Patents by Inventor Chris Mack
Chris Mack has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20200211813Abstract: Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.Type: ApplicationFiled: December 30, 2019Publication date: July 2, 2020Applicant: FRACTILIA, LLCInventor: Chris MACK
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Patent number: 10665418Abstract: Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.Type: GrantFiled: February 20, 2019Date of Patent: May 26, 2020Assignee: FRACTILIA, LLCInventor: Chris Mack
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Patent number: 10665417Abstract: Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.Type: GrantFiled: February 20, 2019Date of Patent: May 26, 2020Assignee: FRACTILIA, LLCInventor: Chris Mack
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Patent number: 10664955Abstract: Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method includes generating, using an imaging device, a set of one or more images, each including an instance of a feature within a respective pattern structure. The method also includes detecting edges of the features within the pattern structure of each image using an inverse linescan model, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure provided as part of a training data set to a machine learning model.Type: GrantFiled: May 17, 2019Date of Patent: May 26, 2020Assignee: FRACTILIA, LLCInventor: Chris Mack
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Patent number: 10656532Abstract: Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.Type: GrantFiled: February 22, 2019Date of Patent: May 19, 2020Assignee: FRACTILIA, LLCInventor: Chris Mack
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Patent number: 10648801Abstract: Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.Type: GrantFiled: February 22, 2019Date of Patent: May 12, 2020Assignee: FRACTILIA, LLCInventor: Chris Mack
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Publication number: 20200118789Abstract: An edge detection system is provided that generates a scanning electron microscope (SEM) linescan image of a pattern structure including a feature with edges that require detection. The edge detection system includes an inverse linescan model tool that receives measured linescan information for the feature from the SEM. In response, the inverse linescan model tool provides feature geometry information that includes the position of the detected edges of the feature.Type: ApplicationFiled: December 16, 2019Publication date: April 16, 2020Applicant: FRACTILIA, LLCInventor: Chris MACK
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Patent number: 10522322Abstract: Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.Type: GrantFiled: December 12, 2018Date of Patent: December 31, 2019Assignee: Fractilia, LLCInventor: Chris Mack
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Patent number: 10510509Abstract: An edge detection system is provided that generates a scanning electron microscope (SEM) linescan image of a pattern structure including a feature with edges that require detection. The edge detection system includes an inverse linescan model tool that receives measured linescan information for the feature from the SEM. In response, the inverse linescan model tool provides feature geometry information that includes the position of the detected edges of the feature.Type: GrantFiled: December 17, 2018Date of Patent: December 17, 2019Assignee: Fractilia, LLCInventor: Chris Mack
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Patent number: 10488188Abstract: Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.Type: GrantFiled: December 12, 2018Date of Patent: November 26, 2019Assignee: Fractilia, LLCInventor: Chris Mack
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Publication number: 20190272623Abstract: Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method includes generating, using an imaging device, a set of one or more images, each including an instance of a feature within a respective pattern structure. The method also includes detecting edges of the features within the pattern structure of each image using an inverse linescan model, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure provided as part of a training data set to a machine learning model.Type: ApplicationFiled: May 17, 2019Publication date: September 5, 2019Applicant: FRACTILIA, LLCInventor: Chris MACK
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Publication number: 20190187570Abstract: Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.Type: ApplicationFiled: February 22, 2019Publication date: June 20, 2019Applicant: FRACTILIA, LLCInventor: Chris MACK
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Publication number: 20190186909Abstract: Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.Type: ApplicationFiled: February 22, 2019Publication date: June 20, 2019Applicant: FRACTILIA, LLCInventor: Chris MACK
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Publication number: 20190180977Abstract: Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.Type: ApplicationFiled: February 20, 2019Publication date: June 13, 2019Applicant: FRACTILIA, LLCInventor: Chris MACK
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Publication number: 20190180976Abstract: Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.Type: ApplicationFiled: February 20, 2019Publication date: June 13, 2019Applicant: FRACTILIA, LLCInventor: Chris MACK
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Publication number: 20190164303Abstract: Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.Type: ApplicationFiled: December 12, 2018Publication date: May 30, 2019Applicant: FRACTILIA, LLCInventor: Chris MACK
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Publication number: 20190139736Abstract: An edge detection system is provided that generates a scanning electron microscope (SEM) linescan image of a pattern structure including a feature with edges that require detection. The edge detection system includes an inverse linescan model tool that receives measured linescan information for the feature from the SEM. In response, the inverse linescan model tool provides feature geometry information that includes the position of the detected edges of the feature.Type: ApplicationFiled: December 17, 2018Publication date: May 9, 2019Applicant: FRACTILIA, LLCInventor: Chris MACK
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Publication number: 20190113338Abstract: Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.Type: ApplicationFiled: December 12, 2018Publication date: April 18, 2019Applicant: FRACTILIA, LLCInventor: Chris MACK
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Patent number: 10176966Abstract: An edge detection system is provided that generates a scanning electron microscope (SEM) linescan image of a pattern structure including a feature with edges that require detection. The edge detection system includes an inverse linescan model tool that receives measured linescan information for the feature from the SEM. In response, the inverse linescan model tool provides feature geometry information that includes the position of the detected edges of the feature.Type: GrantFiled: February 8, 2018Date of Patent: January 8, 2019Assignee: Fractilia, LLCInventor: Chris Mack
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Patent number: 9188974Abstract: Various computer-implemented methods are provided. One method includes determining errors across a field of a lens of a lithography system based on wafer measurements. In addition, the method includes separating the errors into correctable and non-correctable errors across the field. The errors may include dose errors, focus errors, or dose and focus errors. In another embodiment, the method may include determining correction terms for parameter(s) of the lithography system, which if applied to the parameter(s), the correctable errors would be eliminated resulting in approximately optimal imaging performance of the lithography system. Another method includes controlling one or more parameters of features within substantially an entire printed area on a product wafer using a limited number of wafer measurements performed on a test wafer. The wafer measurements may be performed on a first feature type, and the features that are controlled may include a second, different feature type.Type: GrantFiled: July 17, 2011Date of Patent: November 17, 2015Assignee: KLA-Tencor Technologies Corp.Inventors: Chris Mack, Moshe E Preil