{"id":"https://openalex.org/W4321064113","doi":"https://doi.org/10.1109/m2vip55626.2022.10041049","title":"Chip Surface Defect Recognition based on Improved Faster R-CNN","display_name":"Chip Surface Defect Recognition based on Improved Faster R-CNN","publication_year":2022,"publication_date":"2022-11-16","ids":{"openalex":"https://openalex.org/W4321064113","doi":"https://doi.org/10.1109/m2vip55626.2022.10041049"},"language":"en","primary_location":{"id":"doi:10.1109/m2vip55626.2022.10041049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/m2vip55626.2022.10041049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 28th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","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/A5054837333","display_name":"Chengxia Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I4210144214","display_name":"Jiangsu University of Technology","ror":"https://ror.org/04jabhf80","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210144214"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chengxia Ma","raw_affiliation_strings":["School of Mechanical Engineering, Jiangsu University of Technology,Changzhou,China","School of Mechanical Engineering, Jiangsu University of Technology, Changzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Jiangsu University of Technology,Changzhou,China","institution_ids":["https://openalex.org/I4210144214"]},{"raw_affiliation_string":"School of Mechanical Engineering, Jiangsu University of Technology, Changzhou, China","institution_ids":["https://openalex.org/I4210144214"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100610039","display_name":"Chao Yuan","orcid":"https://orcid.org/0000-0002-6931-0440"},"institutions":[{"id":"https://openalex.org/I4210144214","display_name":"Jiangsu University of Technology","ror":"https://ror.org/04jabhf80","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210144214"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Chao","raw_affiliation_strings":["School of Mechanical Engineering, Jiangsu University of Technology,Changzhou,China","School of Mechanical Engineering, Jiangsu University of Technology, Changzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Jiangsu University of Technology,Changzhou,China","institution_ids":["https://openalex.org/I4210144214"]},{"raw_affiliation_string":"School of Mechanical Engineering, Jiangsu University of Technology, Changzhou, China","institution_ids":["https://openalex.org/I4210144214"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100370140","display_name":"Junjie Zhu","orcid":"https://orcid.org/0000-0001-7503-4502"},"institutions":[{"id":"https://openalex.org/I4210144214","display_name":"Jiangsu University of Technology","ror":"https://ror.org/04jabhf80","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210144214"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjie Zhu","raw_affiliation_strings":["School of Mechanical Engineering, Jiangsu University of Technology,Changzhou,China","School of Mechanical Engineering, Jiangsu University of Technology, Changzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Jiangsu University of Technology,Changzhou,China","institution_ids":["https://openalex.org/I4210144214"]},{"raw_affiliation_string":"School of Mechanical Engineering, Jiangsu University of Technology, Changzhou, China","institution_ids":["https://openalex.org/I4210144214"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011791047","display_name":"Yaqian Wang","orcid":"https://orcid.org/0000-0001-5060-1347"},"institutions":[{"id":"https://openalex.org/I4210144214","display_name":"Jiangsu University of Technology","ror":"https://ror.org/04jabhf80","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210144214"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaqian Wang","raw_affiliation_strings":["School of Mechanical Engineering, Jiangsu University of Technology,Changzhou,China","School of Mechanical Engineering, Jiangsu University of Technology, Changzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Jiangsu University of Technology,Changzhou,China","institution_ids":["https://openalex.org/I4210144214"]},{"raw_affiliation_string":"School of Mechanical Engineering, Jiangsu University of Technology, Changzhou, China","institution_ids":["https://openalex.org/I4210144214"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100429035","display_name":"Wenhui Liu","orcid":"https://orcid.org/0009-0000-3940-908X"},"institutions":[{"id":"https://openalex.org/I4210144214","display_name":"Jiangsu University of Technology","ror":"https://ror.org/04jabhf80","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210144214"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhui Liu","raw_affiliation_strings":["School of Mechanical Engineering, Jiangsu University of Technology,Changzhou,China","School of Mechanical Engineering, Jiangsu University of Technology, Changzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Jiangsu University of Technology,Changzhou,China","institution_ids":["https://openalex.org/I4210144214"]},{"raw_affiliation_string":"School of Mechanical Engineering, Jiangsu University of Technology, Changzhou, China","institution_ids":["https://openalex.org/I4210144214"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005888416","display_name":"Zhenhua Han","orcid":null},"institutions":[{"id":"https://openalex.org/I4210144214","display_name":"Jiangsu University of Technology","ror":"https://ror.org/04jabhf80","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210144214"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenhua Han","raw_affiliation_strings":["School of Mechanical Engineering, Jiangsu University of