An object is to provide a measurement system or the like that enables selection of appropriate new measurement targets by performing measurement on a limited number of measurement points.

Proposed is a system including a measurement tool; and a computer system configured to communicate with the measurement tool, in which the computer system is configured to calculate, based on feature data of a plurality of locations on a wafer received from the measurement tool, an in-plane distribution of the feature data on the wafer (C), select, based on the calculated in-plane distribution, a new measurement point for acquiring the feature data (D), calculate, based on feature data acquired by measuring the selected new measurement point (B), a new in-plane distribution of the feature data on the wafer (F), and output at least one of the feature data of the new measurement point and the in-plane distribution which are acquired by executing the selection of the new measurement point and the calculation of the new in-plane distribution at least once (H).

A method of fabricating a semiconductor device includes generating a mask based on second layout data obtained by applying an OPC model to first layout data and performing a semiconductor process using the mask on a substrate, obtaining a plurality of pattern images by selecting a plurality of sample patterns from the substrate, selecting sample images corresponding to the sample patterns from each of the first layout data, the second layout data, and simulation data obtained by performing a simulation based on the second layout data, generating a plurality of input images corresponding to the sample patterns by blending the sample images corresponding to the sample patterns, respectively, and generating an error prediction model for the OPC model by training a machine learning model using a data set including the input images and the pattern images.

Methods and systems for learnable defect detection for semiconductor applications are provided. One system includes a deep metric learning defect detection model configured for projecting a test image for a specimen and a corresponding reference image into latent space, determining a distance in the latent space between one or more different portions of the test image and corresponding portion(s) of the corresponding reference image, and detecting defects in the one or more different portions of the test image based on the determined distances. Another system includes a learnable low-rank reference image generator configured for removing noise from one or more test images for a specimen thereby generating one or more reference images corresponding to the one or more test images.

Disclosed is a semiconductor integrated circuit fabricating method of a semiconductor fabricating device which includes a processor executing a defect detection module includes receiving, at the defect detection module, a first capture image of the semiconductor integrated circuit and a first layout image, generating, at the defect detection module, a second layout image from the first capture image, generating, at the defect detection module, a contour image from the first capture image and the second layout image, detecting, at the defect detection module, a defect of the semiconductor integrated circuit based on the first layout image and the contour image, analyzing, at the semiconductor fabricating device, a correlation between a kind of the defect and process variations of the semiconductor integrated circuit, and changing, at the semiconductor fabricating device, at least one process variation having a correlation with the defect from among the process variations.

An improved method and apparatus for enhancing an inspection image in a charged-particle beam inspection system. An improved method for enhancing an inspection image comprises acquiring a first image and a second image of multiple stacked layers of a sample that are taken with a first focal point and a second focal point, respectively, associating a first segment of the first image with a first layer among the multiple stacked layers and associating a second segment of the second image with a second layer among the multiple stacked layers, updating the first segment based on a first reference image corresponding to the first layer and updating the second segment based on a second reference image corresponding to the second layer, and combining the updated first segment and the updated second segment to generate a combined image including the first layer and the second layer.

Methods and systems for milling and imaging a sample based on multiple fiducials at different sample depths include forming a first fiducial on a first sample surface at a first sample depth; milling at least a portion of the sample surface to expose a second sample surface at a second sample depth; forming a second fiducial on the second sample surface; and milling at least a portion of the second sample surface to expose a third sample surface including a region of interest (ROI) at a third sample depth. The location of the ROI at the third sample depth relative to the first fiducial may be calculated based on an image of the ROI and the second fiducial as well as relative position between the first fiducial and the second fiducial.

This disclosure relates to a system for performing efficient learning of a specific portion. To achieve this purpose, there is proposed a system configured to generate a converted image on the basis of input of an input image, the system comprising a learning model in which parameters are adjusted so as to suppress an error between the input image and a second image converted upon input of the input image, the learning model being subjected to different learning at least between a first area in the image and a second area different from the first area.

Disclosed herein are CPM support systems, as well as related apparatuses, methods, computing devices, and computer-readable media. For example, in some embodiments, a charged particle microscope computational support apparatus may include: first logic to, for each angle of a plurality of angles, receive an associated image of a specimen at the angle, and generate an associated scan mask based on one or more regions-of-interest in the associated image; second logic to, for each angle of the plurality of angles, generate an associated data set of the specimen by processing data from a scan, in accordance with the associated scan mask, by a charged particle microscope of the specimen at the angle; and third logic to provide, for each angle of the plurality of angles, the associated data set of the specimen to reconstruction logic to generate a three-dimensional reconstruction of the specimen.

An inspection apparatus includes an image distortion estimation unit that estimates a distortion amount between a reference image and an inspection image, an image distortion correction unit that corrects the inspection image and/or the reference image using an estimated distortion amount, and an inspection unit that performs inspection using a corrected inspection image and the reference image or the inspection image and a corrected reference image. The image distortion estimation unit estimates a distortion amount in which only distortion occurring in an entire image can be corrected by adjustment of a correction condition.

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.