Novel methods for identification and analysis of various yield problems in semiconductor manufacturing

Huhn Lee Chang; Yun Moon Jae; Whan Chong Kyu; Dong Woo Hyung; Hee Kang Seog; Seok Oh Kyung; Woo Hong Seok; Cheol Lee Jae

doi:10.1109/ASMC.2006.1638749

Novel methods for identification and analysis of various yield problems in semiconductor manufacturing

Huhn Lee Chang
, Yun Moon Jae
, Whan Chong Kyu
, Dong Woo Hyung
, Hee Kang Seog
, Seok Oh Kyung
, Woo Hong Seok
, Cheol Lee Jae

Samsung

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Overwhelming data is produced during semiconductor processing and it becomes more important to classify a large number of wafers into various types of failures for the root cause analysis of the yield excursion as quickly as possible. In this paper, feature vector based methods have been suggested for the classification of wafers and their application to the root cause analysis. Local bin profile has been calculated to generate a feature vector for a wafer. K-means clustering method has been used to cluster these vectors for the classification of wafers. ANOVA or Kruscal-Wallis test has been applied to one of the components of a feature vector for the yield analysis, depending on its normality. Our yield analysis examples have proven that these analysis methods are very effective and quick in pinpointing the root cause for the various types of failures, especially the equipment-originated ones, including those otherwise would be impossible with the conventional methods.

Original language	English
Title of host publication	17th Annual SEMI/IEEE Advanced Semiconductor Manufacturing Conference, ASMC 2006
Pages	227-232
Number of pages	6
DOIs	https://doi.org/10.1109/ASMC.2006.1638749
State	Published - 2006
Externally published	Yes
Event	17th Annual SEMI/IEEE Advanced Semiconductor Manufacturing Conference, ASMC 2006 - Boston, MA, United States Duration: 22 May 2006 → 24 May 2006

Publication series

Name	ASMC (Advanced Semiconductor Manufacturing Conference) Proceedings
Volume	2006
ISSN (Print)	1078-8743

Conference

Conference	17th Annual SEMI/IEEE Advanced Semiconductor Manufacturing Conference, ASMC 2006
Country/Territory	United States
City	Boston, MA
Period	22/05/06 → 24/05/06

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

ANOVA
Clustering
Feature vector
K-means
Kruscal-Wallis
Pattern recognition
Scheffe test
Semiconductor manufacturing
Software
Statistical analysis
Yield analysis

Access to Document

10.1109/ASMC.2006.1638749

Cite this

Chang, H. L., Jae, Y. M., Kyu, W. C., Hyung, D. W., Seog, H. K., Kyung, S. O., Seok, W. H., & Jae, C. L. (2006). Novel methods for identification and analysis of various yield problems in semiconductor manufacturing. In 17th Annual SEMI/IEEE Advanced Semiconductor Manufacturing Conference, ASMC 2006 (pp. 227-232). Article 1638749 (ASMC (Advanced Semiconductor Manufacturing Conference) Proceedings; Vol. 2006). https://doi.org/10.1109/ASMC.2006.1638749

@inproceedings{c667718519654b5fbf3168567fc38f8c,

title = "Novel methods for identification and analysis of various yield problems in semiconductor manufacturing",

abstract = "Overwhelming data is produced during semiconductor processing and it becomes more important to classify a large number of wafers into various types of failures for the root cause analysis of the yield excursion as quickly as possible. In this paper, feature vector based methods have been suggested for the classification of wafers and their application to the root cause analysis. Local bin profile has been calculated to generate a feature vector for a wafer. K-means clustering method has been used to cluster these vectors for the classification of wafers. ANOVA or Kruscal-Wallis test has been applied to one of the components of a feature vector for the yield analysis, depending on its normality. Our yield analysis examples have proven that these analysis methods are very effective and quick in pinpointing the root cause for the various types of failures, especially the equipment-originated ones, including those otherwise would be impossible with the conventional methods.",

keywords = "ANOVA, Clustering, Feature vector, K-means, Kruscal-Wallis, Pattern recognition, Scheffe test, Semiconductor manufacturing, Software, Statistical analysis, Yield analysis",

