@inproceedings{eaa05a3bc2204e68b98c358c2d82673d,
title = "Early Diagnosis and Prediction of Wafer Quality Using Machine Learning on sub-10nm Logic Technology",
abstract = "This paper proposes to use machine learning (ML) methods to predict wafer quality using Fab inline measured items, DC measurements, and DVS (Dynamic Voltage Stress) at wafer sort. With developed ML approach, the predicted accuracy is more than 80% in 8 nm products used in this study. We believe this method can be further fine-tuned to help enable ICs at the high level expected for automotive systems. By assigning predictive rankings, the method also helps enable best tooling system for higher quality.",
keywords = "Gradient Boosting, Machine Learning, Mice, Risk Prediction",
author = "Ko, {Heung Kook} and Sena Park and Jihyun Ryu and Kim, {Sung Ryul} and Giwon Lee and Dongjoon Lee and Sangwoo Pae and Euncheol Lee and Yongsun Ji and Hia Jiang and Jeong, {Tae Young} and Taiki Uemura and Dongkyun Kwon and Hyungrok Do and Hyungu Kahng and Cho, {Yoon Sang} and Jiyoon Lee and Kim, {Seoung Bum}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2020 IEEE International Reliability Physics Symposium, IRPS 2020 ; Conference date: 28-04-2020 Through 30-05-2020",
year = "2020",
month = apr,
doi = "10.1109/IRPS45951.2020.9128932",
language = "English",
series = "IEEE International Reliability Physics Symposium Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE International Reliability Physics Symposium, IRPS 2020 - Proceedings",
}