{"id":"https://openalex.org/W1991934649","doi":"https://doi.org/10.1145/2818869.2818934","title":"A Hybrid Big Data Analytics Method for Yield Improvement in Semiconductor Manufacturing","display_name":"A Hybrid Big Data Analytics Method for Yield Improvement in Semiconductor Manufacturing","publication_year":2015,"publication_date":"2015-10-07","ids":{"openalex":"https://openalex.org/W1991934649","doi":"https://doi.org/10.1145/2818869.2818934","mag":"1991934649"},"language":"en","primary_location":{"id":"doi:10.1145/2818869.2818934","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2818869.2818934","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ASE BigData &amp; SocialInformatics 2015","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/A5008093202","display_name":"Chung-Hong Lee","orcid":"https://orcid.org/0000-0003-4178-5388"},"institutions":[{"id":"https://openalex.org/I89178830","display_name":"National Kaohsiung University of Applied Sciences","ror":"https://ror.org/04wydzr61","country_code":"TW","type":"education","lineage":["https://openalex.org/I89178830"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Chung-Hong Lee","raw_affiliation_strings":["Dept. Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan","[Department Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan]"],"affiliations":[{"raw_affiliation_string":"Dept. Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan","institution_ids":["https://openalex.org/I89178830"]},{"raw_affiliation_string":"[Department Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan]","institution_ids":["https://openalex.org/I89178830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031539003","display_name":"Hsin-Chang Yang","orcid":"https://orcid.org/0000-0001-5851-2760"},"institutions":[{"id":"https://openalex.org/I192168892","display_name":"National University of Kaohsiung","ror":"https://ror.org/013zjb662","country_code":"TW","type":"education","lineage":["https://openalex.org/I192168892"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Hsin-Chang Yang","raw_affiliation_strings":["Dept. Information Management, National University of Kaohsiung, Kaohsiung, Taiwan"],"affiliations":[{"raw_affiliation_string":"Dept. Information Management, National University of Kaohsiung, Kaohsiung, Taiwan","institution_ids":["https://openalex.org/I192168892"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081863004","display_name":"Shou-Chen Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I89178830","display_name":"National Kaohsiung University of Applied Sciences","ror":"https://ror.org/04wydzr61","country_code":"TW","type":"education","lineage":["https://openalex.org/I89178830"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Shou-Chen Cheng","raw_affiliation_strings":["Dept. Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan","[Department Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan]"],"affiliations":[{"raw_affiliation_string":"Dept. Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan","institution_ids":["https://openalex.org/I89178830"]},{"raw_affiliation_string":"[Department Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan]","institution_ids":["https://openalex.org/I89178830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049605978","display_name":"Sheng-Wen Tsai","orcid":null},"institutions":[{"id":"https://openalex.org/I89178830","display_name":"National Kaohsiung University of Applied Sciences","ror":"https://ror.org/04wydzr61","country_code":"TW","type":"education","lineage":["https://openalex.org/I89178830"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Sheng-Wen Tsai","raw_affiliation_strings":["Dept. Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan","[Department Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan]"],"affiliations":[{"raw_affiliation_string":"Dept. Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan","institution_ids":["https://openalex.org/I89178830"]},{"raw_affiliation_string":"[Department Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan]","institution_ids":["https://openalex.org/I89178830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5008093202"],"corresponding_institution_ids":["https://openalex.org/I89178830"],"apc_list":null,"apc_paid":null,"fwci":1.1317,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.81125865,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"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":0.9994000196456909,"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":0.9994000196456909,"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/T11159","display_name":"Manufacturing Process and Optimization","score":0.9488000273704529,"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/T13049","display_name":"Surface Roughness and Optical Measurements","score":0.9330999851226807,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/support-vector-machine","display_name":"Support vector machine","score":0.675821840763092},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6202669739723206},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5936219096183777},{"id":"https://openalex.org/keywords/profitability-index","display_name":"Profitability index","score":0.5618539452552795},{"id":"https://openalex.org/keywords/semiconductor-device-fabrication","display_name":"Semiconductor device