Yield estimation and control
First Claim
1. A defect prediction method for a device manufacturing process involving production substrates processed by a lithographic apparatus, the method comprising:
- training, by a hardware computer system, a classification model using a training set comprising measured or determined values of a process parameter associated with the production substrates processed by the device manufacturing process and an indication regarding existence of defects associated with the production substrates processed in the device manufacturing process under the values of the process parameter, the process parameter representing a setting or condition of a physical process, a material, an object or an apparatus that occurred during, or is occurring during, application of physical processing to the production substrates; and
producing, by the hardware computer system, an output from the classification model that indicates a prediction of a defect for a substrate.
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Abstract
A defect prediction method for a device manufacturing process involving production substrates processed by a lithographic apparatus, the method including training a classification model using a training set including measured or determined values of a process parameter associated with the production substrates processed by the device manufacturing process and an indication regarding existence of defects associated with the production substrates processed in the device manufacturing process under the values of the process parameter, and producing an output from the classification model that indicates a prediction of a defect for a substrate.
31 Citations
26 Claims
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1. A defect prediction method for a device manufacturing process involving production substrates processed by a lithographic apparatus, the method comprising:
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training, by a hardware computer system, a classification model using a training set comprising measured or determined values of a process parameter associated with the production substrates processed by the device manufacturing process and an indication regarding existence of defects associated with the production substrates processed in the device manufacturing process under the values of the process parameter, the process parameter representing a setting or condition of a physical process, a material, an object or an apparatus that occurred during, or is occurring during, application of physical processing to the production substrates; and producing, by the hardware computer system, an output from the classification model that indicates a prediction of a defect for a substrate. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A method of training a classification model, the method comprising:
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predicting, by a hardware computer system, a defect in or on a substrate using the classification model, the classification model having, as an independent variable, a process parameter of a device manufacturing process for lithographically exposing substrates and/or a layout parameter of a pattern to be provided on a substrate using a lithographic apparatus, the process parameter representing a setting or condition of a physical process, a material, an object or an apparatus that occurred during, or is occurring during, application of physical processing to production substrates; receiving information regarding existence of a defect for a measured or determined value of the process parameter and/or layout parameter; and training, by a hardware computer system, the classification model based on the predicted defect and the information regarding existence of the defect for the measured or determined value of the process parameter and/or layout parameter. - View Dependent Claims (20, 21, 22, 23)
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- 16. A method of producing a classification model to facilitate defect prediction in a device manufacturing process involving production substrates processed by a lithographic apparatus, the method comprising training, by a hardware computer system, the classification model using a training set comprising measured or determined values of a process parameter of a plurality of substrates processed by the device manufacturing process and an indication regarding existence of defects associated with the values of the process parameter, the process parameter representing a setting or condition of a physical process, a material, an object or an apparatus that occurred during, or is occurring during, application of physical processing to production substrates.
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24. A non-transitory computer-readable medium comprising instructions therein, the instructions, when executed, configured to cause a computer apparatus to at least:
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train a classification model using a training set comprising measured or determined values of a process parameter associated with production substrates processed by the device manufacturing process and an indication regarding existence of defects associated with the production substrates processed under the values of the process parameter in a device manufacturing process involving production substrates processed by a lithographic apparatus, the process parameter representing a setting or condition of a physical process, a material, an object or an apparatus that occurred during, or is occurring during, application of physical processing to the production substrates; and produce an output from the classification model that indicates a prediction of a defect for a substrate.
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25. A non-transitory computer-readable medium comprising instructions therein, the instructions, when executed, configured to cause a computer apparatus to at least:
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predict a defect in or on a substrate using a classification model, the classification model having, as an independent variable, a process parameter of a device manufacturing process for lithographically exposing substrates and/or a layout parameter of a pattern to be provided on a substrate using a lithographic apparatus, the process parameter representing a setting or condition of a physical process, a material, an object or an apparatus that occurred during, or is occurring, during application of physical processing to production substrates; receive information regarding existence of a defect for a measured or determined value of the process parameter and/or layout parameter; and train the classification model based on the predicted defect and the information regarding existence of the defect for the measured or determined value of the process parameter and/or layout parameter.
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26. A non-transitory computer-readable medium comprising instructions therein, the instructions, when executed, configured to cause a computer apparatus to at least:
train a classification model to facilitate defect prediction in a device manufacturing process involving production substrates processed by a lithographic apparatus, using a training set comprising measured or determined values of a process parameter of a plurality of substrates processed by the device manufacturing process and an indication regarding existence of defects associated with the values of the process parameter, the process parameter representing a setting or condition of a physical process, a material, an object or an apparatus that occurred during, or is occurring during, application of physical processing to the production substrates.
Specification