Pattern grouping method based on machine learning

Abstract

A pattern grouping method may include receiving an image of a first pattern, generating a first fixed-dimensional feature vector using trained model parameters applying to the received image, and assigning the first fixed-dimensional feature vector a first bucket ID. The method may further include creating a new bucket ID for the first fixed-dimensional feature vector in response to determining that the first pattern does not belong to one of a plurality of buckets corresponding to defect patterns, or mapping the first fixed-dimensional feature vector to the first bucket ID in response to determining that the first pattern belongs to one of a plurality of buckets corresponding to defect patterns.

1 - 15 . (canceled) 
     
     
         16 . A non-transitory computer readable medium storing a set of instructions that is executable by one or more processors of a system to cause the system to perform a method comprising:
 receiving an image of a first pattern;   generating a first fixed-dimensional feature vector using trained model parameters, the model parameters being based on the received image; and   assigning the first fixed-dimensional feature vector a first bucket identity (ID).   
     
     
         17 . The computer readable medium of  claim 16 , wherein in assigning the first fixed-dimensional feature vector the first bucket ID, the set of instructions causes the system to further perform:
 creating a new bucket ID for the first fixed-dimensional feature vector in response to a determination that the first pattern does not belong to one of a plurality of buckets corresponding to defect patterns.   
     
     
         18 . The computer readable medium of  claim 16 , wherein in assigning the first fixed-dimensional feature vector the first bucket ID, the set of instructions causes the system to further perform:
 mapping the first fixed-dimensional feature vector to the first bucket ID in response to a determination that the first pattern belongs to one of a plurality of buckets corresponding to defect patterns.   
     
     
         19 . The computer readable medium of  claim 17 , wherein the defect patterns comprise GDS information associated with defects. 
     
     
         20 . The computer readable medium of  claim 19 , wherein the defect patterns comprise information derived from the GDS information that includes number of sides, number of angles, dimension, shape, or a combination thereof. 
     
     
         21 . The computer readable medium of  claim 16 , wherein the fixed-dimensional feature vector is a one-dimensional feature vector. 
     
     
         22 . The computer readable medium of  claim 16 , wherein the set of instructions causes the system to further perform the following to obtain the trained model parameters:
 obtaining a plurality of images of a plurality of patterns with assigned bucket IDs; and   training model parameters for a deep learning network.   
     
     
         23 . The computer readable medium of  claim 22 , wherein the set of instructions causes the system to further perform the following to obtain the trained model parameters:
 applying parameters of a single polygon located in a center of one of a plurality of images for the deep learning network.   
     
     
         24 . The computer readable medium of  claim 16 , wherein the set of instructions causes the system to further perform:
 pre-training a linear classifier network based on Graphic Data System (GDS) of a sample.   
     
     
         25 . The computer readable medium of  claim 24 , wherein the set of instructions causes the system to further perform:
 identifying a portion of the GDS that is associated with a region,   generating label data for the portion of the GDS that indicates a location of the region, and that indicates a type of shape of polygon data associated with the portion of the GDS, and   pre-training the linear classifier network based on the portion of the GDS and based on the label data.   
     
     
         26 . An apparatus comprising:
 a memory storing a set of instructions; and   at least one processor configured to execute the set of instructions to cause the apparatus to perform:
 receiving an image of a first pattern; 
 generating a first fixed-dimensional feature vector using trained model parameters, the model parameters being based on the received image; and 
 assigning the first fixed-dimensional feature vector a first bucket identity (ID). 
   
     
     
         27 . The apparatus of  claim 26 , wherein in assigning the first fixed-dimensional feature vector the first bucket ID, the at least one processor is configured to execute the set of instructions to cause the apparatus to further perform:
 creating a new bucket ID for the first fixed-dimensional feature vector in response to a determination that the first pattern does not belong to one of a plurality of buckets corresponding to defect patterns.   
     
     
         28 . The apparatus of  claim 26 , wherein in assigning the first fixed-dimensional feature vector the first bucket ID, the at least one processor is configured to execute the set of instructions to cause the apparatus to further perform:
 mapping the first fixed-dimensional feature vector to the first bucket ID in response to a determination that the first pattern belongs to one of a plurality of buckets corresponding to defect patterns.   
     
     
         29 . The apparatus of  claim 27 , wherein the defect patterns comprise GDS information associated with defects. 
     
     
         30 . The apparatus of  claim 29 , wherein the defect patterns comprise information derived from the GDS information that includes number of sides, number of angles, dimension, shape, or a combination thereof. 
     
     
         31 . The apparatus of  claim 26 , wherein the fixed-dimensional feature vector is a one-dimensional feature vector. 
     
     
         32 . The apparatus of  claim 26 , wherein the at least one processor is configured to execute the set of instructions to cause the apparatus to further perform the following to obtain the trained model parameters:
 obtaining a plurality of images of a plurality of patterns with assigned bucket IDs; and   training model parameters for a deep learning network.   
     
     
         33 . The apparatus of  claim 32 , wherein the at least one processor is configured to execute the set of instructions to cause the apparatus to further perform the following to obtain the trained model parameters:
 applying parameters of a single polygon located in a center of one of a plurality of images for the deep learning network.   
     
     
         34 . The apparatus of  claim 26 , wherein the at least one processor is configured to execute the set of instructions to cause the apparatus to further perform:
 pre-training a linear classifier network based on Graphic Data System (GDS) of a sample.   
     
     
         35 . The apparatus of  claim 34 , wherein the at least one processor is configured to execute the set of instructions to cause the apparatus to further perform:
 identifying a portion of the GDS that is associated with a region,   generating label data for the portion of the GDS that indicates a location of the region, and that indicates a type of shape of polygon data associated with the portion of the GDS, and   pre-training the linear classifier network based on the portion of the GDS and based on the label data.