{"id":"https://openalex.org/W4293025085","doi":"https://doi.org/10.1145/3489517.3530514","title":"An efficient yield optimization method for analog circuits via gaussian process classification and varying-sigma sampling","display_name":"An efficient yield optimization method for analog circuits via gaussian process classification and varying-sigma sampling","publication_year":2022,"publication_date":"2022-07-10","ids":{"openalex":"https://openalex.org/W4293025085","doi":"https://doi.org/10.1145/3489517.3530514"},"language":"en","primary_location":{"id":"doi:10.1145/3489517.3530514","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3489517.3530514","pdf_url":null,"source":{"id":"https://openalex.org/S4363608816","display_name":"Proceedings of the 59th ACM/IEEE Design Automation Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 59th ACM/IEEE 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/A5100382649","display_name":"Xiaodong Wang","orcid":"https://orcid.org/0000-0002-0555-8199"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaodong Wang","raw_affiliation_strings":["Fudan University, China"],"affiliations":[{"raw_affiliation_string":"Fudan University, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037328458","display_name":"Changhao Yan","orcid":"https://orcid.org/0000-0002-8936-3945"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changhao Yan","raw_affiliation_strings":["Fudan University, China"],"affiliations":[{"raw_affiliation_string":"Fudan University, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045464812","display_name":"Fan Yang","orcid":"https://orcid.org/0000-0003-2164-8175"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Yang","raw_affiliation_strings":["Fudan University, China"],"affiliations":[{"raw_affiliation_string":"Fudan University, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054960059","display_name":"Dian Zhou","orcid":"https://orcid.org/0000-0002-2648-5232"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dian Zhou","raw_affiliation_strings":["University of Texas at Dallas"],"affiliations":[{"raw_affiliation_string":"University of Texas at Dallas","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064213921","display_name":"Xuan Zeng","orcid":"https://orcid.org/0000-0002-8097-4053"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuan Zeng","raw_affiliation_strings":["Fudan University, China"],"affiliations":[{"raw_affiliation_string":"Fudan University, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100382649"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":1.5541,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.82920792,"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":"625","last_page":"630"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9998000264167786,"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/T11338","display_name":"Advancements in Photolithography Techniques","score":0.9986000061035156,"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/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/bayesian-optimization","display_name":"Bayesian optimization","score":0.7182944416999817},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6963405609130859},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5417805910110474},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5394284725189209},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5118557810783386},{"id":"https://openalex.org/keywords/sigma","display_name":"Sigma","score":0.5085471868515015},{"id":"https://openalex.org/keywords/analogue-electronics","display_name":"Analogue electronics","score":0.47694912552833557},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4526790976524353},{"id":"https://openalex.org/keywords/surrogate-model","display_name":"Surrogate model","score":0.43848344683647156},{"id":"https://openalex.org/keywords/yield","display_name":"Yield (engineering)","score":0.41137224435806274},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4087650775909424},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37131449580192566},{"id":"https://openalex.org/keywords/electronic-circuit","display_name":"Electronic circuit","score":0.36148735880851746},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35340166091918945},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3481833338737488},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33564937114715576},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13117104768753052},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.09305435419082642}],"concepts":[{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.7182944416999817},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6963405609130859},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5417805910110474},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5394284725189209},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5118557810783386},{"id":"https://openalex.org/C2778049214","wikidata":"https://www.wikidata.org/wiki/Q7512234","display_name":"Sigma","level":2,"score":0.5085471868515015},{"id":"https://openalex.org/C29074008","wikidata":"https://www.wikidata.org/wiki/Q174925","display_name":"Analogue electronics","level":3,"score":0.47694912552833557},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4526790976524353},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.43848344683647156},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.41137224435806274},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4087650775909424},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37131449580192566},{"id":"https://openalex.org/C134146338","wikidata":"https://www.wikidata.org/wiki/Q1815901","display_name":"Electronic circuit","level":2,"score":0.36148735880851746},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35340166091918945},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3481833338737488},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33564937114715576},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13117104768753052},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.09305435419082642},{"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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","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:10.1145/3489517.3530514","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3489517.3530514","pdf_url":null,"source":{"id":"https://openalex.org/S4363608816","display_name":"Proceedings of the 59th ACM/IEEE Design Automation Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 59th ACM/IEEE Design Automation Conference","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":17,"referenced_works":["https://openalex.org/W147475332","https://openalex.org/W1510052597","https://openalex.org/W1965520710","https://openalex.org/W1988698377","https://openalex.org/W2006440698","https://openalex.org/W2021819681","https://openalex.org/W2063686375","https://openalex.org/W2101197344","https://openalex.org/W2117261126","https://openalex.org/W2126639080","https://openalex.org/W2192203593","https://openalex.org/W2586938721","https://openalex.org/W2626010172","https://openalex.org/W2768460200","https://openalex.org/W3013336438","https://openalex.org/W3092014264","https://openalex.org/W4248670092"],"related_works":["https://openalex.org/W4380627621","https://openalex.org/W4292081304","https://openalex.org/W2950792054","https://openalex.org/W3104422856","https://openalex.org/W4206864338","https://openalex.org/W4287867179","https://openalex.org/W3134690064","https://openalex.org/W4210726438","https://openalex.org/W4287752080","https://openalex.org/W4237912051"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"an":[3],"efficient":[4],"yield":[5,24,40,82],"optimization":[6,64],"method":[7,46,94],"for":[8],"analog":[9],"circuits":[10],"via":[11],"Gaussian":[12,43],"process":[13,32,44],"classification":[14,45],"and":[15,60],"varying-sigma":[16],"sampling.":[17],"To":[18],"quickly":[19],"determine":[20],"the":[21,61,70,81,89,92],"better":[22],"design,":[23],"estimations":[25],"are":[26],"executed":[27],"at":[28,84],"varying":[29],"sigma":[30],"of":[31,35,54,102],"variations.":[33],"Instead":[34],"regression":[36],"methods":[37],"requiring":[38],"accurate":[39],"values,":[41],"a":[42,73],"is":[47,66,77],"applied":[48],"to":[49,68,79,97],"model":[50,76],"these":[51],"preference":[52],"information":[53],"designs":[55],"with":[56,88],"binary":[57],"comparison":[58],"results,":[59],"preferential":[62],"Bayesian":[63],"framework":[65],"implemented":[67],"guide":[69],"search.":[71],"Additionally,":[72],"multi-fidelity":[74],"surrogate":[75],"adopted":[78],"learn":[80],"correlation":[83],"different":[85],"sigmas.":[86],"Compared":[87],"state-of-the-art":[90],"methods,":[91],"proposed":[93],"achieves":[95],"up":[96],"12\u00d7":[98],"speed-up":[99],"without":[100],"loss":[101],"accuracy.":[103]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
