A Unified Defect Pattern Analysis of Wafer Maps Using Density-Based Clustering

Jaehoon Koo,Sangheum Hwang

doi:10.1109/access.2021.3084221

Jaehoon Koo, Sangheum Hwang

Open Access

https://doi.org/10.1109/access.2021.3084221

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Abstract

For a yield enhancement in semiconductor manufacturing, it is necessary to analyze wafer maps since they contain information gathered during the manufacturing such as test results of each chip. Especially, spatial patterns of defective chips, e.g. zone, scratch, ring patterns, etc. presented on a wafer map provide valuable information on the potential causes of malfunctions in a fabrication process. Numerous automatic analysis methods have been developed for identifying such defect patterns. We propose a defect pattern analysis method based on density-based clustering (DBC), which consists of two steps: conducting a statistical test to detect wafer maps that contain abnormal defects and clustering the defect patterns. Specifically, we develop a new statistic based on the core points from DBC for the spatial randomness test, which requires much fewer examinations to identify abnormal wafer maps than the existing joint-count based statistics. With those core points, clustering of abnormal defects can be coherently performed in the subsequent clustering step. The main advantage of our method over previous automatic detection methods is that it performs both steps simultaneously based on the core points from DBC. The proposed method is evaluated on simulated and real wafer map datasets. Experimental results show that the proposed method identifies spatial dependence among defects as accurate as the existing methods, but with much less computational effort.

Highlights

Silocon wafer is a raw material used to manufacture integrated circuit chips through a fabrication process [1]
After computing the probability Q and labeling core points by examining every defective chip according to Equation 9, we can conduct a statistical test for the null hypothesis H0 with the number of core points
As the sample wafer maps are obtained in the real settings, the distribution of local defective chips is less clear than the simulated dataset, which leads to more difficult clustering

Summary

INTRODUCTION

Silocon wafer is a raw material used to manufacture integrated circuit chips through a fabrication process [1]. We develop a new statistic based on the number of core points for identifying wafers with a global defect pattern. After the SRT is performed, clustering of defective chips should be conducted to further analyze the defect patterns To this end, the proposed method adopts the DBC algorithm. Our proposed framework inherits the advantages of the DBC, e.g., an ability to identify simultaneously occurred several defect patterns on a single wafer map and arbitrary shaped clustered patterns such as a ring. It is confirmed that the proposed framework shows better SRT performance than the previous methods and finds local defect patterns well even if there exist multiple clusters or arbitrary shaped clusters.

THE PROPOSED FRAMEWORK

EXPERIMENTS ON PUBLIC DATA

Findings

CONCLUSION