The likely causes of each of these types of defects are:  
* Center type: due to abnormality of RF(Radio Frequency) power, abnormality in liquid flow, or abnormality in liquid pressure. * Local type: due to silt valve leak, abnormality during robot handoffs or abnormality in the pump. * Random type: due to contaminated pipes, abnormality in showerhead, or abnormality in control wafers. * Scrape type: mainly due to abnormality during robot handoffs or wafer impacts. If the defect type can be correctly identified, then by the use of the process of elimination, the engineers can localize the cause(s) of the defects in the process and thus should be able to correct them and increase the yield. In previous studies on wafer defects, [PERSON] et. al. [7] used an SVM(Support Vector Machine) classifier to classify 1150 wafer images into two classes, high and low yields and reported to have  
Figure 3: Defect types: (**a**) center, (**b**) local, (**c**) random, and (**d**) scrape. Figure 2: Wafer defects: (**a**) random defect, (**b**) repeatable defect, (**c**) systematic defect, and (**d**) combinational defect. achieved an accuracy of 95.6%. [PERSON] et al. [8] used the machine-learning algorithm of logistic regression to detect whether a wafer contains defects in order to predict yield, and the testing was done using synthetically generated images simulating six types of wafer defects; F-scores (F-measure) varying from 77.9% to 96.6% were reported. [PERSON] et al. [9] used random forest as a similarity measurement to separate faulty wafers from normal wafers using a total of 1600 wafers in the experiment; however, no accuracy was reported. [PERSON] et al. [10] used a voting ensemble classifier (SVE) using density as a feature to detect wafer defect patterns using 25,519 unequally distributed wafer images containing eight classes of defects: center, donut, edge-local, edge-ring, local, random, scratch, and near-full, and the paper reported that it was able to achieve an average of 95.8% accuracy. [PERSON] et al. [11] proposed a light-weight CNN model for training and classifying 13,514 28 \(\times\) 28 wafer images into 4 classes: no defect, mechanical defects, crystal defects, and redundant defects; however, the paper did not mention how many images were used for training, but it did report that an average of 99.7% accuracy was achieved, which included a 100% detection rate on wafers with no defect. In the current semiconductor manufacturing industries, the inspection process for visible defects still mostly relies on manual labor for errors. However, identification by manpower alone will eventually result in false identifications due to fatigue and lack of objectivity. Therefore, the purpose of this research is to find a reliable machine vision-based method to correctly identify and classify the type of wafer defects in the hope of replacing manual inspection. This paper investigates methods involved using variants of the convolution neural networks, which are deep-learning neural networks and have been shown to be effective in various complex vision-based tasks, because they are able to learn the nonlinear relationship(s) between the inputs and the expected outputs [12]. In the following section, we will discuss the parameters and design of the proposed convolution neural network, followed by experimental comparisons with the performances of other machine-learning methods presented in the literatures. In the conclusion section, we will discuss possible improvements and future works.