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Patent

Edge detection system

Chris A. Mack
TLDR
In this paper, an edge detection system is presented that generates a scanning electron microscope (SEM) linescan image of a pattern structure including a feature with edges that require detection.
Abstract
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.

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Citations
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Patent

Edge detection system and its use for optical proximity correction

Mack Chris
TL;DR: In this article, 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, and then 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.
Patent

System and method for removing noise from roughness measurements

Mack Chris
TL;DR: In this paper, 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, and then 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.
Patent

System and method for generating and analyzing roughness measurements

Mack Chris
TL;DR: In this article, 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, and then 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.
Patent

System and method for generating and analyzing roughness measurements and their use for process monitoring and control

Mack Chris
TL;DR: In this paper, 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, and then 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.
Patent

Edge detection system and its use for machine learning

Mack Chris
TL;DR: In this paper, the authors proposed a method to remove noise from roughness measurements to determine roughness of a feature in a pattern structure using an inverse linescan model, and evaluated a high-frequency portion of the biased power spectral density (PSD) dataset to determine a noise model for predicting noise over all frequencies, and subtracting the noise predicted by the determined noise model from a biased roughness measure.
References
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Journal ArticleDOI

Unbiased roughness measurements: Subtracting out SEM effects

TL;DR: In this paper, an inverse linescan model is used for edge detection and the noise in SEM edge and width measurements can be measured and removed statistically from roughness measurements, using different pixel sizes, magnifications, and frames of averaging on several different post-lithography and post-etch patterns.
Patent

Pattern inspection method

TL;DR: In this paper, a pattern inspection method of extracting a pattern edge shape from an image obtained by a scanning microscope and inspecting the pattern is presented. But the method is not suitable for inspection in the case of high dimensional images.
Proceedings ArticleDOI

Reducing roughness in extreme ultraviolet lithography

Chris A. Mack
TL;DR: In this paper, a new global optimization approach is proposed, taking advantage of the different strengths and weaknesses of lithography and etch, for the insertion of extreme ultraviolet (EUV) lithography as proposed in the coming years.
Proceedings ArticleDOI

Using the analytical linescan model for SEM metrology

TL;DR: The analytical linescan model (ALM) accomplishes this goal by using a physical model for linescan generation to constrain the possible shape of a linescan to allow edge positions to be estimated with very low sensitivity to noise.
Patent

Apparatus for data analysis

TL;DR: In this article, edge points are extracted by specifying a height (values indicating a distance from a substrate) on a pattern when edges of the pattern are extracted from a CD-SEM image.