{"id":"https://openalex.org/W1588700481","doi":"https://doi.org/10.1109/vts.2015.7116280","title":"Improving accuracy of on-chip diagnosis via incremental learning","display_name":"Improving accuracy of on-chip diagnosis via incremental learning","publication_year":2015,"publication_date":"2015-04-01","ids":{"openalex":"https://openalex.org/W1588700481","doi":"https://doi.org/10.1109/vts.2015.7116280","mag":"1588700481"},"language":"en","primary_location":{"id":"doi:10.1109/vts.2015.7116280","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vts.2015.7116280","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 33rd VLSI Test Symposium (VTS)","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/A5031481481","display_name":"Xuanle Ren","orcid":"https://orcid.org/0000-0002-8272-1164"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xuanle Ren","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA","[Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, USA]"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"[Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, USA]","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086164979","display_name":"M.C. Martin","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mitchell Martin","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA","[Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, USA]"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"[Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, USA]","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111967389","display_name":"R.D. Blanton","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"R. D. Blanton","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, US","[Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, USA]"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, US","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"[Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, USA]","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5031481481"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":2.2609,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.87302875,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"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/T11032","display_name":"VLSI and Analog Circuit Testing","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T11032","display_name":"VLSI and Analog Circuit Testing","score":0.9998000264167786,"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"}},{"id":"https://openalex.org/T10363","display_name":"Low-power high-performance VLSI design","score":0.994700014591217,"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/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9927999973297119,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8156555891036987},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.657950222492218},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5876385569572449},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46976515650749207},{"id":"https://openalex.org/keywords/cache","display_name":"Cache","score":0.46949058771133423},{"id":"https://openalex.org/keywords/chip","display_name":"Chip","score":0.4657272696495056},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4194064140319824},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.41899868845939636},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41758018732070923},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.11423695087432861}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8156555891036987},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.657950222492218},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5876385569572449},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46976515650749207},{"id":"https://openalex.org/C115537543","wikidata":"https://www.wikidata.org/wiki/Q165596","display_name":"Cache","level":2,"score":0.46949058771133423},{"id":"https://openalex.org/C165005293","wikidata":"https://www.wikidata.org/wiki/Q1074500","display_name":"Chip","level":2,"score":0.4657272696495056},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4194064140319824},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.41899868845939636},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41758018732070923},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.11423695087432861},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vts.2015.7116280","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vts.2015.7116280","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 33rd VLSI Test Symposium (VTS)","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":31,"referenced_works":["https://openalex.org/W1971670074","https://openalex.org/W1974836518","https://openalex.org/W1990989733","https://openalex.org/W2011352087","https://openalex.org/W2015594753","https://openalex.org/W2025554908","https://openalex.org/W2033882786","https://openalex.org/W2035960619","https://openalex.org/W2059965612","https://openalex.org/W2061726056","https://openalex.org/W2081714388","https://openalex.org/W2083930772","https://openalex.org/W2098417235","https://openalex.org/W2103753221","https://openalex.org/W2108807072","https://openalex.org/W2125169487","https://openalex.org/W2141565132","https://openalex.org/W2142679274","https://openalex.org/W2151113616","https://openalex.org/W2160512933","https://openalex.org/W2161033118","https://openalex.org/W2162465831","https://openalex.org/W2164529645","https://openalex.org/W2169061104","https://openalex.org/W2281759336","https://openalex.org/W2321908408","https://openalex.org/W4285719527","https://openalex.org/W6676485797","https://openalex.org/W6680973776","https://openalex.org/W6695804766","https://openalex.org/W6700560842"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W2087343574","https://openalex.org/W4246352526","https://openalex.org/W2121910908"],"abstract_inverted_index":{"On-chip":[0],"test/diagnosis":[1],"is":[2,30,79],"proposed":[3],"to":[4,9,19,81,98],"be":[5,39,151],"an":[6,25,27],"effective":[7],"method":[8],"ensure":[10],"the":[11,21,57,63,83,90,100,105,123,137,141],"lifetime":[12],"reliability":[13],"of":[14,23,49,65,85,122],"integrated":[15,28],"systems.":[16],"In":[17],"order":[18],"manage":[20],"complexity":[22],"such":[24],"approach,":[26],"system":[29],"partitioned":[31],"into":[32],"multiple":[33],"modules":[34],"where":[35],"each":[36],"module":[37],"can":[38,107,150],"periodically":[40],"tested,":[41],"diagnosed":[42],"and":[43,52,126],"repaired":[44],"if":[45],"necessary.":[46],"The":[47],"limitation":[48],"on-chip":[50,86],"memory":[51],"computing":[53],"capability,":[54],"coupled":[55],"with":[56],"inherent":[58],"uncertainty":[59],"in":[60],"diagnosis,":[61],"causes":[62],"occurrence":[64],"misdiagnoses.":[66],"To":[67],"address":[68],"this":[69],"challenge,":[70],"a":[71],"novel":[72],"incremental-learning":[73],"algorithm,":[74],"namely":[75],"dynamic":[76],"k-nearest-neighbor":[77],"(DKNN),":[78],"developed":[80],"improve":[82],"accuracy":[84,149],"diagnosis.":[87],"Different":[88],"from":[89,140],"conventional":[91],"KNN,":[92],"DKNN":[93],"employs":[94],"online":[95,117],"diagnosis":[96,112,118],"data":[97,113,119],"update":[99],"learned":[101],"classifier":[102,106],"so":[103],"that":[104,147],"keep":[108],"evolving":[109],"as":[110],"new":[111],"becomes":[114],"available.":[115],"Incorporating":[116],"enables":[120],"tracking":[121],"fault":[124],"distribution":[125],"thus":[127],"improves":[128],"diagnostic":[129,148],"accuracy.":[130],"Experiments":[131],"using":[132],"various":[133],"benchmark":[134],"circuits":[135],"(e.g.,":[136],"cache":[138],"controller":[139],"OpenSPARC":[142],"T2":[143],"processor":[144],"design)":[145],"demonstrate":[146],"more":[152],"than":[153],"doubled.":[154]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}