{"id":"https://openalex.org/W4415968992","doi":"https://doi.org/10.1109/iecon58223.2025.11221765","title":"Fine-Grained Region Perception Network for Few-Shot Defect Classification of IC Package Substrates: Benchmark Methodology and Dataset","display_name":"Fine-Grained Region Perception Network for Few-Shot Defect Classification of IC Package Substrates: Benchmark Methodology and Dataset","publication_year":2025,"publication_date":"2025-10-14","ids":{"openalex":"https://openalex.org/W4415968992","doi":"https://doi.org/10.1109/iecon58223.2025.11221765"},"language":null,"primary_location":{"id":"doi:10.1109/iecon58223.2025.11221765","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon58223.2025.11221765","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2025 \u2013 51st Annual Conference of the IEEE Industrial Electronics Society","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/A5100673440","display_name":"Haoyuan Li","orcid":"https://orcid.org/0009-0007-4678-9923"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoyuan Li","raw_affiliation_strings":["Northeastern University,Software College,Shenyang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeastern University,Software College,Shenyang,China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060088885","display_name":"Ruiyun Yu","orcid":"https://orcid.org/0000-0003-0523-6242"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruiyun Yu","raw_affiliation_strings":["Northeastern University,Software College,Shenyang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeastern University,Software College,Shenyang,China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101900881","display_name":"Bin Guo","orcid":"https://orcid.org/0000-0002-7749-725X"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bingyang Guo","raw_affiliation_strings":["Northeastern University,Software College,Shenyang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeastern University,Software College,Shenyang,China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048650159","display_name":"Zhengtao Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengtao Zhang","raw_affiliation_strings":["Institute of Automation,Chinese Academy of Sciences,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Automation,Chinese Academy of Sciences,Beijing,China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210094879"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.51301326,"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":"8"},"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":0.7049999833106995,"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":0.7049999833106995,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.14239999651908875,"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"}},{"id":"https://openalex.org/T12122","display_name":"Physical Unclonable Functions (PUFs) and Hardware Security","score":0.04340000078082085,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/benchmark","display_name":"Benchmark (surveying)","score":0.6355999708175659},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5997999906539917},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.5317999720573425},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5256999731063843},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4733999967575073},{"id":"https://openalex.org/keywords/integrated-circuit","display_name":"Integrated circuit","score":0.46299999952316284},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.42399999499320984}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6495000123977661},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6355999708175659},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5997999906539917},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.5317999720573425},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5256999731063843},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4733999967575073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4668000042438507},{"id":"https://openalex.org/C530198007","wikidata":"https://www.wikidata.org/wiki/Q80831","display_name":"Integrated circuit","level":2,"score":0.46299999952316284},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45260000228881836},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.42399999499320984},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.35670000314712524},{"id":"https://openalex.org/C138331895","wikidata":"https://www.wikidata.org/wiki/Q11650","display_name":"Electronics","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32359999418258667},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C134146338","wikidata":"https://www.wikidata.org/wiki/Q1815901","display_name":"Electronic circuit","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C199672914","wikidata":"https://www.wikidata.org/wiki/Q4241353","display_name":"Hot spot (computer programming)","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.2847000062465668},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.26019999384880066}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iecon58223.2025.11221765","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon58223.2025.11221765","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2025 \u2013 51st Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1977976515","https://openalex.org/W2119751209","https://openalex.org/W2884364435","https://openalex.org/W3034187513","https://openalex.org/W3034312118","https://openalex.org/W3035143213","https://openalex.org/W3035163205","https://openalex.org/W3096805028","https://openalex.org/W3108975329","https://openalex.org/W3176341011","https://openalex.org/W4214507219","https://openalex.org/W4214562728","https://openalex.org/W4223897252","https://openalex.org/W4226189252","https://openalex.org/W4226300953","https://openalex.org/W4292974152","https://openalex.org/W4309708317","https://openalex.org/W4312935555","https://openalex.org/W4315434857","https://openalex.org/W4377819788","https://openalex.org/W4380318427","https://openalex.org/W4382203447","https://openalex.org/W4382462216","https://openalex.org/W4382467570","https://openalex.org/W4386026523","https://openalex.org/W4386076270","https://openalex.org/W4386076678","https://openalex.org/W4390357076","https://openalex.org/W4390874276","https://openalex.org/W4396629482","https://openalex.org/W4399566317","https://openalex.org/W4411245027"],"related_works":[],"abstract_inverted_index":{"As":[0],"the":[1,4,19,24,29,54,59,90,103,110,122,132,170,176],"core":[2],"of":[3,23,39,56,82,87,112,134,172,178],"modern":[5],"electronics":[6],"industry,":[7],"integrated":[8,40],"circuits":[9,41],"(IC)":[10],"involve":[11],"highly":[12],"complex":[13],"design":[14,20],"and":[15,21,32,37,58,78,117,121,149,166,175],"manufacturing":[16],"processes,":[17],"with":[18,163],"fabrication":[22],"package":[25,42],"substrates":[26,43],"particularly":[27],"impacting":[28],"circuit\u2019s":[30],"performance":[31],"reliability.":[33],"Therefore,":[34],"defect":[35],"detection":[36],"classification":[38,81],"(ICPS)":[44],"are":[45],"crucial":[46],"in":[47,61,156],"IC":[48],"production.":[49],"Addressing":[50],"issues":[51],"such":[52],"as":[53],"scarcity":[55],"data":[57,62],"challenges":[60],"perception":[63,77,98],"for":[64,95],"ICPS,":[65],"we":[66,142],"propose":[67],"a":[68,144],"Fine-grained":[69],"Region":[70,105],"Perception":[71],"Network":[72],"(FRPNet)":[73],"to":[74,130,138,158],"achieve":[75],"multi-view":[76],"precise":[79],"few-shot":[80],"ICPS.":[83,151],"Specifically,":[84],"FRPNet":[85],"consists":[86],"three":[88],"modules:":[89],"Category-Perceptive":[91],"Interaction":[92],"Module,":[93,107,125],"responsible":[94],"feature":[96],"aggregation":[97],"during":[99],"class":[100],"simulation":[101],"changes;":[102],"Fine-Grained":[104],"Aggregation":[106],"which":[108,126],"observes":[109],"regions":[111],"interest":[113],"from":[114,136],"multiple":[115],"views":[116],"ensures":[118],"intra-class":[119],"connectivity;":[120],"Localization":[123],"Refinement":[124],"enhances":[127],"positional":[128],"information":[129],"ensure":[131],"stability":[133],"features":[135],"local":[137],"global":[139],"scales.":[140],"Additionally,":[141],"construct":[143],"CPS2D-FSC":[145,157],"dataset":[146],"comprising":[147],"single-layer":[148],"multi-layer":[150],"We":[152],"conducted":[153],"extensive":[154],"experiments":[155],"validate":[159],"FRPNet,":[160],"including":[161],"comparisons":[162],"SOTA":[164],"algorithms":[165],"ablation":[167],"studies,":[168],"demonstrating":[169],"superiority":[171],"our":[173],"algorithm":[174],"effectiveness":[177],"each":[179],"module.":[180]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-11-06T00:00:00"}
