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US 2021/0334608 A1Published application
Abnormal Wafer Image Classification
AssigneePDF Solutions, Inc.
InventorsTomonori Honda, Richard Burch, Qing Zhu, Jeffrey Drue David
Life of this patent
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Abstract
A semiconductor image classifier. Convolution functions are applied to modify the wafer images in order to extract key information about the image. The modified images are condensed then processed through a series of pairwise classifiers, each classifier configured to determine that the image is more like one of the pair than the other. Probabilities from each classifier are collected to form a prediction for each image.
Claims20
A method comprising:
- obtaining a first plurality of sample images of semiconductor wafers formed on a semiconductor substrate, the first plurality of sample images including a plurality of different image types;
- applying at least a first convolution function to each of the first plurality of images to generate a plurality of modified images each corresponding to a respective one of the plurality of sample images;
- applying a pooling function to each of the modified images to form a plurality of corresponding vectors each defining a plurality of features for each respective modified image;
- performing variable selection on each corresponding vector in order to select a smaller set of features from the plurality of features that are more important in determining image type for the vector;
- processing each corresponding vector in a plurality of machine-learning-based pairwise classifier models, each of the pairwise classifier models configured to determine a corresponding probability that the vector is more like one of unique plurality of pairs of the plurality of different image types;
- inputting the determined probabilities from the processing step into a final machine-learning-based classifier model configured to assign one of the plurality of image types to each corresponding vector on the basis of the determined probabilities from the processing step;
- and
- running the final machine-learning-based classifier model.
5 more elements
A method comprising:
- obtaining a first plurality of sample images of semiconductor wafers formed on a semiconductor substrate, the first plurality of sample images including a plurality of different classes of defects appearing on the sample images;
- applying a plurality of mathematical operators, individually or in various combinations, to each of the first plurality of images thereby generating a plurality of modified images, each modified image corresponding to a respective one of the plurality of sample images;
- condensing the plurality of modified images to a plurality of single-dimensional representations, each single-dimensional representation defining a plurality of features for each corresponding modified image;
- performing variable selection on each single-dimensional representation in order to select a smaller set of features from the plurality of features that are more important in determining the class of defect for the single-dimensional representation;
- processing each corresponding single-dimensional representation in a plurality of machine-learning-based pairwise classifier models, each of the pairwise classifier models configured to determine a corresponding probability that the single-dimensional representation is more indicative of one of a unique plurality of pairs of the plurality of different classes of defects;
- and
- running a final machine-learning-based classifier model configured to assign one of the plurality of classes of defects to each corresponding single-dimensional representation on the basis of the determined probabilities from the processing step.
4 more elements
A method comprising:
- obtaining a sample image;
- processing the sample image through a series of pairwise classifiers, each pairwise classifier configured to determine a probability that the sample image is more like one than the other of a unique pair of a plurality of classes of defects;
- and
- processing the determined probabilities to generate a final prediction for assigning one of the plurality of classes of defects to the sample image.
1 more element
The method of claim 19, further comprising:
- repeating the method steps for a plurality of sample images.
A method comprising:
- obtaining a first plurality of sample images of semiconductor wafers formed on a semiconductor substrate, the first plurality of sample images including a plurality of different image types;
- computing a plurality of single-dimension metrics each corresponding to a respective one of the plurality of sample images, each metric each defining a plurality of features for the respective sample image;
- performing variable selection on each metric in order to select a smaller set of features from the plurality of features that are more important in determining image type for the metric;
- processing each corresponding metric in a plurality of machine-learning-based pairwise classifier models, each of the pairwise classifier models configured to determine a corresponding probability that the metric is more indicative of one of a unique plurality of pairs of the plurality of different image types;
- and
- running a final machine-learning-based classifier model configured to assign one of the plurality of image types to each corresponding vector on the basis of the determined probabilities from the processing step.
3 more elements
Dependencies are inferred from the claim text.