{"id":"https://openalex.org/W4388208192","doi":"https://doi.org/10.1109/tim.2023.3325873","title":"Dual Entropy-Controlled Convolutional Neural Network for Mini/Micro LED Defect Recognition","display_name":"Dual Entropy-Controlled Convolutional Neural Network for Mini/Micro LED Defect Recognition","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4388208192","doi":"https://doi.org/10.1109/tim.2023.3325873"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2023.3325873","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3325873","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Instrumentation and Measurement","raw_type":"journal-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/A5010239868","display_name":"Yuxiang Wang","orcid":"https://orcid.org/0009-0009-3914-4486"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxiang Wang","raw_affiliation_strings":["School of Microelectronics, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0009-0009-3914-4486","affiliations":[{"raw_affiliation_string":"School of Microelectronics, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074151166","display_name":"Jie Chu","orcid":"https://orcid.org/0000-0001-7505-1994"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Chu","raw_affiliation_strings":["School of Microelectronics, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0001-7505-1994","affiliations":[{"raw_affiliation_string":"School of Microelectronics, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103278328","display_name":"Yu Chen","orcid":"https://orcid.org/0009-0009-6634-7107"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]},{"id":"https://openalex.org/I2800372957","display_name":"China Electronics Technology Group Corporation","ror":"https://ror.org/0098hst83","country_code":"CN","type":"company","lineage":["https://openalex.org/I2800372957"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Chen","raw_affiliation_strings":["School of Microelectronics, Xidian University, Xi&#x2019;an, China","The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, China"],"raw_orcid":"https://orcid.org/0009-0009-6634-7107","affiliations":[{"raw_affiliation_string":"School of Microelectronics, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, China","institution_ids":["https://openalex.org/I2800372957"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044214379","display_name":"Dong Liang","orcid":"https://orcid.org/0009-0008-9184-7421"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]},{"id":"https://openalex.org/I2800372957","display_name":"China Electronics Technology Group Corporation","ror":"https://ror.org/0098hst83","country_code":"CN","type":"company","lineage":["https://openalex.org/I2800372957"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Liang","raw_affiliation_strings":["School of Microelectronics, Xidian University, Xi&#x2019;an, China","The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, China"],"raw_orcid":"https://orcid.org/0009-0008-9184-7421","affiliations":[{"raw_affiliation_string":"School of Microelectronics, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, China","institution_ids":["https://openalex.org/I2800372957"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012721846","display_name":"Kailin Wen","orcid":"https://orcid.org/0000-0002-3373-2561"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kailin Wen","raw_affiliation_strings":["School of Microelectronics, Xidian University, Xi&#x2019;an, China","Suzhou Honghu Qiji Electronic Technology Company Ltd., Suzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-3373-2561","affiliations":[{"raw_affiliation_string":"School of Microelectronics, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Suzhou Honghu Qiji Electronic Technology Company Ltd., Suzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101425172","display_name":"Jueping Cai","orcid":"https://orcid.org/0000-0002-1852-0982"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jueping Cai","raw_affiliation_strings":["School of Microelectronics, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-1852-0982","affiliations":[{"raw_affiliation_string":"School of Microelectronics, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.5881,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.85542313,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"72","issue":null,"first_page":"1","last_page":"14"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9812999963760376,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7141095995903015},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6695070266723633},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6560438871383667},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6446986198425293},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.548995852470398},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5477357506752014},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5284958481788635},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5189759135246277},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5095487236976624},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5004839897155762},{"id":"https://openalex.org/keywords/chip","display_name":"Chip","score":0.4181230068206787},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36739906668663025},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07771238684654236}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7141095995903015},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6695070266723633},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6560438871383667},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6446986198425293},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.548995852470398},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5477357506752014},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5284958481788635},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5189759135246277},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5095487236976624},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5004839897155762},{"id":"https://openalex.org/C165005293","wikidata":"https://www.wikidata.org/wiki/Q1074500","display_name":"Chip","level":2,"score":0.4181230068206787},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36739906668663025},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07771238684654236},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2023.3325873","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3325873","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2936612942","display_name":null,"funder_award_id":"2021ZDLGY02-01","funder_id":"https://openalex.org/F4320336350","funder_display_name":"Key