Technology,Changzhou,China","School of Mechanical Engineering, Jiangsu University of Technology, Changzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Jiangsu University of Technology,Changzhou,China","institution_ids":["https://openalex.org/I4210144214"]},{"raw_affiliation_string":"School of Mechanical Engineering, Jiangsu University of Technology, Changzhou, China","institution_ids":["https://openalex.org/I4210144214"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5054837333"],"corresponding_institution_ids":["https://openalex.org/I4210144214"],"apc_list":null,"apc_paid":null,"fwci":0.5525,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.649575,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"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/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":1.0,"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/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/T12039","display_name":"Electron and X-Ray Spectroscopy Techniques","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/2508","display_name":"Surfaces, Coatings and Films"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8346191644668579},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7408946752548218},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6841309070587158},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.619042694568634},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6082268953323364},{"id":"https://openalex.org/keywords/chip","display_name":"Chip","score":0.5753505825996399},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5544306635856628},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5436453819274902},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.49185895919799805},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.4585227370262146},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.43631428480148315},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42444857954978943},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10576292872428894}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8346191644668579},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7408946752548218},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6841309070587158},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.619042694568634},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6082268953323364},{"id":"https://openalex.org/C165005293","wikidata":"https://www.wikidata.org/wiki/Q1074500","display_name":"Chip","level":2,"score":0.5753505825996399},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5544306635856628},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5436453819274902},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.49185895919799805},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.4585227370262146},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.43631428480148315},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42444857954978943},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10576292872428894},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/m2vip55626.2022.10041049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/m2vip55626.2022.10041049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 28th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2034856412","https://openalex.org/W2054831422","https://openalex.org/W2102605133","https://openalex.org/W2332733735","https://openalex.org/W2912069721","https://openalex.org/W2922520051","https://openalex.org/W3088039753","https://openalex.org/W4389905414","https://openalex.org/W6620707391"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4313906399","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"The":[0,118],"surface":[1,131,172],"defects":[2,37],"on":[3,38],"integrated":[4],"circuit":[5],"chips":[6],"are":[7,138],"small":[8],"and":[9,11,16,21,72,126,140,154,166],"slender,":[10],"there":[12],"exist":[13],"low":[14],"accuracy":[15,94,125,165],"difficulty":[17],"in":[18,55,115],"feature":[19],"extraction":[20],"classification":[22],"using":[23,134],"networks":[24],"models":[25],"for":[26],"defect":[27,132,173],"recognition.":[28,174],"Aiming":[29],"at":[30],"the":[31,59,82,85,92,96,98,104,109,123,129,135,146,155,164],"recognition":[32,93,133],"of":[33,36,67,84,95,128,169],"five":[34],"categories":[35],"chip":[39,130,171],"surfaces,":[40],"an":[41],"improved":[42,73],"Faster":[43,116,157],"R-CNN":[44,158],"(Faster":[45],"Region-based":[46],"Convolutional":[47,76],"Neural":[48],"Network)":[49,79],"deep":[50],"learning":[51],"network":[52,100,106,114],"is":[53,62,101],"proposed":[54,136],"this":[56],"paper.":[57],"Firstly,":[58],"sample":[60],"data":[61,70],"enhanced":[63],"by":[64],"a":[65],"combination":[66],"traditional":[68],"image":[69],"augmentation":[71],"DCGAN":[74],"(Deep":[75],"Generative":[77],"Adversarial":[78],"to":[80,90,107],"ensure":[81],"effectiveness":[83],"model":[86],"training":[87],"process.":[88],"Meanwhile,":[89],"improve":[91],"model,":[97],"ResNet50":[99],"introduced":[102],"into":[103],"backbone":[105],"replace":[108],"original":[110,156],"VGG16":[111],"(GG-Very-Deep-16":[112],"CNN)":[113],"R-CNN.":[117],"experimental":[119],"results":[120],"show":[121],"that":[122],"average":[124],"speed":[127,167],"method":[137],"94.92%":[139],"27":[141],"FPS,":[142],"respectively.":[143],"Compared":[144],"with":[145],"YOLO-v4":[147],"(You":[148],"Only":[149],"Look":[150],"Once":[151],"v4)":[152],"algorithm":[153],"algorithm,":[159],"it":[160],"can":[161],"better":[162],"meet":[163],"requirements":[168],"actual":[170]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-25T14:56:36.534964","created_date":"2025-10-10T00:00:00"}
