{"id":"https://openalex.org/W4415178694","doi":"https://doi.org/10.1109/access.2025.3621299","title":"Mixed-Defect Wafer Map Classification Using CapsNet-Based Models With Precise Scratch-Pattern Reconstruction","display_name":"Mixed-Defect Wafer Map Classification Using CapsNet-Based Models With Precise Scratch-Pattern Reconstruction","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415178694","doi":"https://doi.org/10.1109/access.2025.3621299"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3621299","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3621299","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3621299","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029255606","display_name":"Yoshikazu Nagamura","orcid":"https://orcid.org/0000-0002-9364-9997"},"institutions":[{"id":"https://openalex.org/I4210153176","display_name":"Renesas Electronics (Japan)","ror":"https://ror.org/058wb7691","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210153176"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yoshikazu Nagamura","raw_affiliation_strings":["Analysis and Evaluation Technology Department, Renesas Electronics Corporation, Horiguchi, Japan"],"affiliations":[{"raw_affiliation_string":"Analysis and Evaluation Technology Department, Renesas Electronics Corporation, Horiguchi, Japan","institution_ids":["https://openalex.org/I4210153176"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006786260","display_name":"Yuki Yamanaka","orcid":null},"institutions":[{"id":"https://openalex.org/I69740276","display_name":"Tokyo Metropolitan University","ror":"https://ror.org/00ws30h19","country_code":"JP","type":"education","lineage":["https://openalex.org/I69740276"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuki Yamanaka","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Graduate School of Systems Design, Tokyo Metropolitan University, Hino, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Graduate School of Systems Design, Tokyo Metropolitan University, Hino, Japan","institution_ids":["https://openalex.org/I69740276"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073052006","display_name":"Itsuki Fujita","orcid":"https://orcid.org/0000-0002-8265-1944"},"institutions":[{"id":"https://openalex.org/I69740276","display_name":"Tokyo Metropolitan University","ror":"https://ror.org/00ws30h19","country_code":"JP","type":"education","lineage":["https://openalex.org/I69740276"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Itsuki Fujita","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Graduate School of Systems Design, Tokyo Metropolitan University, Hino, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Graduate School of Systems Design, Tokyo Metropolitan University, Hino, Japan","institution_ids":["https://openalex.org/I69740276"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033483661","display_name":"Masayuki Arai","orcid":"https://orcid.org/0000-0002-4636-6310"},"institutions":[{"id":"https://openalex.org/I104946051","display_name":"Nihon University","ror":"https://ror.org/05jk51a88","country_code":"JP","type":"education","lineage":["https://openalex.org/I104946051"]},{"id":"https://openalex.org/I52706244","display_name":"College of Industrial Technology","ror":"https://ror.org/054a9s036","country_code":"JP","type":"education","lineage":["https://openalex.org/I52706244"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masayuki Arai","raw_affiliation_strings":["Department of Mathematical Information Engineering, College of Industrial Technology, Nihon University, Narashino, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Mathematical Information Engineering, College of Industrial Technology, Nihon University, Narashino, Japan","institution_ids":["https://openalex.org/I52706244","https://openalex.org/I104946051"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084127116","display_name":"Satoshi Fukumoto","orcid":"https://orcid.org/0000-0002-5025-6552"},"institutions":[{"id":"https://openalex.org/I69740276","display_name":"Tokyo Metropolitan University","ror":"https://ror.org/00ws30h19","country_code":"JP","type":"education","lineage":["https://openalex.org/I69740276"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Satoshi Fukumoto","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Graduate School of Systems Design, Tokyo Metropolitan University, Hino, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Graduate School of Systems Design, Tokyo Metropolitan University, Hino, Japan","institution_ids":["https://openalex.org/I69740276"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5029255606"],"corresponding_institution_ids":["https://openalex.org/I4210153176"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.403,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.872005,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"13","issue":null,"first_page":"182491","last_page":"182502"},"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":0.9990000128746033,"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":0.9990000128746033,"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/T11338","display_name":"Advancements in Photolithography