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    Automatic detection of photoresist residual layer in lithography using a neural classification approach

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    Type
    Article
    Authors
    Gereige, Issam
    Robert, Stéphane
    Eid, Jessica
    KAUST Department
    KAUST Solar Center (KSC)
    Physical Science and Engineering (PSE) Division
    Date
    2012-09
    Permanent link to this record
    http://hdl.handle.net/10754/562289
    
    Metadata
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    Abstract
    Photolithography is a fundamental process in the semiconductor industry and it is considered as the key element towards extreme nanoscale integration. In this technique, a polymer photo sensitive mask with the desired patterns is created on the substrate to be etched. Roughly speaking, the areas to be etched are not covered with polymer. Thus, no residual layer should remain on these areas in order to insure an optimal transfer of the patterns on the substrate. In this paper, we propose a nondestructive method based on a classification approach achieved by artificial neural network for automatic residual layer detection from an ellipsometric signature. Only the case of regular defect, i.e. homogenous residual layer, will be considered. The limitation of the method will be discussed. Then, an experimental result on a 400 nm period grating manufactured with nanoimprint lithography is analyzed with our method. © 2012 Elsevier B.V. All rights reserved.
    Publisher
    Elsevier BV
    Journal
    Microelectronic Engineering
    DOI
    10.1016/j.mee.2012.02.032
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.mee.2012.02.032
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; KAUST Solar Center (KSC)

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