Prediction of integral type failures in semiconductor manufacturing through classification methods

• Published in: 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA) pp. 1 - 4
• Main Authors: Susto, Gian Antonio, McLoone, Sean, Pagano, Daniele, Schirru, Andrea, Pampuri, Simone, Beghi, Alessandro
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2013
• Subjects: Educational institutions; Ion implantation; Maintenance engineering; Manufacturing; Stress; Support vector machines; Tungsten
• DOI: 10.1109/ETFA.2013.6648127
• ISSN: 1946-0740

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset.