Automatic Inspection for Wafer Defect Pattern Recognition with Unsupervised Clustering

Katherine Shu Min Li; Leon Li-Yang Chen; Ken Chau Cheung Cheng; Peter Yi-Yu Liao; Sying Jyan Wang; Andrew Yi An Huang; Nova Tsai; Leon Chou; Gus Chang Hung Han; Jwu E. Chen; Hsing Chung Liang; Chun Lung Hsu

doi:10.1109/ETS50041.2021.9465457

Automatic Inspection for Wafer Defect Pattern Recognition with Unsupervised Clustering

Katherine Shu Min Li
, Leon Li-Yang Chen
, Ken Chau Cheung Cheng
, Peter Yi-Yu Liao
, Sying Jyan Wang
, Andrew Yi An Huang
, Nova Tsai
, Leon Chou
, Gus Chang Hung Han
, Jwu E. Chen
, Hsing Chung Liang
, Chun Lung Hsu

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

1 Scopus citations

Abstract

we propose an automatic wafer defect maps detection method based on unsupervised learning. There is no need for human labeling, and similar defect clusters are identified automatically without human intervention. As a result, the process is less error-prone. Whenever the wafer test result of a WUT is available, it can be compared immediately with existing clusters. If the wafer map matches one of the known defect patterns, then RCA can be done efficiently.

Original language	English
Title of host publication	Proceedings - 2021 IEEE European Test Symposium, ETS 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665418492
DOIs	https://doi.org/10.1109/ETS50041.2021.9465457
State	Published - 24 05 2021
Externally published	Yes
Event	26th IEEE European Test Symposium, ETS 2021 - Virtual, Bruges, Belgium Duration: 24 05 2021 → 28 05 2021

Publication series

Name	Proceedings of the European Test Workshop
Volume	2021-May
ISSN (Print)	1530-1877
ISSN (Electronic)	1558-1780

Conference

Conference	26th IEEE European Test Symposium, ETS 2021
Country/Territory	Belgium
City	Virtual, Bruges
Period	24/05/21 → 28/05/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

clustering
defect pattern
machine learning
mask
wafermap
yield learning

Access to Document

10.1109/ETS50041.2021.9465457

Cite this

Li, K. S. M., Li-Yang Chen, L., Cheng, K. C. C., Yi-Yu Liao, P., Wang, S. J., Huang, A. Y. A., Tsai, N., Chou, L., Han, G. C. H., Chen, J. E., Liang, H. C., & Hsu, C. L. (2021). Automatic Inspection for Wafer Defect Pattern Recognition with Unsupervised Clustering. In Proceedings - 2021 IEEE European Test Symposium, ETS 2021 Article 9465457 (Proceedings of the European Test Workshop; Vol. 2021-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ETS50041.2021.9465457

@inproceedings{0e4f53b60fd04f67aa2f338ad904be19,

title = "Automatic Inspection for Wafer Defect Pattern Recognition with Unsupervised Clustering",

abstract = "we propose an automatic wafer defect maps detection method based on unsupervised learning. There is no need for human labeling, and similar defect clusters are identified automatically without human intervention. As a result, the process is less error-prone. Whenever the wafer test result of a WUT is available, it can be compared immediately with existing clusters. If the wafer map matches one of the known defect patterns, then RCA can be done efficiently.",

keywords = "clustering, defect pattern, machine learning, mask, wafermap, yield learning",

author = "Li, \{Katherine Shu Min\} and \{Li-Yang Chen\}, Leon and Cheng, \{Ken Chau Cheung\} and \{Yi-Yu Liao\}, Peter and Wang, \{Sying Jyan\} and Huang, \{Andrew Yi An\} and Nova Tsai and Leon Chou and Han, \{Gus Chang Hung\} and Chen, \{Jwu E.\} and Liang, \{Hsing Chung\} and Hsu, \{Chun Lung\}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 26th IEEE European Test Symposium, ETS 2021 ; Conference date: 24-05-2021 Through 28-05-2021",

