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Automatic Control and Machine Learning for Semiconductor Manufacturing: Review and Challenges

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Susto, Gian Antonio, Pampuri, Simone, Schirru, Andrea, De Nicolao, Guiseppe, McLoone, Sean F. and Beghi, Alessandro (2012) Automatic Control and Machine Learning for Semiconductor Manufacturing: Review and Challenges. In: 10th European Workshop on Advanced Control and Diagnosis (ACD 2012), Nov. 8-9, 2012, Technical University of Denmark, Kgs. Lyngby, Denmark.

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Abstract

Semiconductor manufacturing is one of the most technologically advanced industrial sectors. Process quality and control are critical for decreasing costs and increasing yield. The contribution of automatic control and statistical modeling in this area can drastically impact production performance. For this reason in the past decade major collaborative research projects have been undertaken between fab industries and academia in the areas of Virtual Metrology, Predictive Maintenance, Fault Detection, Run-to-Run control and modeling. In this paper we review some this research, discuss its impact on production and highlight current challenges.

Item Type:	Conference or Workshop Item (Paper)
Keywords:	Intelligent manufacturing systems; Predictive maintenance; Fault detection; Soft Sensor; Virtual Metrology; Run-to-Run Control; Machine Learning;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering
Item ID:	4184
Depositing User:	Sean McLoone
Date Deposited:	04 Feb 2013 12:54
Journal or Publication Title:	Proceedings of the 10th European Workshop on Advanced Control and Diagnosis (ACD 2012)
Refereed:	Yes
URI:	https://mural.maynoothuniversity.ie/id/eprint/4184
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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