Generative adversarial networks (GANs) for simulating specimen images
AssigneeKLA Corporation
InventorBjorn Brauer
Life of this patent
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Abstract
Methods and systems for generating a simulated image for a specimen are provided. One system includes one or more computer subsystems and one or more components executed by the one or more computer subsystems. The one or more components include a generative adversarial network (GAN), e.g., a conditional GAN (cGAN), trained with a training set that includes portions of design data for one or more specimens designated as training inputs and corresponding images of the one or more specimens designated as training outputs. The one or more computer subsystems are configured for generating a simulated image for a specimen by inputting a portion of design data for the specimen into the GAN.
Claims18
A system configured to generate a simulated image of a specimen, 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 generative adversarial network trained with a training set comprising portions of design data for one or more specimens designated as training inputs and corresponding images of the one or more specimens designated as training outputs;
- and
- wherein the one or more computer subsystems are configured for:
- generating a portion of design data for a specimen input to the generative adversarial network by modifying original design data for the specimen with a synthetic defect;
- and
- generating a simulated image for the specimen by inputting the portion of the design data for the specimen into the generative adversarial network, wherein the simulated image comprises an augmented defect image of the synthetic defect.
A non-transitory computer-readable medium, storing program instructions executable on one or more computer systems for performing a computer-implemented method for generating a simulated image of a specimen, wherein the computer-implemented method comprises:
- generating a portion of design data for a specimen input to a generative adversarial network by modifying original design data for the specimen with a synthetic defect;
- generating a simulated image for the specimen by inputting the portion of the design data for the specimen into the generative adversarial network, wherein the simulated image comprises an augmented defect image of the synthetic defect, and wherein said inputting is performed by the one or more computer systems;
- wherein one or more components are executed by the one or more computer systems, wherein the one or more components comprise the generative adversarial network;
- and
- wherein the generative adversarial network is trained with a training set comprising portions of design data for one or more specimens designated as training inputs and corresponding images of the one or more specimens designated as training outputs.
A computer-implemented method for generating a simulated image of a specimen, comprising:
- generating a portion of design data for a specimen input to a generative adversarial network by modifying original design data for the specimen with a synthetic defect;
- generating a simulated image for the specimen by inputting the portion of the design data for the specimen into the generative adversarial network, wherein the simulated image comprises an augmented defect image of the synthetic defect, and wherein said inputting is performed by one or more computer subsystems;
- wherein one or more components are executed by the one or more computer subsystems, wherein the one or more components comprise the generative adversarial network;
- and
- wherein the generative adversarial network is trained with a training set comprising portions of design data for one or more specimens designated as training inputs and corresponding images of the one or more specimens designated as training outputs.
Dependencies are inferred from the claim text.