@inproceedings{be65c91e90bb4a10ba75a5c23a1b91fd,
title = "Machine Learning and Big Data in optical CD metrology for process control",
abstract = "In this technical paper we explore the use of machine learning techniques to enable, andmake better, process control that uses optical CD metrology. We focus on showing how the combination of machine learning algorithms that, by their nature, enable automation, with a Big Data infrastructure, allows the automation of recipe creation, recipe monitoring, and recipe control and update. This automation is essential for semiconductor manufacturing, where process stability is of utmost importance and is, however, hard to achieve. We also discuss how this combination of machine-learning algorithms and a Big-Data system improves accuracy, throughput, tool matching and repeatability.",
keywords = "big data, machine learning, matching, optical metrology, process control, repeatability, sampling, throughput",
author = "Barak Bringoltz and Eitan Rothstein and Ilya Rubinovich and Kim, {Yong Ha} and Noam Tal and Oded Cohen and Shay Yogev and Ariel Broitman and Eylon Rabinovich and Tal Zaharoni",
note = "Publisher Copyright: {\textcopyright} 2018 Taiwan Semiconductor Industry Association.; 2018 e-Manufacturing and Design Collaboration Symposium, eMDC 2018 ; Conference date: 07-09-2018",
year = "2018",
month = oct,
day = "29",
language = "English",
series = "e-Manufacturing and Design Collaboration Symposium 2018, eMDC 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "e-Manufacturing and Design Collaboration Symposium 2018, eMDC 2018 - Proceedings",
address = "United States",
}
