{"id":"https://openalex.org/W4416963414","doi":"https://doi.org/10.1109/islped65674.2025.11261767","title":"Development of a Physics-Informed Neural Network Model for Rapid Power Integrity Analysis in Die-Level and Die-Package Co-Design for 2.5-D Chiplet Solutions","display_name":"Development of a Physics-Informed Neural Network Model for Rapid Power Integrity Analysis in Die-Level and Die-Package Co-Design for 2.5-D Chiplet Solutions","publication_year":2025,"publication_date":"2025-08-06","ids":{"openalex":"https://openalex.org/W4416963414","doi":"https://doi.org/10.1109/islped65674.2025.11261767"},"language":null,"primary_location":{"id":"doi:10.1109/islped65674.2025.11261767","is_oa":false,"landing_page_url":"https://doi.org/10.1109/islped65674.2025.11261767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)","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/A5100330041","display_name":"Xi Chen","orcid":"https://orcid.org/0000-0003-3276-2918"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xi Chen","raw_affiliation_strings":["Northwestern University,Department of Electrical and Computer Engineering,Evanston,IL,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern University,Department of Electrical and Computer Engineering,Evanston,IL,USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041367243","display_name":"Yuhao Ju","orcid":"https://orcid.org/0000-0003-2509-400X"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuhao Ju","raw_affiliation_strings":["Northwestern University,Department of Electrical and Computer Engineering,Evanston,IL,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern University,Department of Electrical and Computer Engineering,Evanston,IL,USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005991726","display_name":"Jie Gu","orcid":"https://orcid.org/0000-0003-2912-7294"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jie Gu","raw_affiliation_strings":["Northwestern University,Department of Electrical and Computer Engineering,Evanston,IL,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern University,Department of Electrical and Computer Engineering,Evanston,IL,USA","institution_ids":["https://openalex.org/I111979921"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I111979921"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31891981,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T10363","display_name":"Low-power high-performance VLSI design","score":0.6747999787330627,"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":0.6747999787330627,"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/T11444","display_name":"Electromagnetic Compatibility and Noise Suppression","score":0.09269999712705612,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.054999999701976776,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5949000120162964},{"id":"https://openalex.org/keywords/transient","display_name":"Transient (computer programming)","score":0.47850000858306885},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.44339999556541443},{"id":"https://openalex.org/keywords/interconnection","display_name":"Interconnection","score":0.43689998984336853},{"id":"https://openalex.org/keywords/voltage-droop","display_name":"Voltage droop","score":0.43369999527931213},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.40779998898506165},{"id":"https://openalex.org/keywords/superposition-principle","display_name":"Superposition principle","score":0.38449999690055847},{"id":"https://openalex.org/keywords/power-integrity","display_name":"Power integrity","score":0.3774000108242035},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.37700000405311584}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6345000267028809},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5949000120162964},{"id":"https://openalex.org/C2780799671","wikidata":"https://www.wikidata.org/wiki/Q17087362","display_name":"Transient (computer programming)","level":2,"score":0.47850000858306885},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.44339999556541443},{"id":"https://openalex.org/C123745756","wikidata":"https://www.wikidata.org/wiki/Q1665949","display_name":"Interconnection","level":2,"score":0.43689998984336853},{"id":"https://openalex.org/C40760162","wikidata":"https://www.wikidata.org/wiki/Q10920295","display_name":"Voltage droop","level":4,"score":0.43369999527931213},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.4221000075340271},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.40779998898506165},{"id":"https://openalex.org/C27753989","wikidata":"https://www.wikidata.org/wiki/Q284885","display_name":"Superposition