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.
|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
|Kang Li, Yusheng Xue, Shumei Cui, Qun Niu
|Published - 2014
|Communications in Computer and Information Science