fabrication","score":0.5270493030548096},{"id":"https://openalex.org/keywords/yield","display_name":"Yield (engineering)","score":0.4685830771923065},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4364906847476959},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.43160945177078247},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.41891491413116455},{"id":"https://openalex.org/keywords/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.4125223159790039},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3973065912723541},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34141480922698975},{"id":"https://openalex.org/keywords/manufacturing-engineering","display_name":"Manufacturing engineering","score":0.3374531865119934},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.22401946783065796},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17535650730133057},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1341395080089569},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.1315310001373291}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.675821840763092},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6202669739723206},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5936219096183777},{"id":"https://openalex.org/C129361004","wikidata":"https://www.wikidata.org/wiki/Q2470236","display_name":"Profitability index","level":2,"score":0.5618539452552795},{"id":"https://openalex.org/C66018809","wikidata":"https://www.wikidata.org/wiki/Q1570432","display_name":"Semiconductor device fabrication","level":3,"score":0.5270493030548096},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.4685830771923065},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4364906847476959},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.43160945177078247},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.41891491413116455},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.4125223159790039},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3973065912723541},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34141480922698975},{"id":"https://openalex.org/C117671659","wikidata":"https://www.wikidata.org/wiki/Q11049265","display_name":"Manufacturing engineering","level":1,"score":0.3374531865119934},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.22401946783065796},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17535650730133057},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1341395080089569},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.1315310001373291},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C160671074","wikidata":"https://www.wikidata.org/wiki/Q267131","display_name":"Wafer","level":2,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"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:10.1145/2818869.2818934","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2818869.2818934","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ASE BigData &amp; SocialInformatics 2015","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1503468974","https://openalex.org/W1986033385","https://openalex.org/W1988518729","https://openalex.org/W2007154098","https://openalex.org/W2007280555","https://openalex.org/W2102661631","https://openalex.org/W2110209169","https://openalex.org/W2113952909","https://openalex.org/W2119821739","https://openalex.org/W2131020804","https://openalex.org/W2138939294","https://openalex.org/W2156909104","https://openalex.org/W2171151720","https://openalex.org/W4206686222","https://openalex.org/W7033336652"],"related_works":["https://openalex.org/W3125099825","https://openalex.org/W25115902","https://openalex.org/W2753408573","https://openalex.org/W4285147705","https://openalex.org/W1986338457","https://openalex.org/W2066844180","https://openalex.org/W4390608645","https://openalex.org/W4293192099","https://openalex.org/W3132513730","https://openalex.org/W4253553932"],"abstract_inverted_index":{"In":[0,36],"the":[1,6,27,30,34,41,52,56,61,75,79,101],"manufacturing":[2],"of":[3,11,29,60,81],"semiconductor":[4,122],"encapsulation,":[5],"production":[7,105],"yield":[8,22,119],"is":[9,19],"one":[10],"critical":[12],"issues":[13],"concerned":[14],"by":[15],"all":[16],"foundries.":[17],"It":[18],"because":[20],"that":[21,111],"rate":[23],"can":[24],"directly":[25],"affects":[26],"quality":[28],"final":[31],"product":[32],"and":[33,45,54,58,73],"profitability.":[35],"this":[37],"work":[38],"we":[39,89],"take":[40],"defect-products":[42],"as":[43],"samples":[44,53],"use":[46,64],"machine":[47],"learning":[48],"techniques":[49],"to":[50,70,99],"classify":[51],"verify":[55],"accuracy":[57,77],"feasibility":[59],"experiment.":[62],"We":[63],"Support":[65],"Vector":[66],"Machines":[67],"(SVM)":[68],"model":[69],"perform":[71],"classification":[72],"compare":[74],"resulting":[76],"with":[78],"results":[80],"Back":[82],"Propagation":[83],"Neural":[84],"Network":[85],"(BPN)":[86],"model.":[87],"Furthermore,":[88],"employ":[90],"a":[91],"statistical":[92],"method":[93,114],"namely":[94],"Pearson":[95],"product-moment":[96],"correlation":[97],"coefficient":[98],"identify":[100],"influential":[102],"factors":[103],"for":[104,118],"quality.":[106],"The":[107],"experimental":[108],"result":[109],"demonstrates":[110],"our":[112],"hybrid":[113],"has":[115],"great":[116],"potentials":[117],"improvement":[120],"in":[121],"manufacturing.":[123]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
