{"id":"https://openalex.org/W1506391929","doi":"https://doi.org/10.1109/cec.2005.1554880","title":"Swarm Intelligence in Automated Electrical Wafer Sort Classification","display_name":"Swarm Intelligence in Automated Electrical Wafer Sort Classification","publication_year":2005,"publication_date":"2005-12-13","ids":{"openalex":"https://openalex.org/W1506391929","doi":"https://doi.org/10.1109/cec.2005.1554880","mag":"1506391929"},"language":"en","primary_location":{"id":"doi:10.1109/cec.2005.1554880","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2005.1554880","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2005 IEEE Congress on Evolutionary Computation","raw_type":"proceedings-article"},"type":"conference-paper","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/A5044601243","display_name":"Emilio Miguela\u00f1ez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"E. Miguelanez","raw_affiliation_strings":["Test Advantage Limited, Falkirk, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Test Advantage Limited, Falkirk, UK","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111377875","display_name":"A.M.S. Zalzala","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107404","display_name":"Clinical Research Solutions","ror":"https://ror.org/01m2t4z13","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210107404"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"A.M.S. Zalzala","raw_affiliation_strings":["FZ-LLC, Technology and Research Solutions, Dubai, UAE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"FZ-LLC, Technology and Research Solutions, Dubai, UAE","institution_ids":["https://openalex.org/I4210107404"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034447135","display_name":"Paul Buxton","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"P. Buxton","raw_affiliation_strings":["Test Advantage Limited, Falkirk, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Test Advantage Limited, Falkirk, UK","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.06193209,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"2","issue":null,"first_page":"1597","last_page":"1604"},"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/T11301","display_name":"Advanced Surface Polishing Techniques","score":0.9667999744415283,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9621999859809875,"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/computer-science","display_name":"Computer science","score":0.6671874523162842},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5544012188911438},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.5018646717071533},{"id":"https://openalex.org/keywords/semiconductor-device-fabrication","display_name":"Semiconductor device fabrication","score":0.4890042543411255},{"id":"https://openalex.org/keywords/wafer-testing","display_name":"Wafer testing","score":0.48385322093963623},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4566454291343689},{"id":"https://openalex.org/keywords/wafer","display_name":"Wafer","score":0.4405529499053955},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43110066652297974},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4270769953727722},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39963293075561523},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3440214991569519},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17929229140281677}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6671874523162842},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5544012188911438},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.5018646717071533},{"id":"https://openalex.org/C66018809","wikidata":"https://www.wikidata.org/wiki/Q1570432","display_name":"Semiconductor device fabrication","level":3,"score":0.4890042543411255},{"id":"https://openalex.org/C44445679","wikidata":"https://www.wikidata.org/wiki/Q2538844","display_name":"Wafer testing","level":3,"score":0.48385322093963623},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4566454291343689},{"id":"https://openalex.org/C160671074","wikidata":"https://www.wikidata.org/wiki/Q267131","display_name":"Wafer","level":2,"score":0.4405529499053955},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43110066652297974},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4270769953727722},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39963293075561523},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3440214991569519},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17929229140281677},{"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/cec.2005.1554880","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2005.1554880","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2005 