## 4 Experiments and Discussion
### Results of Prediction Models and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Method** & **Classifier** & **Measure** & **Center** & **Donat** & **Edge-Ring** & **Edge-Local** & **Local** & **Random** & **Scatch** & **None** & **Average** \\ \hline \multirow{8}{*}{ML-based Methods} & \multirow{4}{*}{LR} & Precision & 0.9573 & 0.0424 & 0.7643 & **0.9899** & 0.7228 & 0.9783 & 0.9456 & 0.7879 & 0.7876 \\  & & Recall & 0.9106 & 0.8605 & 0.8214 & 0.9703 & 0.7019 & **1** & 0.9121 & 0.8387 & 0.8747 \\  & & F1-score & 0.9313 & 0.8811 & 0.7897 & 0.98 & 0.7122 & 0.9899 & **0.9222** & 0.8125 & 0.8729 \\  & & AUC & 0.9972 & 0.9862 & 0.9774 & 0.9992 & 0.9642 & 0.9993 & 0.9957 & 0.9772 & 0.9672 \\ \cline{2-11}  & \multirow{4}{*}{RF} & Precision & 0.9512 & 0.9102 & 0.7799 & 0.9706 & 0.7307 & **0.9839** & 0.9316 & 0.9414 \\  & & Recall & 0.9512 & 0.8488 & 0.8036 & **0.9802** & 0.7308 & **1** & 0.8791 & 0.8495 & 0.9004 \\  & & F1-score & 0.9512 & 0.8743 & 0.7895 & **0.9754** & 0.717 & 0.9899 & 0.9195 & 0.8404 & 0.8998 \\  & & AUC & 0.9983 & 0.9871 & 0.9765 & **0.9997** & 0.9523 & **0.9999** & **0.9974** & 0.9846 & 0.9915 \\ \cline{2-11}  & \multirow{4}{*}{GBDT} & Precision & **0.9684** & 0.8899 & 0.7902 & 0.98 & 0.7054 & **0.899** & 0.9425 & 0.7961 & 0.8313 \\  & & Recall & 0.9503 & 0.8722 & 0.8123 & 0.7033 & 0.7946 & **1** & 0.8462 & 0.8817 & 0.8084 \\  & & F1-score & 0.9504 & 0.8623 & 0.8603 & 0.9751 & 0.7315 & **0.9445** & 0.9006 & 0.8367 & 0.8906 \\  & & AUC & **0.9988** & 0.9774 & 0.9752 & **0.9997** & 0.9866 & **0.9999** & 0.9999 & 0.987 & 0.9929 \\ \hline \multirow{8}{*}{MC-based Methods} & \multirow{4}{*}{VCG16} & Precision & 0.9608 & 0.9524 & 0.9706 & 0.8266 & 0.4999 & 0.9566 & 0.7864 & 0.978 & 0.9464 \\  & & F1-score & 0.9449 & 0.9412 & 0.9754 & 0.8018 & 0.9467 & 0.9503 & 0.8265 & 0.9564 & 0.9562 \\  & & AUC & 0.9982 & **0.9984** & 0.9996 & 0.9899 &However, the highest F1-score achieved by the proposed DCNN model for the 'Local' failure pattern type was 0.8081, which is not a satisfactory result when compared to the results obtained for the other wafer failure pattern types. A possible reason for this problem is that there was noise in the original image dataset, or that the experimental dataset was extracted by random sampling. The overall performance comparison results of different prediction models are shown in Table 4. As can be seen from the results, the performance of the proposed DCNN-based prediction model outperformed the other eight prediction models in terms of precision, recall, F1-score, AUC, and accuracy, with values of 0.9381, 0.9379, 0.9376, 0.9933, and 0.9375, respectively, while the ResNet50-based prediction model exhibited an equivalent performance in AUC of 0.9933. It can also be seen that the overall results for all prediction models mentioned in Table 4 decreased with respect to the performance of each class mentioned in Table 3. This may have been a result of the significantly lower performance of the 'Local' failure pattern type compared to the other failure types. Additionally, the results also showed that the DL-based techniques exhibited improved performances for the recognition of wafer map failure pattern types compared to the ML-based methods when comparing the average and overall performances of all ML- and DL-based prediction models. We also developed several DCNN-based models with fewer or much deeper layers, denoted as DCNN1, DCNN2, DCNN3, and DCNN4, which had very similar structures to the proposed method. To develop these models, the Adam optimizer was also used in the experiments due to its efficiency. We tested different learning rates for the DCNN1-, DCNN2-, DCNN3-, and DCNN4-based prediction models, and found that the optimal learning rate was 0.001 for all four models during training and validation. Figure 8 illustrates how the DCNN1-, DCNN2-, DCNN3-, and DCNN4-based prediction models performed in terms of accuracy and loss learning curves.