Categorical based feature modeling on a zero inflated performance measure

Abstract

ASML produces TwinScan NXT machines that are used for the production of microchips. The machines ensure that an accurate pattern of DUV-light passes a lens and that it is projected as accurate as possible on the wafer. To ensure that the focal point of the converged DUV-light falls exactly onto the wafer, the leveling functionality is of great importance. That is, placing the wafer in the correct depth of focus by rotating the wafer and moving the wafer up or down during exposure.
In order to meet the imaging requirements, the performance is investigated by analyzing errors of the machines at customers' site, considering one-year data. The most important errors are A, B, C and D. To reduce the total unscheduled down (USD) time of those errors, we should focus on reducing the mean USD time for errors A and C; and focus on reducing the frequency for errors B andD where these last two errors are likely to be solved together.
Different nominal customer-related variables are considered as possible causes of USD times such as location, system type or type of sensors. After applying hierarchical clustering and multidimensional scaling, the variable set is reduced. This set is used to model the USD time of one error: B. Significant differences in USD times are found, showed by the robust and distribution free rank tests: Wilcoxon and Kruskal-Wallis test.
To discover interesting patterns among variables, regression models are applied. The linear regression model and generalized linear model not seem to be the right model to the data. The zero adjusted exponential model seems to be the correct model and show that AG type, location and FSM flex package are the most important explanatory variables. This directs to a potential root cause where ASML is working further upon.