how to use JMP to calculate Lot to Lot, Wafer to Wafer, Within wafer variation?

[#1]

My question is how to use JMP to calculate Lot to Lot, Wafer to Wafer, Within wafer variation and quantify the uniformity?

for example, I have one table, in this table have column "lot", column "wafer", column "site", also includes one data column.

within same lot have 15 wafers, within same wafer have 20 sites, so the question is how to use JMP to calculate Lot to Lot, Wafer to Wafer, Within wafer variation and quantify the uniformity?

thanks

[#2]

You might want to start with something simple like a variability chart...but there may be some spatial features to the data that are of interest...seems like there always is with wafer quality evaluation/quantification/visualization.

There have been many blogs, JMP Discovery Summit presentations, Add Ins, and other resources on this topic...a search through the JMP User Community should yield some useful resources. My former colleague @MikeD_Anderson  has forgotten more about semi conductor data visualization than I'll ever know. Might want to search on his name to find his contributions?

[#3]

You are describing a nested (or hierarchical) sampling plan.  The sampling tree might look something like this:

As @P_Bartell suggests, looking at the data with variability charts is a good first step.  I'm not sure what you mean by "uniformity"? To understand consistency you can use Range charts.  Start at the bottom of the sampling tree (the smallest rational subgroup).  If this is stable, roll up the tree and assess the variability at the next layer.

If you want to quantify the variance components (without understanding consistency), make sure the data types are nominal, Analyze>Quality and Process>Variability/Attribute Gauge, enter the response variable in the Y, Response box.  Enter the tree from top to bottom in the X, Grouping box.  Choose model type Nested.  This will produce a variability chart.  Select the red triangle next to the Variability Gauge and then select Variance Components.  This will give you ANOVA and Variance Components.