Algorithm

Process (computing)

Engineering

Fuzzy clustering

DBSCAN

Operating system

Data mining

Computer science

Outlier

Pattern recognition (psychology)

Cluster (spacecraft)

Wafer

Artificial intelligence

CURE data clustering algorithm

Cluster analysis

Programming language

Semiconductor device fabrication

Electrical engineering

IEEE Transactions on Semiconductor Manufacturing

Defective die on a wafer map tend to cluster in distinguishable patterns, and such defect patterns can provide crucial information to identify equipment problems or process failures in the semiconductor manufacturing. Therefore, it is important to accurately and efficiently classify the defect patterns. In this paper, we propose a novel clustering-based defect pattern detection and classification framework for wafer bin map (WBM). The proposed framework has many advantages. Outlier detection and defect cluster pattern extraction can be done at the same time; arbitrarily shaped cluster patterns can be detected; and there is no need to specify the number of clusters in advance. Based on WBM property, the parameters used in the clustering algorithm are fixed so that parameter sensitivity can be avoided. Since the defect patterns are classified based on extracted features, no labeled data and no supervised classification training are needed and single-type patterns as well as mixed-type patterns can be found. Extensive experiments conducted on a real-world WM-811K dataset has shown the superiority of proposed framework. The proposed framework can also be used in a big data environment to accelerate performance.

A Novel DBSCAN-Based Defect Pattern Detection and Classification Framework for Wafer Bin Map