Background
Consider a manufacturing
process for an integrated circuit.* The
objective is to improve the process yield. The five factors that may
affect the process are:
It is too expensive to run a
full factorial design, which has 25
= 32 runs. Therefore, the engineers decide to run a half
fractional factorial design using generator E = ABCD.
Experiment
Design
The engineers use DOE++ to design a two level fractional factorial
design. The design-specific settings, the factor properties and the
response properties used are shown next.
The design matrix and the response data are given
in the "Fractional Factorial Design Model 1" Folio.
Analysis
Part I
Step 1: After
performing the experiment according to the design and recording
the results, the engineers enter the data set into the Standard Folio, as shown next.
[Click
to Enlarge]
Step 2: In the Select
Effects window, all effects up to order 2 are selected for
inclusion, as shown next.
Step 3: The data set is
analyzed with the default risk (significance) level of 0.1,
using individual terms.
Step 4: An Effect
Probability plot is
created, as shown next.
The Effect Probability plot shows
that effects A, B, C and AB are significant.
Analysis
Part II
The results for the reduced model are given in the
"Fractional Factorial Design Model 2" Folio.
Step 1: The design Folio is
duplicated and the copy is named "Fractional Factorial
Design Model 2."
Step 2: In the Select
Effects window, only the significant effects are selected to
calculate the new model, as shown next.
Step 3: The reduced
model is calculated. The coefficients for A, B, C and AB, found
in the Regression Information table in the Analysis tab, are:
Conclusions
As shown in the Regression Information table in the Analysis
tab, each of the effects in the reduced model has a positive
effect on the yield. Therefore, in order to increase the yield,
the high level setting of factors A, B, and C should be applied.
*
Montgomery, D. C. Design and Analysis of
Experiments, 5th edition, John Wiley & Sons, New York,
2001. | 
