{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120436"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120436","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Physics-driven parametric macromodeling for high-speed electronic circuits","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","abstract_has_math":false,"creators":["Page, Andrew Joseph"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Chen, Xu"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Inverse Design","Uncertainty Quantification"],"languages":["en","eng"],"rights":["Copyright 2023 Andrew Page"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120436","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chen, Xu"]},{"key":"dc:creator","label":"Author","values":["Page, Andrew Joseph"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-05-01"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Inverse Design","Uncertainty Quantification"]}]},{"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 2023 Andrew Page"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120436"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","The student, Andrew Page, accepted the attached license on 2023-04-28 at 08:29.","The student, Andrew Page, submitted this Thesis for approval on 2023-04-28 at 08:40.","This Thesis was approved for publication on 2023-05-01 at 16:46.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19232 on 2023-09-01 at 17:15:17","This thesis presents three projects, each demonstrating parametric macromodeling techniques driven by physics-based acceleration or accuracy improvements. These projects are supplemented with a literature review concerning fundamental material modeling, manufacturing procedures, and error tolerances. The parameterizations are applied to uncertainty quantification and design optimization procedures, steered toward engineering applications. The first project involves parameterizing the pulse response of a lossy dispersive channel using a sparse interpolation method fed by the finite difference time-domain electromagnetic simulation algorithm. A novel delay extraction routine is proposed to aid in the prediction accuracy of the surrogate model. Principal component analysis is used to compress the vast amounts of data used for this project to improve prediction efficiency. The literature review surveys the origins of process-borne manufacturing defects to incorporate design parameter uncertainty in a realistic sense. Such details are often either overlooked or under-emphasized in uncertainty quantification attempts, where precise definition of design space uncertainty is as important as an accurate parametric macromodeling scheme. Chemical etching effects, metal deposition including impurities and roughness, and dispersive dielectric material details are covered. The second project demonstrates a parameterization of the per-unit-length parameters of a multi-conductor channel. Modern effects such as surface roughness and etching edge profiles are included in the parameterization, in contrast with many existing studies. Various domain sizes in the design space are tested alongside complexity reduction leveraging the physical behaviors of various per-unit-length parameters. A numerical implementation of the Kramers-Kronig relations is developed and implemented to demonstrate the preservation of causality in the parametric macromodel. This parametric macromodel is used to calculate the statistics of the line parasitics, modal impedances, propagation characteristics, and channel scattering parameters for a PCIe 5.0 link with an uncertain cross-section and dispersive substrate . The third involves the forward modeling and inverse design of a test coupon launch structure used in the board measurement practice known as the “delta-L method.” An inverse model is trained to synthesize a launch design to exhibit a desired electrical performance and to be physically realizable. A forward model is constructed and used to evaluate the electrical performance of the designs synthesized by the inverse model during training. The training of this inverse model is treated as a convex optimization problem with constraints on the synthesized designs. These constraints inspire the development of a novel implementation of constraint loss by a two-sided everywhere-differentiable barrier function. The finished inverse model is applied to a swift multi-criteria design optimization. All computational work is performed on a workstation with an Intel®Core™i7- 8700 processor with 16GB of DDR4 RAM and an Nvidia Geforce GTX 1080 Ti graphics card."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Physics-driven parametric macromodeling for high-speed electronic circuits"]}]}],"canonical_facts":{"dc:contributor":["Chen, Xu"],"dc:creator":["Page, Andrew Joseph"],"dc:date":["2023-05","2023-05-01"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","The student, Andrew Page, accepted the attached license on 2023-04-28 at 08:29.","The student, Andrew Page, submitted this Thesis for approval on 2023-04-28 at 08:40.","This Thesis was approved for publication on 2023-05-01 at 16:46.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19232 on 2023-09-01 at 17:15:17","This thesis presents three projects, each demonstrating parametric macromodeling techniques driven by physics-based acceleration or accuracy improvements. These projects are supplemented with a literature review concerning fundamental material modeling, manufacturing procedures, and error tolerances. The parameterizations are applied to uncertainty quantification and design optimization procedures, steered toward engineering applications. The first project involves parameterizing the pulse response of a lossy dispersive channel using a sparse interpolation method fed by the finite difference time-domain electromagnetic simulation algorithm. A novel delay extraction routine is proposed to aid in the prediction accuracy of the surrogate model. Principal component analysis is used to compress the vast amounts of data used for this project to improve prediction efficiency. The literature review surveys the origins of process-borne manufacturing defects to incorporate design parameter uncertainty in a realistic sense. Such details are often either overlooked or under-emphasized in uncertainty quantification attempts, where precise definition of design space uncertainty is as important as an accurate parametric macromodeling scheme. Chemical etching effects, metal deposition including impurities and roughness, and dispersive dielectric material details are covered. The second project demonstrates a parameterization of the per-unit-length parameters of a multi-conductor channel. Modern effects such as surface roughness and etching edge profiles are included in the parameterization, in contrast with many existing studies. Various domain sizes in the design space are tested alongside complexity reduction leveraging the physical behaviors of various per-unit-length parameters. A numerical implementation of the Kramers-Kronig relations is developed and implemented to demonstrate the preservation of causality in the parametric macromodel. This parametric macromodel is used to calculate the statistics of the line parasitics, modal impedances, propagation characteristics, and channel scattering parameters for a PCIe 5.0 link with an uncertain cross-section and dispersive substrate . The third involves the forward modeling and inverse design of a test coupon launch structure used in the board measurement practice known as the “delta-L method.” An inverse model is trained to synthesize a launch design to exhibit a desired electrical performance and to be physically realizable. A forward model is constructed and used to evaluate the electrical performance of the designs synthesized by the inverse model during training. The training of this inverse model is treated as a convex optimization problem with constraints on the synthesized designs. These constraints inspire the development of a novel implementation of constraint loss by a two-sided everywhere-differentiable barrier function. The finished inverse model is applied to a swift multi-criteria design optimization. All computational work is performed on a workstation with an Intel®Core™i7- 8700 processor with 16GB of DDR4 RAM and an Nvidia Geforce GTX 1080 Ti graphics card."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120436"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Andrew Page"],"dc:subject":["Inverse Design","Uncertainty Quantification"],"dc:title":["Physics-driven parametric macromodeling for high-speed electronic circuits"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}