Using Unsupervised Machine Learning for Plasma Etching Endpoint Detection

Imen Chakroun, Thomas J. Ashby, Sayantan Das, Sandip Halder, Roel Wuyts, Wilfried Verachtert

2020

Abstract

Much has been discussed around the advent of Industry 4.0 tools to improve yield across front-end and backend semiconductor manufacturers. One of these tools is the etch endpoint detection (EPD) systems. It is essential to optimize the etch process by precisely landing on the underlying layers, because over-etching can cause underlying layer damage. In this work, we explore unsupervised machine learning for automatically identifying the endpoint during plasma etching of low open-area wafers using optical emission spectroscopy.

Paper Citation

in Harvard Style

Chakroun I., Ashby T., Das S., Halder S., Wuyts R. and Verachtert W. (2020). Using Unsupervised Machine Learning for Plasma Etching Endpoint Detection. In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-397-1, pages 273-279. DOI: 10.5220/0008877502730279

in Bibtex Style

@conference{icpram20,
author={Imen Chakroun and Thomas Ashby and Sayantan Das and Sandip Halder and Roel Wuyts and Wilfried Verachtert},
title={Using Unsupervised Machine Learning for Plasma Etching Endpoint Detection},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2020},
pages={273-279},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008877502730279},
isbn={978-989-758-397-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Using Unsupervised Machine Learning for Plasma Etching Endpoint Detection
SN - 978-989-758-397-1
AU - Chakroun I.
AU - Ashby T.
AU - Das S.
AU - Halder S.
AU - Wuyts R.
AU - Verachtert W.
PY - 2020
SP - 273
EP - 279
DO - 10.5220/0008877502730279