author = "Chang, \{Huhn Lee\} and Jae, \{Yun Moon\} and Kyu, \{Whan Chong\} and Hyung, \{Dong Woo\} and Seog, \{Hee Kang\} and Kyung, \{Seok Oh\} and Seok, \{Woo Hong\} and Jae, \{Cheol Lee\}",

year = "2006",

doi = "10.1109/ASMC.2006.1638749",

language = "English",

isbn = "1424402549",

series = "ASMC (Advanced Semiconductor Manufacturing Conference) Proceedings",

pages = "227--232",

booktitle = "17th Annual SEMI/IEEE Advanced Semiconductor Manufacturing Conference, ASMC 2006",

note = "17th Annual SEMI/IEEE Advanced Semiconductor Manufacturing Conference, ASMC 2006 ; Conference date: 22-05-2006 Through 24-05-2006",

}

Chang, HL, Jae, YM, Kyu, WC, Hyung, DW, Seog, HK, Kyung, SO, Seok, WH & Jae, CL 2006, Novel methods for identification and analysis of various yield problems in semiconductor manufacturing. in 17th Annual SEMI/IEEE Advanced Semiconductor Manufacturing Conference, ASMC 2006., 1638749, ASMC (Advanced Semiconductor Manufacturing Conference) Proceedings, vol. 2006, pp. 227-232, 17th Annual SEMI/IEEE Advanced Semiconductor Manufacturing Conference, ASMC 2006, Boston, MA, United States, 22/05/06. https://doi.org/10.1109/ASMC.2006.1638749

Novel methods for identification and analysis of various yield problems in semiconductor manufacturing. / Chang, Huhn Lee; Jae, Yun Moon; Kyu, Whan Chong et al.
17th Annual SEMI/IEEE Advanced Semiconductor Manufacturing Conference, ASMC 2006. 2006. p. 227-232 1638749 (ASMC (Advanced Semiconductor Manufacturing Conference) Proceedings; Vol. 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Chang, Huhn Lee

AU - Jae, Yun Moon

AU - Kyu, Whan Chong

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AU - Seog, Hee Kang

AU - Kyung, Seok Oh

AU - Seok, Woo Hong

AU - Jae, Cheol Lee

PY - 2006

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AB - Overwhelming data is produced during semiconductor processing and it becomes more important to classify a large number of wafers into various types of failures for the root cause analysis of the yield excursion as quickly as possible. In this paper, feature vector based methods have been suggested for the classification of wafers and their application to the root cause analysis. Local bin profile has been calculated to generate a feature vector for a wafer. K-means clustering method has been used to cluster these vectors for the classification of wafers. ANOVA or Kruscal-Wallis test has been applied to one of the components of a feature vector for the yield analysis, depending on its normality. Our yield analysis examples have proven that these analysis methods are very effective and quick in pinpointing the root cause for the various types of failures, especially the equipment-originated ones, including those otherwise would be impossible with the conventional methods.

KW - ANOVA

KW - Clustering

KW - Feature vector

KW - K-means

KW - Kruscal-Wallis

KW - Pattern recognition

KW - Scheffe test

KW - Semiconductor manufacturing

KW - Software

KW - Statistical analysis

KW - Yield analysis

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T3 - ASMC (Advanced Semiconductor Manufacturing Conference) Proceedings

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BT - 17th Annual SEMI/IEEE Advanced Semiconductor Manufacturing Conference, ASMC 2006

T2 - 17th Annual SEMI/IEEE Advanced Semiconductor Manufacturing Conference, ASMC 2006

Y2 - 22 May 2006 through 24 May 2006

ER -

Chang HL, Jae YM, Kyu WC, Hyung DW, Seog HK, Kyung SO et al. Novel methods for identification and analysis of various yield problems in semiconductor manufacturing. In 17th Annual SEMI/IEEE Advanced Semiconductor Manufacturing Conference, ASMC 2006. 2006. p. 227-232. 1638749. (ASMC (Advanced Semiconductor Manufacturing Conference) Proceedings). doi: 10.1109/ASMC.2006.1638749

Novel methods for identification and analysis of various yield problems in semiconductor manufacturing

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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