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

Research output: Chapter in Book/Published conference outputConference publication


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 languageEnglish
Title of host publicationIntelligent Computing in Smart Grid and Electrical Vehicles
Subtitle of host publicationInternational 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
EditorsKang Li, Yusheng Xue, Shumei Cui, Qun Niu
ISBN (Electronic)9783662452868
ISBN (Print)978366245281
Publication statusPublished - 2014

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0937
ISSN (Electronic)1865-0929


  • PM
  • PdM
  • OES
  • RUL
  • Ion Beam Etch


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