Making the Best Choice for Defect Count Models

Least Squares Regression is often the go-to method for building statistical models. But should it be? Quality measures, like defect counts that are used as response variables, often violate assumptions that must be met for standard least squares regression models to be successful. What should we use instead?

In this session, we use the following steps to develop a useful statistical model to minimize defect counts in a chemical mechanical planarization (CMP) process that is used for material removal and planarization of wafers in semiconductor manufacturing. 

• Explore trade-offs of using standard least squares regression methodology that assumes a continuous response on defect count data.
• Develop and compare appropriate models of CMP defect count data in JMP and JMP Pro.
• Use the best model to find an optimal tool recipe that minimizes defect counts.

Suggested Prerequisites:

• Some experience evaluating process data.
• Basic familiarity with least squares regression models.
• Attending a relateds Jan. 24 session or viewing its video after the session: Designing Process Optimization Experiments Using Models Based on Production Data.