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    Matrix Factorisation Techniques for Endpoint Detection in Plasma Etching


    Ragnoli, Emanuele and McLoone, Seamus and Ringwood, John and Macgerailt, N. (2008) Matrix Factorisation Techniques for Endpoint Detection in Plasma Etching. In: IEEE/SEMI Advanced Semiconductor Manufacturing Conference, 2008. ASMC 2008. IEEE, pp. 156-161. ISBN 9781424419647

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    Abstract

    Advanced data mining techniques such as variable selection through matrix factorization have been intensively applied in the last ten years in the area of plasma-etch point detection using optimal emission spectroscopy (OES). OES data sets are enormous, consisting of measurements of over 2000 wavelength recorded at sample rates of 1 - 3 Hertz, and consequently, these techniques are needed in order to generate compact representations of the relevant process characteristics. To date, the main technique employed in this regard has been PCA (Principal Components Analysis), a matrix factorisation technique which generates linear combinations of the original variables that best capture the information in the data (in terms of variance explained). Recently, an alternative matrix factorisation technique, Non-Negative Matrix Factorisation (NMF) [1], has been gaining increasing attention in the fields of image feature extraction and blind source separation due to its tendency to yield sparse representations of data. The aim of this work is to introduce Non-Negative Matrix Factorisation to the semiconductor research community and to provide a comparison with PCA in order to highlight its properties.

    Item Type: Book Section
    Keywords: sputter etching; data mining; matrix decomposition; principal component analysis; spectroscopy;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Item ID: 8838
    Identification Number: https://doi.org/10.1109/ASMC.2008.4529021
    Depositing User: Professor John Ringwood
    Date Deposited: 20 Sep 2017 15:47
    Publisher: IEEE
    Refereed: Yes
    URI:

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