A pattern according to an embodiment includes first and second line patterns, each of the first and second line patterns extends in a direction intersecting a <111> direction and has a side surface, the side surface has at least one {111} crystal plane, the side surface of the first line pattern has a first roughness, and the side surface of the second line pattern has a second roughness larger than the first roughness.

A pattern according to an embodiment includes first and second line patterns, each of the first and second line patterns extends in a direction intersecting a <111> direction and has a side surface, the side surface has at least one {111} crystal plane, the side surface of the first line pattern has a first roughness, and the side surface of the second line pattern has a second roughness larger than the first roughness.

An apparatus for determining a functional index for stenosis assessment of a vessel is provided. The apparatus comprises an input interface (40) and a processing unit (50). The input interface is configured to obtain image data (30) representing a two-dimensional representation of a vessel (6). The processing unit (50) is configured to determine a course of the vessel (6) and a width (w1, w2) of the vessel along its course in the image data and is further configured to determine the functional index for stenosis assessment of the vessel based on the width of the vessel in the image data.

The present disclosure relates to a dimension measuring device that shortens a time required for dimension measurement and eliminates errors caused by an operator. A dimension measuring device that measures a dimension of a measurement target using an input image is provided, in which a first image in which each region of the input image is labeled by region is generated by machine learning, an intermediate image including a marker indicating each region of the first image is generated based on the generated first image, a second image in which each region of the input image is labeled by region is generated based on the input image and the generated intermediate image, coordinates of a boundary line between adjacent regions are obtained by using the generated second image, coordinates of a feature point that defines a dimension condition of the measurement target are obtained by using the obtained coordinates of the boundary line, and the dimension of the measurement target is measured by using the obtained coordinates of the feature point.

A computer includes a processor and a memory storing instructions executable by the processor to receive radar data from a radar, the radar data including radar pixels having respective measured depths; receive camera data from a camera, the camera data including an image frame including camera pixels; map the radar pixels to the image frame; generate respective regions of the image frame surrounding the respective radar pixels; for each region, determine confidence scores for the respective camera pixels in that region; output a depth map of projected depths for the respective camera pixels based on the confidence scores; and operate a vehicle including the radar and the camera based on the depth map. The confidence scores indicate confidence in applying the measured depth of the radar pixel for that region to the respective camera pixels.

A computer includes a processor and a memory storing instructions executable by the processor to receive radar data from a radar, the radar data including radar pixels having respective measured depths; receive camera data from a camera, the camera data including an image frame including camera pixels; map the radar pixels to the image frame; generate respective regions of the image frame surrounding the respective radar pixels; for each region, determine confidence scores for the respective camera pixels in that region; output a depth map of projected depths for the respective camera pixels based on the confidence scores; and operate a vehicle including the radar and the camera based on the depth map. The confidence scores indicate confidence in applying the measured depth of the radar pixel for that region to the respective camera pixels.

Methods and systems for characterizing dimensions and material properties of semiconductor devices by transmission small angle x-ray scatterometry (TSAXS) systems having relatively small tool footprint are described herein. The methods and systems described herein enable Q space resolution adequate for metrology of semiconductor structures with reduced optical path length. In general, the x-ray beam is focused closer to the wafer surface for relatively small targets and closer to the detector for relatively large targets. In some embodiments, a high resolution detector with small point spread function (PSF) is employed to mitigate detector PSF limits on achievable Q resolution. In some embodiments, the detector locates an incident photon with sub-pixel accuracy by determining the centroid of a cloud of electrons stimulated by the photon conversion event. In some embodiments, the detector resolves one or more x-ray photon energies in addition to location of incidence.

Methods and systems for characterizing dimensions and material properties of semiconductor devices by transmission small angle x-ray scatterometry (TSAXS) systems having relatively small tool footprint are described herein. The methods and systems described herein enable Q space resolution adequate for metrology of semiconductor structures with reduced optical path length. In general, the x-ray beam is focused closer to the wafer surface for relatively small targets and closer to the detector for relatively large targets. In some embodiments, a high resolution detector with small point spread function (PSF) is employed to mitigate detector PSF limits on achievable Q resolution. In some embodiments, the detector locates an incident photon with sub-pixel accuracy by determining the centroid of a cloud of electrons stimulated by the photon conversion event. In some embodiments, the detector resolves one or more x-ray photon energies in addition to location of incidence.

Through-tubing, cased-hole sealed material density can be evaluated using gamma ray measurements. Density evaluation comprises detecting, by at least one detector positioned within a casing of a wellbore including a sealing material positioned between the casing and a subsurface formation, electromagnetic radiation generated in response to nuclear radiation being emitted outward toward the subsurface formation, determining an electromagnetic radiation count based on the detected electromagnetic radiation, selecting at least one of a first reference material having a density that is less than a density of the sealing material and a second reference material having a density that is greater than the density of the sealing material, adjusting the electromagnetic radiation count based on the density of the at least one of the first reference material and the second reference material, and determining a density of the sealing material based on the adjusted electromagnetic radiation count.

Example implementations relate to an inspection method for training a measurement machine to accurately measure side joint lengths and detecting a defect among a plurality of solder joints. The method includes receiving a first data representing the side joint lengths of the plurality of solder joints measured by a first measurement machine and a second data representing the side joint lengths measured by a second measurement machine. Further, the method includes determining a correlation value based on a statistical analysis of a relationship between the first data and the second data. The method further includes updating an algorithm used by the first measurement machine to measure the side joint lengths, based on the correlation value to reduce deviation between the first data and the second data. Later, the updated algorithm is used as a dimensional metrology in the first measurement machine for detecting the defect in the solder joints.