Wafer-to-wafer process fault detection using data stream mining techniques

In this paper, we develop a wafer-to-wafer fault detection system using data stream mining techniques for a semiconductor etch tool. The system consists of two data stream mining modules: a trace segmentation module and a multivariate trace comparison module. Each time a wafer exits the processing chamber, the trace segmentation module extracts the traces of monitored tool parameters from raw sensor data streams. We propose a novel trace segmentation algorithm called multisensor-based trace segmentation. The algorithm finds the individual start and end times of monitored tool parameters in a wafer-to-wafer fashion. For analyzing faulty tool operations, the multivariate trace comparison module performs a new principal component analysis (PCA) called a trace structure-based PCA. For each tool parameter, the structural similarity distance between a template and the extracted trace is measured using a dynamic time warping algorithm. Then, the measurements are used to build the PCA model. This approach is contrasted with the traditional PCA procedure in which the trace means are used as the building blocks for the PCA model. Experiments using the data collected from a worksite reactive ion etch tool showed that the performance of the proposed system is very encouraging.