Abstract: A population of data points each having three or more parameters associated therewith, such as multi-channel defect data from an optical scanner, are plotted in three dimensions, and groupings of data points are identified. Boundary surfaces are defined in the three-dimensional space for delineating groupings of data points. The different groupings correspond to different data classifications or types. Classification algorithms based on the boundary surfaces are defined. When applied to defect classification, the algorithms can be exported to an optical scanner for runtime classification of defects. An algorithm for identifying a particular grouping of data points can be defined as a Boolean combination of grouping rules from two or more different n-dimensional representations, where n can be either 2 or 3 for each representation.

Abstract: A population of data points each having three or more parameters associated therewith, such as multi-channel defect data from an optical scanner, are plotted in three dimensions, and groupings of data points are identified. Boundary surfaces are defined in the three-dimensional space for delineating groupings of data points. The different groupings correspond to different data classifications or types. Classification algorithms based on the boundary surfaces are defined. When applied to defect classification, the algorithms can be exported to an optical scanner for runtime classification of defects. An algorithm for identifying a particular grouping of data points can be defined as a Boolean combination of grouping rules from two or more different n-dimensional representations, where n can be either 2 or 3 for each representation.