Semantic Autoencoder for Modeling BEOL and MOL Dielectric Lifetime Distributions

Weiman Yan, Ernest Wu, Alexander G. Schwing, Elyse Rosenbaum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper 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 and that including time-zero leakage current as an input improves the model performance. The autoencoder may be configured to model intrinsic break-down or to model breakdown resulting from competing failure mechanisms, e.g. intrinsic and extrinsic.

Original languageEnglish (US)
Title of host publication2023 IEEE International Reliability Physics Symposium, IRPS 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665456722
DOIs
StatePublished - 2023
Externally publishedYes
Event61st IEEE International Reliability Physics Symposium, IRPS 2023 - Monterey, United States
Duration: Mar 26 2023Mar 30 2023

Publication series

NameIEEE International Reliability Physics Symposium Proceedings
Volume2023-March
ISSN (Print)1541-7026

Conference

Conference61st IEEE International Reliability Physics Symposium, IRPS 2023
Country/TerritoryUnited States
CityMonterey
Period3/26/233/30/23

Keywords

  • BEOL
  • MOL
  • TDDB
  • Time-dependent dielectric breakdown
  • lifetime distribution
  • machine learning

ASJC Scopus subject areas

  • General Engineering

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