High-speed signal integrity analysis and channel modeling using neural networks
Konduru, Juhitha
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Permalink
https://hdl.handle.net/2142/129618
Description
Title
High-speed signal integrity analysis and channel modeling using neural networks
Author(s)
Konduru, Juhitha
Issue Date
2025-05-02
Director of Research (if dissertation) or Advisor (if thesis)
Schutt-Ainé, José
Doctoral Committee Chair(s)
Schutt-Ainé, José
Committee Member(s)
Bernhard, Jennifer
Peng, Zhen
Hanumolu, Pavan Kumar
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
neural networks
machine learning, channel modeling
signal integrity
high-speed channels
electro-thermal analysis
Abstract
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.
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