6. Process or Product Monitoring and Control
6.6. Case Studies in Process Monitoring
6.6.1. Lithography Process

6.6.1.5.

Work This Example Yourself

View Dataplot Macro for this Case Study

This page allows you to repeat the analysis outlined in the case study description on the previous page using Dataplot . It is required that you have already downloaded and installed Dataplot and configured your browser. to run Dataplot. Output from each analysis step below will be displayed in one or more of the Dataplot windows. The four main windows are the Output Window, the Graphics window, the Command History window, and the data sheet window. Across the top of the main windows there are menus for executing Dataplot commands. Across the bottom is a command entry window where commands can be typed in.

Data Analysis Steps	Results and Conclusions
Click on the links below to start Dataplot and run this case study yourself. Each step may use results from previous steps, so please be patient. Wait until the software verifies that the current step is complete before clicking on the next step.	The links in this column will connect you with more detailed information about each analysis step from the case study description.
1. Invoke Dataplot and read data. 1. Read in the data.	1. You have read 5 columns of numbers into Dataplot, variables CASSETTE, WAFER, SITE, WIDTH, and RUNSEQ.
2. Plot of the response variable 1. Numerical summary of WIDTH. 2. 4-Plot of WIDTH. 3. Run sequence plot of WIDTH.	1. The summary shows the mean line width is 2.53 and the standard deviation of the line width is 0.69. 2. The 4-plot shows non-constant location and scale and moderate autocorrelation. 3. The run sequence plot shows non-constant location and scale.
3. Generate scatter and box plots against individual factors. 1. Scatter plot of WIDTH versus CASSETTE. 2. Box plot of WIDTH versus CASSETTE. 3. Scatter plot of WIDTH versus WAFER. 4. Box plot of WIDTH versus WAFER. 5. Scatter plot of WIDTH versus SITE. 6. Box plot of WIDTH versus SITE. 7. DOE mean plot of WIDTH versus CASSETTE, WAFER, and SITE. 8. DOE sd plot of WIDTH versus CASSETTE, WAFER, and SITE.	1. The scatter plot shows considerable variation in location. 2. The box plot shows considerable variation in location and scale and the prescence of some outliers. 3. The scatter plot shows minimal variation in location and scale. 4. The box plot shows minimal variation in location and scale. It also show some outliers. 5. The scatter plot shows some variation in location. 6. The box plot shows some variation in location. Scale seems relatively constant. Some outliers. 7. The DOE mean plot shows effects for CASSETTE and SITE, no effect for WAFER. 8. The DOE sd plot shows effects for CASSETTE and SITE, no effect for WAFER.
4. Subgroup analysis. 1. Generate a moving mean control chart. 2. Generate a moving range control chart. 3. Generate a mean control chart for WAFER. 4. Generate a sd control chart for WAFER. 5. Generate a mean control chart for CASSETTE. 6. Generate a sd control chart for CASSETTE. 7. Generate an analysis of variance. This is not currently implemented in DATAPLOT for nested datasets. 8. Generate a mean control chart using lot-to-lot variation.	1. The moving mean plot shows a large number of out-of- control points. 2. The moving range plot shows a large number of out-of- control points. 3. The mean control chart shows a large number of out-of- control points. 4. The sd control chart shows no out-of-control points. 5. The mean control chart shows a large number of out-of- control points. 6. The sd control chart shows no out-of-control points. 7. The analysis of variance and components of variance calculations show that cassette to cassette variation is 54% of the total and site to site variation is 36% of the total. 8. The mean control chart shows one point that is on the boundary of being out of control.