Feature extraction, condition monitoring, and fault modeling in semiconductor manufacturing systems
Reliable feature extraction, condition monitoring, and fault modeling are critical to understanding equipment degradation and implementing the proper maintenance decisions in manufacturing processes. Semiconductor manufacturing machines are highly sophisticated systems, consisting of multiple interacting components operating in highly variable operating conditions. This complicates performance monitoring since equipment condition must often be inferred through concurrent interpretation of multiple sensor readings originating from potentially very different subsystems of the tool. This paper presents an integrated approach to feature extraction, condition monitoring, and fault modeling applied to a set of standard built-in sensors on a modern 300-mm technology industrial Plasma Enhanced Chemical Vapor Deposition (PECVD) tool. Linear Discriminant Analysis was utilized to determine the set of dynamic features that are the most sensitive to different tool conditions brought about by chamber cleaning or various faults. Gaussian Mixture Models of the dynamic feature distributions were used to statistically quantify changes of these features as the condition of the tool changed. In addition, four highly detrimental faults were analyzed to demonstrate the fault modeling methodology. Data collected over 8 months from a PECVD tool being operated by a major microelectronics manufacturer was used to verify the methodology. Top sensitive features from various faults observed in this period were examined and physical connections to the chamber condition were interpreted through their behavior. (C) 2012 Elsevier B.V. All rights reserved.
condition monitoring linear discriminant analysis gaussian mixture model multivariate gaussian