Multivariate fault detection and visualization in the semiconductor industry

The semiconductor industry provides vast opportunities for process monitoring and multivariate fault detection. Most of the multivariate methods currently used in the industry are statistically-based techniques. These methods are also extended to monitor batch processes such as the process tools used in semiconductor manufacturing. In this dissertation, the existing statistical fault detection methodologies are discussed and compared to non-parametric modeling techniques for multivariate outlier detection. Inspired by these non-parametric modeling techniques, a new k Nearest Neighbor (KNN) multivariate fault detection method is proposed to augment the existing statistical methods. In this technique, instead of pre-computing a model, only a window of historic reference data is retained. The fault detection performance metric used in this algorithm provides universal scaling and confidence limits for the overall metric value, the block contributions, and individual variable contributions. It also has the flexibility to be tuned for local or global sensitivity when multiple populations are present within the reference data. This new KNN method also is extended to monitor batch processes. Two applications of the KNN method are created by simply unfolding the batch data or by selecting only reference data similar in batch time for each individual trace sample. Both KNN batch methods are compared against other existing batch methods to detect induced faults using a plasma etch experiment. The trace sample method performs among the best of all investigated batch techniques. This dissertation also introduces additional methods for monitoring systems with multivariate models. A complete software architecture is presented for reporting and visualization of multivariate results. This method takes advantage of block and variable contributions to guide users to the process variables with the most extreme and most frequent excursions. This system is applied to monitor final wafer electrical test data. In addition, methods are presented which assist the monitoring of drifting processes. A simple technique to recursively adapt the centering and scaling coefficients of a principal component analysis (PCA) model is presented. Movement metrics are also introduced to monitor the changes in these coefficients over time. These movement metrics allow visibility into the process changes which caused the model to adapt.