Similarity searching for fault diagnosis of defect patterns in wafer bin maps

Abstract

Due to the growing complexity of processes in semiconductor manufacturing, high volumes of data are automatically generated, resulting in a greater challenge for fault detection and yield improvement. Wafer bin maps (WBMs), which represent the spatial distribution of defective dies on the wafer, may contain specific defect patterns that offer useful insight into the underlying causes of anomalies in the processes. Hence, the identification of these defect patterns helps the early detection and diagnosis of the faults. Nowadays, rare and mixed-type defects are more frequently observed, which increases the difficulty for the recognition of defect patterns. In this study, we propose a similarity searching approach for the identification of defect patterns and their potential causes. The comparison of similar patterns provides valuable information to trace the problems in the process history which may narrow the scope of troubleshooting. In particular, the tensor voting algorithm is applied to highlight the structural information in the patterns, and then the weighted best-buddies similarity (WBBS) is proposed to measure the degree of similarity. Experimental results verify the effectiveness of the proposed method in the context of both single and mixed-type defect patterns.