Outliers are often removed when modelling. However, detecting outliers is the essence of statistical process control. Understanding how they are created is an opportunity for quality improvement.  

Outliers are defined as "unusual" observations and require a convention, such as the three-sigma rule or another more subjective criterion. In this presentation, we demonstrate three approaches to modelling DOE in semiconductor process development, the goal of which was to understand the mechanism that generates outliers:

• The first approach takes experimental data, uses a rule to categorize observations as outliers, and then uses logistic/Poisson regression to model the rate they are generated.
• The second uses the Functional Data Explorer in JMP Pro to model the inverse empirical cumulative density function so one can see which combinations of factors cause or prevent outlier generation in a semiconductor manufacturing process.  
• The third approach uses the nonlinear platform to model the data with a t-distribution so one can see the outlier distribution, as well as detect shifts in the process mean.  

We discuss how the three approaches differ in terms of the quality of the information they supply and the difficulty of the analyses.