## 6 Results and Discussion
### Article by Article Discussion  
As shown in Table 2, three different methods have been used to carry out the task of detecting and classifying defects in one of the articles [23]. The article has two main objectives. The first is to implement a CNN to perform the detection and classification of defects on the surface of the wafers, and the second is to detect defects of unknown class. Two different datasets are used. One of them, consisting of \(2123\)\(160\times 160\) images, after being augmented to \(6369\) images by means of flipping and rotating the original images, is used for training and testing. The other is composed of \(30\) unknown defect images. To achieve the first objective of the paper, a CNN designed by the authors themselves is implemented. It contains four convolutional layers, two max pooling layers, one FC layer and ReLU as its activation function. This CNN, which achieves a test accuracy of \(0.962\), is compared with a classical ML method. Specifically, it is an SVM with the radial base function, which achieves a test accuracy of \(0.925\). Thus, we can clearly see that the designed CNN exceeds the SVM in this case and for this particular data set. Finally, to achieve the second objective, a K-NN is fed with the CNN output. K-NN only fails in \(2\) of the \(30\) images, and only in one of the five categories that are analysed, so that its accuracy is \(0.933\). In the following article [27], the authors implemented a CNN designed by themselves to perform the task of defect detection and classification. This CNN was composed of three convolutional layers, one max pooling layer and two FC layers. The available dataset for this experiment, once augmented, contained \(12{,}000\) images. The test accuracy achieved was \(0.953\). The authors also applied the same dataset to a random forest method, obtaining a test accuracy of \(0.942\). Thus, it can be stated that, although the results are adequate and very similar, the designed CNN outperforms the random forest method in this case. Regarding the average training time for this experiment, it depends on the number of clients used when computing. The average training lasts for about \(160\) s when there is only one client, while when there are from six to ten clients, it decreases to \(80\) s.  
A k-means approach is then adopted in the article [33]. The objective of the study is to analyse an SEM image to detect, quantify and classify the image features using clusters in which the defects are included. As can be seen in Table 2, the authors do not provide quantitative results. They present a novel tool that allows to quantify and group the features, defects in this case, in different clusters according to their features and to manage them together. The next article that appears in Table 2 is [36]. Again, there is no result on accuracy in the table but for another reason. The authors designed an SOM-based method to perform automatic defect detection and classification on wafers. The results have not been included in the table because different combinations of mask sizes and number of clusters were attempted to determine the best one. The results were expressed in terms of specificity (number of negative or non-defective true ROIs (regions of interest) detected divided by total negative or non-defective ROIs) and sensitivity (number of positive or defective true ROIs detected divided by total positive or defective ROIs). The best combination of results was reached by using \(8\)\(\times\)\(8\) masks with three, four or five groups, achieving a value of \(0.967\) for sensitivity and \(1\) for specificity. Then, in Reference [55], the authors used three different CNN models in order to classify different wafer maps: Inception V2, ResNet-50 and VGGNet-16. The achieved accuracy was, \(0.9\), \(0.875\) and \(0.844\), respectively. It seems clear that Inception V2 and ResNet-50, which have almost identical results, outperform VGGNet-16.