@inproceedings{61715730984746bb8e6738a851fb8298,
title = "Semantic Autoencoder for Modeling BEOL and MOL Dielectric Lifetime Distributions",
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.",
keywords = "BEOL, MOL, TDDB, Time-dependent dielectric breakdown, lifetime distribution, machine learning",
author = "Weiman Yan and Ernest Wu and Schwing, {Alexander G.} and Elyse Rosenbaum",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 61st IEEE International Reliability Physics Symposium, IRPS 2023 ; Conference date: 26-03-2023 Through 30-03-2023",
year = "2023",
doi = "10.1109/IRPS48203.2023.10117878",
language = "English (US)",
series = "IEEE International Reliability Physics Symposium Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE International Reliability Physics Symposium, IRPS 2023 - Proceedings",
address = "United States",
}