A Scalable Deep Learning-Based Approach for Anomaly Detection in Semiconductor Manufacturing
Simone Tedesco, Gian Antonio Susto, Natalie Gentner, Andreas Kyek, Yao Yang · 2021
The diffusion of the Industry 4.0 paradigm lead to the creation and collection of huge manufacturing datasets; such datasets contain for example measurements coming from physical sensors located in different equipment or even in different productive manufacturing organizations. Such large and heterogeneous datasets represent a challenge when aiming for developing data-driven approaches like Anomaly Detection or Predictive Maintenance. In this work we present a new approach for performing Anomaly Detection that is able to handle heterogeneous data coming from different equipment, work centers or production sites. The proposed approach exploits Deep Learning architectures: Autoencoders are employed to derive a 'normal’ behaviour of the system under exam that is then used for comparison when monitoring new data. The main strength of the proposed approach is its generality and scalability: the effectiveness of the proposed approach is demonstrated through experiments performed on real world data extracted from different workcenters in semiconductor manufacturing facilities.