Patents by Inventor Mark Dishner

Mark Dishner has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Computer-implemented methods for performing one or more defect-related functions
    Patent number: 9037280
    Abstract: Computer-implemented methods for performing one or more defect-related functions are provided. One method for identifying noise in inspection data includes identifying events detected in a number of sets of inspection data that is less than a predetermined number as noise. One method for binning defects includes binning the defects into groups based on defect characteristics and the sets of the inspection data in which the defects were detected. One method for selecting defects for defect analysis includes binning defects into group(s) based on proximity of the defects to each other and spatial signatures formed by the group(s). A different method for selecting defects for defect analysis includes selecting defects having the greatest diversity of defect characteristic(s) for defect analysis. One method includes classifying defects on a specimen using inspection data generated for the specimen combined with defect review data generated for the specimen.
    Type: Grant
    Filed: June 6, 2005
    Date of Patent: May 19, 2015
    Assignee: KLA-Tencor Technologies Corp.
    Inventors: Mark Dishner, Chris W. Lee, Sharon McCauley, Patrick Huet, David Wang
  • Computer-implemented methods for performing one or more defect-related functions
    Publication number: 20060287751
    Abstract: Computer-implemented methods for performing one or more defect-related functions are provided. One method for identifying noise in inspection data includes identifying events detected in a number of sets of inspection data that is less than a predetermined number as noise. One method for binning defects includes binning the defects into groups based on defect characteristics and the sets of the inspection data in which the defects were detected. One method for selecting defects for defect analysis includes binning defects into group(s) based on proximity of the defects to each other and spatial signatures formed by the group(s). A different method for selecting defects for defect analysis includes selecting defects having the greatest diversity of defect characteristic(s) for defect analysis. One method includes classifying defects on a specimen using inspection data generated for the specimen combined with defect review data generated for the specimen.
    Type: Application
    Filed: June 6, 2005
    Publication date: December 21, 2006
    Inventors: Mark Dishner, Chris Lee, Sharon McCauley, Patrick Huet, David Wang