{"id":"https://openalex.org/W3108299574","doi":"https://doi.org/10.1109/idsta50958.2020.9264135","title":"A Defect Detection Model for Imbalanced Wafer Image Data Using CAE and Xception","display_name":"A Defect Detection Model for Imbalanced Wafer Image Data Using CAE and Xception","publication_year":2020,"publication_date":"2020-10-19","ids":{"openalex":"https://openalex.org/W3108299574","doi":"https://doi.org/10.1109/idsta50958.2020.9264135","mag":"3108299574"},"language":"en","primary_location":{"id":"doi:10.1109/idsta50958.2020.9264135","is_oa":false,"landing_page_url":"https://doi.org/10.1109/idsta50958.2020.9264135","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)","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/A5060880131","display_name":"Jaegyeong Cha","orcid":"https://orcid.org/0000-0002-2616-5415"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Jaegyeong Cha","raw_affiliation_strings":["Sungkyunkwan University,Department of Smart Factory Convergence,Suwon,Gyeonggi-do,Republic of Korea,16419","Department of Smart Factory Convergence, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University,Department of Smart Factory Convergence,Suwon,Gyeonggi-do,Republic of Korea,16419","institution_ids":["https://openalex.org/I848706"]},{"raw_affiliation_string":"Department of Smart Factory Convergence, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032789488","display_name":"Seokju Oh","orcid":"https://orcid.org/0000-0003-2473-6621"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seokju Oh","raw_affiliation_strings":["Sungkyunkwan University,Department of Smart Factory Convergence,Suwon,Gyeonggi-do,Republic of Korea,16419","Department of Smart Factory Convergence, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University,Department of Smart Factory Convergence,Suwon,Gyeonggi-do,Republic of Korea,16419","institution_ids":["https://openalex.org/I848706"]},{"raw_affiliation_string":"Department of Smart Factory Convergence, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100454683","display_name":"Donghyun Kim","orcid":"https://orcid.org/0000-0002-7132-4454"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Donghyun Kim","raw_affiliation_strings":["Sungkyunkwan University,Department of Smart Factory Convergence,Suwon,Gyeonggi-do,Republic of Korea,16419","Department of Smart Factory Convergence, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University,Department of Smart Factory Convergence,Suwon,Gyeonggi-do,Republic of Korea,16419","institution_ids":["https://openalex.org/I848706"]},{"raw_affiliation_string":"Department of Smart Factory Convergence, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031383405","display_name":"Jongpil Jeong","orcid":"https://orcid.org/0000-0002-4061-9532"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jongpil Jeong","raw_affiliation_strings":["Sungkyunkwan University,Department of Smart Factory Convergence,Suwon,Gyeonggi-do,Republic of Korea,16419","Department of Smart Factory Convergence, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University,Department of Smart Factory Convergence,Suwon,Gyeonggi-do,Republic of Korea,16419","institution_ids":["https://openalex.org/I848706"]},{"raw_affiliation_string":"Department of Smart Factory Convergence, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5060880131"],"corresponding_institution_ids":["https://openalex.org/I848706"],"apc_list":null,"apc_paid":null,"fwci":0.7132,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.77819307,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"28","last_page":"33"},"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/T14319","display_name":"Currency Recognition and Detection","score":0.983299970626831,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9761000275611877,"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/wafer","display_name":"Wafer","score":0.8133315443992615},{"id":"https://openalex.org/keywords/semiconductor-device-fabrication","display_name":"Semiconductor device fabrication","score":0.6501246094703674},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5956172943115234},{"id":"https://openalex.org/keywords/semiconductor","display_name":"Semiconductor","score":0.5662454962730408},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5341315269470215},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.49918198585510254},{"id":"https://openalex.org/keywords/electronics","display_name":"Electronics","score":0.4935246706008911},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4814288318157196},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.45303165912628174},{"id":"https://openalex.org/keywords/manufacturing-engineering","display_name":"Manufacturing engineering","score":0.439557284116745},{"id":"https://openalex.org/keywords/wafer-testing","display_name":"Wafer testing","score":0.4334610104560852},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4308663308620453},{"id":"https://openalex.org/keywords/semiconductor-device-modeling","display_name":"Semiconductor device modeling","score":0.4224664866924286},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.3635846674442291},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3303712010383606},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.3175787925720215},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2456255555152893},{"id":"https://openalex.org/keywords/cmos","display_name":"CMOS","score":0.21782022714614868},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.16924583911895752},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.07205522060394287}],"concepts":[{"id":"https://openalex.org/C160671074","wikidata":"https://www.wikidata.org/wiki/Q267131","display_name":"Wafer","level":2,"score":0.8133315443992615},{"id":"https://openalex.org/C66018809","wikidata":"https://www.wikidata.org/wiki/Q1570432","display_name":"Semiconductor