Detection and classification of defect patterns on semiconductor wafers

Chih Hsuan Wang, Way Kuo, Halima Bensmail

Research output: Contribution to journalArticle

46 Citations (Scopus)


The detection of process problems and parameter drift at an early stage is crucial to successful semiconductor manufacture. The defect patterns on the wafer can act as an important source of information for quality engineers allowing them to isolate production problems. Traditionally, defect recognition is performed by quality engineers using a scanning electron microscope. This manual approach is not only expensive and time consuming but also it leads to high misidentification levels. In this paper, an automatic approach consisting of a spatial filter, a classification module and an estimation module is proposed to validate both real and simulated data. Experimental results show that three types of typical defect patterns: (i) a linear scratch; (ii) a circular ring; and (iii) an elliptical zone can be successfully extracted and classified. A Gaussian EM algorithm is used to estimate the elliptic and linear patterns, and a spherical-shell algorithm is used to estimate ring patterns. Furthermore, both convex and nonconvex defect patterns can be simultaneously recognized via a hybrid clustering method. The proposed method has the potential to be applied to other industries.

Original languageEnglish
Pages (from-to)1059-1068
Number of pages10
JournalIIE Transactions (Institute of Industrial Engineers)
Issue number12
Publication statusPublished - 1 Dec 2006
Externally publishedYes


ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Management Science and Operations Research

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