Note: See Complete Factorial Experiment in the SAS/QC Sample Library.

Yin and Jillie (1987) describe an experiment on a nitride etch process for a single-wafer plasma etcher. The experiment has four factors: cathode power (Power), gas flow (Flow), reactor chamber pressure (Pressure), and electrode gap (Gap). A single replicate of a design is run, and the etch rate (Rate) is measured. You can use the following statements to construct a 16-run design in the four factors:

proc factex;
   factors Power Flow Pressure Gap;
   output out=EtcherDesign
          Power    nvals=(0.80 1.20)
          Flow     nvals=(4.50 550)
          Pressure nvals=(125  200)
          Gap      nvals=(275  325);
run;

The design with the actual (decoded) factor levels is saved in the data set EtcherDesign. The experiment that uses the 16-run design is performed, and the etch rate is measured. The following DATA step updates the data set EtcherDesign with the values of Rate:

data EtcherDesign;
   set EtcherDesign;
   input Rate @@;
   datalines;
   550   669  604   650  633   642  601   635
   1037  749  1052  868  1075  860  1063  729
   ;

title 'Nitride Etch Process Experiment';
proc print;
run;

The data set DESGNDAT is listed in Output 7.18.1.

To perform an analysis of variance on the responses, you can use the GLM procedure, as follows:

proc glm data=EtcherDesign;
   class Power Flow Pressure Gap;
   model rate=Power|Flow|Pressure|Gap@2 / ss1;
run;

The factors are listed in both the CLASS and MODEL statements, and the response as a function of the factors is modeled by using the MODEL statement. The MODEL statement requests Type I sum of squares (SS1) and lists all effects that contain two or fewer factors. It is assumed that three-factor and higher interactions are not significant.

Part of the output from the GLM procedure is shown in Output 7.18.2. The main effect of the factors Power and Gap and the interaction between Power and Gap are significant (their p-values are less than 0.01).