  The evolution of industry has enabled the integration of physical and digital
systems, facilitating the collection of extensive data on manufacturing
processes. This integration provides a reliable solution for improving process
quality and managing equipment health. However, data collected from real
manufacturing processes often exhibit challenging properties, such as severe
class imbalance, high rates of missing values, and noisy features, which hinder
effective machine learning implementation. In this study, a rare class
prediction approach is developed for in situ data collected from a smart
semiconductor manufacturing process. The primary objective is to build a model
that addresses issues of noise and class imbalance, enhancing class separation.
The developed approach demonstrated promising results compared to existing
literature, which would allow the prediction of new observations that could
give insights into future maintenance plans and production quality. The model
was evaluated using various performance metrics, with ROC curves showing an AUC
of 0.95, a precision of 0.66, and a recall of 0.96
