Apparatus and method for inspecting pattern

Abstract

An operation part in a pattern inspection apparatus includes a defect candidate image generator for generating a binary defect candidate image representing a defect candidate area in an inspection image by comparing the inspection image with a reference image, in an inspection image masking part the inspection image is masked with the defect candidate image to obtain a masked inspection image. In a feature value calculation part, an autocorrelation feature value is obtained from the masked inspection image, and outputted to a classifying part. The classifying part comprises a classifier outputting a classification result on the basis of the autocorrelation feature value and a classifier construction part for constructing the classifier by learning. It is thereby possible to easily perform the high accurate classification of defect candidate using the autocorrelation feature value which is hard to characterize as compared with geometric feature value or feature value representing density.

1 . An apparatus for inspecting pattern on an object, comprising:
 a differential image generator for generating a differential image representing a difference between a grayscale inspection image representing pattern on an object and a grayscale reference image or a difference between two images obtained from said inspection image and said reference image, respectively, or a differential image obtained from an image representing said difference;   a defect candidate image generator for generating a defect candidate image representing an area which includes a defect candidate in said inspection image by comparing said inspection image with said reference image;   a differential image masking part for masking said differential image with said defect candidate image to obtain a masked differential image;   a feature value calculation part for obtaining an autocorrelation feature value from said masked differential image; and   a classifying part for performing a classification of said defect candidate on the basis of said autocorrelation feature value.   
   
   
       2 . The apparatus according to  claim 1 , further comprising
 an inspection image masking part for masking said inspection image with said defect candidate image to obtain a masked inspection image, wherein   said classification is also based on an autocorrelation feature value obtained from said masked inspection image.   
   
   
       3 . The apparatus according to  claim 2 , further comprising
 a reference image masking part for masking said reference image with said defect candidate image to obtain a masked reference image, wherein   said classification is also based on an autocorrelation feature value obtained from said masked reference image.   
   
   
       4 . The apparatus according to  claim 1 , wherein
 said classification is also based on an autocorrelation feature value obtained from said defect candidate image.   
   
   
       5 . The apparatus according to  claim 1 , wherein
 said classifying part comprises a classifier construction part for constructing a classifier by learning which outputs a classification result in accordance with autocorrelation feature value.   
   
   
       6 - 8 . (canceled) 
   
   
       9 . A method for inspecting pattern on an object, comprising the steps of:
 a) generating a differential image representing a difference between a grayscale inspection image representing pattern on an object and a grayscale reference image or a difference between two images obtained from said inspection image and said reference image, respectively, or a differential image obtained from an image representing said difference;   b) generating a defect candidate image representing an area which includes a defect candidate in said inspection image by comparing said inspection image with said reference image;   c) masking said differential image with said defect candidate image to obtain a masked differential image;   d) obtaining an autocorrelation feature value from said masked differential image; and   e) performing a classification of said defect candidate on the basis of said autocorrelation feature value.   
   
   
       10 . The method according to  claim 9 , further comprising the step of
 masking said inspection image with said defect candidate image to obtain a masked inspection image, wherein   said classification is also based on an autocorrelation feature value obtained from said masked inspection image.   
   
   
       11 . The method according to  claim 10 , further comprising the step of
 masking said reference image with said defect candidate image to obtain a masked reference image, wherein   said classification is also based on an autocorrelation feature value obtained from said masked reference image.   
   
   
       12 . The method according to  claim 9 , wherein
 said classification is also based on an autocorrelation feature value obtained from said defect candidate image.   
   
   
       13 . The method according to  claim 9 , wherein
 said classification is performed by a classifier constructed by learning which outputs a classification result in accordance with said autocorrelation feature value.   
   
   
       14 - 16 . (canceled)