Optimization of OC-SVM engine used for out-of-control detection in semiconductor industry

Abstract

Considering the importance of detecting anomalies as soon as they occur in the semiconductor industry, we propose in this paper to study the effectiveness of a robust machine learning classification technique, which is the One-Class Support Vector Machine (OC-SVM), used for out-of-control detection in production line. An optimization of the OC-SVM is proposed to improve its performance with a brief overview of the different methods used in this purpose. Numerical results are then presented based on industrial data provided by STMicroelectronics Crolles.