{"id":"https://openalex.org/W7140161155","doi":"https://doi.org/10.1016/j.aei.2026.104621","title":"A deep active learning framework for defect classification of wafer bin maps under noisy labels","display_name":"A deep active learning framework for defect classification of wafer bin maps under noisy labels","publication_year":2026,"publication_date":"2026-03-24","ids":{"openalex":"https://openalex.org/W7140161155","doi":"https://doi.org/10.1016/j.aei.2026.104621"},"language":"en","primary_location":{"id":"doi:10.1016/j.aei.2026.104621","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.aei.2026.104621","pdf_url":null,"source":{"id":"https://openalex.org/S112141509","display_name":"Advanced Engineering Informatics","issn_l":"1474-0346","issn":["1474-0346","1873-5320"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advanced Engineering Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.aei.2026.104621","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Chansung Lim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chansung Lim","raw_affiliation_strings":["School of Industrial and Management Engineering, College of Engineering, Korea University, 145 Anam-ro, Seoul 02841, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Industrial and Management Engineering, College of Engineering, Korea University, 145 Anam-ro, Seoul 02841, Republic of Korea","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Gyeongho Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gyeongho Kim","raw_affiliation_strings":["Institute of Engineering Research, Korea University, 145 Anam-ro, Seoul 02841, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Engineering Research, Korea University, 145 Anam-ro, Seoul 02841, Republic of Korea","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Sunghoon Lim","orcid":"https://orcid.org/0000-0001-9534-7397"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sunghoon Lim","raw_affiliation_strings":["School of Industrial and Management Engineering, College of Engineering, Korea University, 145 Anam-ro, Seoul 02841, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0001-9534-7397","affiliations":[{"raw_affiliation_string":"School of Industrial and Management Engineering, College of Engineering, Korea University, 145 Anam-ro, Seoul 02841, Republic of Korea","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":3570,"currency":"USD","value_usd":3570},"apc_paid":{"value":3570,"currency":"USD","value_usd":3570},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.6466476,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"74","issue":null,"first_page":"104621","last_page":"104621"},"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":0.8238000273704529,"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":0.8238000273704529,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.03590000048279762,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.022299999371170998,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/oracle","display_name":"Oracle","score":0.6708999872207642},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5907999873161316},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.534500002861023},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5084999799728394},{"id":"https://openalex.org/keywords/bin","display_name":"Bin","score":0.48420000076293945},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.47850000858306885},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.46459999680519104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.708299994468689},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.6708999872207642},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6692000031471252},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5907999873161316},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.534500002861023},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5134000182151794},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5113000273704529},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5084999799728394},{"id":"https://openalex.org/C156273044","wikidata":"https://www.wikidata.org/wiki/Q4913766","display_name":"Bin","level":2,"score":0.48420000076293945},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.47850000858306885},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.46459999680519104},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.447299987077713},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.41609999537467957},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.3853999972343445},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3662000000476837},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.34049999713897705},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.2687999904155731}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.aei.2026.104621","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.aei.2026.104621","pdf_url":null,"source":{"id":"https://openalex.org/S112141509","display_name":"Advanced Engineering Informatics","issn_l":"1474-0346","issn":["1474-0346","1873-5320"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advanced Engineering Informatics","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.aei.2026.104621","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.aei.2026.104621","pdf_url":null,"source":{"id":"https://openalex.org/S112141509","display_name":"Advanced Engineering Informatics","issn_l":"1474-0346","issn":["1474-0346","1873-5320"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advanced Engineering Informatics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321373","display_name":"Korea