High-speed signal integrity analysis and channel modeling using neural networks

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Konduru, Juhitha

Contributors dc:contributor

• Schutt-Ainé, José
• Bernhard, Jennifer
• Peng, Zhen
• Hanumolu, Pavan Kumar

• neural networks
• machine learning, channel modeling
• signal integrity
• high-speed channels
• electro-thermal analysis

Statement dc:rights

• Copyright 2025 Juhitha Konduru

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129618

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129618