Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning

Predicting unscheduled breakdowns of plasma etching equipment can reduce maintenance costs and production losses in the semiconductor industry. However, plasma etching is a complex procedure and it is hard to capture all relevant equipment properties and behaviors in a single physical model. Machine...

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Bibliographic Details
Published in:arXiv.org 2019-04
Main Authors: Jalali, Anahid, Heistracher, Clemens, Schindler, Alexander, Haslhofer, Bernhard, Nemeth, Tanja, Glawar, Robert, Sihn, Wilfried, De Boer, Peter
Format: Article
Language:English
Subjects:
Artificial intelligence
Data points
Decision making
Equipment costs
Etching equipment
Judgments
Machine learning
Maintenance costs
Maintenance management
Mathematical models
Mean time to failure
Plasma etching
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Summary:Predicting unscheduled breakdowns of plasma etching equipment can reduce maintenance costs and production losses in the semiconductor industry. However, plasma etching is a complex procedure and it is hard to capture all relevant equipment properties and behaviors in a single physical model. Machine learning offers an alternative for predicting upcoming machine failures based on relevant data points. In this paper, we describe three different machine learning tasks that can be used for that purpose: (i) predicting Time-To-Failure (TTF), (ii) predicting health state, and (iii) predicting TTF intervals of an equipment. Our results show that trained machine learning models can outperform benchmarks resembling human judgments in all three tasks. This suggests that machine learning offers a viable alternative to currently deployed plasma etching equipment maintenance strategies and decision making processes.
ISSN:2331-8422