Systems and methods incorporating a neural network and a forward physical model for semiconductor applications
First Claim
1. A system configured to train a neural network, comprising:
- one or more computer subsystems; and
one or more components executed by the one or more computer subsystems, wherein the one or more components comprise;
a neural network configured for determining inverted features of input images in a training set for a specimen input to the neural network;
a forward physical model configured for reconstructing the input images from the inverted features thereby generating a set of output images corresponding to the input images in the training set; and
a residue layer configured for determining differences between the input images in the training set and their corresponding output images in the set;
wherein the one or more computer subsystems are configured for altering one or more parameters of the neural network based on the determined differences thereby training the neural network,wherein the one or more computer subsystems are further configured to input a runtime image for the specimen or another specimen into the trained neural network such that the trained neural network determines the inverted features for the runtime image, and wherein the inverted features are features of an optically corrected version of the runtime image.
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Abstract
Methods and systems for training a neural network are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a neural network configured for determining inverted features of input images in a training set for a specimen input to the neural network, a forward physical model configured for reconstructing the input images from the inverted features thereby generating a set of output images corresponding to the input images in the training set, and a residue layer configured for determining differences between the input images in the training set and their corresponding output images in the set. The one or more computer subsystems are configured for altering one or more parameters of the neural network based on the determined differences thereby training the neural network.
37 Citations
30 Claims
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1. A system configured to train a neural network, comprising:
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one or more computer subsystems; and one or more components executed by the one or more computer subsystems, wherein the one or more components comprise; a neural network configured for determining inverted features of input images in a training set for a specimen input to the neural network; a forward physical model configured for reconstructing the input images from the inverted features thereby generating a set of output images corresponding to the input images in the training set; and a residue layer configured for determining differences between the input images in the training set and their corresponding output images in the set; wherein the one or more computer subsystems are configured for altering one or more parameters of the neural network based on the determined differences thereby training the neural network, wherein the one or more computer subsystems are further configured to input a runtime image for the specimen or another specimen into the trained neural network such that the trained neural network determines the inverted features for the runtime image, and wherein the inverted features are features of an optically corrected version of the runtime image. - 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)
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27. A system configured to train a neural network, comprising:
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an imaging subsystem configured for generating images of a specimen; one or more computer subsystems configured for acquiring the images and generating a training set of input images from the acquired images; and one or more components executed by the one or more computer subsystems, wherein the one or more components comprise; a neural network configured for determining inverted features of the input images in the training set for the specimen input to the neural network; a forward physical model configured for reconstructing the input images from the inverted features thereby generating a set of output images corresponding to the input images in the training set; and a residue layer configured for determining differences between the input images in the training set and their corresponding output images in the set; wherein the one or more computer subsystems are configured for altering one or more parameters of the neural network based on the determined differences thereby training the neural network, wherein the one or more computer subsystems are further configured to input a runtime image for the specimen or another specimen into the trained neural network such that the trained neural network determines the inverted features for the runtime image, and wherein the inverted features are features of an optically corrected version of the runtime image.
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28. 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, wherein the computer-implemented method comprises:
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determining inverted features of input images in a training set for a specimen by inputting the training set of input images to a neural network; reconstructing the input images from the inverted features by inputting the inverted features into a forward physical model thereby generating a set of output images corresponding to the input images in the training set; determining differences between the input images in the training set and their corresponding output images in the set; altering one or more parameters of the neural network based on the determined differences thereby training the neural network; and inputting a runtime image for the specimen or another specimen into the trained neural network such that the trained neural network determines the inverted features for the runtime image, wherein the inverted features are features of an optically corrected version of the runtime image, wherein said determining the inverted features, reconstructing the input images, determining the differences, altering the one or more parameters, and inputting the runtime image are performed by one or more computer systems, wherein one or more components are executed by the one or more computer systems, and wherein the one or more components comprise the neural network and the forward physical model.
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29. A computer-implemented method for training a neural network, comprising:
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determining inverted features of input images in a training set for a specimen by inputting the training set of input images to a neural network; reconstructing the input images from the inverted features by inputting the inverted features into a forward physical model thereby generating a set of output images corresponding to the input images in the training set; determining differences between the input images in the training set and their corresponding output images in the set; altering one or more parameters of the neural network based on the determined differences thereby training the neural network; and inputting a runtime image for the specimen or another specimen into the trained neural network such that the trained neural network determines the inverted features for the runtime image, wherein the inverted features are features of an optically corrected version of the runtime image, wherein said determining the inverted features, reconstructing the input images, determining the differences, altering the one or more parameters, and inputting the runtime image are performed by one or more computer systems, wherein one or more components are executed by the one or more computer systems, and wherein the one or more components comprise the neural network and the forward physical model.
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30. A system configured to train a neural network, comprising:
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one or more computer subsystems; and one or more components executed by the one or more computer subsystems, wherein the one or more components comprise; a neural network configured for determining inverted features of input images in a training set for a specimen input to the neural network; a forward physical model configured for reconstructing the input images from the inverted features thereby generating a set of output images corresponding to the input images in the training set; and a residue layer configured for determining differences between the input images in the training set and their corresponding output images in the set; wherein the one or more computer subsystems are configured for altering one or more parameters of the neural network based on the determined differences thereby training the neural network, wherein the one or more computer subsystems are further configured to input a runtime image for the specimen or another specimen into the trained neural network such that the trained neural network determines the inverted features for the runtime image, and wherein the inverted features are features of an amplitude and phase version of the runtime image.
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Specification