Planned Maintenance Schedule Update Method for Predictive Maintenance of Semiconductor Plasma Etcher

Abstract

In a semiconductor plasma etcher, it is becoming increasingly necessary to improve productivity by reducing unplanned equipment maintenance. Thus, predictive maintenance (PdM) is typically conducted using equipment data to predict the failure timing, after which proactive measures should be taken. In PdM, the planned maintenance schedule is updated on the basis of the predicted failure timing. However, in practice, the predicted failure timing has a probabilistic variability. Therefore, we propose a maintenance schedule update method based on the expected maintenance cost calculated from the probabilistic variability of the failure timing. We applied our method and conventional methods to a dataset of failure cases that model actual component failures of etchers and found that our method was effective in terms of reducing maintenance costs.