Fractilia, LLC

Kla-tencor Technologies Corporation

Fractilla, LLC

Chris Mack

Ady Levy

Michael E Adel

Stanley E Stokowski

Zain Saidin

Sterling G Watson

Mark Ghinovker

Walter Dean Mieher

Jeffrey Byers

Robert J Jones

Thaddeus Gerard Dziura

Moshe E Preil

Vladimir Levinski

Mark Davis Smith

John Charles Robinson

Boris Golovanevsky

Pavel Izikson

Matt Hankinson

Larry Steven Zurbrick

Aviv Frommer

Jorge Poplawski

Elyakim Kassel

Jonathan Yannuzzi

Methods, systems, and computer-readable mediums for configuring a lithography tool to manufacture a semiconductor device. The method includes selecting a first variable, selecting a second variable, selecting at least one response variable that is a function of the first variable and second variable, determining a measurement uncertainty for each response variable, determining, based on a measurement of the response variable, and the measurement uncertainty for the response variable, a plurality of probabilities representing a plurality of indications of whether a plurality of points associated with a lithography process meet a specification requirement for each response variable, wherein the plurality of probabilities represent a process window, and configuring, based on the process window, a lithography tool to manufacture a semiconductor device.

Detection of probabilistic process windows

In one embodiment, a method includes receiving measured linescan information describing a pattern structure of a feature, applying the received measured linescan information to an inverse linescan model that relates measured linescan information to feature geometry information, and identifying, based at least in part on the applying the received measured linescan model to the inverse linescan model, feature geometry information that describes a feature that would produce a linescan corresponding to the received measured linescan information. The method also includes determining, at least in part using the inverse linescan model, feature edge positions of the identified feature, analyzing the feature edge positions to determine errors in the manufacture of the pattern structure, and controlling a lithography tool based on the analysis of the feature edge positions.

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

System and method for generating and analyzing roughness measurements

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.

Edge detection system

In one embodiment, a method includes generating a model trained to predict a low-probability stochastic defect, using the model to predict the low-probability stochastic defect, determining a process window based on the low-probability stochastic defect, and controlling, based on the process window, a lithography tool to manufacture a device.

System and method for predicting stochastic-aware process window and yield and their use for process monitoring and control

In one embodiment, a method includes generating a model trained to predict a low-probability stochastic defect, calibrating, using unbiased measurement data, the model to a specific lithography process, patterning process, or both to generate a calibrated model, using the calibrated model to predict the low-probability stochastic defect; and modifying, based on the low-probability stochastic defect, a variable, parameter, setting, or some combination of a manufacturing process of a device.

System and method for low-noise edge detection and its use for process monitoring and control

A method is disclosed. The method includes receiving measured linescan information describing a pattern structure of a feature, applying the received measured linescan information to an inverse linescan model that relates measured linescan information to feature geometry information, identifying, based at least in part on the applying the received measured linescan model to the inverse linescan model, feature geometry information that describes a feature that would produce a linescan corresponding to the received measured linescan information, determining, at least in part using the inverse linescan model, feature edge positions of the identified feature, and analyzing the feature edge positions to detect the presence or absence of defects in the pattern structure.

An edge detection system is disclosed. The edge detection system includes an imaging device configured for imaging a pattern structure to form a first image, wherein the pattern structure includes a predetermined feature, and the imaging device images the pattern structure to generate measured linescan information that includes image noise. The edge detection system includes a processor, coupled to the imaging device, configured to receive the measured linescan information including image noise from the imaging device, wherein the processor is configured to: apply the measured linescan information to an inverse linescan model that relates the measured linescan information to feature geometry information, determine, from the inverse linescan model, feature geometry information that describes feature edge positions of the predetermined feature corresponding to the measured linescan information, determine from the feature geometry information at least one metric that describes a property of the edge detection system.

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.

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.

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Austin, TX, United States of America

Chris Mack