Abstract: Disclosed are a magnetic core structure and a magnetic component. The magnetic core structure includes N winding columns and two cover plates, and N is a positive integer, wherein each winding column is provided with a first hollow channel, the two cover plates are disposed at two ends of each winding column, each cover plate is provided with N first through holes, the N winding columns are in a one-to-one correspondence with the N first through holes of each cover plate, and the first hollow channel of each winding column is communicated with the first through holes located on two sides thereof and corresponding thereto. Therefore, the channels for air flow can be added, so that the heat dissipation efficiency is improved when the magnetic core structure is applied to the magnetic component.

Abstract: A light guide assembly includes a housing element, at least one light guide element, and at least one light-shielding element. The housing element includes an outer surface and an inner surface. The light guide element is configured for a light to pass therethrough. The light guide element includes a light entrance surface, at least one light exit surface, and at least one wall surface. The wall surface surrounds and is connected to an edge of the light exit surface and extends toward the light entrance surface. The light exit surface is in contact with the inner surface. The at least one light-shielding element surrounds the at least one wall surface and is in contact with the inner surface.

Abstract: A method of estimating a distance in an endoscopic image is implemented by an endoscopic system. The method includes steps of: irradiating a lesion of a subject and obtaining an image of the lesion and a scale of the endoscopic system as a to-be-analyzed image while the lesion is being irradiated and the scale is placed adjacent to the lesion; using a lesion-contour prediction model to generate a prediction result that indicates a contour of the lesion; displaying the prediction result; generating two selected points on the contour of the lesion; and estimating, based on the scale in the to-be-analyzed image, an actual distance between the two selected points.

Abstract: In some embodiments, the present disclosure relates to a method that includes forming a dielectric layer over a conductive structure on a substrate. A removal process is performed to remove a portion of the dielectric layer to expose a portion of the conductive structure. The substrate is transported into a cleaning chamber having a wafer chuck below a bell jar structure. A cleaning process is performed to clean the exposed portion of the conductive structure by turning on a noble gas source to introduce a noble gas within the cleaning chamber, turning on an oxygen gas source to introduce oxygen within the cleaning chamber, applying a first bias to a plasma coil to form a plasma gas, and applying a second bias to the wafer chuck. The substrate is removed from the cleaning chamber. A conductive layer is formed over the dielectric layer and coupled to the conductive structure.

Abstract: A method including: obtaining data based an optical proximity correction for a spatially shifted version of a training design pattern; and training a machine learning model configured to predict optical proximity corrections for design patterns using data regarding the training design pattern and the data based on the optical proximity correction for the spatially shifted version of the training design pattern.

Abstract: An etch bias direction is determined based on a curvature of a contour in a substrate pattern. The etch bias direction is configured to be used to enhance an accuracy of a semiconductor patterning process relative to prior patterning processes. In some embodiments, a representation of the substrate pattern is received, which includes the contour in the substrate pattern. The curvature of the contour of the substrate pattern is determined, and an etch bias direction is determined based on the curvature by considering curvatures of adjacent contour portions. A simulation model is used to determine an etch effect based on the etch bias direction for an etching process on the substrate pattern.

Abstract: Methods and systems for determining a mask rule check violation (MRC) associated with a mask feature using a detector having geometric properties corresponding to the MRC. The detector (e.g., elliptical shaped) is configured to include a curved portion to detect a curvature violation, include an enclosed area (e.g., a fully enclosed area or a partially enclosed area having an opening), include a predefined orientation axis configured to guide relative positioning of the detector with a mask feature, and include a length to detect a critical dimension violation. The orientation axis of the detector is aligned with a normal axis at a location on the mask feature. Based on the orientation axis aligned with the normal axis of the mask feature, an MRC violation is determined corresponding to a region of the mask feature that intersects the enclosed area.

Abstract: A method for improving a process model for a patterning process, the method including obtaining a) a measured contour from an image capture device, and b) a simulated contour generated from a simulation of the process model. The method also includes aligning the measured contour with the simulated contour by determining an offset between the measured contour and the simulated contour. The process model is calibrated to reduce a difference, computed based on the determined offset, between the simulated contour and the measured contour.

Abstract: A method including: obtaining data based an optical proximity correction for a spatially shifted version of a training design pattern; and training a machine learning model configured to predict optical proximity corrections for design patterns using data regarding the training design pattern and the data based on the optical proximity correction for the spatially shifted version of the training design pattern.

