Equipment health modeling for deterioration prognosis and fault signatures diagnosis

摘要

Equipment condition monitoring for deterioration prognosis has drawn lots of attention in semiconductor manufacturing. Deterioration model represents healthy state of equipment and enables manufacturers to avoid equipment breakdown and non-essential maintenances. This research details an automated and intelligent approach to build a healthy state model for equipment deterioration monitoring. Wavelet packet decomposition (WPD) method is utilized first to extract energy-based features. A windowing Gaussian Mixture Model (GMM) then is used to track probability distribution function (PDF) of process to model the deterioration. The proposed model is validated through a real industrial case from a local semiconductor manufacturer.