Patent•
System and method for generating and analyzing roughness measurements and their use for process monitoring and control
04 Mar 2021-
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.
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.
Citations
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Patent•
20 Jun 2019
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.
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.
7 citations
Patent•
18 Apr 2019
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.
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.
6 citations
Patent•
30 May 2019
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.
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.
6 citations
Patent•
17 Dec 2018
TL;DR: 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.
5 citations
Patent•
05 Sep 2019
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.
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 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.
1 citations
References
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Patent•
08 Jan 2004TL;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.
Abstract: The present invention may include a pattern inspection method of extracting a pattern edge shape from an image obtained by a scanning microscope and inspecting the pattern A control section and a computer of the scanning microscope process the intensity distribution of reflected electrons or secondary electrons, find the distribution of gate lengths in a single gate from data about edge positions, estimate the transistor performance by assuming a finally fabricated transistor to be a parallel connection of a plurality of transistors having various gate lengths, and determine the pattern quality and grade based on an estimated result In this manner, it is possible to highly, accurately and quickly estimate an effect of edge roughness on the device performance and highly accurately and efficiently inspect patterns in accordance with device specifications
44 citations
Patent•
30 Mar 2009TL;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.
Abstract: 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. Further, LER values obtained by the extraction or a Fourier spectrum of the LER are obtained. When the same sample is previously observed with the AFM and the CD-SEM, a size of the LER obtained by specifying a height, an auto-correlation distance of the LER, or an index called the spectrum is obtained from results of the AFM observation. Further, theses indices obtained by specifying image processing conditions for detecting the edge points from the CD-SEM observation result are obtained. Also, it is determined that heights providing values when the values are matched correspond to the image processing conditions and then, the edge points are extracted from the CD-SEM IMAGE instead of the AFM observation by using the image processing conditions.
23 citations
Patent•
26 Mar 2010TL;DR: In this paper, the authors provide apparatus and methods for processing substrates using pooled statistically based variance data, which can include Pooled Polymer De-protection Variance (PPDV) data that can be used to determine microbridging defect data, LER defect data and LWR defect data.
Abstract: The invention provides apparatus and methods for processing substrates using pooled statistically based variance data. The statistically based variance data can include Pooled Polymer De-protection Variance (PPDV) data that can be used to determine micro-bridging defect data, LER defect data, and LWR defect data.
21 citations
Patent•
21 Jul 2005TL;DR: In this paper, the equipment acquires data of edge roughness over a sufficiently long area, integrates a components corresponding to a spatial frequency region being set on a power spectrum by the operator, and displays them on a length measuring SEM.
Abstract: Equipment extracts components of spatial frequency that need to be evaluated in manufacturing a device or in analyzing a material or process out of edge roughness on fine line patterns and displays them as indexes. The equipment acquires data of edge roughness over a sufficiently long area, integrates a components corresponding to a spatial frequency region being set on a power spectrum by the operator, and displays them on a length measuring SEM. Alternatively, the equipment divides the edge roughness data of the sufficiently long area, computes long-period roughness and short-period roughness that correspond to an arbitrary inspection area by performing statistical processing and fitting based on theoretical calculation, and displays them on the length measuring SEM.
20 citations
Patent•
10 Dec 2004TL;DR: In this paper, a non-circular non-linear shape is fitted to the plurality of points on an edge of the feature in the image and a roughness parameter for the feature is computed in response to the respective distances.
Abstract: A method for evaluating a feature, consisting of receiving an image of the feature and determining respective coordinates of a plurality of points on an edge of the feature in the image. A figure having a non-circular non-linear shape is fitted to the plurality of points, and respective distances between the plurality of points and the figure are determined. A roughness parameter for the feature is computed in response to the respective distances. The method finds application in the analysis of critical dimensions (CD) of integrated circuits and, particularly, in the measurement of the edge roughness of their features and components as imaged by means of eg. The electron scanning microscopy (SEM).
18 citations