Principal component analysis based support vector machine for the end point detection of the metal E

Kyounghoon Han; Seunghyok Kim; Kun Joo Park; En Sup Yoon; Heeyeop Chae

doi:10.3182/20080706-5-KR-1001.3971

Principal component analysis based support vector machine for the end point detection of the metal E

Kyounghoon Han, Seunghyok Kim, Kun Joo Park, En Sup Yoon, Heeyeop Chae

Department of Chemical Engineering

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

Abstract

An endpoint detection using the algorithm of principal component analysis based support vector machine was developed for the plasma etching process. Because many endpoint detection techniques use a few manually selected wavelengths, noise render them ineffective and it is hard to select the important wavelengths. So the principal component algorithm with the whole wavelengths has been developed for the more effective monitoring of end point. And the support vector regression was followed for the real-time end point detection with reduced wavelengths to save the processing time. This approach was applied and demonstrated for a metal etching process of Al and 0.5% Cu on the oxide stack with inductively coupled BCl2/Cl2 plasma.

Original language	English
Title of host publication	Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
Edition	1 PART 1
DOIs	https://doi.org/10.3182/20080706-5-KR-1001.3971
State	Published - 2008
Event	17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of Duration: 6 Jul 2008 → 11 Jul 2008

Publication series

Name	IFAC Proceedings Volumes (IFAC-PapersOnline)
Number	1 PART 1
Volume	17
ISSN (Print)	1474-6670

Conference

Conference	17th World Congress, International Federation of Automatic Control, IFAC
Country/Territory	Korea, Republic of
City	Seoul
Period	6/07/08 → 11/07/08

Keywords

Applications in semiconductor manufacturing
Process modeling and identification
Process observation and parameter estimation

UN SDGs

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

Access to Document

10.3182/20080706-5-KR-1001.3971

Cite this

Han, K., Kim, S., Park, K. J., Yoon, E. S., & Chae, H. (2008). Principal component analysis based support vector machine for the end point detection of the metal E. In Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC (1 PART 1 ed.). (IFAC Proceedings Volumes (IFAC-PapersOnline); Vol. 17, No. 1 PART 1). https://doi.org/10.3182/20080706-5-KR-1001.3971

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title = "Principal component analysis based support vector machine for the end point detection of the metal E",

abstract = "An endpoint detection using the algorithm of principal component analysis based support vector machine was developed for the plasma etching process. Because many endpoint detection techniques use a few manually selected wavelengths, noise render them ineffective and it is hard to select the important wavelengths. So the principal component algorithm with the whole wavelengths has been developed for the more effective monitoring of end point. And the support vector regression was followed for the real-time end point detection with reduced wavelengths to save the processing time. This approach was applied and demonstrated for a metal etching process of Al and 0.5\% Cu on the oxide stack with inductively coupled BCl2/Cl2 plasma.",

keywords = "Applications in semiconductor manufacturing, Process modeling and identification, Process observation and parameter estimation",

author = "Kyounghoon Han and Seunghyok Kim and Park, \{Kun Joo\} and Yoon, \{En Sup\} and Heeyeop Chae",

year = "2008",

doi = "10.3182/20080706-5-KR-1001.3971",

language = "English",

isbn = "9783902661005",

series = "IFAC Proceedings Volumes (IFAC-PapersOnline)",

number = "1 PART 1",

booktitle = "Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC",

edition = "1 PART 1",

note = "17th World Congress, International Federation of Automatic Control, IFAC ; Conference date: 06-07-2008 Through 11-07-2008",

}

Han, K, Kim, S, Park, KJ, Yoon, ES & Chae, H 2008, Principal component analysis based support vector machine for the end point detection of the metal E. in Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC. 1 PART 1 edn, IFAC Proceedings Volumes (IFAC-PapersOnline), no. 1 PART 1, vol. 17, 17th World Congress, International Federation of Automatic Control, IFAC, Seoul, Korea, Republic of, 6/07/08. https://doi.org/10.3182/20080706-5-KR-1001.3971

Principal component analysis based support vector machine for the end point detection of the metal E. / Han, Kyounghoon; Kim, Seunghyok; Park, Kun Joo et al.
Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC. 1 PART 1. ed. 2008. (IFAC Proceedings Volumes (IFAC-PapersOnline); Vol. 17, No. 1 PART 1).

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

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T1 - Principal component analysis based support vector machine for the end point detection of the metal E

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AU - Kim, Seunghyok

AU - Park, Kun Joo

AU - Yoon, En Sup

AU - Chae, Heeyeop

PY - 2008

Y1 - 2008

N2 - An endpoint detection using the algorithm of principal component analysis based support vector machine was developed for the plasma etching process. Because many endpoint detection techniques use a few manually selected wavelengths, noise render them ineffective and it is hard to select the important wavelengths. So the principal component algorithm with the whole wavelengths has been developed for the more effective monitoring of end point. And the support vector regression was followed for the real-time end point detection with reduced wavelengths to save the processing time. This approach was applied and demonstrated for a metal etching process of Al and 0.5% Cu on the oxide stack with inductively coupled BCl2/Cl2 plasma.

AB - An endpoint detection using the algorithm of principal component analysis based support vector machine was developed for the plasma etching process. Because many endpoint detection techniques use a few manually selected wavelengths, noise render them ineffective and it is hard to select the important wavelengths. So the principal component algorithm with the whole wavelengths has been developed for the more effective monitoring of end point. And the support vector regression was followed for the real-time end point detection with reduced wavelengths to save the processing time. This approach was applied and demonstrated for a metal etching process of Al and 0.5% Cu on the oxide stack with inductively coupled BCl2/Cl2 plasma.

KW - Applications in semiconductor manufacturing

KW - Process modeling and identification

KW - Process observation and parameter estimation

UR - https://www.scopus.com/pages/publications/79961020060

U2 - 10.3182/20080706-5-KR-1001.3971

DO - 10.3182/20080706-5-KR-1001.3971

M3 - Conference contribution

AN - SCOPUS:79961020060

SN - 9783902661005

T3 - IFAC Proceedings Volumes (IFAC-PapersOnline)

BT - Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC

T2 - 17th World Congress, International Federation of Automatic Control, IFAC

Y2 - 6 July 2008 through 11 July 2008

ER -

Principal component analysis based support vector machine for the end point detection of the metal E

Abstract

Publication series

Conference

Keywords

UN SDGs

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