{"id":"https://openalex.org/W7154354561","doi":"https://doi.org/10.48550/arxiv.2604.11202","title":"CapBench: A Multi-PDK Dataset for Machine-Learning-Based Post-Layout Capacitance Extraction","display_name":"CapBench: A Multi-PDK Dataset for Machine-Learning-Based Post-Layout Capacitance Extraction","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154354561","doi":"https://doi.org/10.48550/arxiv.2604.11202"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.11202","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11202","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.11202","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032850232","display_name":"Hector R. Rodriguez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rodriguez, Hector R.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015867887","display_name":"Jiechen Huang","orcid":"https://orcid.org/0000-0002-9748-1829"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Jiechen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133561553","display_name":"Wenjian Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Wenjian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.37560001015663147,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.37560001015663147,"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/T10363","display_name":"Low-power high-performance VLSI design","score":0.12280000001192093,"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":0.0786999985575676,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6176999807357788},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5684000253677368},{"id":"https://openalex.org/keywords/capacitance","display_name":"Capacitance","score":0.5447999835014343},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.5009999871253967},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.49950000643730164},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4830000102519989},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.48019999265670776},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42500001192092896},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.40700000524520874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6771000027656555},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6176999807357788},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5684000253677368},{"id":"https://openalex.org/C30066665","wikidata":"https://www.wikidata.org/wiki/Q164399","display_name":"Capacitance","level":3,"score":0.5447999835014343},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.5009999871253967},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.49950000643730164},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4830000102519989},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.48019999265670776},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42500001192092896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4092999994754791},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.40700000524520874},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.36039999127388},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3601999878883362},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3531999886035919},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.3472999930381775},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.3409999907016754},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29989999532699585},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29339998960494995},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.2678999900817871},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C27458966","wikidata":"https://www.wikidata.org/wiki/Q1187693","display_name":"Control flow graph","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.11202","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11202","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.11202","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11202","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.6287694573402405}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,104],"present":[1],"CapBench,":[2],"a":[3,72,83,150],"fully":[4,28],"reproducible,":[5],"multi-PDK":[6],"dataset":[7,12,112,156],"for":[8,89],"capacitance":[9,66],"extraction.":[10],"The":[11],"is":[13,94],"derived":[14],"from":[15],"open-source":[16],"designs,":[17],"including":[18,118],"single-core":[19],"CPUs,":[20],"systems-on-chip,":[21],"and":[22,30,44,62,76,101,114,126,155],"media":[23],"accelerators.":[24],"All":[25],"designs":[26],"are":[27,68,139,157],"placed":[29],"routed":[31],"using":[32,70],"14":[33],"independent":[34],"OpenROAD":[35],"flow":[36],"runs":[37],"spanning":[38],"three":[39,55],"technology":[40],"nodes:":[41],"ASAP7,":[42],"NanGate45,":[43],"Sky130HD.":[45],"From":[46],"these":[47],"layouts,":[48],"we":[49],"extract":[50],"61,855":[51],"3D":[52],"windows":[53],"across":[54],"size":[56],"tiers":[57],"to":[58,141],"enable":[59],"transfer":[60],"learning":[61,108],"scalability":[63],"studies.":[64],"High-fidelity":[65],"labels":[67],"generated":[69],"RWCap,":[71],"state-of-the-art":[73],"random-walk":[74],"solver,":[75],"validated":[77],"against":[78],"the":[79,133],"industry-standard":[80],"Raphael,":[81],"achieving":[82],"mean":[84],"absolute":[85],"error":[86],"of":[87],"0.64%":[88],"total":[90],"capacitance.":[91],"Each":[92],"window":[93],"pre-processed":[95],"into":[96],"density":[97],"maps,":[98],"graph":[99,127],"representations,":[100],"point":[102,123],"clouds.":[103],"evaluate":[105],"10":[106],"machine":[107],"architectures":[109],"that":[110],"illustrate":[111],"usage":[113],"serve":[115],"as":[116],"baselines,":[117],"convolutional":[119],"neural":[120,128],"networks":[121,129],"(CNNs),":[122],"cloud":[124],"transformers,":[125],"(GNNs).":[130],"CNNs":[131],"demonstrate":[132],"lowest":[134],"errors":[135,147],"(1.75%),":[136],"while":[137],"GNNs":[138],"up":[140],"41.4x":[142],"faster":[143],"but":[144],"exhibit":[145],"larger":[146],"(10.2%),":[148],"illustrating":[149],"clear":[151],"accuracy-speed":[152],"trade-off.":[153],"Code":[154],"available":[158],"at":[159],"https://github.com/THU-numbda/CapBench.":[160]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-15T00:00:00"}
