## 3 Results
### Discussion of Results  
In previous studies, [PERSON] et al. [14] achieved an accuracy of 95.6% in classifying 1150 wafers into two classes, normal and faulty, using an SVM classifier. [PERSON] et al. [16] did a similar experiment using random forest as the similarity measurement using 1600 wafer images. However, both the number of wafer images and the number of classes were too few and would not be able to help semiconductor engineers to identify the sources of the defects. The experiment of [PERSON] et al. [15] using logistic regression on synthetic images lacks useful performance evaluation on real wafer images. The experiment of [PERSON] et al. [17] using a voting ensemble classifier on eight classes of defects achieved an accuracy of 95.8% on 25,519 unequally distributed wafer images. [PERSON], et al. [19] trained and tested a light-weight CNN with 13,514 \(28\times 28\) wafer images with four classes, including one for no defect and report an average accuracy 99.7%. Not only were the few defect classes not very helpful in helping to identify the causes of the defects, the image size could be too small to catch smaller, but still visible, defects. For example, assuming a wafer size of one inch in diameter, \(28\times 28\) can classify a defective pixel no smaller than 0.1 cm\({}^{2}\), while \(256\times 256\) can be useful in classifying a defective pixel as small as 0.01 cm\({}^{2}\). Compared to these previous studies, the proposed CNN classifier, shows better performance with real, larger wafer images, as shown using the proposed measurements. However, to ascertain this, experiments were designed to compare the proposed CNN classifier against the classifiers mentioned using the same test dataset.  
In the experiments performed in this paper, \(256\times 256\) wafer images with defects were trained and classified into four classes, each with known causes, and compared with methods presented in previous studies: SVM, logistic regression, random forest and soft voting ensemble. Because the testing test sets are the same, the performance results can then be compared. According to the experiment results shown above, the proposed CNN architecture with 19,112 training samples performed better than the other methods in terms of accuracy, precision, recall, F-Measure, and AUC. With this we can conclude that the proposed trained CNN model can more accurately classify defective wafer images, according to the four classes with known causes of defects, than the other methods. With an input size of \(256\times 256\), it should be possible to capture smaller defects than the light-weight CNN model proposed by [PERSON] et al. [19]. In terms of accuracy, the proposed CNN classifier achieved an average accuracy of 98.88%, while the SVM achieved only an average of 83% across the four tested defect types. In the comparison experiment against the other classifiers, SVE and RF methods achieved nearly 90% in average precision; however, lower F-measures in the local and random classes indicate that, as classifiers for the given test dataset, their performances lag behind our proposed CNN classifier. However, the AUC measurements show that the SVE may be slightly better than the proposed CNN classifier in the random class.  
In the transfer learning experiment using two pretrained faster-R-CNN models, by re-using the pretrained models from Tensorflow [23], it was possible to retrain them using a smaller training set for a few new classes that were not in the original labels. By running a larger testing set of 9411 wafer images through these retrained models, the results show that the KITTI model was a slightly better performer than the COCO model, and the overall performance in terms of accuracy was no worse than the proposed CNN model with a larger training set. Based on the result of this experiment, the use of the technology of transfer learning appears to be a feasible avenue of research in the future for wafer defect classification.