{"id":"https://openalex.org/W4312754249","doi":"https://doi.org/10.1109/iscas48785.2022.9937457","title":"Transfer Learning for Reuse of Analog Circuit Sizing Models Across Technology Nodes","display_name":"Transfer Learning for Reuse of Analog Circuit Sizing Models Across Technology Nodes","publication_year":2022,"publication_date":"2022-05-28","ids":{"openalex":"https://openalex.org/W4312754249","doi":"https://doi.org/10.1109/iscas48785.2022.9937457"},"language":"en","primary_location":{"id":"doi:10.1109/iscas48785.2022.9937457","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas48785.2022.9937457","pdf_url":null,"source":{"id":"https://openalex.org/S4363604393","display_name":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","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":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","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/A5090272656","display_name":"Zhengfeng Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I72816309","display_name":"Drexel University","ror":"https://ror.org/04bdffz58","country_code":"US","type":"education","lineage":["https://openalex.org/I72816309"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhengfeng Wu","raw_affiliation_strings":["Drexel University,Department of Electrical and Computer Engineering,Philadelphia,PA","Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA"],"affiliations":[{"raw_affiliation_string":"Drexel University,Department of Electrical and Computer Engineering,Philadelphia,PA","institution_ids":["https://openalex.org/I72816309"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA","institution_ids":["https://openalex.org/I72816309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059641297","display_name":"Ioannis Savidis","orcid":"https://orcid.org/0000-0003-4230-1795"},"institutions":[{"id":"https://openalex.org/I72816309","display_name":"Drexel University","ror":"https://ror.org/04bdffz58","country_code":"US","type":"education","lineage":["https://openalex.org/I72816309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ioannis Savidis","raw_affiliation_strings":["Drexel University,Department of Electrical and Computer Engineering,Philadelphia,PA","Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA"],"affiliations":[{"raw_affiliation_string":"Drexel University,Department of Electrical and Computer Engineering,Philadelphia,PA","institution_ids":["https://openalex.org/I72816309"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA","institution_ids":["https://openalex.org/I72816309"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5090272656"],"corresponding_institution_ids":["https://openalex.org/I72816309"],"apc_list":null,"apc_paid":null,"fwci":4.5064,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.96777468,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1033","last_page":"1037"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10558","display_name":"Advancements in Semiconductor Devices and Circuit Design","score":0.9987000226974487,"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/T10558","display_name":"Advancements in Semiconductor Devices and Circuit Design","score":0.9987000226974487,"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T12676","display_name":"Machine Learning and ELM","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/computer-science","display_name":"Computer science","score":0.6831720471382141},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.6083661913871765},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5808866024017334},{"id":"https://openalex.org/keywords/sizing","display_name":"Sizing","score":0.5268646478652954},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.4888738989830017},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48411333560943604},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.466418594121933},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45709648728370667},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.4142824709415436},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3768894076347351},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16742849349975586}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6831720471382141},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.6083661913871765},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5808866024017334},{"id":"https://openalex.org/C2777767291","wikidata":"https://www.wikidata.org/wiki/Q1080291","display_name":"Sizing","level":2,"score":0.5268646478652954},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.4888738989830017},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48411333560943604},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.466418594121933},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45709648728370667},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.4142824709415436},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3768894076347351},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16742849349975586},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas48785.2022.9937457","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas48785.2022.9937457","pdf_url":null,"source":{"id":"https://openalex.org/S4363604393","display_name":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","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":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1815076433","https://openalex.org/W2067964949","https://openalex.org/W2074046446","https://openalex.org/W2165698076","https://openalex.org/W2885735575","https://openalex.org/W2887280559","https://openalex.org/W2963640828","https://openalex.org/W3036532492","https://openalex.org/W3092618035","https://openalex.org/W3108416903","https://openalex.org/W3200168084","https://openalex.org/W6638545294","https://openalex.org/W6753517993"],"related_works":["https://openalex.org/W4313168671","https://openalex.org/W2791025012","https://openalex.org/W4313443708","https://openalex.org/W2028462208","https://openalex.org/W4285337533","https://openalex.org/W2982831492","https://openalex.org/W2027071967","https://openalex.org/W2187490799","https://openalex.org/W1574942924","https://openalex.org/W3138055416"],"abstract_inverted_index":{"A":[0],"transfer":[1,51,78,177,254,262],"learning":[2,79,151,178,255,263],"technique":[3,80],"is":[4,81,224,299],"proposed":[5],"that":[6,99,228],"utilizes":[7],"models":[8,59],"trained":[9,37,108,233,248],"on":[10,24,38,95,109,186],"data":[11,39,71,237,251,298],"in":[12,29,72,191,256,302],"one":[13],"technology":[14,31,43,75],"node":[15,44],"to":[16,83,124,196,200],"predict":[17,125],"the":[18,25,41,53,57,63,73,84,101,126,130,144,147,150,164,172,180,187,205,212,219,229,245,265,269,272,277,280,293,303],"performance":[19,88,127,281],"of":[20,27,56,86,90,103,129,146,168,211,218,221,268,271,279,283,295],"a":[21,110,118,139,159,208,216,240,257,284],"circuit":[22,297],"based":[23,94],"sizing":[26,102],"transistors":[28,104],"another":[30],"node.":[32,76,306],"Specifically,":[33],"neural":[34,273],"networks":[35,274],"optimally":[36],"from":[40,117,138,171],"source":[42],"are":[45,60,66,122],"adopted":[46],"as":[47,198,215],"pre-trained":[48,58],"models.":[49,203],"During":[50,143],"training,":[52],"front":[54],"layers":[55,65,223],"frozen":[61,222],"while":[62],"remaining":[64],"re-trained":[67],"with":[68,234,249],"significantly":[69],"less":[70],"target":[74,304],"The":[77],"applied":[82],"prediction":[85,278],"seven":[87,96],"metrics":[89,128],"an":[91,154],"operational":[92],"amplifier":[93],"design":[97,115,136,290],"variables":[98],"include":[100],"and":[105,158],"capacitors.":[106],"Models":[107],"dataset":[111],"containing":[112],"1602":[113],"simulated":[114,135],"points":[116,137,170,238,252],"180":[119],"nm":[120,141,174,259],"process":[121,305],"transferred":[123,148,206,230],"op-amp":[131],"utilizing":[132],"only":[133,235],"100":[134,169,236],"65":[140,173,258],"process.":[142,260],"training":[145,166,201,270],"models,":[149,207],"curve":[152],"exhibits":[153],"improved":[155],"starting":[156],"point":[157],"lower":[160,241],"asymptotic":[161],"error.":[162],"Utilizing":[163],"same":[165],"set":[167,190],"process,":[175],"applying":[176],"reduces":[179],"normalized":[181],"mean":[182],"average":[183],"error":[184,214,243],"(MAE)":[185],"test":[188,213,242],"(inference)":[189],"all":[192],"cases":[193],"by":[194],"up":[195],"50%":[197],"compared":[199],"standalone":[202,246],"For":[204],"detailed":[209],"characterization":[210],"function":[217],"number":[220],"performed.":[225],"Results":[226],"indicate":[227],"gain":[231],"predictor":[232],"provides":[239,287],"than":[244],"model":[247],"1000":[250],"without":[253],"Therefore,":[261],"improves":[264],"sample":[266],"efficiency":[267],"used":[275],"for":[276,289],"parameters":[282],"circuit,":[285],"which":[286],"benefit":[288],"migration":[291],"when":[292],"collection":[294],"new":[296],"computationally":[300],"costly":[301]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
