  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.
