Adaptive Local Threshold and Color Filtering

A computer-implemented method for detecting defects on a wafer, comprising:

• determining local statistics of pixels in output for a wafer generated using an inspection system;
• determining which of the pixels are outliers based on the local statistics;
• comparing the outliers to the pixels surrounding the outliers to identify the outliers that do not belong to a cluster of outliers as defect candidates and to further identify the outliers that belong to a cluster of outliers as local color nuisances;
• determining a value for a difference in color between the pixels of the defect candidates and the pixels surrounding the defect candidates;
• identifying the defect candidates that have a value for the difference in the color that is greater than or equal to a predetermined value as nuisance defects;
• and
• identifying the defect candidates that have a value for the difference in the color that is less than the predetermined value as real defects, wherein determining the local statistics, determining which of the pixels are the outliers, comparing the outliers, determining the value for the difference in the color, identifying the defect candidates as the nuisance defects, and identifying the defect candidates as the real defects are performed by a computer system.

A non-transitory computer-readable medium, storing program instructions executable on a computer system for performing a computer-implemented method for detecting defects on a wafer, wherein the computer-implemented method comprises:

• determining local statistics of pixels in output for a wafer generated using an inspection system;
• determining which of the pixels are outliers based on the local statistics;
• comparing the outliers to the pixels surrounding the outliers to identify the outliers that do not belong to a cluster of outliers as defect candidates and to further identify the outliers that belong to a cluster of outliers as local color nuisances;
• determining a value for a difference in color between the pixels of the defect candidates and the pixels surrounding the defect candidates;
• identifying the defect candidates that have a value for the difference in the color that is greater than or equal to a predetermined value as nuisance defects;
• and
• identifying the defect candidates that have a value for the difference in the color that is less than the predetermined value as real defects.