An attached substance determination method causes a computer to determine whether or not an attached substance is attached to an inspection target object, in which the computer includes at least one processor, and the at least one processor is configured to (a) acquire, as learning data, a spectroscopic image obtained by imaging a first type sample having the attached substance attached to a base with a spectroscopic camera and a spectroscopic image obtained by imaging a second type sample having no attached substance attached to the base with the spectroscopic camera, in which spectroscopic images of a plurality of kinds of the first type samples having different kinds of the bases and different kinds of the attached substances and spectroscopic images of a plurality of kinds of the second type samples having different kinds of the bases are acquired as the learning data, (b) generate, based on the learning data, a determination model with a spectroscopic image of the inspection target object as an input and a determination result relating to presence or absence of the attached substance as an output, (c) acquire the spectroscopic image of the inspection target object, and (d) input the spectroscopic image of the inspection target object to the determination model and determine the presence or absence of the attached substance based on the determination result output from the determination model.

A spectroscopic metrology system includes a spectroscopic metrology tool and a controller. The controller generates a model of a multilayer grating including two or more layers, the model including geometric parameters indicative of a geometry of a test layer of the multilayer grating and dispersion parameters indicative of a dispersion of the test layer. The controller further receives a spectroscopic signal of a fabricated multilayer grating corresponding to the modeled multilayer grating from the spectroscopic metrology tool. The controller further determines values of the one or more parameters of the modeled multilayer grating providing a simulated spectroscopic signal corresponding to the measured spectroscopic signal within a selected tolerance. The controller further predicts a bandgap of the test layer of the fabricated multilayer grating based on the determined values of the one or more parameters of the test layer of the fabricated structure.

A system is disclosed, in accordance with one or more embodiment of the present disclosure. The system may include a controller including one or more processors configured to execute a set of program instructions. The set of program instructions may be configured to cause the processors to: receive images of a sample from a characterization sub-system; identify target clips from patch clips; prepare processed clips based on the target clips; generate encoded images by transforming the processed clips; sort the encoded images into a set of clusters; display sorted images from the set of clusters; receive labels for the displayed sorted images; determine whether the received labels are sufficient to train a deep learning classifier; and upon determining the received labels are sufficient to train the deep learning classifier, train the deep learning classifier via the displayed sorted images and the received labels.

A thickness estimation method may include: obtaining a test spectrum image; obtaining test spectrum data; measuring a thickness of a test layer formed on the test substrate at the plurality of positions; generating a regression analysis model using a correlation between the thickness of the test layer and the test spectrum data; obtaining a spectrum image; and estimating a thickness of a target layer over the entire area of the semiconductor substrate by applying the spectrum image to the regression analysis model. The thickness corresponding to the entire area of the semiconductor substrate that is being transferred is estimated using the thickness estimation method according to an exemplary embodiment in the present disclosure, such that whether or not processing is normally performed may be examined without requiring a separate time. In addition, an examination result may be feedbacked to processing equipment to improve production yield.

A method includes receiving one or more images of three or more die of a wafer, determining a median intensity value of a set of pixel intensity values acquired from a same location on each of the three or more die, determining a difference intensity value for the set of pixel intensity values by comparing the median intensity value of the set of pixel intensity values to each pixel intensity value, grouping the pixel intensity values into an intensity bin based on the median intensity value of the set of pixel intensity values, generating an initial noise boundary based on a selected difference intensity value in the intensity bin, generating a final noise boundary by adjusting the initial noise boundary, generating a detection boundary by applying a threshold to the final noise boundary, and classifying one or more pixel intensity values outside the detection boundary as a defect.

There are provided a system and a method of automatic tire inspection, the method comprising: obtaining at least one image capturing a wheel of a vehicle; segmenting the at least one image into image segments including a tire image segment corresponding to a tire of the wheel; straightening the tire image segment from a curved shape to a straight shape, giving rise to a straight tire segment; identifying text marked on the tire from the straight tire segment, comprising: detecting locations of a plurality of text portions on the straight tire segment, and recognizing text content for each of the text portions; and analyzing the recognized text content based on one or more predefined rules indicative of association between text content of different text portions at given relative locations, giving rise to a text analysis result indicative of condition of the tire.

Disclosed is a method of determining a complex-valued field relating to a structure, comprising: obtaining image data relating to a series of images of the structure, for which at least one measurement parameter is varied over the series and obtaining a trained network operable to map a series of images to a corresponding complex-valued field. The method comprises inputting the image data into said trained network and non-iteratively determining the complex-valued field relating to the structure as the output of the trained network. A method of training the trained network is also disclosed.

A product defect detection method, device and system are disclosed. The method comprises: acquiring a sample image of a product, extracting candidate image blocks probably including a product defect from the sample image, and extracting preset shape features corresponding to the candidate image blocks and texture features corresponding to the candidate image blocks; training a first-level classifier using the preset shape features to obtain a first-level classifier that can further screen out target image blocks probably including a product defect from the candidate image blocks; training a second-level classifier using the texture features to obtain a second-level classifier that can correctly identify a product defect; and when performing product defect detection, inputting preset shape features of candidate image blocks extracted from a product image into the first-level classifier, and then inputting texture features of obtained target image blocks into the second-level classifier to detect a defect in the product.

An electronic apparatus including a display and one or more processor is disclosed. The one or more processor is configured to: divide a first error value of each of a plurality of first components with respect to a mounting position acquired through inspection of a plurality of substrates of a first type, into a plurality of error values, generate a graph of a tree structure including a plurality of nodes corresponding to the plurality of first components, component types of each of the plurality of first components and a plurality of components included in a mounter, adjust attributes of each of the plurality of nodes using the plurality of error values divided from the first error value of each of the plurality of first components, and display the graph in which the attributes of each of the plurality of nodes are adjusted, on the display.

Wafer taping apparatuses and methods are provided for determining whether taping defects are present on a semiconductor wafer, based on image information acquired by an imaging device. In some embodiments, a method includes applying an adhesive tape on a surface of a semiconductor wafer. An imaging device acquires image information associated with the adhesive tape on the semiconductor wafer. The presence or absence of taping defects is determined by defect recognition circuitry based on the acquired image information.