University","ror":"https://ror.org/047dqcg40"},{"id":"https://openalex.org/F4320321408","display_name":"Ministry of Education","ror":"https://ror.org/01p262204"},{"id":"https://openalex.org/F4320321681","display_name":"Ministry of Trade, Industry and Energy","ror":"https://ror.org/008nkqk13"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1979091955","https://openalex.org/W2032099903","https://openalex.org/W2194775991","https://openalex.org/W2553245249","https://openalex.org/W2590150025","https://openalex.org/W2764262416","https://openalex.org/W2805484002","https://openalex.org/W2970409000","https://openalex.org/W3082906739","https://openalex.org/W3127923644","https://openalex.org/W3177093435","https://openalex.org/W4206254019","https://openalex.org/W4292722430","https://openalex.org/W4367396929","https://openalex.org/W4375946254","https://openalex.org/W4385694318","https://openalex.org/W4386950711","https://openalex.org/W4387171859","https://openalex.org/W4388795502","https://openalex.org/W4390707243","https://openalex.org/W4392121419","https://openalex.org/W4392878051","https://openalex.org/W4393153130","https://openalex.org/W4393318763","https://openalex.org/W4394733541","https://openalex.org/W4394770858","https://openalex.org/W4400852168","https://openalex.org/W4401107346","https://openalex.org/W4402494730","https://openalex.org/W4403154537","https://openalex.org/W4403278235","https://openalex.org/W4403433038","https://openalex.org/W4403933585","https://openalex.org/W4404006891","https://openalex.org/W4405742398","https://openalex.org/W4405861610","https://openalex.org/W4407707402","https://openalex.org/W4408955675","https://openalex.org/W4411405499","https://openalex.org/W4412804079","https://openalex.org/W4413444580","https://openalex.org/W4413911928","https://openalex.org/W4413996869","https://openalex.org/W4415769633","https://openalex.org/W4417190680","https://openalex.org/W7083197204","https://openalex.org/W7083320777","https://openalex.org/W7108203948","https://openalex.org/W7126179099"],"related_works":[],"abstract_inverted_index":{"In":[0],"modern":[1],"semiconductor":[2],"manufacturing,":[3],"accurately":[4],"classifying":[5],"wafer":[6,246],"bin":[7,247],"map":[8,248],"(WBM)":[9],"defect":[10,100,249],"patterns":[11],"is":[12,185,243,255,266,277],"essential":[13],"for":[14,98,245],"ensuring":[15],"productivity.":[16],"While":[17],"recent":[18],"studies":[19,224],"increasingly":[20],"employ":[21],"deep":[22,95],"learning-based":[23],"approaches,":[24],"their":[25,163],"effectiveness":[26,180],"often":[27,75],"depends":[28],"on":[29,138,234],"large-scale":[30],"labeled":[31],"datasets":[32],"that":[33,120,204],"are":[34,165],"costly":[35],"to":[36,64,77,173,257,279],"obtain.":[37],"Active":[38],"learning":[39,242],"(AL)":[40],"offers":[41],"a":[42,93,113,130,142,148,188,195],"practical":[43],"solution":[44],"by":[45],"querying":[46],"the":[47,65,126,135,182,205,227,230],"most":[48],"informative":[49],"samples,":[50,162],"thereby":[51],"reducing":[52],"labeling":[53],"costs.":[54],"However,":[55],"existing":[56,290],"AL":[57,96,193,211,215],"strategies":[58],"cannot":[59],"be":[60],"effectively":[61],"utilized":[62,244],"due":[63],"existence":[66],"of":[67,125,159,181,229],"noisy":[68,103,156,161,196,281,293],"labels":[69],"from":[70,155,282],"human":[71],"annotation":[72],"errors,":[73],"which":[74],"leads":[76],"incorrect":[78],"decision":[79],"boundaries,":[80],"confirmation":[81,177],"bias,":[82],"and":[83,175,213,236],"performance":[84],"deterioration.":[85],"To":[86],"address":[87],"these":[88],"limitations,":[89],"this":[90],"work":[91],"proposes":[92],"hybrid":[94],"framework":[97,107,184,207],"WBM":[99,190],"classification":[101],"under":[102,192,217,292],"labels.":[104,284],"The":[105,179,199,286],"proposed":[106,183,206,231,287],"presents":[108],"three":[109],"novel":[110],"techniques.":[111],"First,":[112],"coverage-based":[114],"diversity":[115,171,253],"sampling":[116,133,172,254],"identifies":[117],"candidate":[118],"samples":[119,154],"provide":[121],"broad,":[122],"non-redundant":[123],"coverage":[124],"unlabeled":[127],"pool.":[128],"Second,":[129],"Bayesian-based":[131],"uncertainty":[132,265],"ranks":[134],"candidates":[136],"based":[137],"information":[139],"gain.":[140],"Third,":[141],"loss-based":[143],"noise":[144,220],"filtering":[145],"mechanism":[146],"using":[147,187],"Gaussian":[149,274],"mixture":[150,275],"model":[151],"distinguishes":[152],"clean":[153,283],"ones.":[157],"Instead":[158],"discarding":[160],"neighborhoods":[164],"marked":[166],"as":[167],"unexplored,":[168],"allowing":[169],"subsequent":[170],"revisit":[174],"mitigate":[176],"bias.":[178],"validated":[186],"real-world":[189],"dataset":[191],"with":[194,268],"oracle":[197],"setup.":[198],"comprehensive":[200],"experimental":[201],"results":[202],"demonstrate":[203],"substantially":[208],"outperforms":[209,289],"conventional":[210],"baselines":[212],"state-of-the-art":[214],"methods":[216,291],"different":[218],"label":[219,237,294],"rates.":[221],"Extensive":[222],"ablation":[223],"also":[225],"verify":[226],"effects":[228],"framework\u2019s":[232],"techniques":[233],"robustness":[235],"efficiency.":[238],"\u2022":[239,251,261,272,285],"Deep":[240],"active":[241],"classification.":[250],"Coverage-based":[252],"performed":[256],"select":[258],"representative":[259],"candidates.":[260],"Efficient":[262],"Bayesian":[263],"epistemic":[264],"employed":[267],"Monte":[269],"Carlo":[270],"dropout.":[271],"Loss-based":[273],"modeling":[276],"applied":[278],"filter":[280],"method":[288],"conditions.":[295]},"counts_by_year":[],"updated_date":"2026-03-25T13:09:30.665167","created_date":"2026-03-25T00:00:00"}