## 4 Results  
The time analytics system has various types of input data. The event log of the equipment, one of the most commonly used data, provides accurate information and detailed time and provides approximately 200 pieces of information per wafer. Hence, the performance of the model was evaluated as a wafer transport delay classification with relatively little accuracy. Sensor signals only provide second-by-second resolution and on/off data. Performance was evaluated via three items (precision, recall, and accuracy) based on the confusion matrix (Equation (6)) (Table 1). Classification evaluation metrics using precision, recall, and accuracy are used to measure the performance of the classification by the machine learning model [18; 19]. The ratio of time-increased wafers is approximately 12% of the total wafers, and the ratio of time-increased wafers due to transportation delay is less than 1%. Since the number of targets is extremely small, we have limited the evaluation target to time-increased wafers. The trueness/falseness was determined by analyzing the equipment on the other side of the wafer transportation. The cross-validation test was performed with the 5-fold dataset. Table 2 shows the results of the experiment. The result showed more than 90% precision, 96% recall, and 99% accuracy. Fold 4 has a higher number of false positives and a lower precision than other folds. As a result of equipment analysis, we have confirmed that the sensor signal of this equipment is delayed. To verify the performance of cause classification, we have applied the system for three months to six EUVs currently being mass-produced. Table 3 shows the ratio of time-increased wafers for each piece of equipment and the classification ratio of each classification model. The proportion of time-increased wafers in the total wafers has been classified from 7.4% to 23.5%, and the cause classification has been classified at an average of 98.6%. Table 4 shows the number of occurrences for each cause classified by cause \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & & **Actual** & \\ \cline{3-8}  & & & & & **True** & & **False** \\ \hline \multirow{2}{*}{Predict} & & True & TP & FP & \\ \cline{2-8}  & & False & FN & TN & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: True positive (TP), false positive (FP), false negative (FN), and true negative (TN) are defined based on the confusion matrix. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Wafers** & **TP** & **FP** & **FN** & **TN** & **Precision (\%)** & **Recall (\%)** & **Accuracy (\%)** \\ \hline Fold 1 & 5120 & 452 & 22 & 11 & 4635 & 95.36 & 97.62 & 99.36 \\ \hline Fold 2 & 5034 & 320 & 18 & 9 & 4687 & 94.67 & 97.26 & 99.46 \\ \hline Fold 3 & 4825 & 351 & 12 & 8 & 4454 & 96.69 & 97.77 & 99.59 \\ \hline Fold 4 & 5244 & 414 & 42 & 10 & 4778 & 90.79 & 97.64 & 99.01 \\ \hline Fold 5 & 4151 & 337 & 16 & 13 & 3785 & 95.47 & 96.29 & 99.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cross-validation test result. classification. The distribution of classification provides a lot of information to engineers. First, cause 7 has a high number of occurrences in all equipment. It shows the possibility that it is a structural issue or an essential process. Second, most causes are equipment dependent. Each cause has a large number of occurrences in a specific piece of equipment. The distribution of causes shows both problems and solutions for each piece of equipment. Finally, the time analytics model not only defines the number of occurrences and the amount of time delay by cause in real-time, but also suggests the solution and amount of productivity improvement. Time-increased wafers (1.4%) that are not classified by the cause classification are classified by time classification. Time classification classifies time-increased wafers through normalizing and clustering processes. We found a system defect in the normalizing process during the experiment. Figure 4a shows an example of clustering without a normalizing process.