{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/116242"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/116242","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Neural ordinary differential equation models for circuits","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_has_math":false,"creators":["Xiong, Jie"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Rosenbaum, Elyse","Raginsky, Maxim","Schutt-Ainé, José E.","Zhou, Jin"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08","date_published":"2022-08","updated_at":"2026-07-22T22:24:55Z","subjects":["Nonlinear circuit","behavioral modeling","neural ordinary differential equation","stability","circuit aging","process variation."],"languages":["en","eng"],"rights":["Copyright 2022 Jie Xiong"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/116242","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Rosenbaum, Elyse","Raginsky, Maxim","Schutt-Ainé, José E.","Zhou, Jin"]},{"key":"dc:creator","label":"Author","values":["Xiong, Jie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-08","2022-07-14"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Nonlinear circuit","behavioral modeling","neural ordinary differential equation","stability","circuit aging","process variation."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Jie Xiong"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/116242"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","The student, Jie Xiong, accepted the attached license on 2022-07-14 at 10:47.","The student, Jie Xiong, submitted this Dissertation for approval on 2022-07-14 at 10:59.","This Dissertation was approved for publication on 2022-07-14 at 16:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18305 on 2022-11-15 at 18:21:04","Behavioral models are widely used for circuit simulation; examples include I/O Buffer Information Specification (IBIS) models, gate timing models, and neural networks. They are often preferred by circuit designers over physics-based models because they obscure intellectual property and are time-efficient to be simulated. In prior works, the recurrent neural network (RNN), a class of neural networks which features state feedback connections, was used to model the dynamic behavior of several types of circuits. This work advances the prior work in three aspects. First, the usual approximation of a discrete-time RNN by a continuous-time Verilog-A model introduces numerical error, which may not be negligible, especially for stiff systems. In this work, neural ordinary differential equations (ODEs) are proposed for circuit modeling, which facilitates the direct learning of continuous-time models including continuous-time RNNs. Second, an aging-aware model framework is proposed to incorporate circuit aging effects. It captures the varying degradation under different aging profiles and is applicable to various types of neural ODEs. A similar two-part model structure is proposed for modeling of process variations. Third, the stability of certain neural ODEs is investigated. A practical input-to-state stability constraint is applied to the training of neural ODE models, with and without aging effects. Finally, the system-level stability is analyzed for neural ODEs connected to generic source(s) and load(s), which may or may not be neural ODEs. Small-gain conditions for different feedback connections are derived, which can be used to identify suitable use conditions for neural ODE models, or to constrain the model parameters."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Neural ordinary differential equation models for circuits"]}]}],"canonical_facts":{"dc:contributor":["Rosenbaum, Elyse","Raginsky, Maxim","Schutt-Ainé, José E.","Zhou, Jin"],"dc:creator":["Xiong, Jie"],"dc:date":["2022-08","2022-07-14"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","The student, Jie Xiong, accepted the attached license on 2022-07-14 at 10:47.","The student, Jie Xiong, submitted this Dissertation for approval on 2022-07-14 at 10:59.","This Dissertation was approved for publication on 2022-07-14 at 16:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18305 on 2022-11-15 at 18:21:04","Behavioral models are widely used for circuit simulation; examples include I/O Buffer Information Specification (IBIS) models, gate timing models, and neural networks. They are often preferred by circuit designers over physics-based models because they obscure intellectual property and are time-efficient to be simulated. In prior works, the recurrent neural network (RNN), a class of neural networks which features state feedback connections, was used to model the dynamic behavior of several types of circuits. This work advances the prior work in three aspects. First, the usual approximation of a discrete-time RNN by a continuous-time Verilog-A model introduces numerical error, which may not be negligible, especially for stiff systems. In this work, neural ordinary differential equations (ODEs) are proposed for circuit modeling, which facilitates the direct learning of continuous-time models including continuous-time RNNs. Second, an aging-aware model framework is proposed to incorporate circuit aging effects. It captures the varying degradation under different aging profiles and is applicable to various types of neural ODEs. A similar two-part model structure is proposed for modeling of process variations. Third, the stability of certain neural ODEs is investigated. A practical input-to-state stability constraint is applied to the training of neural ODE models, with and without aging effects. Finally, the system-level stability is analyzed for neural ODEs connected to generic source(s) and load(s), which may or may not be neural ODEs. Small-gain conditions for different feedback connections are derived, which can be used to identify suitable use conditions for neural ODE models, or to constrain the model parameters."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/116242"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Jie Xiong"],"dc:subject":["Nonlinear circuit","behavioral modeling","neural ordinary differential equation","stability","circuit aging","process variation."],"dc:title":["Neural ordinary differential equation models for circuits"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:55Z"}