Training a neural network for defect detection in low resolution images
First Claim
1. A system configured to train a neural network for defect detection in low resolution images, comprising:
- an inspection tool comprising a high resolution imaging subsystem and a low resolution imaging subsystem, wherein the high and low resolution imaging subsystems comprise at least an energy source and a detector, wherein the energy source is configured to generate energy that is directed to a specimen, and wherein the detector is configured to detect energy from the specimen and to generate images responsive to the detected energy;
one or more computer subsystems configured for acquiring the images of the specimen generated by the high and low resolution imaging subsystems; and
one or more components executed by the one or more computer subsystems, wherein the one or more components comprise a high resolution neural network and a low resolution neural network; and
wherein the one or more computer subsystems are further configured for;
generating a training set of defect images, wherein at least one of the defect images is generated synthetically by the high resolution neural network using at least one of the images generated by the high resolution imaging subsystem;
training the low resolution neural network using the training set of defect images as input; and
detecting defects on another specimen by inputting the images generated for the other specimen by the low resolution imaging subsystem into the trained low resolution neural network.
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Abstract
Methods and systems for training a neural network for defect detection in low resolution images are provided. One system includes an inspection tool that includes high and low resolution imaging subsystems and one or more components that include a high resolution neural network and a low resolution neural network. Computer subsystem(s) of the system are configured for generating a training set of defect images. At least one of the defect images is generated synthetically by the high resolution neural network using an image generated by the high resolution imaging subsystem. The computer subsystem(s) are also configured for training the low resolution neural network using the training set of defect images as input. In addition, the computer subsystem(s) are configured for detecting defects on another specimen by inputting the images generated for the other specimen by the low resolution imaging subsystem into the trained low resolution neural network.
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Citations
31 Claims
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1. A system configured to train a neural network for defect detection in low resolution images, comprising:
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an inspection tool comprising a high resolution imaging subsystem and a low resolution imaging subsystem, wherein the high and low resolution imaging subsystems comprise at least an energy source and a detector, wherein the energy source is configured to generate energy that is directed to a specimen, and wherein the detector is configured to detect energy from the specimen and to generate images responsive to the detected energy; one or more computer subsystems configured for acquiring the images of the specimen generated by the high and low resolution imaging subsystems; and one or more components executed by the one or more computer subsystems, wherein the one or more components comprise a high resolution neural network and a low resolution neural network; and wherein the one or more computer subsystems are further configured for; generating a training set of defect images, wherein at least one of the defect images is generated synthetically by the high resolution neural network using at least one of the images generated by the high resolution imaging subsystem; training the low resolution neural network using the training set of defect images as input; and detecting defects on another specimen by inputting the images generated for the other specimen by the low resolution imaging subsystem into the trained low resolution neural network. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29)
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30. A non-transitory computer-readable medium, storing program instructions executable on one or more computer systems for performing a computer-implemented method for training a neural network for defect detection in low resolution images, wherein the computer-implemented method comprises:
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generating images for a specimen with high and low resolution imaging subsystems of an inspection tool, wherein the high and low resolution imaging subsystems comprise at least an energy source and a detector, wherein the energy source is configured to generate energy that is directed to the specimen, and wherein the detector is configured to detect energy from the specimen and to generate images responsive to the detected energy; wherein one or more components are executed by the one or more computer systems, and wherein the one or more components comprise a high resolution neural network and a low resolution neural network; generating a training set of defect images, wherein at least one of the defect images is generated synthetically by the high resolution neural network using at least one of the images generated by the high resolution imaging subsystem; training the low resolution neural network using the training set of defect images as input; and detecting defects on another specimen by inputting the images generated for the other specimen by the low resolution imaging subsystem into the trained low resolution neural network, wherein generating the training set, training the low resolution neural network, and detecting the defects are performed by the one or more computer systems.
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31. A computer-implemented method for training a neural network for defect detection in low resolution images, comprising:
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generating images for a specimen with high and low resolution imaging subsystems of an inspection tool, wherein the high and low resolution imaging subsystems comprise at least an energy source and a detector, wherein the energy source is configured to generate energy that is directed to the specimen, and wherein the detector is configured to detect energy from the specimen and to generate images responsive to the detected energy; wherein one or more components are executed by one or more computer systems, and wherein the one or more components comprise a high resolution neural network and a low resolution neural network; generating a training set of defect images, wherein at least one of the defect images is generated synthetically by the high resolution neural network using at least one of the images generated by the high resolution imaging subsystem; training the low resolution neural network using the training set of defect images as input; and detecting defects on another specimen by inputting the images generated for the other specimen by the low resolution imaging subsystem into the trained low resolution neural network, wherein generating the training set, training the low resolution neural network, and detecting the defects are performed by the one or more computer systems.
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Specification