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.

A sub-pixel rendering method for generating a target image according to a source image is provided. The method includes: obtaining the source image; determining a target pixel to be rendered in the target image; calculating an edge code of the source pixel corresponding to a sub-pixel of the target pixel to be rendered in the source image; determining texture information around the sub-pixel of the target pixel to be rendered according to the edge code; and calculating a pixel value for the sub-pixel of the target pixel to be rendered according to the texture information and based on distance when the edge code is not a specific pattern.

A sub-pixel rendering method for generating a target image according to a source image is provided. The method includes: obtaining the source image; determining a target pixel to be rendered in the target image; calculating an edge code of the source pixel corresponding to a sub-pixel of the target pixel to be rendered in the source image; determining texture information around the sub-pixel of the target pixel to be rendered according to the edge code; and calculating a pixel value for the sub-pixel of the target pixel to be rendered according to the texture information and based on distance when the edge code is not a specific pattern.

Disclosed are systems and methods for rendering augmented videos on mobile devices and computing environment with limited computational resources. The disclosed systems and methods provide a novel framework for performing automatic detection of surfaces in video frames resulting in the creation of a seamless in-video augmentation object experience for viewing users. The disclosed framework operates by leveraging available surfaces in digital content to show augmentation objects in compliance with various pre-established contextual and technical constraints. The disclosed framework evidences a streamlined, automatic and computationally efficient process(es) that modifies digital content at the surface level within the frames of the digital content based on the contextual and technical constraints, and the computational resources of the device augmented digital content is rendered on.

Disclosed are systems and methods for rendering augmented videos on mobile devices and computing environment with limited computational resources. The disclosed systems and methods provide a novel framework for performing automatic detection of surfaces in video frames resulting in the creation of a seamless in-video augmentation object experience for viewing users. The disclosed framework operates by leveraging available surfaces in digital content to show augmentation objects in compliance with various pre-established contextual and technical constraints. The disclosed framework evidences a streamlined, automatic and computationally efficient process(es) that modifies digital content at the surface level within the frames of the digital content based on the contextual and technical constraints, and the computational resources of the device augmented digital content is rendered on.

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 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.

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.

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.

Methods and systems are provided for performing material capture to determine properties of an imaged surface. A plurality of images can be received depicting a material surface. The plurality of images can be calibrated to align corresponding pixels of the images and determine reflectance information for at least a portion of the aligned pixels. After calibration, a set of reference materials from a material library can be selected using the calibrated images. The set of reference materials can be used to determine a material model that accurately represents properties of the material surface.