device fabrication","level":3,"score":0.6501246094703674},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5956172943115234},{"id":"https://openalex.org/C108225325","wikidata":"https://www.wikidata.org/wiki/Q11456","display_name":"Semiconductor","level":2,"score":0.5662454962730408},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5341315269470215},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.49918198585510254},{"id":"https://openalex.org/C138331895","wikidata":"https://www.wikidata.org/wiki/Q11650","display_name":"Electronics","level":2,"score":0.4935246706008911},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4814288318157196},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.45303165912628174},{"id":"https://openalex.org/C117671659","wikidata":"https://www.wikidata.org/wiki/Q11049265","display_name":"Manufacturing engineering","level":1,"score":0.439557284116745},{"id":"https://openalex.org/C44445679","wikidata":"https://www.wikidata.org/wiki/Q2538844","display_name":"Wafer testing","level":3,"score":0.4334610104560852},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4308663308620453},{"id":"https://openalex.org/C4775677","wikidata":"https://www.wikidata.org/wiki/Q7449393","display_name":"Semiconductor device modeling","level":3,"score":0.4224664866924286},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3635846674442291},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3303712010383606},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.3175787925720215},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2456255555152893},{"id":"https://openalex.org/C46362747","wikidata":"https://www.wikidata.org/wiki/Q173431","display_name":"CMOS","level":2,"score":0.21782022714614868},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.16924583911895752},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.07205522060394287},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/idsta50958.2020.9264135","is_oa":false,"landing_page_url":"https://doi.org/10.1109/idsta50958.2020.9264135","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.6399999856948853,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320327819","display_name":"Ministry of SMEs and Startups","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2020286945","https://openalex.org/W2097117768","https://openalex.org/W2136655611","https://openalex.org/W2147800946","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2531409750","https://openalex.org/W2963446712","https://openalex.org/W2968153557","https://openalex.org/W2970340508","https://openalex.org/W2970409000","https://openalex.org/W2996443765","https://openalex.org/W2997442117","https://openalex.org/W3011969759","https://openalex.org/W6637373629","https://openalex.org/W6655633911","https://openalex.org/W6679997575","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W4386114661","https://openalex.org/W2327254200","https://openalex.org/W2287022550","https://openalex.org/W2027697249","https://openalex.org/W1965337273","https://openalex.org/W1593076923","https://openalex.org/W1849611347","https://openalex.org/W2118048293","https://openalex.org/W2050503762","https://openalex.org/W2284242891"],"abstract_inverted_index":{"The":[0],"development":[1,29],"of":[2,30,51,57,155,162,172,196,203],"technology":[3,32],"in":[4,95],"modern":[5],"society":[6],"causes":[7],"consumers":[8],"to":[9,17,107,144,167,190],"create":[10],"new":[11],"demands.":[12],"And":[13],"consumers'":[14],"demands":[15],"lead":[16],"improved":[18],"product":[19],"quality.":[20],"In":[21,38,67,82,133,165],"particular,":[22],"as":[23,43],"the":[24,28,49,55,60,68,108,169,173,180,184,192,201],"mobile":[25],"era":[26],"enters,":[27],"semiconductor":[31,65,69,72,83,109],"is":[33,59,115,142],"essential":[34],"for":[35,64],"electronic":[36],"products.":[37,52],"electronics,":[39],"semiconductors":[40,58],"are":[41,75],"used":[42],"various":[44,92],"precision":[45],"parts":[46],"and":[47,100,124,149,159,194],"control":[48],"performance":[50],"Therefore,":[53],"improving":[54],"yield":[56],"most":[61],"laborious":[62],"task":[63],"companies.":[66],"manufacturing":[70,110],"industry,":[71],"wafer":[73,89,146,197],"defects":[74,90,102,198],"a":[76,136,153],"major":[77],"problem":[78,171,202],"causing":[79],"large":[80],"losses.":[81,97],"manufacturing,":[84],"which":[85],"includes":[86],"many":[87],"processes,":[88],"cause":[91],"variations,":[93],"resulting":[94],"great":[96,105],"Accurately":[98],"identifying":[99],"classifying":[101],"would":[103],"bring":[104],"benefits":[106],"industry.":[111],"Wafer":[112],"defect":[113,147],"inspection":[114],"being":[116],"conducted":[117],"passively":[118],"by":[119],"experts.":[120],"Wasting":[121],"such":[122],"passive":[123],"human":[125],"resources":[126],"can":[127],"be":[128],"prevented":[129],"through":[130],"machine":[131],"learning.":[132],"this":[134],"paper,":[135],"deep":[137],"learning-based":[138],"model":[139],"using":[140,179],"Xception":[141,151],"proposed":[143,185],"proceed":[145],"detection":[148,193],"classification.":[150],"has":[152],"total":[154],"36":[156],"convolution":[157],"layers":[158],"consists":[160],"largely":[161],"three":[163],"flows.":[164],"addition,":[166],"solve":[168],"imbalance":[170],"dataset,":[174],"data":[175,204],"augmentation":[176],"was":[177,188],"performed":[178],"convolutional":[181],"autoencoder.":[182],"Through":[183],"method,":[186],"it":[187],"possible":[189],"improve":[191],"classification":[195],"while":[199],"solving":[200],"imbalance.":[205]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3}],"updated_date":"2026-01-29T23:13:10.619473","created_date":"2025-10-10T00:00:00"}
