## 6 Results and Discussion
### Article by Article Discussion
Then, the Radon transform was merged with the previously mentioned CNN models, creating new models named R-Inception V2, R-ResNet-50 and R-VGGNet-16. These new models had an accuracy of \(0.974\), \(0.968\) and \(0.96\). As we can appreciate, every model of this new approach outperformed its corresponding simpler model. Among the new models, the same tendency was maintained. R-Inception V2 and R-ResNet-50 offered similar results and both slightly outperformed R-VGGNet-16. Moreover, in [56], the authors classified the chemical composition of particle defects on semiconductor wafers. A VGGNet-based model was assigned for achieving that goal. The idea of the article was to merge the features extracted along the convolution operations with data extracted from an energy-dispersive X-ray (EDX) microscope. In order to do that, the authors started from a dataset of 5761 SEM images belonging to 8 classes, along with 98 images with no defect. The average training time was of 0.264 s/ defect/epoch/GPU. The test accuracy reached was 0.992 for the Top-3 accuracy and 0.821 for the Top-1 accuracy. Then, two different transfer learning approaches were followed, obtaining worse results without saving computing time significantly. Continuing with article [58], the authors developed a CNN model based on Inception V1 that surpassed the commercial state-of-the-art automatic defect classification (ADC) system in the field of semiconductor defect detection and classification. Up to 5388 images of eight different classes constitute the dataset for this experiment. In this case, the CNN selected achieved a test accuracy of 0.873, whereas the ADC system achieved a test accuracy of 0.772. Therefore, the CNN model clearly outperforms the ADC system. Regarding the run-time, it varies from 40 h when no transfer learning is included to 4 h once transfer learning is included. In [59], the authors compared two different CNN models, Inception V3 and ResNet-50, in semiconductor defect detection and classification. As shown in Table 2, the accuracy of the models was 0.6 and 0.7, respectively. These values are poor, probably due to the initial dataset's distinctiveness and/or quality, as it contains only 736 images, which belong to seven classes. Nevertheless, the ResNet-50 model outperformed the Inception V3 one. Regarding the training time, it takes for about 4.5 h to train Inception V3 and 9 h to train ResNet-50. Finally, in [64], the authors proposed three different CNN models to carry out the post-sawing inspection task. The three CNNs were designed by the proper authors. The first CNN used a back propagation algorithm that reached an accuracy of 1. The second one used a linear vector quantisation (LVQ) algorithm and also achieved an accuracy of 1. Finally, the third one implemented a radial basis algorithm and obtained an accuracy of 0.9. For this dataset, backpropagation and LVQ offered the same result, outperforming the radial basis one. Although these results are included, it seems clear that they are not really comparable with the ones of the previous papers, as obtaining accuracy values of 100% reveals that the classification task was not that complex.