Research and Development Projects of Shaanxi Province"},{"id":"https://openalex.org/G5214772924","display_name":null,"funder_award_id":"XJSJ23054","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G8615985595","display_name":null,"funder_award_id":"62274123","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null},{"id":"https://openalex.org/F4320336350","display_name":"Key Research and Development Projects of Shaanxi Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1664710111","https://openalex.org/W1999818284","https://openalex.org/W2012496675","https://openalex.org/W2027320904","https://openalex.org/W2139577851","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2324566014","https://openalex.org/W2531409750","https://openalex.org/W2565639579","https://openalex.org/W2598457882","https://openalex.org/W2618530766","https://openalex.org/W2895572611","https://openalex.org/W2963351448","https://openalex.org/W2964267765","https://openalex.org/W2979135096","https://openalex.org/W2979906247","https://openalex.org/W2997355859","https://openalex.org/W3009435157","https://openalex.org/W3009886659","https://openalex.org/W3011208721","https://openalex.org/W3012245929","https://openalex.org/W3015335398","https://openalex.org/W3036700343","https://openalex.org/W3039229032","https://openalex.org/W3120940860","https://openalex.org/W3152567630","https://openalex.org/W3160412534","https://openalex.org/W3164791059","https://openalex.org/W3180954604","https://openalex.org/W3183048323","https://openalex.org/W3184164445","https://openalex.org/W3196435190","https://openalex.org/W3200673624","https://openalex.org/W4210374670","https://openalex.org/W4213235780","https://openalex.org/W4213350031","https://openalex.org/W4220892690","https://openalex.org/W4280568747","https://openalex.org/W4293075756","https://openalex.org/W6637373629","https://openalex.org/W6737664043","https://openalex.org/W6751733626","https://openalex.org/W6762718338","https://openalex.org/W6769229775"],"related_works":["https://openalex.org/W2180954594","https://openalex.org/W2052835778","https://openalex.org/W2049003611","https://openalex.org/W2127804977","https://openalex.org/W2108418243","https://openalex.org/W164103134","https://openalex.org/W2040545019","https://openalex.org/W4391590134","https://openalex.org/W2590770961","https://openalex.org/W3046039077"],"abstract_inverted_index":{"Neural":[0],"network-based":[1],"computer":[2],"vision":[3],"is":[4,68,133,136,165,198],"widely":[5],"used":[6],"in":[7,104],"industrial":[8,40,94],"image":[9,67,80,235],"detection":[10],"due":[11,70],"to":[12,26,71,138,147,171,206,210,214,238],"the":[13,27,46,57,72,76,83,88,149,173,180,185,190,203,208,240,246],"outstanding":[14],"performance":[15],"of":[16,30,39,75,100,107,144,151,175,184,202,252,256,261],"fast":[17],"and":[18,42,63,82,109,128,142,167,193,223,258],"accurate":[19],"defect":[20,54,267],"recognition,":[21],"which":[22],"can":[23],"be":[24],"applied":[25],"healthy":[28,61],"recognition":[29],"mini/micro":[31,51,92,229,264],"LED":[32,52,93,230,265],"chips.":[33],"However,":[34],"limited":[35,73,98],"by":[36,87],"optical":[37],"imaging":[38],"cameras":[41],"chip":[43,53,62,66,77,266],"physical":[44],"size,":[45],"following":[47],"challenges":[48],"exist":[49],"for":[50,263],"recognition:":[55],"1)":[56],"difference":[58],"between":[59],"a":[60,64,97,116,154,228,254],"defective":[65,101],"small":[69],"size":[74],"image,":[78],"low":[79],"resolution,":[81],"few":[84,155],"pixels":[85],"occupied":[86],"defect;":[89],"2)":[90],"standardized":[91],"manufacturing":[95],"produces":[96],"number":[99],"products,":[102],"resulting":[103],"an":[105,250,259],"imbalance":[106],"positive":[108,192],"negative":[110,194],"samples.":[111],"To":[112,187],"overcome":[113],"these":[114],"challenges,":[115],"dual":[117],"entropy-controlled":[118],"convolutional":[119],"neural":[120],"network":[121],"(DENC-CNN)":[122],"combining":[123],"feature":[124,140,162,181],"entropy":[125,131],"consistency":[126,143],"(FEC)":[127],"gradient":[129,204],"contribution":[130],"(GCE)":[132],"proposed.":[134],"FEC":[135,170],"proposed":[137,241,247],"improve":[139],"enrichment":[141],"information":[145],"transfer":[146],"enhance":[148],"learnable":[150],"samples,":[152,216],"with":[153,169,189],"fusion":[156,163],"parameters.":[157],"Global":[158],"attention":[159,213],"multi-scale":[160],"low-resolution":[161,177],"(GAMLF)":[164],"constructed":[166],"combined":[168],"retain":[172],"detail":[174],"multiscale":[176],"features,":[178],"enhancing":[179],"description":[182],"capability":[183],"model.":[186,242],"deal":[188],"inevitable":[191],"sample":[195],"imbalance,":[196],"GCE":[197],"designed":[199],"as":[200],"part":[201],"weighting":[205],"guide":[207],"model":[209],"pay":[211],"more":[212],"hard-to-classify":[215,221],"while":[217],"avoiding":[218],"over-focusing":[219],"on":[220,233],"samples":[222],"over-fitting.":[224],"We":[225],"also":[226],"construct":[227],"dataset":[231],"based":[232],"self-built":[234],"acquisition":[236],"system":[237],"validate":[239],"Experiments":[243],"show":[244],"that":[245],"DENC-CNN":[248],"achieves":[249],"accuracy":[251],"99.12%,":[253],"G-mean":[255],"97.86%":[257],"F1-score":[260],"97.87%":[262],"recognition.":[268]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
