{"id":"https://openalex.org/W2060639108","doi":"https://doi.org/10.1145/2463209.2488813","title":"Efficient moment estimation with extremely small sample size via bayesian inference for analog/mixed-signal validation","display_name":"Efficient moment estimation with extremely small sample size via bayesian inference for analog/mixed-signal validation","publication_year":2013,"publication_date":"2013-05-28","ids":{"openalex":"https://openalex.org/W2060639108","doi":"https://doi.org/10.1145/2463209.2488813","mag":"2060639108"},"language":"en","primary_location":{"id":"doi:10.1145/2463209.2488813","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2463209.2488813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 50th Annual Design Automation Conference","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/A5088326953","display_name":"Chenjie Gu","orcid":"https://orcid.org/0000-0002-1339-4534"},"institutions":[{"id":"https://openalex.org/I1343180700","display_name":"Intel (United States)","ror":"https://ror.org/01ek73717","country_code":"US","type":"company","lineage":["https://openalex.org/I1343180700"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chenjie Gu","raw_affiliation_strings":["Intel Strategic CAD Labs"],"affiliations":[{"raw_affiliation_string":"Intel Strategic CAD Labs","institution_ids":["https://openalex.org/I1343180700"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087685981","display_name":"Eli Chiprout","orcid":null},"institutions":[{"id":"https://openalex.org/I1343180700","display_name":"Intel (United States)","ror":"https://ror.org/01ek73717","country_code":"US","type":"company","lineage":["https://openalex.org/I1343180700"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eli Chiprout","raw_affiliation_strings":["Intel Strategic CAD Labs"],"affiliations":[{"raw_affiliation_string":"Intel Strategic CAD Labs","institution_ids":["https://openalex.org/I1343180700"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100353869","display_name":"Xin Li","orcid":"https://orcid.org/0000-0002-4510-2436"},"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":"Xin Li","raw_affiliation_strings":["Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088326953"],"corresponding_institution_ids":["https://openalex.org/I1343180700"],"apc_list":null,"apc_paid":null,"fwci":5.3578,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.96006344,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11032","display_name":"VLSI and Analog Circuit Testing","score":0.9980000257492065,"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/T11798","display_name":"Optimal Experimental Design Methods","score":0.9975000023841858,"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/estimator","display_name":"Estimator","score":0.7344877123832703},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.5904369950294495},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5531755089759827},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5421178936958313},{"id":"https://openalex.org/keywords/moment","display_name":"Moment (physics)","score":0.5099993944168091},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.4565832018852234},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4049481153488159},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.39441895484924316},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3317410349845886},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.07596331834793091}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7344877123832703},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.5904369950294495},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5531755089759827},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5421178936958313},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.5099993944168091},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.4565832018852234},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4049481153488159},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.39441895484924316},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3317410349845886},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.07596331834793091},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2463209.2488813","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2463209.2488813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 50th Annual Design Automation Conference","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.421.390","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.421.390","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://users.ece.cmu.edu/~xinli/papers/2013_DAC_moment.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1506806321","https://openalex.org/W1663973292","https://openalex.org/W1976041275","https://openalex.org/W1983837873","https://openalex.org/W2017361567","https://openalex.org/W2077788215","https://openalex.org/W2090692734","https://openalex.org/W2123695884","https://openalex.org/W2159160377","https://openalex.org/W2942228371","https://openalex.org/W3104887532"],"related_works":["https://openalex.org/W4287880334","https://openalex.org/W4366700029","https://openalex.org/W4285230481","https://openalex.org/W4385769873","https://openalex.org/W4281634296","https://openalex.org/W4319161863","https://openalex.org/W2371687270","https://openalex.org/W4206455007","https://openalex.org/W2112927832","https://openalex.org/W2966643660"],"abstract_inverted_index":{"A":[0],"critical":[1],"problem":[2],"in":[3],"pre-Silicon":[4],"and":[5,25,35,102,110,115,137,163,184],"post-Silicon":[6],"validation":[7,182],"of":[8,16,23,47,157,169,181],"analog/mixed-signal":[9],"circuits":[10],"is":[11,84,95],"to":[12,55,87,96,151,171,178],"estimate":[13],"the":[14,21,81,98,144,148],"distribution":[15,136],"circuit":[17,33,108],"performances,":[18],"from":[19],"which":[20,173],"probability":[22],"failure":[24],"parametric":[26],"yield":[27],"can":[28,112,174],"be":[29,113,175],"estimated":[30],"at":[31],"all":[32],"configurations":[34,109],"corners.":[36],"With":[37],"extremely":[38],"small":[39,76],"sample":[40,77],"size,":[41],"traditional":[42],"estimators":[43],"are":[44,116],"only":[45],"capable":[46],"achieving":[48],"a":[49,65,128,134,140,158],"very":[50],"low":[51],"confidence":[52],"level,":[53],"leading":[54],"either":[56],"over-validation":[57],"or":[58],"under-validation.":[59],"In":[60,79],"this":[61],"paper,":[62],"we":[63],"propose":[64],"multi-population":[66],"moment":[67,90],"estimation":[68,73,142],"method":[69,150],"that":[70,100],"significantly":[71],"improves":[72],"accuracy":[74],"under":[75,106],"size.":[78],"fact,":[80],"proposed":[82,149],"estimator":[83],"theoretically":[85],"guaranteed":[86],"outperform":[88],"usual":[89],"estimators.":[91],"The":[92],"key":[93],"idea":[94],"exploit":[97,120],"fact":[99],"simulation":[101],"measurement":[103],"data":[104],"collected":[105],"different":[107,124],"corners":[111],"correlated,":[114],"conditionally":[117],"independent.":[118],"We":[119,146],"such":[121],"correlation":[122],"among":[123],"populations":[125],"by":[126,132],"employing":[127],"Bayesian":[129],"framework,":[130],"i.e.,":[131],"learning":[133],"prior":[135],"applying":[138],"maximum":[139],"posteriori":[141],"using":[143],"prior.":[145],"apply":[147],"several":[152],"datasets":[153],"including":[154],"post-silicon":[155],"measurements":[156],"commercial":[159],"high-speed":[160],"I/O":[161],"link,":[162],"demonstrate":[164],"an":[165],"average":[166],"error":[167],"reduction":[168,180],"up":[170],"2x,":[172],"equivalently":[176],"translated":[177],"significant":[179],"time":[183],"cost.":[185]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":6},{"year":2013,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
