org/004zhad81","country_code":"AT","type":"company","lineage":["https://openalex.org/I4210088621"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Roman Kern","raw_affiliation_strings":["Know-Center GmbH, Graz, Austria"],"affiliations":[{"raw_affiliation_string":"Know-Center GmbH, Graz, Austria","institution_ids":["https://openalex.org/I4210088621"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5067345261"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9855,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.82317116,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.9997000098228455,"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":0.9997000098228455,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9732999801635742,"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"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9696999788284302,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.7442216277122498},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7281261086463928},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6832310557365417},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6442946195602417},{"id":"https://openalex.org/keywords/multiclass-classification","display_name":"Multiclass classification","score":0.5860363841056824},{"id":"https://openalex.org/keywords/semiconductor-device-fabrication","display_name":"Semiconductor device fabrication","score":0.5734272003173828},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.5167400240898132},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4901054799556732},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.47358137369155884},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4491163194179535},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.42068442702293396},{"id":"https://openalex.org/keywords/wafer","display_name":"Wafer","score":0.40638959407806396},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3992927372455597},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.186967670917511},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.16991183161735535},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.14415273070335388},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12643367052078247},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10946792364120483}],"concepts":[{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.7442216277122498},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7281261086463928},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6832310557365417},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6442946195602417},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.5860363841056824},{"id":"https://openalex.org/C66018809","wikidata":"https://www.wikidata.org/wiki/Q1570432","display_name":"Semiconductor device fabrication","level":3,"score":0.5734272003173828},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5167400240898132},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4901054799556732},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.47358137369155884},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4491163194179535},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.42068442702293396},{"id":"https://openalex.org/C160671074","wikidata":"https://www.wikidata.org/wiki/Q267131","display_name":"Wafer","level":2,"score":0.40638959407806396},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3992927372455597},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.186967670917511},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.16991183161735535},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.14415273070335388},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12643367052078247},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10946792364120483},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmla.2018.00131","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla.2018.00131","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.550000011920929}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W572368116","https://openalex.org/W1480376833","https://openalex.org/W1647292442","https://openalex.org/W1663973292","https://openalex.org/W1679220824","https://openalex.org/W1980073965","https://openalex.org/W2003381792","https://openalex.org/W2009086942","https://openalex.org/W2020286945","https://openalex.org/W2180965481","https://openalex.org/W2182415974","https://openalex.org/W2342204193","https://openalex.org/W2737795851","https://openalex.org/W2767053056","https://openalex.org/W2787894218","https://openalex.org/W2790327024","https://openalex.org/W4285719527","https://openalex.org/W6636681779","https://openalex.org/W6655633911","https://openalex.org/W6685606147"],"related_works":["https://openalex.org/W1470425429","https://openalex.org/W4328134586","https://openalex.org/W4205478082","https://openalex.org/W4281385048","https://openalex.org/W4361795583","https://openalex.org/W4313001487","https://openalex.org/W4308191010","https://openalex.org/W3127425528","https://openalex.org/W2940336242","https://openalex.org/W4318350883"],"abstract_inverted_index":{"The":[0,133],"semiconductor":[1],"industry":[2],"is":[3,21,29,41,80],"currently":[4],"leveraging":[5],"to":[6,11,66],"exploit":[7],"machine":[8],"learning":[9,61],"techniques":[10],"improve":[12],"and":[13,88,112,125,148],"automate":[14],"the":[15,22,37,44,76,137,141],"manufacturing":[16],"process.":[17],"An":[18],"essential":[19],"step":[20],"wafer":[23],"test,":[24],"where":[25],"each":[26],"single":[27],"device":[28],"measured":[30],"electrically,":[31],"resulting":[32],"in":[33,70],"an":[34,71,129],"image":[35],"of":[36,48],"wafer.":[38],"Our":[39],"work":[40],"based":[42],"on":[43,57],"hypothesis":[45],"that":[46,136],"deviations":[47],"production":[49],"processes":[50],"can":[51,96],"be":[52],"detected":[53],"via":[54],"spatial":[55],"patterns":[56,69],"these":[58],"wafermaps.":[59],"Supervised":[60],"methods":[62,91,108,139],"are":[63],"one":[64],"possibility":[65],"recognize":[67],"such":[68],"automated":[72],"way":[73],"-":[74],"however,":[75],"training":[77],"sample":[78],"size":[79],"very":[81],"low.":[82],"In":[83],"our":[84],"work,":[85],"we":[86,120],"present":[87],"compare":[89,121],"several":[90],"for":[92],"multiclass":[93,101,142],"classification,":[94],"which":[95],"deal":[97],"with":[98],"this":[99],"limitation:":[100],"decision":[102,123,143],"trees,":[103],"as":[104,106],"well":[105],"decomposition":[107,138],"like":[109],"round":[110],"robin":[111],"error-correcting":[113],"output":[114],"coding":[115],"(ECOC).":[116],"As":[117],"elementary":[118],"classifiers,":[119],"binary":[122],"trees":[124],"logistic":[126],"regression":[127],"using":[128],"elastic":[130],"net":[131],"regularization.":[132],"evaluation":[134],"shows":[135],"outperform":[140],"tree":[144],"regarding":[145],"both,":[146],"accuracy":[147],"practical":[149],"demands.":[150]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
