In the field of semiconductor manufacturing, various processes (such as etching) need to be integrated together. In mass production, multiple machines of the same type need to be used in the same process. Therefore, it is crucial to eliminate performance differences between machines and improve process capabilities and stability. The main goal of this project is to verify whether the new machine (chamber) matches the standard machine (chamber) and to evaluate the capability and stability of the current process. In the machine matching part, we first determine the appropriate subgroups and verify whether they belong to the normal distribution. After that, for normally distributed groups or non-normal groups whose sample means and standard deviations can be trusted, one-way analysis of variance is used to check the group mean variance. Then useJMP tools such as multiple comparisons and equivalence testing find out which machines are significantly different from the standard machine and how different they are. Additionally, regression models can be used to analyze correlations between responses and underlying factors. Through linear or polynomial fitting, we can find out the reasons for differences between machines. Alternatively, we can use a paired t- test (no normality required) to analyze pairwise differences and compare the results with an ANOVA model. In process capability and stability analysis, we first need to find alternative values for the sample mean and standard deviation of subpopulations that do not belong to the normal distribution. For example, we can often use robust mean and robust standard deviation to replace one-sided or two-sided outlier problems. Finally, we analyzed the stability of process capabilities through Ppk , Cpk and stability index. We also used target plots and process stability performance plots to help better visualize the results.
