A pattern inspection apparatus includes an illumination optical system to illuminate an inspection substrate on which a pattern is formed, an offset calculation circuit to calculate an offset amount which depends on an image accumulation time of each of a plurality of photo sensor elements arrayed two-dimensionally, a time delay integration (TDI) sensor to include the plurality of photo sensor elements, to acquire an image of the inspection substrate by receiving a transmitted light or a reflected light from the inspection substrate by the plurality of photo sensor elements, to correct, using the offset amount, a pixel value of optical image data of an acquired image, and to output the optical image data having been corrected, and a comparison circuit to compare an optical image formed by the optical image data output from the TDI sensor with a reference image.

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 for generating metrology sampling scheme for a patterning process, the method including: obtaining a parameter map of a parameter of a patterning process for a substrate; decomposing the parameter map to generate a fingerprint specific to an apparatus of the patterning process and/or a combination of apparatuses of the patterning process; and based on the fingerprint, generating a metrology sampling scheme for a subsequent substrate at the apparatus of the patterning process and/or the combination of apparatuses of the patterning process, wherein the sampling scheme is configured to distribute sampling points on the subsequent substrate so as to improve a metrology sampling density.

Multilayered product structures are formed on substrates by a combination of patterning steps, physical processing steps and chemical processing steps. An inspection apparatus illuminates a plurality of target structures and captures pupil images representing the angular distribution of radiation scattered by each target structure. The target structures have the same design but are formed at different locations on a substrate and/or on different substrates. Based on a comparison of the images the inspection apparatus infers the presence of process-induced stack variations between the different locations. In one application, the inspection apparatus separately measures overlay performance of the manufacturing process based on dark-field images, combined with previously determined calibration information. The calibration is adjusted for each target, depending on the stack variations inferred from the pupil images.

Disclosed herein are systems and methods for synthesizing a diamond using a diamond synthesis machine. A processor receives a plurality of images of a diamond during synthesis within a diamond synthesis machine, each of the plurality of images captured within a time period. The processor executes a diamond state prediction machine learning model using the plurality of images to obtain a predicted data object, the predicted data object indicating a predicted state of the diamond within the diamond synthesis machine at a time subsequent to the time period. The processor detects a predicted defect, a number of defects, defect types, and/or sub-features of such defects and/or other characteristics (e.g., a predicted shape, size, and/or other properties of predicted contours for the diamond and/or pocket holder) of the predicted state of the diamond. The processor adjusts operation of the diamond synthesis machine.

An image acquisition method includes storing a coefficient of a relational expression between a parameter corresponding to a light quantity incident on an imaging sensor including a photo sensor element and an output value of the imaging sensor in the case of the light incident on the imaging sensor which employs a reference image accumulation time, inputting a desired image accumulation time, and calculating a parameter for obtaining a desired output value of the imaging sensor by using a corrected relational expression obtained by correcting using an output value of the imaging sensor employing the desired image accumulation time in the case of the incident light quantity being zero, adjusting the light quantity incident on the imaging sensor to be a calculated parameter, and acquiring a target image by the imaging sensor on which an adjusted light quantity is incident, and outputting data of the acquired image.

An image acquisition method includes storing a coefficient of a relational expression between a parameter corresponding to a light quantity incident on an imaging sensor including a photo sensor element and an output value of the imaging sensor in the case of the light incident on the imaging sensor which employs a reference image accumulation time, inputting a desired image accumulation time, and calculating a parameter for obtaining a desired output value of the imaging sensor by using a corrected relational expression obtained by correcting using an output value of the imaging sensor employing the desired image accumulation time in the case of the incident light quantity being zero, adjusting the light quantity incident on the imaging sensor to be a calculated parameter, and acquiring a target image by the imaging sensor on which an adjusted light quantity is incident, and outputting data of the acquired image.

Embodiments of the present disclosure relate to a metrology method and system for critical dimensions based on a dispersion relation in momentum space. The method comprises: establishing, in accordance with parameters of incident light and a modeled geometric topography of the target to be measured, a simulation dataset associated with a dispersion curve of the target to be measured in momentum space; training a neural-network-based prediction model based on the simulation dataset; obtaining, based on an actual measurement of the target to be measured by incident light, a dispersion relation pattern of the target to be measured in momentum space, wherein the dispersion relation pattern at least indicates a dispersion curve associated with the critical dimensions of the target to be measured; extracting, based on the dispersion relation pattern, features related to the dispersion curve from the dispersion relation pattern via the trained prediction model, to determine an estimated value associated with at least one critical dimension of the target to be measured. According to the method disclosed herein, at least one critical dimension is measured in a more efficient, economical and accurate way.

A metrology apparatus for determining a characteristic of interest of a structure on a substrate, the apparatus comprising: a radiation source configured to generate illumination radiation; at least two illumination branches comprising at least one optical fiber and configured to illuminate a structure on a substrate from different angles; and a radiation switch configured to receive the illumination radiation and transfer at least part of the radiation to a selectable one of the at least two illumination branches.

A substrate inspection apparatus includes: an image sensor which obtains first image data of a first substrate and second image data of a second substrate; and a processor which obtains synthetic image data by using the first and second image data, where the processor obtains first first spot information and first non-spot information of a first first spot area and a first non-spot area on the first substrate, based on the first image data, obtain first second spot information and second non-spot information of a first second spot area and a second non-spot area on locations on the second substrate corresponding to locations of the first first spot area and the first non-spot area on the first substrate, based on the second image data, and obtain the synthetic image data by using the first first spot information, the first second spot information and the first and second non-spot information.