University of Illinois Urbana-Champaign

Machine learning surrogate modeling methods in inverse high-speed link design

Wang, Yuechen

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https://hdl.handle.net/2142/117692

Description

Title
Machine learning surrogate modeling methods in inverse high-speed link design
Author(s)
Wang, Yuechen
Issue Date
2022-12-09
Director of Research (if dissertation) or Advisor (if thesis)
Chen, Xu
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
  • Signal Integrity
  • Inverse Problem
  • Machine Learning
  • Neural Network
  • High-Speed Link Design
Abstract
This thesis implements and compares the performance of several Machine Learning surrogate modeling methods for the inverse high-speed channel design problem. A Tandem Neural Network structure and a User-Choice Inverse Neural Network structure are purposed and thoroughly described for the inverse optimization of high-speed link problems. Comparisons are made between the newly purposed methods and the traditional Machine Learning methods. There are discussions on inverse optimization with mixed continuous-discrete variables. Three high-speed link designs are constructed as examples to evaluate the performance of the Machine Learning methods. Different types of error calculation are displayed to better compare the prediction results of different Machine Learning methods.
Graduation Semester
2022-12
Type of Resource
Thesis
Copyright and License Information
Copyright 2022 Yuechen Wang

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