year = "2021",

month = may,

day = "24",

doi = "10.1109/ETS50041.2021.9465457",

language = "英语",

series = "Proceedings of the European Test Workshop",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings - 2021 IEEE European Test Symposium, ETS 2021",

address = "美国",

}

Li, KSM, Li-Yang Chen, L, Cheng, KCC, Yi-Yu Liao, P, Wang, SJ, Huang, AYA, Tsai, N, Chou, L, Han, GCH, Chen, JE, Liang, HC & Hsu, CL 2021, Automatic Inspection for Wafer Defect Pattern Recognition with Unsupervised Clustering. in Proceedings - 2021 IEEE European Test Symposium, ETS 2021., 9465457, Proceedings of the European Test Workshop, vol. 2021-May, Institute of Electrical and Electronics Engineers Inc., 26th IEEE European Test Symposium, ETS 2021, Virtual, Bruges, Belgium, 24/05/21. https://doi.org/10.1109/ETS50041.2021.9465457

Automatic Inspection for Wafer Defect Pattern Recognition with Unsupervised Clustering. / Li, Katherine Shu Min; Li-Yang Chen, Leon; Cheng, Ken Chau Cheung et al.
Proceedings - 2021 IEEE European Test Symposium, ETS 2021. Institute of Electrical and Electronics Engineers Inc., 2021. 9465457 (Proceedings of the European Test Workshop; Vol. 2021-May).

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

TY - GEN

T1 - Automatic Inspection for Wafer Defect Pattern Recognition with Unsupervised Clustering

AU - Li, Katherine Shu Min

AU - Li-Yang Chen, Leon

AU - Cheng, Ken Chau Cheung

AU - Yi-Yu Liao, Peter

AU - Wang, Sying Jyan

AU - Huang, Andrew Yi An

AU - Tsai, Nova

AU - Chou, Leon

AU - Han, Gus Chang Hung

AU - Chen, Jwu E.

AU - Liang, Hsing Chung

AU - Hsu, Chun Lung

PY - 2021/5/24

Y1 - 2021/5/24

N2 - we propose an automatic wafer defect maps detection method based on unsupervised learning. There is no need for human labeling, and similar defect clusters are identified automatically without human intervention. As a result, the process is less error-prone. Whenever the wafer test result of a WUT is available, it can be compared immediately with existing clusters. If the wafer map matches one of the known defect patterns, then RCA can be done efficiently.

AB - we propose an automatic wafer defect maps detection method based on unsupervised learning. There is no need for human labeling, and similar defect clusters are identified automatically without human intervention. As a result, the process is less error-prone. Whenever the wafer test result of a WUT is available, it can be compared immediately with existing clusters. If the wafer map matches one of the known defect patterns, then RCA can be done efficiently.

KW - clustering

KW - defect pattern

KW - machine learning

KW - mask

KW - wafermap

KW - yield learning

UR - https://www.scopus.com/pages/publications/85113716266

U2 - 10.1109/ETS50041.2021.9465457

DO - 10.1109/ETS50041.2021.9465457

M3 - 会议稿件

AN - SCOPUS:85113716266

T3 - Proceedings of the European Test Workshop

BT - Proceedings - 2021 IEEE European Test Symposium, ETS 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 26th IEEE European Test Symposium, ETS 2021

Y2 - 24 May 2021 through 28 May 2021

ER -

Li KSM, Li-Yang Chen L, Cheng KCC, Yi-Yu Liao P, Wang SJ, Huang AYA et al. Automatic Inspection for Wafer Defect Pattern Recognition with Unsupervised Clustering. In Proceedings - 2021 IEEE European Test Symposium, ETS 2021. Institute of Electrical and Electronics Engineers Inc. 2021. 9465457. (Proceedings of the European Test Workshop). doi: 10.1109/ETS50041.2021.9465457

Automatic Inspection for Wafer Defect Pattern Recognition with Unsupervised Clustering

Abstract

Publication series

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Bibliographical note

Keywords

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