A method of inspecting images on printed products by a computer in a printing machine. Printed products are recorded and digitized by an image sensor of an image inspection system in the course of the image inspection process, and the computer compares them to a digital reference image. If deviations are found, the defective printed products are removed. The computer analyzes the deviations found in the course of the image inspection process together with further data from other system parts and from the machine, determines specific defect classes and the causes thereof based on the defects by machine learning processes, assigns the defects found in the image inspection process to the defect classes in a corresponding way, and displays the classified detected defects with their defect classes and causes to an operator of the machine so that the operator can initiate specific measures to eliminate the defect causes.

Methods and apparatuses are disclosed herein for parameter estimation for metrology. An example method at least includes optimizing, using a parameter estimation network, a parameter set to fit a feature in an image based on one or more models of the feature, the parameter set defining the one or more models, and providing metrology data of the feature in the image based on the optimized parameter set.

A semiconductor structure and a method for managing semiconductor wafer stress are disclosed. The semiconductor structure includes a semiconductor wafer, a first stress layer disposed on and in contact with a backside of the semiconductor wafer, and a second stress layer on and in contact with the first stress layer. The first stress layer exerts a first stress on the semiconductor wafer and the second layer exerts a second stress on the semiconductor wafer that is opposite the first backside stress. The method includes forming a first stress layer on and in contact with a backside of a semiconductor wafer, and further forming a second stress layer on and in contact with the first stress layer. The first stress layer exerts a first stress on the semiconductor wafer and the second stress layer exerts a second stress on the semiconductor wafer that is opposite to the first stress.

A defect observation method includes, as steps executed by a computer system, a first step of acquiring, as a bevel image, an image captured using defect candidate coordinates in a bevel portion as an imaging position by using a microscope or an imaging apparatus; and a second step of detecting a defect in the bevel image. The second step includes a step of determining whether there is at least one portion among a wafer edge, a wafer notch, and an orientation flat in the bevel image, a step of switching and selectively applying a defect detection scheme of detecting the defect from the bevel image from a plurality of schemes which are candidates based on a determination result, and a step of executing a process of detecting the defect from the bevel image in conformity with the switched scheme.

A system and methods for OCD metrology are provided including receiving training data for training an OCD machine learning (ML) model, including multiple pairs of corresponding sets of scatterometric data and reference parameters. For each of the pairs, one or more corresponding outlier metrics are by calculated and corresponding outlier thresholds are applied whether a given pair is an outlier pair. The OCD MIL model is then trained with the training data less the outlier pairs.

A screen mask inspection device inspects a screen mask including a screen opening that forms a printing pattern, and includes: an inspection control device that detects solder position information of a solder paste printed on a substrate via the screen opening, and based on the solder position information, determines whether a quality of printing using the screen mask is good or bad, the solder position information being based on an amount of positional misalignment of the solder paste actually printed on the substrate relative to a predetermined reference position.

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).

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.