Detection and clustering of mixed-type defect patterns in wafer bin maps

A new method for detecting and clustering mixed-type defect patterns in wafer bin maps from semiconductor manufacturing is developed

Article  |  Fall 2018

The wafer fabrication process consists of multiple sequential processes, such as oxidation, photolithography, etching, ion implantation, and metallization. After the wafer fabrication, a verification test is performed on each die of a wafer to detect any process change and to verify whether all dies meet product specifications. Based on wafer test results, each die can be assigned a binary value (e.g., value one for good dies and value zero for defective dies).

The resulting spatial map of a wafer is called a wafer bin map (WBM). The bin values of adjacent dies in a WBM are often spatially correlated, forming some systematic defect patterns (e.g., circle, ring, scratch, shot, and zone patterns). These non-random defect patterns occur because of assignable causes. For example, a circular pattern concentrated in the center of a wafer typically occurs when there is a uniformity problem during the chemical-mechanical planarization. Therefore, it is important to identify systematic defect patterns in order to know the root causes of failure and to take actions for quality management.

In particular, as wafer-fabrication processes have become more complicated, mixed-type defect patterns (two or more different types of defect patterns occur simultaneously in a single wafer) occur more frequently than in the past. For more effective classification of wafers according to their defect patterns, mixed-type defect patterns need to be detected and separated into several clusters of different patterns. Subsequently, each cluster of a single pattern can be matched to a well-known defect type (e.g., circle, ring) or it may indicate the emergence of a new defect pattern. However, there are several challenges: the separation of random defects from systematic defect patterns, determining the number of clusters, and the clustering of defect patterns of complex shapes.

This problem is investigated in the paper “Detection and clustering of mixed-type defect patterns in wafer bin maps” by Jinho Kim, a project leader of DRAM test engineering team at SK hynix, Youngmin Lee, a Ph.D. student, and Heeyoung Kim, an associate professor in the Department of Industrial and Systems Engineering at KAIST. The article was published in IISE Transactions and selected as a feature article. To detect systematic defects before they become clustered, the authors propose a new filtering method called connected-path filtering. The method aims to detect paths that are longer than a prespecified threshold. The detected systematic defects are subsequently clustered using the infinite warped mixture model. This model can effectively cluster mixed-type defect patterns with complex shapes, and furthermore, the number of clusters does not need to be specified in advance but is automatically determined during the clustering procedure.

 

The proposed method was tested on real WBM data from SK hynix, a global semiconductor company, and the results demonstrate that the proposed method can successfully separate mixed-type defect patterns into several clusters of different patterns with the right number of clusters.