3. Production Process Characterization
3.4. Data Analysis for PPC
3.4.2. Exploring Relationships

3.4.2.2.

Exploring Main Effects

The next step is to look for main effects

The next step in the exploratory analysis of our data is to see which factors have an effect on which response variables and to quantify that effect. Scatter plots and box plots will be the tools of choice here.

Watch out for varying sample sizes across levels

This step is relatively self explanatory. However there are two points of caution. First, be cognizant of not only the trends in these graphs but also the amount of data represented in those trends. This is especially true for categorical explanatory variables. There may be many more observations in some levels of the categorical variable than in others. In any event, take unequal sample sizes into account when making inferences.

Graph implicit as well as explicit explanatory variables

The second point is to be sure to graph the responses against implicit explanatory variables (such as observation order) as well as the explicit explanatory variables. There may be interesting insights in these hidden explanatory variables.

Example: wafer processing

In the example below, we have collected data on the particles added to a wafer during a particular processing step. We ran a number of cassettes through the process and sampled wafers from certain slots in the cassette. We also kept track of which load lock the wafers passed through. This was done for two different process temperatures. We measured both small particles (< 2 microns) and large particles (> 2 microns). We plot the responses (particle counts) against each of the explanatory variables.

Cassette does not appear to be an important factor for small or large particles

This first graph is a box plot of the number of small particles added for each cassette type. The "X"'s in the plot represent the maximum, median, and minimum number of particles.

The second graph is a box plot of the number of large particles added for each cassette type.

We conclude from these two box plots that cassette does not appear to be an important factor for small or large particles.

There is a difference between slots for small particles, one slot is different for large particles

We next generate box plots of small and large particles for the slot variable. First, the box plot for small particles.

Next, the box plot for large particles.

We conclude that there is a difference between slots for small particles. We also conclude that one slot appears to be different for large particles.

Load lock may have a slight effect for small and large particles

We next generate box plots of small and large particles for the load lock variable. First, the box plot for small particles.

Next, the box plot for large particles.

We conclude that there may be a slight effect for load lock for small and large particles.

For small particles, temperature has a strong effect on both location and spread. For large particles, there may be a slight temperature effect but this may just be due to the outliers

We next generate box plots of small and large particles for the temperature variable. First, the box plot for small particles.

Next, the box plot for large particles.

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We conclude that temperature has a strong effect on both location and spread for small particles. We conclude that there might be a small temperature effect for large particles, but this may just be due to outliers.