{"id":"https://openalex.org/W4388117314","doi":"https://doi.org/10.1109/itc-asia58802.2023.10301159","title":"Toward Improvement and Evaluation of Reconstruction Capability of CapsNet-Based Wafer Map Defect Pattern Classifier","display_name":"Toward Improvement and Evaluation of Reconstruction Capability of CapsNet-Based Wafer Map Defect Pattern Classifier","publication_year":2023,"publication_date":"2023-09-12","ids":{"openalex":"https://openalex.org/W4388117314","doi":"https://doi.org/10.1109/itc-asia58802.2023.10301159"},"language":"en","primary_location":{"id":"doi:10.1109/itc-asia58802.2023.10301159","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itc-asia58802.2023.10301159","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Test Conference in Asia (ITC-Asia)","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/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":true,"raw_author_name":"Yuki Yamanaka","raw_affiliation_strings":["Tokyo Metropolitan University,Graduate School of System Design,Japan","Graduate School of System Design, Tokyo Metropolitan University, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Metropolitan University,Graduate School of System Design,Japan","institution_ids":["https://openalex.org/I69740276"]},{"raw_affiliation_string":"Graduate School of System Design, Tokyo Metropolitan University, 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":["Nihon University,College of Industrial Technology,Japan","College of Industrial Technology, Nihon University, Japan"],"affiliations":[{"raw_affiliation_string":"Nihon University,College of Industrial Technology,Japan","institution_ids":["https://openalex.org/I52706244","https://openalex.org/I104946051"]},{"raw_affiliation_string":"College of Industrial Technology, Nihon University, Japan","institution_ids":["https://openalex.org/I52706244","https://openalex.org/I104946051"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029255606","display_name":"Yoshikazu Nagamura","orcid":"https://orcid.org/0000-0002-9364-9997"},"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":"Yoshikazu Nagamura","raw_affiliation_strings":["Tokyo Metropolitan University,Graduate School of System Design,Japan","Graduate School of System Design, Tokyo Metropolitan University, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Metropolitan University,Graduate School of System Design,Japan","institution_ids":["https://openalex.org/I69740276"]},{"raw_affiliation_string":"Graduate School of System Design, Tokyo Metropolitan University, Japan","institution_ids":["https://openalex.org/I69740276"]}]},{"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":["Tokyo Metropolitan University,Graduate School of System Design,Japan","Graduate School of System Design, Tokyo Metropolitan University, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Metropolitan University,Graduate School of System Design,Japan","institution_ids":["https://openalex.org/I69740276"]},{"raw_affiliation_string":"Graduate School of System Design, Tokyo Metropolitan University, Japan","institution_ids":["https://openalex.org/I69740276"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5006786260"],"corresponding_institution_ids":["https://openalex.org/I69740276"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24751567,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9998000264167786,"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":0.9998000264167786,"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/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9998000264167786,"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/T11338","display_name":"Advancements in Photolithography Techniques","score":0.9979000091552734,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7465722560882568},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6934658288955688},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6920228004455566},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6739459037780762},{"id":"https://openalex.org/keywords/wafer","display_name":"Wafer","score":0.6524755954742432},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6348188519477844},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49574050307273865},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4215664863586426},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.20961233973503113}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7465722560882568},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6934658288955688},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6920228004455566},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6739459037780762},{"id":"https://openalex.org/C160671074","wikidata":"https://www.wikidata.org/wiki/Q267131","display_name":"Wafer","level":2,"score":0.6524755954742432},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6348188519477844},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49574050307273865},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4215664863586426},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.20961233973503113},{"id":"https://openalex.org/C171250308","wikidata":"https://www.wikidata.org/wiki/Q11468","display_name":"Nanotechnology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itc-asia58802.2023.10301159","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itc-asia58802.2023.10301159","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Test Conference in Asia (ITC-Asia)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1495288325","https://openalex.org/W1849928240","https://openalex.org/W2601564443","https://openalex.org/W2790607928","https://openalex.org/W2963703618","https://openalex.org/W2970409000","https://openalex.org/W4205262863","https://openalex.org/W6743446608"],"related_works":["https://openalex.org/W1998662473","https://openalex.org/W2075391483","https://openalex.org/W2742348144","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Classification":[0],"of":[1,18,25,64,80,112,128,143,157,170,175],"wafer":[2,114,136,161],"map":[3,115,137],"defect":[4,96,116,122,178],"patterns":[5],"is":[6,45],"important":[7],"to":[8,13,35,69,150,172],"monitor":[9],"occurrence":[10],"and":[11,40,58,90,139,162],"further":[12],"assist":[14,77],"root":[15],"cause":[16],"analysis":[17,79],"manufacturing-process-induced":[19],"systematic":[20,81,95],"defects.":[21],"CapsNet,":[22],"a":[23,30,133,141,160,168,176],"variant":[24],"convolutional":[26],"neural":[27],"network,":[28],"contains":[29],"decoder":[31],"network":[32,126],"which":[33],"tries":[34],"reconstruct":[36],"an":[37],"input":[38,59],"image,":[39],"during":[41],"training":[42,146],"loss":[43],"function":[44],"calculated":[46],"not":[47,84],"only":[48,85],"for":[49,54,92],"prediction":[50,72],"error":[51],"but":[52,87],"also":[53,76,88,166],"difference":[55],"between":[56],"reconstructed":[57,65,177],"images.":[60,180],"Improving":[61],"the":[62,93,129],"quality":[63,174],"image":[66],"might":[67],"lead":[68],"improvements":[70,108],"on":[71,106,109,159],"capability.":[73],"It":[74],"would":[75],"root-cause":[78],"defects,":[82],"since":[83],"category":[86],"locations":[89],"shapes":[91],"predicted":[94],"are":[97],"more":[98],"clearly":[99],"reported.":[100],"In":[101],"this":[102],"paper":[103],"we":[104,124],"report":[105],"our":[107],"reconstruction":[110,152],"capability":[111,153],"CapsNet-based":[113],"pattern":[117,179],"classifier.":[118,130],"Mainly":[119],"targeting":[120],"scratch":[121],"pattern,":[123],"tuned":[125],"architecture":[127],"We":[131,165],"generated":[132],"synthesized":[134],"high-resolution":[135],"dataset,":[138],"applied":[140],"mixture":[142],"sub-categories":[144],"as":[145],"data":[147],"in":[148],"order":[149],"improve":[151],"even":[154],"under":[155],"appearances":[156],"noises":[158],"chopped":[163],"curves.":[164],"propose":[167],"set":[169],"measures":[171],"evaluate":[173]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
