Feature extraction of probe mark image and automatic detection of probing pad defects in semiconductor using CSVM

Jeong Hoon Lee; Jee Hyong Lee

doi:10.1117/12.2181518

Feature extraction of probe mark image and automatic detection of probing pad defects in semiconductor using CSVM

Jeong Hoon Lee
, Jee Hyong Lee

Department of Computer Science and Engineering

Samsung
Sungkyunkwan University

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

Abstract

As semiconductor micro-fabrication process continues to advance, the size of probing pads also become smaller in a chip. A probe needle contacts each probing pad for electrical test. However, probe needle may incorrectly touch probing pad. Such contact failures damage probing pads and cause qualification problems. In order to detect contact failures, the current system observes the probing marks on pads. Due to a low accuracy of the system, engineers have to redundantly verify the result of the system once more, which causes low efficiency. We suggest an approach for automatic defect detection to solve these problems using image processing and CSVM. We develop significant features of probing marks to classify contact failures more correctly. We reduce 38% of the workload of engineers.

Original language	English
Title of host publication	Seventh International Conference on Machine Vision, ICMV 2014
Editors	Branislav Vuksanovic, Jianhong Zhou, Antanas Verikas, Petia Radeva
Publisher	SPIE
ISBN (Electronic)	9781628415605
DOIs	https://doi.org/10.1117/12.2181518
State	Published - 2015
Event	7th International Conference on Machine Vision, ICMV 2014 - Milan, Italy Duration: 19 Nov 2014 → 21 Nov 2014

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	9445
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	7th International Conference on Machine Vision, ICMV 2014
Country/Territory	Italy
City	Milan
Period	19/11/14 → 21/11/14

UN SDGs

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

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Cost-Sensitive Support Vector Machine (CSVM)
Feature Extraction
Image Processing
Probe Test
Probing Pad Defects
Semiconductor

Access to Document

10.1117/12.2181518

Cite this

Lee, J. H., & Lee, J. H. (2015). Feature extraction of probe mark image and automatic detection of probing pad defects in semiconductor using CSVM. In B. Vuksanovic, J. Zhou, A. Verikas, & P. Radeva (Eds.), Seventh International Conference on Machine Vision, ICMV 2014 Article 94450H (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9445). SPIE. https://doi.org/10.1117/12.2181518

Lee, Jeong Hoon ; Lee, Jee Hyong. / Feature extraction of probe mark image and automatic detection of probing pad defects in semiconductor using CSVM. Seventh International Conference on Machine Vision, ICMV 2014. editor / Branislav Vuksanovic ; Jianhong Zhou ; Antanas Verikas ; Petia Radeva. SPIE, 2015. (Proceedings of SPIE - The International Society for Optical Engineering).

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title = "Feature extraction of probe mark image and automatic detection of probing pad defects in semiconductor using CSVM",

abstract = "As semiconductor micro-fabrication process continues to advance, the size of probing pads also become smaller in a chip. A probe needle contacts each probing pad for electrical test. However, probe needle may incorrectly touch probing pad. Such contact failures damage probing pads and cause qualification problems. In order to detect contact failures, the current system observes the probing marks on pads. Due to a low accuracy of the system, engineers have to redundantly verify the result of the system once more, which causes low efficiency. We suggest an approach for automatic defect detection to solve these problems using image processing and CSVM. We develop significant features of probing marks to classify contact failures more correctly. We reduce 38\% of the workload of engineers.",

keywords = "Cost-Sensitive Support Vector Machine (CSVM), Feature Extraction, Image Processing, Probe Test, Probing Pad Defects, Semiconductor",

author = "Lee, \{Jeong Hoon\} and Lee, \{Jee Hyong\}",

note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; 7th International Conference on Machine Vision, ICMV 2014 ; Conference date: 19-11-2014 Through 21-11-2014",

year = "2015",

doi = "10.1117/12.2181518",

language = "English",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

publisher = "SPIE",

editor = "Branislav Vuksanovic and Jianhong Zhou and Antanas Verikas and Petia Radeva",

booktitle = "Seventh International Conference on Machine Vision, ICMV 2014",

}

Lee, JH & Lee, JH 2015, Feature extraction of probe mark image and automatic detection of probing pad defects in semiconductor using CSVM. in B Vuksanovic, J Zhou, A Verikas & P Radeva (eds), Seventh International Conference on Machine Vision, ICMV 2014., 94450H, Proceedings of SPIE - The International Society for Optical Engineering, vol. 9445, SPIE, 7th International Conference on Machine Vision, ICMV 2014, Milan, Italy, 19/11/14. https://doi.org/10.1117/12.2181518

Feature extraction of probe mark image and automatic detection of probing pad defects in semiconductor using CSVM. / Lee, Jeong Hoon; Lee, Jee Hyong.
Seventh International Conference on Machine Vision, ICMV 2014. ed. / Branislav Vuksanovic; Jianhong Zhou; Antanas Verikas; Petia Radeva. SPIE, 2015. 94450H (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9445).

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

TY - GEN

T1 - Feature extraction of probe mark image and automatic detection of probing pad defects in semiconductor using CSVM

AU - Lee, Jeong Hoon

AU - Lee, Jee Hyong

PY - 2015

Y1 - 2015

N2 - As semiconductor micro-fabrication process continues to advance, the size of probing pads also become smaller in a chip. A probe needle contacts each probing pad for electrical test. However, probe needle may incorrectly touch probing pad. Such contact failures damage probing pads and cause qualification problems. In order to detect contact failures, the current system observes the probing marks on pads. Due to a low accuracy of the system, engineers have to redundantly verify the result of the system once more, which causes low efficiency. We suggest an approach for automatic defect detection to solve these problems using image processing and CSVM. We develop significant features of probing marks to classify contact failures more correctly. We reduce 38% of the workload of engineers.

AB - As semiconductor micro-fabrication process continues to advance, the size of probing pads also become smaller in a chip. A probe needle contacts each probing pad for electrical test. However, probe needle may incorrectly touch probing pad. Such contact failures damage probing pads and cause qualification problems. In order to detect contact failures, the current system observes the probing marks on pads. Due to a low accuracy of the system, engineers have to redundantly verify the result of the system once more, which causes low efficiency. We suggest an approach for automatic defect detection to solve these problems using image processing and CSVM. We develop significant features of probing marks to classify contact failures more correctly. We reduce 38% of the workload of engineers.

KW - Cost-Sensitive Support Vector Machine (CSVM)

KW - Feature Extraction

KW - Image Processing

KW - Probe Test

KW - Probing Pad Defects

KW - Semiconductor

UR - https://www.scopus.com/pages/publications/84924390266

U2 - 10.1117/12.2181518

DO - 10.1117/12.2181518

M3 - Conference contribution

AN - SCOPUS:84924390266

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Seventh International Conference on Machine Vision, ICMV 2014

A2 - Vuksanovic, Branislav

A2 - Zhou, Jianhong

A2 - Verikas, Antanas

A2 - Radeva, Petia

PB - SPIE

T2 - 7th International Conference on Machine Vision, ICMV 2014

Y2 - 19 November 2014 through 21 November 2014

ER -

Feature extraction of probe mark image and automatic detection of probing pad defects in semiconductor using CSVM

Abstract

Publication series

Conference

UN SDGs

Keywords

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