## 4 Experiments and Discussion
### Results of Prediction Models and Discussion  
In the experiment, we proposed a DCNN prediction model for the recognition of wafer map failures. The performance of the proposed DCNN prediction model was compared with three popular ML-based algorithms--LR, RF, GBDT--and several well-known DL-based algorithms--VGGNet, ResNet, and EfficientNet. The batch size and epochs of the proposed DCNN and the other DL-based prediction models were designated as 96 and 25. The Adam optimizer was also used for the other predefined methods, because of its efficiency. We tested different learning rates for the VGG16-, VGG19-, ResNet50-, ResNet101-, and EfficientNetB0-based prediction models, and found that those five models could be trained accurately using the training and validation dataset when the learning rates were 0.0001, 0.0001, 0.000005, 0.00001, and 0.00001, respectively. The accuracy and loss learning curves of the proposed DCNN model, as well as the VGG16-, VGG19-, ResNet50  
Figure 7: The learning curves of the compared DL-based prediction models: (**a**) the accuracy learning curves of VGG16; (**b**) the loss learning curves of VGG16; (**c**) the accuracy learning curves of VGG19; (**d**) the loss learning curves of VGG19; (**e**) the accuracy learning curves of ResNet50; (**f**) the loss learning curves of ResNet50; (**g**) the accuracy learning curves of ResNet101; (**h**) the loss learning curves of ResNet101; (**i**) the accuracy learning curves of EfficientNetB0; (**j**) the loss learning curves of EfficientNetB0. Figure 6 shows the accuracy and loss learning curves of the proposed DCNN model during training and validation. In Figure 6a, it can be seen that the training accuracy of the proposed DCNN model dropped suddenly at the start of training, and then gradually increased over the next few training epochs, while the validation accuracy was monotonous during the first few epochs and then increased gradually. Figure 6b shows that the training and validation loss declined slowly across all epochs. Figure 7 shows the learning curves of the compared DL-based prediction models--VGG16, VGG19, ResNet50, ResNet101, and EfficientNetB0. In Figure 7a,b, it can be seen that the training and validation accuracy of the VGG16 model increased swiftly at first and then increased gradually over several training epochs, whereas the training and validation loss of the VGG16 model decreased rapidly at the beginning and then decreased gradually over the following epochs. The training and validation learning curves of the VGG19 model were very similar to those of the VGG16 model, as shown in Figure 7c-f, which illustrate the learning curves of ResNet50, where the training accuracy slowly increased over time, and the validation accuracy increased quickly during the initial stage and then steadily increased over a series of training epochs, while the training loss exhibited little change, and the validation loss decreased rapidly at the beginning and then gradually decreased over the following epochs. The training and validation learning curves of ResNet101 and EfficientNetB0 in Figure 7g-j are very similar to those of ResNet50, where it can be seen that the validation accuracy increased faster than the training accuracy, and the range of validation loss changes was higher than the training loss. However, the ResNet50-, ResNet101-, and EfficientNetB0-based prediction models demonstrated a high probability of overfitting compared to the VGG16- and VGG19-based prediction models, because those three models had much deeper structures with much higher training accuracy and lower training loss. Table 3 shows the results of the performance measures precision, recall, F1-score, and AUC of the proposed DCNN and the other compared models for all wafer failure pattern types--Center, Donut, Edge-Ring, Edge-Local, Local, Random, Scratch, and None.