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.

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.

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.

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.

According to at least one aspect, a system is provided. The system comprises at least one hardware processor; and at least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform: generating a 3-dimensional (3D) model of an object at least in part by analyzing a first plurality of images of the object captured using a first scanning device; generating a texture model of a texture of a material at least in part by analyzing a second plurality of images of the material captured using a second scanning device different from the first scanning device, the material being separate and distinct from the object; and applying the texture model to the 3D model to generate a textured 3D model of the object.

According to at least one aspect, a system is provided. The system comprises at least one hardware processor; and at least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform: generating a 3-dimensional (3D) model of an object at least in part by analyzing a first plurality of images of the object captured using a first scanning device; generating a texture model of a texture of a material at least in part by analyzing a second plurality of images of the material captured using a second scanning device different from the first scanning device, the material being separate and distinct from the object; and applying the texture model to the 3D model to generate a textured 3D model of the object.

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 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.