A Predictive Maintenance System for Epitaxy Processes Based on Filtering and Prediction Techniques

Abstract

Silicon epitaxial deposition is a process strongly influenced by wafer temperature behavior, which has to be constantly monitored to avoid the production of defective wafers. However, temperature measurements are not reliable, and the sensors have to be appropriately calibrated with some dedicated procedure. A predictive maintenance (PdM) system is proposed with the aim of predicting process behavior and scheduling control actions on the sensors in advance. Two different prediction techniques have been employed and compared: the Kalman predictor and the particle filter with Gaussian kernel density estimator. The accuracy of the PdM module has been tested on real industrial production datasets.