A study on the Classification of Profile Data with Applications in Semiconductor Manufacturing

• Main Authors: Tseng, Yuan-Yi, 曾源毅
• Other Authors: Horng , Jyh-Jen Shiau
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/73593503080389768517

碩士 === 國立交通大學 === 統計學研究所 === 100 === In the industry, engineers use control charts to monitor process variables for process stability. A point that plots outside of the control limits is interpreted as an evidence that the process is out of control, and investigations and corrective actions are required to find and eliminate the assignable cause or causes responsible for this behavior. It would be helpful for engineers to take right corrective actions if we can identify the type of the problems from the out-of-control data. In this research, we use three well-known classification methods, KNN, LDA and QDA to classify profile data and study the effectiveness of these methods via simulation. The simulation results indicate that the KNN method has the best performance in terms of the accurate classification rate, but takes the longest time in computation. For real-life examples, we simulate profile data for two potential applications in semiconductor manufacturing and apply the three classification methods on them. In the first example, the angle of wafer surface etching classifies the profile into three classes: normal, over-etching, and under-etching. Without knowing the angle, we apply the three classification methods to classify profile data. In this example, among the three methods, KNN performs the best, LDA the second, and QDA the worst. In the second example, the pattern of the defective chips on a wafer determines the class of a wafer. We first transform the 0-1 2-dimensional data into profile data, then apply the three classification methods to classify the wafers. The result shows that the performances of the three methods are fairly similar.