Decision Tree Ensemble-Based Wafer Map Failure Pattern Recognition Based on Radon Transform-Based Features

Abstract

Wafer maps contain information about defects and clustered defects that form failure patterns. Failure patterns exhibit the information related to defect generation mechanisms. The accurate classification of failure patterns in wafer maps can provide crucial information for engineers to recognize the causes of the fabrication problems. In this paper, we proposed a decision tree ensemble learning-based wafer map failure pattern recognition method based on radon transform-based features. Radon transform is applied on raw wafer map data to generate the new features which are exhibiting the geometric information of failure patterns in wafer map. Decision tree algorithm is applied to build decision tree ensemble and the final decision is made by aggregating the prediction results of decision trees. The effectiveness of the proposed method has been verified by using the real world wafer map data set (WM-811K).