Patent Document ID: 7953680
Application ID: 11953274

Base Claim:
1. A computer program product for excluding variations attributable to equipment used in a semiconductor wafer manufacturing process from split analysis procedures, the computer program product including a storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for facilitating a method including: identifying one or more dependent variables related to a split analysis performed using data from a split lot of semiconductor wafers manufactured by equipment the semiconductor wafer manufacturing process; performing a test to ascertain whether or not a variation attributable to the equipment used in the semiconductor wafer manufacturing process exists with respect to any of the one or more identified dependent variables; if a variation attributable to the equipment exists, constructing a target data set and a training data set, wherein the target data set includes the identified dependent variables, a plurality of numerical variables used to implement split definition variables in split analysis, and a plurality of equipment-related variables related to the equipment used in the semiconductor manufacturing process, and wherein the training data set includes any data set from which a statistical model may be constructed; identifying a signature for the variation attributable to the equipment used in the semiconductor manufacturing process; selecting a statistical model based upon the identified signature; constructing the selected statistical model using the training data set to generate a statistical output; joining the target data set with the statistical output; adjusting the identified dependent variables in the target data set using the statistical output; and providing a graphical user interface that visually displays an extent to which at least one of the equipment-related variables impacts the split analysis.

---

Claim 5:
5. The computer program product of claim 1 wherein the target data set is a subset of the training data set, or wherein the target data set is the training data set.