Semantic autoencoder for modeling dielectric lifetime distributions
Yan, Weiman
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https://hdl.handle.net/2142/129156
Description
Title
Semantic autoencoder for modeling dielectric lifetime distributions
Author(s)
Yan, Weiman
Issue Date
2025-03-31
Director of Research (if dissertation) or Advisor (if thesis)
Rosenbaum, Elyse
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Machine Learning
Dielectric breakdown
BEOL
MOL
TDDB
Reliability
Abstract
This thesis presents a physics-based machine learning framework for modeling a dielectric lifetime distribution in the presence of manufacturing process variations. It uses a Semantic Autoencoder that provides insight into the dielectric thickness distribution and parameters of the underlying percolation model. Experiments show that the model is applicable to various types of dielectric films. The autoencoder may be configured to model intrinsic breakdown or to model breakdown resulting from competing failure mechanisms, e.g. intrinsic and extrinsic.
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