Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning
This work addresses maintenance cost reduction for semiconductor manufacturers, but it is incremental as it applies existing machine learning methods to a new domain-specific problem.
The paper tackled the problem of predicting unscheduled breakdowns in plasma etching equipment to reduce maintenance costs and production losses in the semiconductor industry, and found that trained machine learning models outperformed benchmarks resembling human judgments in predicting time-to-failure, health state, and time-to-failure intervals.
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.