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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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: Li, K., Xue, Y., Cui, S., Niu, Q. (eds) Intelligent Computing in Smart Grid and Electrical Vehicles. ICSEE LSMS 2014 2014. Communications in Computer and Information Science, vol 463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45286-8_16
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DOI: https://doi.org/10.1007/978-3-662-45286-8_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45285-1
Online ISBN: 978-3-662-45286-8
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