Predictive maintenance for improved sustainability — an Ion beam etch endpoint detection system use case

Jian Wan; Seán McLoone; Patrick English; Paul O'Hara; Adrian Johnston

Predictive maintenance for improved sustainability — an Ion beam etch endpoint detection system use case

Jian Wan, Seán McLoone, Patrick English, Paul O'Hara, Adrian Johnston

Mechanical, Biomedical & Design Engineering

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

In modern semiconductor manufacturing facilities maintenance strategies are increasingly shifting from traditional preventive maintenance (PM) based approaches to more efficient and sustainable predictive maintenance (PdM) approaches. This paper describes the development of such an online PdM module for the endpoint detection system of an ion beam etch tool in semiconductor manufacturing. The developed system uses optical emission spectroscopy (OES) data from the endpoint detection system to estimate the RUL of lenses, a key detector component that degrades over time. Simulation studies for historical data for the use case demonstrate the effectiveness of the proposed PdM solution and the potential for improved sustainability that it affords.

Original language	English
Title of host publication	Intelligent Computing in Smart Grid and Electrical Vehicles
Subtitle of host publication	International Conference on Life System Modeling and Simulation, LSMS 2014 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2014 Shanghai, China, September 20-23, 2014 Proceedings, Part III
Editors	Kang Li, Yusheng Xue, Shumei Cui, Qun Niu
Publisher	Springer
Pages	147-156
ISBN (Electronic)	9783662452868
ISBN (Print)	978366245281
Publication status	Published - 2014

Publication series

Name	Communications in Computer and Information Science
Publisher	Springer
Volume	463
ISSN (Print)	1865-0937
ISSN (Electronic)	1865-0929

Keywords

PM
PdM
OES
RUL
Ion Beam Etch

Cite this

Wan, J., McLoone, S., English, P., O'Hara, P., & Johnston, A. (2014). Predictive maintenance for improved sustainability — an Ion beam etch endpoint detection system use case. In K. Li, Y. Xue, S. Cui, & Q. Niu (Eds.), Intelligent Computing in Smart Grid and Electrical Vehicles: International Conference on Life System Modeling and Simulation, LSMS 2014 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2014 Shanghai, China, September 20-23, 2014 Proceedings, Part III (pp. 147-156). (Communications in Computer and Information Science; Vol. 463). Springer.

Wan, Jian ; McLoone, Seán ; English, Patrick et al. / Predictive maintenance for improved sustainability — an Ion beam etch endpoint detection system use case. Intelligent Computing in Smart Grid and Electrical Vehicles: International Conference on Life System Modeling and Simulation, LSMS 2014 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2014 Shanghai, China, September 20-23, 2014 Proceedings, Part III. editor / Kang Li ; Yusheng Xue ; Shumei Cui ; Qun Niu. Springer, 2014. pp. 147-156 (Communications in Computer and Information Science).

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title = "Predictive maintenance for improved sustainability — an Ion beam etch endpoint detection system use case",

abstract = "In modern semiconductor manufacturing facilities maintenance strategies are increasingly shifting from traditional preventive maintenance (PM) based approaches to more efficient and sustainable predictive maintenance (PdM) approaches. This paper describes the development of such an online PdM module for the endpoint detection system of an ion beam etch tool in semiconductor manufacturing. The developed system uses optical emission spectroscopy (OES) data from the endpoint detection system to estimate the RUL of lenses, a key detector component that degrades over time. Simulation studies for historical data for the use case demonstrate the effectiveness of the proposed PdM solution and the potential for improved sustainability that it affords.",

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series = "Communications in Computer and Information Science",

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Wan, J, McLoone, S, English, P, O'Hara, P & Johnston, A 2014, Predictive maintenance for improved sustainability — an Ion beam etch endpoint detection system use case. in K Li, Y Xue, S Cui & Q Niu (eds), Intelligent Computing in Smart Grid and Electrical Vehicles: International Conference on Life System Modeling and Simulation, LSMS 2014 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2014 Shanghai, China, September 20-23, 2014 Proceedings, Part III. Communications in Computer and Information Science, vol. 463, Springer, pp. 147-156.

Predictive maintenance for improved sustainability — an Ion beam etch endpoint detection system use case. / Wan, Jian; McLoone, Seán; English, Patrick et al.
Intelligent Computing in Smart Grid and Electrical Vehicles: International Conference on Life System Modeling and Simulation, LSMS 2014 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2014 Shanghai, China, September 20-23, 2014 Proceedings, Part III. ed. / Kang Li; Yusheng Xue; Shumei Cui; Qun Niu. Springer, 2014. p. 147-156 (Communications in Computer and Information Science; Vol. 463).

Research output: Chapter in Book/Published conference output › Conference publication

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T1 - Predictive maintenance for improved sustainability — an Ion beam etch endpoint detection system use case

AU - Wan, Jian

AU - McLoone, Seán

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N2 - In modern semiconductor manufacturing facilities maintenance strategies are increasingly shifting from traditional preventive maintenance (PM) based approaches to more efficient and sustainable predictive maintenance (PdM) approaches. This paper describes the development of such an online PdM module for the endpoint detection system of an ion beam etch tool in semiconductor manufacturing. The developed system uses optical emission spectroscopy (OES) data from the endpoint detection system to estimate the RUL of lenses, a key detector component that degrades over time. Simulation studies for historical data for the use case demonstrate the effectiveness of the proposed PdM solution and the potential for improved sustainability that it affords.

AB - In modern semiconductor manufacturing facilities maintenance strategies are increasingly shifting from traditional preventive maintenance (PM) based approaches to more efficient and sustainable predictive maintenance (PdM) approaches. This paper describes the development of such an online PdM module for the endpoint detection system of an ion beam etch tool in semiconductor manufacturing. The developed system uses optical emission spectroscopy (OES) data from the endpoint detection system to estimate the RUL of lenses, a key detector component that degrades over time. Simulation studies for historical data for the use case demonstrate the effectiveness of the proposed PdM solution and the potential for improved sustainability that it affords.

KW - PM

KW - PdM

KW - OES

KW - RUL

KW - Ion Beam Etch

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BT - Intelligent Computing in Smart Grid and Electrical Vehicles

A2 - Li, Kang

A2 - Xue, Yusheng

A2 - Cui, Shumei

A2 - Niu, Qun

PB - Springer

ER -

Wan J, McLoone S, English P, O'Hara P, Johnston A. Predictive maintenance for improved sustainability — an Ion beam etch endpoint detection system use case. In Li K, Xue Y, Cui S, Niu Q, editors, Intelligent Computing in Smart Grid and Electrical Vehicles: International Conference on Life System Modeling and Simulation, LSMS 2014 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2014 Shanghai, China, September 20-23, 2014 Proceedings, Part III. Springer. 2014. p. 147-156. (Communications in Computer and Information Science).

Predictive maintenance for improved sustainability — an Ion beam etch endpoint detection system use case

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