Lynn, Shane and Ringwood, John and MacGearailt, Niall (2012) Global and Local Virtual Metrology Models for a Plasma Etch Process. IEEE Transactions on Semiconductor Manufacturing, 25 (1). pp. 94-103. ISSN 0894-6507

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Abstract

Virtual metrology (VM) is the estimation of metrology variables that may be expensive or difficult to measure using readily available process information. This paper investigates the application of global and local VM schemes to a data set recorded from an industrial plasma etch chamber. Windowed VM models are shown to be the most accurate local VM scheme, capable of producing useful estimates of plasma etch rates over multiple chamber maintenance events and many thousands of wafers. Partial least-squares regression, artificial neural networks, and Gaussian process regression are investigated as candidate modeling techniques, with windowed Gaussian process regression models providing the most accurate results for the data set investigated.

Item Type:	Article
Keywords:	Gaussian process regression; local modeling; neural network applications; plasma etch; virtual metrology; VM;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering
Item ID:	3560
Depositing User:	Professor John Ringwood
Date Deposited:	30 Mar 2012 14:37
Journal or Publication Title:	IEEE Transactions on Semiconductor Manufacturing
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Refereed:	Yes
URI:	Publisher
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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