Systems and methods for characterizing a region of interest (ROI) in a medical image are provided. An exemplary system may include a memory storing instructions and at least one processor communicatively coupled to the memory to execute the instructions which, when executed by the processor, may cause the processor to perform operations. The operations may include detecting one or more candidate ROIs from the medical image using a three-dimensional (3D) machine learning network. The operations may also include determining a key slice for each candidate ROI. The operations may further include selecting a primary ROI from the one or more candidate ROIs based on the respective key slices. In addition, the operations may include classifying the primary ROI into one of a plurality of categories using a texture-based classifier based on the key slice corresponding to the primary ROI.

An apparatus includes an acquisition unit configured to acquire target data, a determination unit configured to determine whether the target data has material appearance information of an object, and a control unit configured to display, on a display unit, an image corresponding to the target data based on a result of the determination by the determination unit, wherein, in a case where the target data has the material appearance information of the object, the control unit displays a moving image or consecutive still images, which includes a plurality of images under different viewing conditions, corresponding to the target data.

An apparatus includes an acquisition unit configured to acquire target data, a determination unit configured to determine whether the target data has material appearance information of an object, and a control unit configured to display, on a display unit, an image corresponding to the target data based on a result of the determination by the determination unit, wherein, in a case where the target data has the material appearance information of the object, the control unit displays a moving image or consecutive still images, which includes a plurality of images under different viewing conditions, corresponding to the target data.

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.

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.

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 area 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 area when the edge code is not a specific pattern.

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.

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.

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.