{"id":"https://openalex.org/W3004759735","doi":"https://doi.org/10.1109/asicon47005.2019.8983666","title":"A Precise Block-Based Statistical Timing Analysis with MAX Approximation Using Multivariate Adaptive Regression Splines","display_name":"A Precise Block-Based Statistical Timing Analysis with MAX Approximation Using Multivariate Adaptive Regression Splines","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3004759735","doi":"https://doi.org/10.1109/asicon47005.2019.8983666","mag":"3004759735"},"language":"en","primary_location":{"id":"doi:10.1109/asicon47005.2019.8983666","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asicon47005.2019.8983666","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 13th International Conference on ASIC (ASICON)","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/A5050955798","display_name":"Leilei Jin","orcid":"https://orcid.org/0000-0002-0534-3593"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Leilei Jin","raw_affiliation_strings":["National ASIC System Engineering Technology Research Center"],"affiliations":[{"raw_affiliation_string":"National ASIC System Engineering Technology Research Center","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101741811","display_name":"Wenjie Fu","orcid":"https://orcid.org/0000-0002-2772-6712"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenjie Fu","raw_affiliation_strings":["National ASIC System Engineering Technology Research Center"],"affiliations":[{"raw_affiliation_string":"National ASIC System Engineering Technology Research Center","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017256460","display_name":"Yu Zheng","orcid":"https://orcid.org/0000-0002-0411-3868"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu Zheng","raw_affiliation_strings":["National ASIC System Engineering Technology Research Center"],"affiliations":[{"raw_affiliation_string":"National ASIC System Engineering Technology Research Center","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061478876","display_name":"Hao Yan","orcid":"https://orcid.org/0009-0003-0195-0446"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao Yan","raw_affiliation_strings":["National ASIC System Engineering Technology Research Center"],"affiliations":[{"raw_affiliation_string":"National ASIC System Engineering Technology Research Center","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5050955798"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.16229147,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10363","display_name":"Low-power high-performance VLSI design","score":0.9998999834060669,"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/T10363","display_name":"Low-power high-performance VLSI design","score":0.9998999834060669,"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/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.9998000264167786,"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/T11032","display_name":"VLSI and Analog Circuit Testing","score":0.9994999766349792,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multivariate-adaptive-regression-splines","display_name":"Multivariate adaptive regression splines","score":0.7072686553001404},{"id":"https://openalex.org/keywords/skew","display_name":"Skew","score":0.702349066734314},{"id":"https://openalex.org/keywords/standard-deviation","display_name":"Standard deviation","score":0.6343986392021179},{"id":"https://openalex.org/keywords/multivariate-normal-distribution","display_name":"Multivariate normal distribution","score":0.512925386428833},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4786854386329651},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46692726016044617},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4643118381500244},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4554637372493744},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.45326870679855347},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.44709086418151855},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4469645023345947},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4388246536254883},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4318305552005768},{"id":"https://openalex.org/keywords/mars-exploration-program","display_name":"Mars Exploration Program","score":0.4170806407928467},{"id":"https://openalex.org/keywords/polynomial-regression","display_name":"Polynomial regression","score":0.2075815498828888}],"concepts":[{"id":"https://openalex.org/C44882253","wikidata":"https://www.wikidata.org/wiki/Q3455882","display_name":"Multivariate adaptive regression splines","level":4,"score":0.7072686553001404},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.702349066734314},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.6343986392021179},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.512925386428833},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4786854386329651},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46692726016044617},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4643118381500244},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4554637372493744},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.45326870679855347},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.44709086418151855},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4469645023345947},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4388246536254883},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4318305552005768},{"id":"https://openalex.org/C83260615","wikidata":"https://www.wikidata.org/wiki/Q6773121","display_name":"Mars Exploration Program","level":2,"score":0.4170806407928467},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.2075815498828888},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"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/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/asicon47005.2019.8983666","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asicon47005.2019.8983666","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 13th International Conference on ASIC (ASICON)","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":7,"referenced_works":["https://openalex.org/W1966741973","https://openalex.org/W1967709031","https://openalex.org/W1977055509","https://openalex.org/W2102201073","https://openalex.org/W2158750015","https://openalex.org/W2344454022","https://openalex.org/W2914931491"],"related_works":["https://openalex.org/W4256152544","https://openalex.org/W10576317","https://openalex.org/W1481829876","https://openalex.org/W1991715599","https://openalex.org/W2055800560","https://openalex.org/W2519969434","https://openalex.org/W4290802965","https://openalex.org/W97789383","https://openalex.org/W4394644693","https://openalex.org/W4387909643"],"abstract_inverted_index":{"The":[0],"impact":[1,38],"of":[2,52,100],"process":[3,33],"variations":[4,34],"on":[5,69],"timing":[6,61,83],"has":[7],"become":[8],"significant":[9],"in":[10],"advanced":[11],"technology":[12],"nodes.":[13],"In":[14,41],"this":[15,37],"paper,":[16],"a":[17,64],"multivariate":[18],"adaptive":[19],"regression":[20],"splines":[21],"(MARS)":[22],"delay":[23,55,103],"model":[24],"is":[25],"proposed":[26],"that":[27],"considers":[28],"both":[29],"global":[30],"and":[31,96,107],"local":[32],"to":[35,43,63],"characterize":[36],"more":[39],"accurately.":[40],"order":[42],"obtain":[44],"MAX":[45,72],"operation":[46],"results,":[47],"the":[48,60,74,78,82,91,101],"first":[49],"three":[50],"moments":[51],"MARS":[53],"gate":[54],"distribution":[56,62],"are":[57,105],"calculated,":[58],"converting":[59],"skew-normal":[65],"representation.":[66],"Eventually,":[67],"based":[68],"an":[70],"approximation":[71],"operation,":[73],"block-based":[75],"SSTA":[76],"propagates":[77],"arrival":[79],"time":[80],"through":[81],"diagram.":[84],"Tested":[85],"with":[86],"10":[87],"ISCAS85":[88],"benchmark":[89],"circuits,":[90],"average":[92],"mean":[93],"squared":[94],"error":[95,99],"standard":[97],"deviation":[98],"path":[102],"calculation":[104],"0.52%":[106],"0.88%.":[108]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
