{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129618"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129618","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"High-speed signal integrity analysis and channel modeling using neural networks","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","abstract_has_math":false,"creators":["Konduru, Juhitha"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Schutt-Ainé, José","Bernhard, Jennifer","Peng, Zhen","Hanumolu, Pavan Kumar"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-02","date_published":"2025-05-02","updated_at":"2026-07-22T22:25:05Z","subjects":["neural networks","machine learning, channel modeling","signal integrity","high-speed channels","electro-thermal analysis"],"languages":["en","eng"],"rights":["Copyright 2025 Juhitha Konduru"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129618","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Schutt-Ainé, José","Bernhard, Jennifer","Peng, Zhen","Hanumolu, Pavan Kumar"]},{"key":"dc:creator","label":"Author","values":["Konduru, Juhitha"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-05-02","2025-05"]},{"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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["neural networks","machine learning, channel modeling","signal integrity","high-speed channels","electro-thermal analysis"]}]},{"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 2025 Juhitha Konduru"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129618"]}]},{"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 2027-05-01","The student, Juhitha Konduru, accepted the attached license on 2025-05-01 at 05:45.","The student, Juhitha Konduru, submitted this Dissertation for approval on 2025-05-01 at 06:03.","This Dissertation was approved for publication on 2025-05-02 at 12:10.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22143 on 2025-10-19 at 19:16:56","The analysis of high-speed networks is often carried out using transistor-level simulation tools which have large computational time. This leads to a limitation in terms of the amount of time spent generating an optimal design and accurately analyzing the system. Therefore, there is a need for fast and accurate modeling of packages and boards, which is the key for developing high performance devices. With increasing complexity, thermal effects significantly impact the systems performance as well. Hence, the fast model should be able to perform electro-thermal co-simulations as well. By integrating thermal analysis with electrical simulations, we can optimize designs for efficiency without overheating issues. This thesis discusses a machine learning based approach using neural networks to generate a fast model, eliminating the need to run long simulations using EM solvers often. This helps in creating the optimal design faster without going through many iterations. An ML-based fast-learned model is obtained for a differential PTH. An effective way to generate datasets for training the ML model is discussed. The generated ML model shows a 200X improvement over HFSS while simulating a single design using the Inference model of the neural network. This thesis also discusses a method using machine learning to perform electro-thermal simulations. The proposed method shows a 220X speedup when compared to the two-way coupling process for electro-thermal simulations."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["High-speed signal integrity analysis and channel modeling using neural networks"]}]}],"canonical_facts":{"dc:contributor":["Schutt-Ainé, José","Bernhard, Jennifer","Peng, Zhen","Hanumolu, Pavan Kumar"],"dc:creator":["Konduru, Juhitha"],"dc:date":["2025-05-02","2025-05"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","The student, Juhitha Konduru, accepted the attached license on 2025-05-01 at 05:45.","The student, Juhitha Konduru, submitted this Dissertation for approval on 2025-05-01 at 06:03.","This Dissertation was approved for publication on 2025-05-02 at 12:10.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22143 on 2025-10-19 at 19:16:56","The analysis of high-speed networks is often carried out using transistor-level simulation tools which have large computational time. This leads to a limitation in terms of the amount of time spent generating an optimal design and accurately analyzing the system. Therefore, there is a need for fast and accurate modeling of packages and boards, which is the key for developing high performance devices. With increasing complexity, thermal effects significantly impact the systems performance as well. Hence, the fast model should be able to perform electro-thermal co-simulations as well. By integrating thermal analysis with electrical simulations, we can optimize designs for efficiency without overheating issues. This thesis discusses a machine learning based approach using neural networks to generate a fast model, eliminating the need to run long simulations using EM solvers often. This helps in creating the optimal design faster without going through many iterations. An ML-based fast-learned model is obtained for a differential PTH. An effective way to generate datasets for training the ML model is discussed. The generated ML model shows a 200X improvement over HFSS while simulating a single design using the Inference model of the neural network. This thesis also discusses a method using machine learning to perform electro-thermal simulations. The proposed method shows a 220X speedup when compared to the two-way coupling process for electro-thermal simulations."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129618"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Juhitha Konduru"],"dc:subject":["neural networks","machine learning, channel modeling","signal integrity","high-speed channels","electro-thermal analysis"],"dc:title":["High-speed signal integrity analysis and channel modeling using neural networks"],"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 Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}