Abstract: A method including: obtaining a thin-mask transmission function of a patterning device and a M3D model for a lithographic process, wherein the thin-mask transmission function is a continuous transmission mask (CTM) and the M3D model at least represents a portion of M3D attributable to multiple edges of structures on the patterning device; determining a M3D mask transmission function of the patterning device by using the thin-mask transmission function and the M3D model; and determining an aerial image produced by the patterning device and the lithographic process, by using the M3D mask transmission function.

Abstract: A method including: obtaining data based an optical proximity correction for a spatially shifted version of a training design pattern; and training a machine learning model configured to predict optical proximity corrections for design patterns using data regarding the training design pattern and the data based on the optical proximity correction for the spatially shifted version of the training design pattern.

Abstract: A method to determine a curvilinear pattern of a patterning device that includes obtaining (i) an initial image of the patterning device corresponding to a target pattern to be printed on a substrate subjected to a patterning process, and (ii) a process model configured to predict a pattern on the substrate from the initial image, generating, by a hardware computer system, an enhanced image from the initial image, generating, by the hardware computer system, a level set image using the enhanced image, and iteratively determining, by the hardware computer system, a curvilinear pattern for the patterning device based on the level set image, the process model, and a cost function, where the cost function (e.g., EPE) determines a difference between a predicted pattern and the target pattern, where the difference is iteratively reduced.

Abstract: A method for determining a likelihood that an assist feature of a mask pattern will print on a substrate. The method includes obtaining (i) a plurality of images of a pattern printed on a substrate and (ii) variance data the plurality of images of the pattern; determining, based on the variance data, a model configured to generate variance data associated with the mask pattern; and determining, based on model-generated variance data for a given mask pattern and a resist image or etch image associated with the given mask pattern, the likelihood that an assist feature of the given mask pattern will be printed on the substrate. The likelihood can be applied to adjust one or more parameters related to a patterning process or a patterning apparatus to reduce the likelihood that the assist feature will print on the substrate.

Abstract: A method for improving a design of a patterning device. The method includes (i) obtaining mask points of a design of a mask feature, wherein the mask feature corresponds to a target feature in a target pattern to be printed on a substrate; and (ii) adjusting locations of the mask points to generate a modified design of the mask feature based on the adjusted mask points.

Abstract: A method including: obtaining data based an optical proximity correction for a spatially shifted version of a training design pattern; and training a machine learning model configured to predict optical proximity corrections for design patterns using data regarding the training design pattern and the data based on the optical proximity correction for the spatially shifted version of the training design pattern.

Abstract: A method of determining a mask pattern for a target pattern to be printed on a substrate. The method includes partitioning a portion of a design layout including the target pattern into a plurality of cells with reference to a given location on the target pattern; assigning a plurality of variables within a particular cell of the plurality of cells, the particular cell including the target pattern or a portion thereof; and determining, based on values of the plurality of variables, the mask pattern for the target pattern such that a performance metric of a patterning process utilizing the mask pattern is within a desired performance range.

Abstract: A method including: obtaining data based an optical proximity correction for a spatially shifted version of a training design pattern; and training a machine learning model configured to predict optical proximity corrections for design patterns using data regarding the training design pattern and the data based on the optical proximity correction for the spatially shifted version of the training design pattern.

Abstract: A method for training a machine learning model to generate a predicted measured image, the method including obtaining (a) an input target image associated with a reference design pattern, and (b) a reference measured image associated with a specified design pattern printed on a substrate, wherein the input target image and the reference measured image are non-aligned images; and training, by a hardware computer system and using the input target image, the machine learning model to generate a predicted measured image.

Abstract: A method including: obtaining a thin-mask transmission function of a patterning device and a M3D model for a lithographic process, wherein the thin-mask transmission function is a continuous transmission mask (CTM) and the M3D model at least represents a portion of M3D attributable to multiple edges of structures on the patterning device; determining a M3D mask transmission function of the patterning device by using the thin-mask transmission function and the M3D model; and determining an aerial image produced by the patterning device and the lithographic process, by using the M3D mask transmission function.

Abstract: Methods of identifying a hot spot from a design layout or of predicting whether a pattern in a design layout is defective, using a machine learning model. An example method disclosed herein includes obtaining sets of one or more characteristics of performance of hot spots, respectively, under a plurality of process conditions, respectively, in a device manufacturing process; determining, for each of the process conditions, for each of the hot spots, based on the one or more characteristics under that process condition, whether that hot spot is defective; obtaining a characteristic of each of the process conditions; obtaining a characteristic of each of the hot spots; and training a machine learning model using a training set including the characteristic of one of the process conditions, the characteristic of one of the hot spots, and whether that hot spot is defective under that process condition.