• DocumentCode
    671575
  • Title

    Neural decision directed segmentation of silicon defects

  • Author

    Godbole, Aditi S. ; Tyagi, Kanishka ; Manry, Michael T.

  • Author_Institution
    Dept. of Electr. Eng., Univ. of Texas at Arlington, Arlington, TX, USA
  • fYear
    2013
  • fDate
    4-9 Aug. 2013
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    A system is proposed for recognizing four types of defects present in silicon wafer images. After preprocessing, the system applies four segmentation algorithms, one per defect type. Approximate posterior probabilities from a multilayer perceptron classifier aid in fusing the segmentors and making the final defect classification. Numerical results confirm the feasibility of our approach.
  • Keywords
    automatic optical inspection; elemental semiconductors; image classification; image recognition; image segmentation; multilayer perceptrons; probability; production engineering computing; silicon; Si; approximate posterior probabilities; final defect classification; multilayer perceptron classifier; neural decision directed segmentation; segmentation algorithms; segmentor fusion; silicon defect recognition; silicon wafer images; Copper; Feature extraction; Filtering algorithms; Image segmentation; Matrix converters; Plastics; Silicon;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2013 International Joint Conference on
  • Conference_Location
    Dallas, TX
  • ISSN
    2161-4393
  • Print_ISBN
    978-1-4673-6128-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.2013.6706915
  • Filename
    6706915
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=671575