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

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.