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US Patent 10488188 System and method for removing noise from roughness measurements

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10488188
Date of Patent
November 26, 2019
Patent Application Number
16218338
Date Filed
December 12, 2018
Patent Citations
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US Patent 10176966 Edge detection system
Patent Citations Received
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US Patent 11508546 System and method for low-noise edge detection and its use for process monitoring and control
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US Patent 11521825 System and method for predicting stochastic-aware process window and yield and their use for process monitoring and control
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US Patent 11361937 System and method for generating and analyzing roughness measurements and their use for process monitoring and control
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US Patent 11380516 System and method for generating and analyzing roughness measurements and their use for process monitoring and control
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US Patent 11996265 System and method for generating and analyzing roughness measurements and their use for process monitoring and control
10
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US Patent 11004654 System and method for generating and analyzing roughness measurements
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US Patent 11004653 Edge detection system
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US Patent 11664188 Edge detection system
14
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Patent Primary Examiner
‌
Nicole M Ippolito
Patent abstract

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.

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