Many photomask customers require process capability reporting on a regular basis. One of their main purposes is to evaluate product quality based on process variability from period to period. The problem occurs when process improvement is based on a single reported index.
There are a variety of methods used today to calculate process capability, and any one method has proven insufficient (see figure below). Process 'A' looks good with respect to the average-range divided by a constant (R-bar/d<sub>2</sub>), but the standard deviation looks too wide. Conversely, Process 'B' looks good with respect to standard deviation, but its R-bar/d<sub>2</sub> is skewed too far. An explanation for this phenomenon is that the use of the standard deviation method to calculate capability (actually the performance index) ignores within-sample variation, and using the R-bar/d<sub>2</sub> method omits sample-to-sample variation. Therefore, both methods should be employed to better evaluate process performance and capability. This paper presents a means by which to employ the merits of both sigma estimators to achieve a better representation of continual improvement. Utilizing a method of pooling within and between-sample variation (Cvk) indicates that the two processes below actually exhibit almost identical capability.