ICA Based Disturbance Specific Control Charts

This paper demonstrates a general method for simplifying multivariate process monitoring so as to allow the use of traditional SPC tools while facilitating process diagnosis. We pool data recycled from factorial design experiments to develop latent variable representations of complex processes which are directly identified with process steps or segments. Our method models disturbances in the process rather than the process itself. The methodology is illustrated on the problem of monitoring electrical test (E-Test) data from a semiconductor manufacturing process. Development and production data from a working semiconductor plant are used to estimate a factor model that is used to develop univariate control charts for particular types of process disturbances. Detection and false alarm rates for data with known disturbances are given. The charts correctly detect and classify all the disturbance cases with a very low false alarm rate.