| dc.description.abstract | With the increasing cost of wafer manufacturing, it has become crucial to control wafer yield. Analyzing defect patterns of wafer maps is an effective method to achieve this goal. While many studies have focused on defect patterns with cluster distributions, this thesis addresses the analysis of anti-cluster distribution defect patterns. We propose a method to label and classify these patterns. We classify anti-cluster distribution defect patterns into two types: Grid and Sparse. For the Grid pattern, defect dies exhibit a regular distribution across the wafer, while for the Sparse pattern, defect dies are dispersed across the wafer.
Our method consists of three stages. In the first stage, we preprocess the wafer maps to remove cluster and line defect patterns that may interfere with the identification of Grid patterns. Next, we extract relevant features from the wafer maps and perform comprehensive analysis to identify Grid patterns, which may contain multiple types of defect patterns. In the second stage, we employ pre-established random defect models to determine whether the defects on the wafer maps exhibit an anti-cluster distribution. The random defect models include three types: Score, Discrete die, and Under 2-die models. By applying these models and employing weighted calculations on the wafer maps, we can analyze and determine the distribution type of the defect patterns. If the patterns demonstrate an anti-cluster distribution, they progress to the next stage. Finally, we extract features from the wafer maps and analyze them to determine if they represent specific Grid or Sparse patterns.
To validate our approach, we conducted experiments on real wafers and achieved a high accuracy of recognition. Experimental results demonstrate the effectiveness and reliability of our method in identifying and classifying anti-cluster distribution defect patterns. | en_US |