{"id":"https://openalex.org/W3035834061","doi":"https://doi.org/10.23919/date48585.2020.9116242","title":"Fast and Accurate High-Sigma Failure Rate Estimation through Extended Bayesian Optimized Importance Sampling","display_name":"Fast and Accurate High-Sigma Failure Rate Estimation through Extended Bayesian Optimized Importance Sampling","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3035834061","doi":"https://doi.org/10.23919/date48585.2020.9116242","mag":"3035834061"},"language":"en","primary_location":{"id":"doi:10.23919/date48585.2020.9116242","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date48585.2020.9116242","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE)","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/A5038362728","display_name":"Michael Hefenbrock","orcid":"https://orcid.org/0000-0002-7583-2376"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Michael Hefenbrock","raw_affiliation_strings":["Karlsruhe Institute of Technology, Karlsruhe, Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064900456","display_name":"Dennis D. Weller","orcid":"https://orcid.org/0000-0002-0251-4596"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Dennis D. Weller","raw_affiliation_strings":["Karlsruhe Institute of Technology, Karlsruhe, Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082553444","display_name":"Michael Beigl","orcid":"https://orcid.org/0000-0001-5009-2327"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Michael Beigl","raw_affiliation_strings":["Karlsruhe Institute of Technology, Karlsruhe, Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064445713","display_name":"Mehdi B. Tahoori","orcid":"https://orcid.org/0000-0002-8829-5610"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Mehdi B. Tahoori","raw_affiliation_strings":["Karlsruhe Institute of Technology, Karlsruhe, Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5038362728"],"corresponding_institution_ids":["https://openalex.org/I102335020"],"apc_list":null,"apc_paid":null,"fwci":0.4621,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.57723577,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"103","last_page":"108"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11032","display_name":"VLSI and Analog Circuit Testing","score":0.9975000023841858,"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":0.9975000023841858,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11798","display_name":"Optimal Experimental Design Methods","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.710385262966156},{"id":"https://openalex.org/keywords/failure-rate","display_name":"Failure rate","score":0.6736805438995361},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.6417219042778015},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.5939444303512573},{"id":"https://openalex.org/keywords/process-variation","display_name":"Process variation","score":0.5251150727272034},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5061752200126648},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5028857588768005},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.4933101236820221},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.49124616384506226},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.4646361172199249},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.411424458026886},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2885169982910156},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.1729152500629425},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16046211123466492},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.14304053783416748},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11826443672180176},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11745434999465942},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.10637375712394714}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.710385262966156},{"id":"https://openalex.org/C163164238","wikidata":"https://www.wikidata.org/wiki/Q2737027","display_name":"Failure rate","level":2,"score":0.6736805438995361},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.6417219042778015},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.5939444303512573},{"id":"https://openalex.org/C93389723","wikidata":"https://www.wikidata.org/wiki/Q7247313","display_name":"Process variation","level":3,"score":0.5251150727272034},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5061752200126648},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5028857588768005},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.4933101236820221},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.49124616384506226},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.4646361172199249},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.411424458026886},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2885169982910156},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.1729152500629425},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16046211123466492},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.14304053783416748},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11826443672180176},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11745434999465942},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.10637375712394714},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/date48585.2020.9116242","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date48585.2020.9116242","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W202805564","https://openalex.org/W1505220924","https://openalex.org/W1529817821","https://openalex.org/W1701825639","https://openalex.org/W2033246483","https://openalex.org/W2047446678","https://openalex.org/W2061144551","https://openalex.org/W2120353978","https://openalex.org/W2142910310","https://openalex.org/W2192203593","https://openalex.org/W2757344627","https://openalex.org/W2768460200","https://openalex.org/W2769791151","https://openalex.org/W2785885072","https://openalex.org/W2945537713","https://openalex.org/W2989851762","https://openalex.org/W3100083105","https://openalex.org/W3139804307","https://openalex.org/W4211049957","https://openalex.org/W4232939033","https://openalex.org/W6608206471","https://openalex.org/W6637653338","https://openalex.org/W6746080594"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W2146343568","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W2013643406","https://openalex.org/W2027972911","https://openalex.org/W2157978810","https://openalex.org/W2597809628","https://openalex.org/W3046370962"],"abstract_inverted_index":{"Due":[0],"to":[1,24,32,56,74,203],"the":[2,16,57,76,90,98,120,133,136,158,204],"aggressive":[3],"technology":[4],"downscaling,":[5],"process":[6],"variations":[7],"are":[8],"becoming":[9],"pre-dominent,":[10],"causing":[11],"performance":[12],"fluctuations":[13],"and":[14,36,148],"impacting":[15],"chip":[17],"yield.":[18],"Therefore,":[19],"individual":[20],"circuit":[21,63,134,143],"components":[22],"have":[23],"be":[25,47,95],"designed":[26],"with":[27,49],"very":[28],"small":[29],"failure":[30,43,79,87,91,137,179,198],"rates":[31,44],"guarantee":[33],"functional":[34],"correctness":[35],"robust":[37],"operation.":[38],"The":[39,139],"assessment":[40],"of":[41,60,125,132,141,157,169,186,195],"high-sigma":[42],"however":[45],"cannot":[46],"achieved":[48],"conventional":[50],"Monte":[51],"Carlo":[52],"(MC)":[53],"methods":[54,71],"due":[55],"huge":[58],"amount":[59,168],"required":[61],"time-consuming":[62],"simulations.":[64],"To":[65],"this":[66,107],"end,":[67],"Importance":[68],"Sampling":[69],"(IS)":[70],"were":[72],"proposed":[73],"solve":[75],"otherwise":[77],"intractable":[78],"rate":[80,92,180,199],"estimation":[81,149,200],"problem":[82],"by":[83,123,154],"focusing":[84],"on":[85,176],"high-probable":[86],"regions.":[88],"However,":[89],"could":[93],"largely":[94],"underestimated":[96],"while":[97],"computational":[99],"effort":[100],"for":[101],"deriving":[102],"them":[103],"is":[104,145,151],"high.":[105],"In":[106],"paper,":[108],"we":[109,172],"propose":[110],"an":[111,126],"eXtended":[112],"Bayesian":[113],"Optimized":[114],"IS":[115],"(XBOIS)":[116],"method,":[117],"which":[118],"addresses":[119],"aforementioned":[121],"shortcomings":[122],"deployment":[124],"accurate":[127],"surrogate":[128],"model":[129],"(e.g.":[130],"delay)":[131],"around":[135],"region.":[138],"number":[140],"costly":[142],"simulations":[144],"therefore":[146],"minimized":[147],"accuracy":[150,201],"substantially":[152],"improved":[153],"efficient":[155],"exploration":[156],"variation":[159],"space.":[160],"As":[161],"especially":[162],"memory":[163],"elements":[164],"occupy":[165],"a":[166,184,192],"large":[167],"on-chip":[170],"resources,":[171],"evaluate":[173],"our":[174],"approach":[175],"SRAM":[177],"cell":[178],"estimation.":[181],"Results":[182],"show":[183],"speedup":[185],"about":[187],"16x":[188],"as":[189,191],"well":[190],"two":[193],"orders":[194],"magnitude":[196],"higher":[197],"compared":[202],"best":[205],"state-of-the-art":[206],"techniques.":[207]},"counts_by_year":[{"year":2025,"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"}
