PATTERN GROUPING METHOD BASED ON MACHINE LEARNING
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.