## 4 Experimental Results
### Performance Evaluation  
#### 4.3.1 Comparison of Original Data Classification Performance of Models Trained via Augmented Wafers  
Tables 6 and 7 show the classification performance of the WBM defect pattern classifier trained through the DCGAN-based model and the WBM defect pattern classifier trained through the CAE-based model, respectively. As can be seen in Tables 6 and 7, the difference in the classification accuracy for each defect pattern of the CNN classifier trained with augmented WBMs in the two augmented models varied from a minimum of 0.57% to a maximum of 10.81%. Furthermore, for all defect patterns, the performance of the CNN classifier trained using augmented data from the DCGAN-based model was higher than that of the classifier trained using augmented data from the CAE-based model. Figure 7: Results generated by each augmented model for the ‘donut’ defect pattern: (**a**) original wafer (‘donut’); (**b**) augmented wafers from the CAE-based model (\(\sigma\) = 0.2); (**c**) augmented wafers from the DCGAN-based model; (**d**) randomly generated defect patterns and their labels from the DCGAN-based models. In evaluating industrial data classifiers, distinguishing abnormal from normal is as important as the classification accuracy of each abnormality. The misclassification rate of all original WBMs was 1.41% lower in classifiers trained with DCGAN-based augmented wafers than in classifiers trained with CAE-based augmented wafers. In addition, the classification accuracy for each defect pattern was approximately 5.31% higher in the classifier trained with DCGAN-based augmented wafers than in the classifier trained with CAE-based augmented wafers. This showed that the WBMs generated by the DCGAN-based model had a probability distribution which was more similar to the original WBMs than the WBMs generated by the CAE-based model. 3.2 Comparison of the Performance of Different Augmented Data Classification Models Trained via Augmented Wafers  
Table 8 shows the classification accuracy for each defect pattern in the WBMs generated by the CAE-based augmentation model using the classifier trained with the WBMs generated by the DCGAN-based augmentation method. In the same way, Table 9 shows the classification accuracy for each defect pattern in the WBMs generated by the DCGAN-based augmentation model using the classifier trained with the WBMs generated by the CAE-based augmentation method. The experimental results showed that each defect \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Actual**Predicted** & **Center** & **Donut** & **Edge-Loc** & **Edge-Ring** & **Loc** & **Near-Full** & **Random** & **Scratch** & **None** \\ \hline Center & \begin{tabular}{c} 57 \\ (63.33\%) \\ \end{tabular} & 0 & 0 & 2 & 0 & 0 & 0 & 1 & \begin{tabular}{c} 30 \\ (33.33\%) \\ \end{tabular} \\ Donut & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\  & & & & & & & (0\%) \\ Edge-Loc & 1 & 0 & \begin{tabular}{c} 108 \\ (36.48\%) \\ \end{tabular} & 11 & 12 & 0 & 0 & 4 & \begin{tabular}{c} 160 \\ (54.05\%) \\ \end{tabular} \\ Edge-Ring & 0 & 0 & 0 & 20 & 0 & 0 & 0 & \begin{tabular}{c} 11 \\ (35.48\%) \\ \end{tabular} \\ Loc & 21 & 0 & 32 & 0 & 90 & 0 & 0 & \begin{tabular}{c} 50 \\ (35.02\%) \\ \end{tabular} \\ Near-Full & 0 & 0 & 0 & 0 & 0 & 16 & 0 & \begin{tabular}{c} 0 \\ (0\%) \\ \end{tabular} \\ Random & 2 & 1 & 23 & 0 & 9 & 1 & \begin{tabular}{c} 35 \\ (47.29\%) \\ \end{tabular} & 3 & 0 \\ Scratch & 0 & 0 & 0 & 0 & 1 & 0 & \
\begin{tabular}{c} 21 \\ (29.16\%) \\ \end{tabular} & 50 \\ None & 7 & 0 & 3 & 0 & 20 & 0 & 0 & 44 &
\begin{tabular}{c} 13,415 \\ (99.45\%) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix of classifier trained with augmented data from DCGAN-based model.