IEEE Congress on Evolutionary Computation","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1490670270","https://openalex.org/W1502205195","https://openalex.org/W1553290591","https://openalex.org/W1580876177","https://openalex.org/W1628481780","https://openalex.org/W1875348914","https://openalex.org/W1926832021","https://openalex.org/W2082236085","https://openalex.org/W2084128131","https://openalex.org/W2084812512","https://openalex.org/W2089382082","https://openalex.org/W2109364787","https://openalex.org/W2118144345","https://openalex.org/W2119588524","https://openalex.org/W2124290836","https://openalex.org/W2124776405","https://openalex.org/W2125409337","https://openalex.org/W2139339670","https://openalex.org/W2142334564","https://openalex.org/W2145833756","https://openalex.org/W2149306144","https://openalex.org/W2162012556","https://openalex.org/W2169245194","https://openalex.org/W3023540311","https://openalex.org/W4248214229","https://openalex.org/W4302168679","https://openalex.org/W4388297464"],"related_works":["https://openalex.org/W2112424816","https://openalex.org/W2992897358","https://openalex.org/W2054845823","https://openalex.org/W2540312267","https://openalex.org/W2070188681","https://openalex.org/W2367528910","https://openalex.org/W2156694894","https://openalex.org/W2031579205","https://openalex.org/W2631724279","https://openalex.org/W4229506424"],"abstract_inverted_index":{"The":[0,162,230],"semiconductor":[1,25],"manufacturing":[2,82,106,138],"domain":[3],"is":[4,71,97,167,193,212],"by":[5,154],"no":[6],"doubt":[7],"a":[8,168],"rich":[9],"and":[10,33,40,48,53,145,183,225],"challenging":[11],"environment":[12],"for":[13],"the":[14,21,66,77,105,113,122,143,149,155,159,174,178,206,209,226,240],"application":[15],"of":[16,20,24,31,68,79,148,164,202],"machine":[17],"learning.":[18],"Some":[19],"demanding":[22],"characteristic":[23],"data":[26,70],"include":[27],"high":[28],"dimensionality,":[29],"mixtures":[30],"categorical":[32],"numerical":[34],"data,":[35,38],"non-randomly":[36],"Gaussian":[37],"non-Gaussian":[39],"multi-modal":[41],"distributions,":[42],"highly":[43],"non-linear":[44],"complex":[45],"relationships,":[46],"noise":[47],"outliers":[49],"in":[50,104,121,158,239],"both":[51],"x":[52],"y":[54],"dimensions,":[55],"temporal":[56],"dependencies,":[57],"etc.":[58,136],"These":[59,137],"challenges":[60],"are":[61,126,140,152,233],"becoming":[62],"particularly":[63],"crucial":[64],"as":[65,115,134],"quantity":[67],"available":[69,238],"growing":[72],"dramatically.":[73],"This":[74],"paper":[75],"addresses":[76],"problem":[78],"automatic":[80],"wafer":[81,89],"process":[83,107],"error":[84],"detection":[85],"based":[86,172],"on":[87,142,173],"electrical":[88,146],"sort":[90],"(EWS)":[91],"parametric":[92,156],"tests.":[93],"A":[94],"wafer-to-wafer":[95],"analysis":[96],"presented":[98],"that":[99,108,190],"automatically":[100],"detect":[101,196],"possible":[102],"errors":[103,139],"causes":[109,125],"systematic":[110],"damage":[111],"to":[112,195,205],"product":[114],"it":[116],"passes":[117],"through":[118],"some":[119],"step":[120],"process.":[123,161],"Possible":[124],"equipment":[127],"mishandling,":[128],"operator":[129],"error,":[130],"material":[131],"issues":[132],"such":[133],"contamination,":[135],"reflected":[141],"physical":[144],"properties":[147],"wafers,":[150],"which":[151],"measured":[153],"tests":[157],"EWS":[160,207],"core":[163],"this":[165,191],"research":[166],"novel":[169],"classifier":[170],"system":[171,192,211],"benefits":[175],"arising":[176],"from":[177],"interaction":[179],"between":[180],"evolutionary":[181],"algorithms":[182],"artificial":[184],"neural":[185],"networks.":[186],"Experimental":[187],"results":[188,232],"demonstrates":[189],"able":[194],"defective":[197],"wafers":[198],"with":[199,214,235],"an":[200],"accuracy":[201],"82%.":[203],"Prior":[204],"classification,":[208],"proposed":[210],"evaluated":[213],"three":[215],"classification":[216],"benchmark":[217],"problems:":[218],"Iris":[219],"dataset,":[220],"Australian":[221],"credit":[222],"card":[223],"problem,":[224],"Pumas":[227],"diabetes":[228],"dataset.":[229],"obtained":[231],"compared":[234],"classifier's":[236],"outcomes":[237],"literature.":[241]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
