Semiconductor etch process monitoring based on multi-stage MPCA

Point at the poor effects of MPCA method for faults monitoring in batch processes with multiple periods, this paper proposes a new multistage modeling method, first, according to the different number of the principal component on the each time slice to fuzzy on the process of division, then using k-means algorithm for precise division of sample data clustering, and finally according to the classification results, establish the typical statistical analysis model at each stage to monitor the whole process. The method for fault monitoring semiconductor etch process,, and are compared with the MPCA method proved that the method has a good monitoring performance and can accurately and timely monitoring the change caused by product quality failures.