{"id":"https://openalex.org/W2898077816","doi":"https://doi.org/10.1109/ijcnn.2018.8489422","title":"Unsupervised Wafermap Patterns Clustering via Variational Autoencoders","display_name":"Unsupervised Wafermap Patterns Clustering via Variational Autoencoders","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2898077816","doi":"https://doi.org/10.1109/ijcnn.2018.8489422","mag":"2898077816"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2018.8489422","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489422","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","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/A5084872091","display_name":"Peter Tulala","orcid":null},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":true,"raw_author_name":"Peter Tulala","raw_affiliation_strings":["Institute of Computer Engineering, Vienna University of Technology (TU Wien)"],"affiliations":[{"raw_affiliation_string":"Institute of Computer Engineering, Vienna University of Technology (TU Wien)","institution_ids":["https://openalex.org/I145847075"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109910135","display_name":"Hamidreza Mahyar","orcid":null},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Hamidreza Mahyar","raw_affiliation_strings":["Institute of Computer Engineering, Vienna University of Technology (TU Wien)"],"affiliations":[{"raw_affiliation_string":"Institute of Computer Engineering, Vienna University of Technology (TU Wien)","institution_ids":["https://openalex.org/I145847075"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011190722","display_name":"Elahe Ghalebi","orcid":null},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Elahe Ghalebi","raw_affiliation_strings":["Institute of Computer Engineering, Vienna University of Technology (TU Wien)"],"affiliations":[{"raw_affiliation_string":"Institute of Computer Engineering, Vienna University of Technology (TU Wien)","institution_ids":["https://openalex.org/I145847075"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034362557","display_name":"Radu Grosu","orcid":"https://orcid.org/0000-0001-5715-2142"},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Radu Grosu","raw_affiliation_strings":["Institute of Computer Engineering, Vienna University of Technology (TU Wien)"],"affiliations":[{"raw_affiliation_string":"Institute of Computer Engineering, Vienna University of Technology (TU Wien)","institution_ids":["https://openalex.org/I145847075"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5084872091"],"corresponding_institution_ids":["https://openalex.org/I145847075"],"apc_list":null,"apc_paid":null,"fwci":3.4488,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.93583569,"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":"1","last_page":"8"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T11159","display_name":"Manufacturing Process and Optimization","score":0.9952999949455261,"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":0.9905999898910522,"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/cluster-analysis","display_name":"Cluster analysis","score":0.8118343353271484},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7010666728019714},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6881017684936523},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6011287569999695},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5425205826759338},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5386798977851868},{"id":"https://openalex.org/keywords/visual-inspection","display_name":"Visual inspection","score":0.49127429723739624},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4694506525993347},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.44093021750450134},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.43679988384246826},{"id":"https://openalex.org/keywords/semiconductor-device-fabrication","display_name":"Semiconductor device fabrication","score":0.42186975479125977},{"id":"https://openalex.org/keywords/wafer","display_name":"Wafer","score":0.39452552795410156},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37552082538604736},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3503122329711914},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13188451528549194}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8118343353271484},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7010666728019714},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6881017684936523},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6011287569999695},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5425205826759338},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5386798977851868},{"id":"https://openalex.org/C168820333","wikidata":"https://www.wikidata.org/wiki/Q448889","display_name":"Visual inspection","level":2,"score":0.49127429723739624},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4694506525993347},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.44093021750450134},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43679988384246826},{"id":"https://openalex.org/C66018809","wikidata":"https://www.wikidata.org/wiki/Q1570432","display_name":"Semiconductor device fabrication","level":3,"score":0.42186975479125977},{"id":"https://openalex.org/C160671074","wikidata":"https://www.wikidata.org/wiki/Q267131","display_name":"Wafer","level":2,"score":0.39452552795410156},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37552082538604736},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3503122329711914},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13188451528549194},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/ijcnn.2018.8489422","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489422","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","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":0.5}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W111762992","https://openalex.org/W1486242743","https://openalex.org/W1629070646","https://openalex.org/W1959608418","https://openalex.org/W1985135956","https://openalex.org/W1987971958","https://openalex.org/W2016381774","https://openalex.org/W2016973091","https://openalex.org/W2021312192","https://openalex.org/W2046040371","https://openalex.org/W2053488654","https://openalex.org/W2099861576","https://openalex.org/W2101234009","https://openalex.org/W2149335224","https://openalex.org/W2166851633","https://openalex.org/W2184898757","https://openalex.org/W2225156818","https://openalex.org/W2286515324","https://openalex.org/W2557283755","https://openalex.org/W2771169143","https://openalex.org/W3101380508","https://openalex.org/W4294562888","https://openalex.org/W6640963894","https://openalex.org/W6675354045","https://openalex.org/W6684578138"],"related_works":["https://openalex.org/W2811390910","https://openalex.org/W3196155444","https://openalex.org/W2146076056","https://openalex.org/W2695951553","https://openalex.org/W2026253357","https://openalex.org/W2775464024","https://openalex.org/W2592385986","https://openalex.org/W2776466379","https://openalex.org/W3093564170","https://openalex.org/W2899683012"],"abstract_inverted_index":{"Semiconductor":[0],"manufacturing":[1],"processes":[2],"are":[3,96],"prone":[4],"to":[5,51,70,113,125],"process":[6],"deviations":[7],"or":[8],"other":[9],"production":[10,59,68],"issues.":[11],"Quality":[12],"assurance":[13],"of":[14,30,38,56,66,86,129,144],"every":[15],"processing":[16,46],"step":[17,123],"and":[18,36,61,133],"measuring":[19],"wafer":[20],"test":[21,94],"values":[22,95],"is":[23,124],"crucial":[24],"for":[25,83],"finding":[26,57],"possible":[27],"root":[28],"causes":[29],"these":[31],"problems.":[32],"Automated":[33],"visual":[34],"inspection":[35],"recognition":[37],"patterns":[39,88],"in":[40,89],"wafermap":[41,90],"data":[42],"obtained":[43],"during":[44],"different":[45],"steps":[47],"has":[48],"a":[49,79,106,118,149],"potential":[50],"signifficantly":[52],"improve":[53],"the":[54,67,127,142,145],"efficiency":[55],"early":[58],"issues":[60],"even":[62],"help":[63],"with":[64],"adjustment":[65],"parameters":[69],"automatically":[71],"resolve":[72],"them.":[73],"In":[74],"this":[75,130],"paper,":[76],"we":[77],"present":[78],"machine":[80],"learning":[81],"approach":[82],"unsupervised":[84],"clustering":[85],"spatial":[87],"measurement":[91],"data.":[92],"Measured":[93],"first":[97],"pre-processed":[98],"using":[99],"some":[100],"computer":[101],"vision":[102],"techniques,":[103],"followed":[104],"by":[105],"feature":[107],"extraction":[108],"based":[109],"on":[110],"variational":[111],"autoencoders":[112],"decompose":[114],"high-dimensional":[115],"wafermaps":[116],"into":[117,137],"low-dimensional":[119],"latent":[120,131],"representation.":[121],"Final":[122],"detect":[126],"structure":[128],"space":[132],"assign":[134],"individual":[135],"wafers":[136],"clusters.":[138],"We":[139],"experimentally":[140],"evaluate":[141],"performance":[143],"proposed":[146],"method":[147],"over":[148],"real":[150],"dataset.":[151]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
