Analysis of defective patterns on wafers in semiconductor manufacturing: A bibliographical review

Author

Yum, Bong-Jin ; Koo, Jae Hoon ; Kim, Seong-Jun

Author_Institution

Dept. of Ind. & Syst. Eng., KAIST, Daejeon, South Korea

fYear

2012

fDate

20-24 Aug. 2012

Firstpage

86

Lastpage

90

Abstract

The existing works on automatic detection and/or classification of clusters of defective dies on wafers is reviewed. The literature is classified into three major categories, namely, spatial randomness test, automatic cluster detection only, and automatic detection and classification of clusters. Future research directions are also discussed.

Keywords

automatic optical inspection; dies (machine tools); image classification; pattern clustering; production engineering computing; semiconductor industry; semiconductor technology; statistical testing; automatic classification; automatic cluster detection; automatic detection; bibliographical review; defective die clusters; defective pattern analysis; semiconductor manufacturing; spatial randomness test; wafers; Clustering algorithms; Decision trees; Manufacturing; Neural networks; Pattern recognition; Semiconductor device modeling; Systematics;

fLanguage

English

Publisher

ieee

Conference_Titel

Automation Science and Engineering (CASE), 2012 IEEE International Conference on

Conference_Location

Seoul

ISSN

2161-8070

Print_ISBN

978-1-4673-0429-0

Type

conf

DOI

10.1109/CoASE.2012.6386471

Filename

6386471