{"id":"https://openalex.org/W3123177384","doi":"https://doi.org/10.1109/itc44778.2020.9325254","title":"Learning A Wafer Feature With One Training Sample","display_name":"Learning A Wafer Feature With One Training Sample","publication_year":2020,"publication_date":"2020-11-01","ids":{"openalex":"https://openalex.org/W3123177384","doi":"https://doi.org/10.1109/itc44778.2020.9325254","mag":"3123177384"},"language":"en","primary_location":{"id":"doi:10.1109/itc44778.2020.9325254","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itc44778.2020.9325254","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Test Conference (ITC)","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/A5057241064","display_name":"Yueling Jenny Zeng","orcid":null},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yueling Jenny Zeng","raw_affiliation_strings":["University of California, Santa Barbara, Santa Barbara, California"],"affiliations":[{"raw_affiliation_string":"University of California, Santa Barbara, Santa Barbara, California","institution_ids":["https://openalex.org/I154570441"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100369642","display_name":"Li-C. Wang","orcid":"https://orcid.org/0000-0003-4851-8004"},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li-C. Wang","raw_affiliation_strings":["University of California, Santa Barbara, Santa Barbara, California"],"affiliations":[{"raw_affiliation_string":"University of California, Santa Barbara, Santa Barbara, California","institution_ids":["https://openalex.org/I154570441"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028980757","display_name":"Chuanhe Jay Shan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chuanhe Jay Shan","raw_affiliation_strings":["IE3A, Inc., Los Angeles, California"],"affiliations":[{"raw_affiliation_string":"IE3A, Inc., Los Angeles, California","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078270682","display_name":"Nik Sumikawa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nik Sumikawa","raw_affiliation_strings":["NXP Semiconductor, Chandler, AZ"],"affiliations":[{"raw_affiliation_string":"NXP Semiconductor, Chandler, AZ","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5057241064"],"corresponding_institution_ids":["https://openalex.org/I154570441"],"apc_list":null,"apc_paid":null,"fwci":0.8916,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.80677278,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"1","issue":null,"first_page":"1","last_page":"10"},"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":0.9991999864578247,"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":0.9991999864578247,"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":0.9975000023841858,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9972000122070312,"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/autoencoder","display_name":"Autoencoder","score":0.8479176759719849},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.68712317943573},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6601152420043945},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6374104022979736},{"id":"https://openalex.org/keywords/wafer","display_name":"Wafer","score":0.6128879189491272},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5519789457321167},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5222098231315613},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.5102992057800293},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4830663502216339},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4484378397464752},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.4139987826347351},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35330140590667725},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.2813107371330261},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2116461992263794},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.07515040040016174}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8479176759719849},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.68712317943573},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6601152420043945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6374104022979736},{"id":"https://openalex.org/C160671074","wikidata":"https://www.wikidata.org/wiki/Q267131","display_name":"Wafer","level":2,"score":0.6128879189491272},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5519789457321167},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5222098231315613},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.5102992057800293},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4830663502216339},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4484378397464752},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.4139987826347351},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35330140590667725},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2813107371330261},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2116461992263794},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.07515040040016174},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itc44778.2020.9325254","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itc44778.2020.9325254","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Test Conference (ITC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5199999809265137,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1560724230","https://openalex.org/W1710476689","https://openalex.org/W1959608418","https://openalex.org/W2064675550","https://openalex.org/W2101234009","https://openalex.org/W2125865219","https://openalex.org/W2173520492","https://openalex.org/W2187089797","https://openalex.org/W2560512785","https://openalex.org/W2601669715","https://openalex.org/W2622563070","https://openalex.org/W2766736793","https://openalex.org/W2913975675","https://openalex.org/W2953046278","https://openalex.org/W2960592935","https://openalex.org/W2963135265","https://openalex.org/W2963223306","https://openalex.org/W2963373786","https://openalex.org/W2963684088","https://openalex.org/W2980442334","https://openalex.org/W3007683907","https://openalex.org/W3118608800","https://openalex.org/W3195149063","https://openalex.org/W4295177495","https://openalex.org/W4298289240","https://openalex.org/W6637568146","https://openalex.org/W6675354045","https://openalex.org/W6678814708","https://openalex.org/W6682610290","https://openalex.org/W6685352114","https://openalex.org/W6718379498"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2145836866","https://openalex.org/W2803255133","https://openalex.org/W2909431601"],"abstract_inverted_index":{"In":[0],"this":[1,49,73],"work,":[2],"we":[3],"consider":[4],"learning":[5,77],"a":[6,39,57],"wafer":[7,64],"plot":[8],"recognizer":[9],"where":[10],"only":[11],"one":[12],"training":[13,44],"sample":[14,45],"is":[15,46,54],"available.":[16],"We":[17],"introduce":[18],"an":[19,69],"approach":[20,36],"called":[21],"Manifestation":[22,41],"Learning":[23],"to":[24,37],"enable":[25],"the":[26,32,52,61,76],"learning.":[27],"The":[28,43],"underlying":[29],"technology":[30],"utilizes":[31],"Variational":[33],"AutoEncoder":[34],"(VAE)":[35],"construct":[38],"so-called":[40],"Space.":[42],"projected":[47],"into":[48],"space":[50],"and":[51,81],"recognition":[53],"achieved":[55],"through":[56],"pre-trained":[58],"model":[59],"in":[60],"space.":[62],"Using":[63],"probe":[65],"test":[66],"data":[67],"from":[68],"automotive":[70],"product":[71],"line,":[72],"paper":[74],"explains":[75],"approach,":[78],"its":[79],"feasibility":[80],"limitation.":[82]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}