When magnetron sputtering deposition meets machine learning: Application to process anomaly detection.

This paper demonstrates how the application of machine learning techniques can be used in Magnetron Sputtering (MS) processes, to detect anomalies and reduce their failure rate. Magnetron Sputtering is a widely used technique in materials science and engineering to deposit thin films of various materials for a range of applications. However, the process is complex and can be prone to various anomalies that can lead to defects in the deposited films, resulting in a non-negligible waste of coated objects. In this paper, we focus on the use of machine learning algorithms for both online and offline anomaly detection, which can help identify and diagnose process anomalies in real-time or post-process. Our results demonstrate that machine learning techniques can be used to develop anomaly detection systems, to limit failure in magnetron sputtering processes. • Simple machine learning tools can be applied to magnetron sputtering processes to detect anomalies, offline or online. • This innovative approach can limit failure in magnetron sputtering processes • Although machine learning is not common for surface scientist, this approach is reliable and based on accessible algorithms • The algorithms assist the operator in the decision making without seeking to replace him. [ABSTRACT FROM AUTHOR]