Inspection and Classification of Semiconductor Wafer Surface Defects Using CNN Deep Learning Networks  
[PERSON]  
1 Degree Program of Digital Space and Product Design, Kainan University, Taoyuan 33587, Taiwan; [EMAIL_ADDRESS]  
[PERSON]  
2 Department of Electrical Engineering, Chang Gung University, Taoyuan 33302, Taiwan; [EMAIL_ADDRESS]  
[PERSON]  
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* Thin-film processing: the use of physical or chemical means to perform vapor deposition of crystals on thin film. * Chemical-mechanical polishing: the principle of polishing to flatten the even contours on the wafers. * Photolithography: using photoresist for exposure and development, so as to leave the photo-masked pattern on the wafer. * Etching: to remove materials from the surface of the wafer by physical or chemical means wherever the surface is not protected by the photoresist. * Diffusion and ion implantation: to use physical phenomena of heat diffusion to alter the semiconductor's electrical conductivity, then ionize the surface substance, then control the electrical current magnitude to control the concentrations of ions. * Oxidation: to reduce the damage that can occur during the ion implantation stage. * Metallization: mainly to perform the connections of metals. The types of manufacturing processing problems that can occur could involve robot handoffs, contaminations, flow leakages, etc. Semiconductor engineers are able to use the defect patterns on the wafers to locate problems in the process, which would then become clues in helping improve the yield. [PERSON] [6] identified, in general, that manufacturing defects can be classified into three types: Type-A, Type-B, and Type-C.  
1. Type-A defects are evenly random with a stable mean density. This type of defect is generated randomly, and no specific clustering phenomenon is visible, as shown in Figure 2a. The cause of this type of defect is complex and not fixed to particular patterns. It is difficult to find the cause of this type of defect. This type of yield abnormality can be reduced by improving the stability and accuracy of the process. 2. Type-B defects are systematic and repeatable from wafer to wafer. This type of defect has obvious clustering phenomenon, as shown in Figure 2b,c. The cause of this type of defect can usually be found by the distribution of defects on the wafer, which is used to find abnormalities in the process or machine, such as the misalignment of the mask position during photo development or excessive etching during the process, etc. 3. Type-C defects vary from wafer to wafer. This type of defect is the most common occurrence in semiconductor manufacturing. That is, it is a combination of Type-A defects and Type-B defects,  
Figure 1: The semiconductor manufacturing diagram. as shown in Figure 2d. In this type of defect, it is very important to eliminate the causes for random defects and keep systemic defects, so that engineers can find the cause of anomalies. There are many different types of defects, both visible and invisible. However, based on [PERSON]'s classification system [6] and suggestions from engineers about different types of visible defects with known causes, the wafer-defect images will be grouped into four major classes: random, local, center, and scrape, for this study. Examples of these four major classes are shown in Figure 3. The defects in the random type are almost randomly distributed across the entire surface of the wafer. The defects in the local type are concentrated on the edge of the wafer but do not exhibit linear or curvy characteristics. The defects in the center type are concentrated around or near the center of the wafer in circular or ring-like patterns. The defects in the scrape type exhibit linear or curvy distribution from the edge moving toward the center of the wafer. These four classes of defect have known possible cause.