@inproceedings{095cbb6764c44609af56839f16919077,
title = "New Loss Function for Learning Dielectric Thickness Distributions and Generative Modeling of Breakdown Lifetime",
abstract = "This work introduces a new loss function for modeling dielectric lifetime distributions with thickness nonuniformity. It is applicable to both maximum likelihood estimation and to a previously introduced machine learning (ML) framework, providing better agreement between measurement data and generated distributions. The ML method is used to model die and wafer level lifetime distributions and thickness variation. An analytic expression relating dielectric thickness to leakage current or vice versa can be extracted.",
keywords = "lifetime distribution, machine learning, TDDB, Time-dependent dielectric breakdown",
author = "Weiman Yan and Ernest Wu and Elyse Rosenbaum",
note = "This work was supported in part by the National Science Foundation under Grant CNS 2137288 and the industry members of the Center for Advanced Electronics Through Machine Learning (CAEML). The authors acknowledge Ron Bolam for providing the MOL TDDB data. Thanks are extended to Prof. Alex Schwing for his useful suggestions.; 2025 IEEE International Reliability Physics Symposium, IRPS 2025 ; Conference date: 30-03-2025 Through 03-04-2025",
year = "2025",
doi = "10.1109/IRPS48204.2025.10983167",
language = "English (US)",
series = "IEEE International Reliability Physics Symposium Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 IEEE International Reliability Physics Symposium, IRPS 2025 - Proceedings",
address = "United States",
}