principle","level":2,"score":0.38449999690055847},{"id":"https://openalex.org/C2777561913","wikidata":"https://www.wikidata.org/wiki/Q19599527","display_name":"Power integrity","level":4,"score":0.3774000108242035},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.37700000405311584},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.3440000116825104},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.34150001406669617},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.33570000529289246},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.31310001015663147},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.3001999855041504},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29420000314712524},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C47949032","wikidata":"https://www.wikidata.org/wiki/Q663542","display_name":"Overcurrent","level":3,"score":0.28200000524520874},{"id":"https://openalex.org/C133833761","wikidata":"https://www.wikidata.org/wiki/Q16001847","display_name":"Admittance parameters","level":3,"score":0.2782000005245209},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.275299996137619},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.26570001244544983},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C32946077","wikidata":"https://www.wikidata.org/wiki/Q618079","display_name":"Network analysis","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/islped65674.2025.11261767","is_oa":false,"landing_page_url":"https://doi.org/10.1109/islped65674.2025.11261767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)","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/W2912768279","https://openalex.org/W2947238692","https://openalex.org/W2966470711","https://openalex.org/W3013164405","https://openalex.org/W3115187376","https://openalex.org/W3186706634","https://openalex.org/W4225162755","https://openalex.org/W4313332230","https://openalex.org/W4362587723","https://openalex.org/W4385192573","https://openalex.org/W4385212413","https://openalex.org/W4385495295","https://openalex.org/W4392739466","https://openalex.org/W4392745848","https://openalex.org/W4400033920","https://openalex.org/W4401880573","https://openalex.org/W4408489371"],"related_works":[],"abstract_inverted_index":{"This":[0],"work":[1,159],"presents":[2],"a":[3,63,75,145],"novel":[4],"power":[5,79],"distribution":[6],"network":[7,15],"(PDN)":[8],"analysis":[9,26,90],"using":[10],"the":[11,31,71,140],"emerging":[12],"physics-informed":[13],"neural":[14,170],"(PINNs)":[16],"model.":[17],"Different":[18],"from":[19,123],"conventional":[20,104,154],"solver-based":[21],"analysis,":[22],"PINN":[23],"allows":[24],"rapid":[25],"and":[27,48,62,91,99,113,120,125,135,147,160],"prediction":[28],"while":[29],"maintaining":[30],"physics":[32],"compliance":[33],"for":[34],"high-fidelity":[35],"analysis.":[36],"An":[37,58],"adaptive":[38],"multi-objective":[39],"training":[40,166],"strategy":[41],"is":[42,84],"introduced,":[43],"incorporating":[44],"an":[45],"interconnection":[46],"matrix":[47],"layer":[49],"labeling":[50],"to":[51,74,86,153,163],"accelerate":[52],"convergence":[53],"across":[54],"complex":[55],"PDN":[56,89],"structures.":[57],"embedded":[59],"workload":[60],"vector":[61],"transient":[64,115],"modulator":[65],"with":[66,103,132],"linear":[67],"superposition":[68],"method":[69,83,143],"extend":[70],"model\u2019s":[72],"applicability":[73],"wide":[76],"range":[77],"of":[78,165],"scenarios.":[80],"The":[81,106],"developed":[82],"applied":[85],"both":[87,109],"chip-level":[88],"Chiplet":[92],"2.5-D":[93,130],"chip-package":[94],"co-analysis,":[95],"showing":[96],"high":[97],"accuracy":[98],"fast":[100],"runtime":[101,151],"compared":[102,152],"methods.":[105],"approach":[107],"captures":[108],"steady-state":[110],"IR":[111,119],"droop":[112],"dynamic":[114],"supply":[116],"droop,":[117],"including":[118],"L\u2022di/dt":[121],"noise":[122],"package":[124],"on-die":[126],"PDN.":[127],"Experiments":[128],"on":[129],"Chiplets":[131],"RISC-V":[133],"processors":[134],"CNN":[136],"accelerators":[137],"show":[138],"that":[139],"proposed":[141],"PINN-based":[142],"achieves":[144],"299x":[146],"7x":[148],"reduction":[149],"in":[150],"EDA":[155],"tools":[156],"or":[157],"prior":[158],"saves":[161],"up":[162],"80%":[164],"data":[167],"than":[168],"traditional":[169],"networks":[171],"models.":[172]},"counts_by_year":[],"updated_date":"2025-12-04T23:47:47.292601","created_date":"2025-12-03T00:00:00"}