## 4 Case Study
### Measure Phase  
During the Define Phase, the key inputs and outputs (KPIVs & KPOVs) for defining the project have been completed. At the beginning of Measure phase, the team completes the C&E (Cause and  
Figure 4: The Supplier–Input–Process–Output-Customer (SIPOC) diagram of issue case’s processes. Effect) matrix, such as Table 6 and converts it to a Pareto chart, as shown in Figure 5, in which the coating parameters, photo resistor characteristics, and action analysis of operators are included. These are the main factors that influence the production variation. The determination of the cause of these failures is mainly through the component analysis of the defective samples, the observation of the on-site personnel operation, and the statistical analysis by the integrated department. Before the follow-up Analyze Phase, the project team asked the field staff to collect the actual production data to facilitate the process capability analysis. The team studies and assesses how risks are managed based on the various possibilities of risk analysis. The risks controlled by each process are analyzed by FMEA. It identifies the potential causes of contamination and the way of control during the processes. If the subsequent assessment has significant impact on the defect loss, it will be controlled in the project's end. After identifying the process risk, we must first confirm the accuracy of the inspection personnel, so we need to analyze the measurement system. Since miscalculation or omission will have an impact \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Importance Degree** & **1** & **3** & **8** & **9** & **9** & \\ \hline
**KPIVs** & **KPOVs** & **Poor Production** & **Poor** & **Fluid** & **Nozzle** & **Photo Resist** & **Total** \\  & **Environment** & **Cleanliness** & **Instability** & **Sticked** & **Stripping** & **Total** \\ \hline Coating parameters & 1 & 1 & 1 & 9 & 9 & 174 \\ Photo resist characteristic & 1 & 1 & 3 & 3 & 9 & 136 \\ Operating process & 1 & 1 & 3 & 9 & 1 & 118 \\ Coater material anomaly & 1 & 1 & 9 & 3 & 1 & 112 \\ The arm of Robot dust & 1 & 9 & 1 & 1 & 1 & 54 \\ CV dust & 1 & 9 & 1 & 1 & 1 & 54 \\ VCD dust & 1 & 9 & 1 & 1 & 1 & 54 \\ HEPA leakage & 9 & 1 & 1 & 1 & 1 & 38 \\ HP sublimation & 1 & 1 & 1 & 1 & 1 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: C&E matrix for dirt defect analysis. Figure 5: Pareto Chart (output from Minitab v14). on the yield of the product, and it should be identified first. We developed analytical plans as shown in Table 7, and the results are shown in Figure 6.  
Table 7 represents the measurement system analysis test planning with the inspector. Figure 6 is a magnified photo of the sample selected in the test plan under an electron microscope. The inspector tests these test samples. The test results of these samples are recorded in the table below. In the table below, the second row indicates the standard test results for each test sample. These words represent the visual inspection defect codes in the case factory. The field below A1 represents the first test result of inspector \"A\" for each test sample. The field below A2 represents the second test result performed by inspector \"A\" for each test sample. The field below B1 represents the first test performed by inspector \"B\" for each test sample. The field below B2 represents the second test result performed by inspector \"B\" for each test sample. The first inspection and the second inspection are separated by two weeks. We avoided inspectors who maintained stereotypes that affect the judgment of the test. We expect the inspectors to agree on the first and second inspection results. The inspectors, as well as the test standards, can have consistent test results. This represents the consistency of the measurement system. The project team analyzed the above research results in statistical software and obtained the analysis results in Figure 7. The analysis chart on the left represents the consistency of the inspector's results at each test.