The Identification System of Wafer Defects Clustering Patterns Constructed by Artificial Neural Network

• Main Authors: Chaio-Kai Chang, 張喬凱
• Other Authors: Lee-Ing Tong
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/06726075683928110634

碩士 === 國立交通大學 === 工業工程與管理系所 === 94 === Being a semiconductor manufacturer, knowing how to improve the yield of wafer production has been regarded as the focus. However the causes of yield problems have much to do with the total number of defects on a wafer and defects clustering phenomenon. As the wafer size increases, the wafer processes get complicated and the defects clustering phenomenon tends to be apparent. Different problems of wafer processes always make different clustering patterns, so process engineers could find the process problems rapidly to improve the yield by identifying the clustering patterns correctly. Some papers make use of Artificial Neural Network (ANN) to identify wafer defects clustering patterns and come to the acceptable effects. However, it costs much time while transferring wafer defects data into input variables of ANN. This study constructs a wafer defects identification system by ANN, which characterize well identification rate and the method for easily getting input variables of ANN. Simulation data is used for training ANN and then to find out the combination of parameters of the best performance. The Real wafer defects data verify the effectiveness and feasibility of the identification system.