Development and Benchmarking of Multivariate Statistical Process Control Tools for a Semiconductor Etch Process: Improving Robustness through Model Updating

Abstract Multivariate Statistical Process Control tools have been developed for monitoring and fault detection on a Lam 9600 Metal Etcher. Application of these methods is complicated because the process data exhibits large amounts of normal variation that is continuous on some time scales and discontinuous on others. Variations due to faults can be minor in comparison. Several models based on principal components analysis and variants which incorporate methods for model updating have been tested for long term robustness and sensitivity to known faults. Model performance was assessed with about six month’s worth of process data and a set of benchmark fault detection problems.