Techniques","score":0.9732000231742859,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9675999879837036,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/wafer","display_name":"Wafer","score":0.816100001335144},{"id":"https://openalex.org/keywords/scratch","display_name":"Scratch","score":0.6380000114440918},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6310999989509583},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6270999908447266},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5859000086784363},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5537999868392944},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5263000130653381},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4691999852657318},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.38609999418258667}],"concepts":[{"id":"https://openalex.org/C160671074","wikidata":"https://www.wikidata.org/wiki/Q267131","display_name":"Wafer","level":2,"score":0.816100001335144},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7271000146865845},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6568999886512756},{"id":"https://openalex.org/C2781235140","wikidata":"https://www.wikidata.org/wiki/Q275131","display_name":"Scratch","level":2,"score":0.6380000114440918},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6310999989509583},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6270999908447266},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5859000086784363},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5537999868392944},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5263000130653381},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4691999852657318},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.38609999418258667},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3603000044822693},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3517000079154968},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.3357999920845032},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.3312999904155731},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C4775677","wikidata":"https://www.wikidata.org/wiki/Q7449393","display_name":"Semiconductor device modeling","level":3,"score":0.31779998540878296},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2888999879360199},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2727000117301941},{"id":"https://openalex.org/C2779751349","wikidata":"https://www.wikidata.org/wiki/Q1474480","display_name":"Scanner","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C165005293","wikidata":"https://www.wikidata.org/wiki/Q1074500","display_name":"Chip","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.2578999996185303},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.25679999589920044}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3621299","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3621299","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:562c7ea5a4b54ce49bed3f8f0799a39f","is_oa":true,"landing_page_url":"https://doaj.org/article/562c7ea5a4b54ce49bed3f8f0799a39f","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 182491-182502 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3621299","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3621299","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Wafer":[0,181],"maps":[1,21,104,182],"of":[2,31,98,135,163,183],"large-scale":[3],"integration":[4],"chips":[5],"are":[6],"matrices":[7],"showing":[8],"the":[9,28,34,75,81,95,99,125,132,160,173,189,208],"defective":[10],"chip":[11],"position":[12],"on":[13,19,55,105,172,207],"a":[14,106,113],"wafer.":[15],"Determining":[16],"defect":[17,204],"patterns":[18,26,52],"wafer":[20,56,103,141,210],"is":[22],"crucial":[23],"because":[24],"these":[25],"indicate":[27],"root":[29],"cause":[30],"defects":[32],"in":[33,59,69,124,200],"manufacturing":[35],"process.":[36],"Convolutional":[37],"neural":[38],"networks":[39,129],"can":[40],"successfully":[41],"extract":[42],"local":[43],"features;":[44],"however,":[45],"they":[46],"cannot":[47],"accurately":[48,158],"identify":[49],"linear":[50],"scratch":[51,136,164],"widely":[53],"spread":[54],"maps,":[57],"resulting":[58],"low":[60],"classification":[61,170],"accuracy.":[62],"By":[63],"contrast,":[64],"CapsNet":[65],"stores":[66],"extracted":[67,91],"features":[68,79,89],"multidimensional":[70],"vectors":[71],"(capsules)":[72],"and":[73,127,144],"learns":[74,86],"positional":[76],"relationship":[77],"between":[78],"using":[80,90,112,188],"capsule":[82],"layers.":[83],"Moreover,":[84],"it":[85,167],"input":[87],"data":[88],"capsules":[92],"by":[93,120],"reducing":[94],"reconstruction":[96,133,152],"errors":[97],"decoder":[100,126],"network.":[101],"Mixed-defect":[102],"public":[107,174],"dataset,":[108],"Mixed-WM38,":[109],"were":[110,148,186,196],"classified":[111,187],"CapsNet-based":[114,190],"model.":[115,191],"This":[116],"model":[117,156],"was":[118],"developed":[119,155],"reconfiguring":[121],"computational":[122],"layers":[123],"classifier":[128],"to":[130],"improve":[131],"accuracy":[134],"patterns.":[137,165],"A":[138],"synthesized":[139],"high-resolution":[140],"map":[142],"dataset":[143,175],"original":[145],"pixel-wise":[146],"metrics":[147],"used":[149],"for":[150],"scratch-pattern":[151],"experiments.":[153],"The":[154],"could":[157],"recognize":[159],"fine":[161],"feature":[162],"Thus,":[166],"exhibited":[168],"higher":[169],"performance":[171],"than":[176],"benchmark":[177],"models":[178],"(F1:":[179],"98.1%).":[180],"real-world":[184],"products":[185],"Defect":[192],"patterns,":[193],"including":[194],"scratches,":[195],"clearly":[197],"identified,":[198],"even":[199],"cases":[201],"with":[202],"multiple":[203],"types":[205],"overlapping":[206],"same":[209],"map.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-15T00:00:00"}