A metrology method for use in determining one or more parameters of a three-dimensional patterned structure, the method including performing a fitting procedure between measured TEM image data of the patterned structure and simulated TEM image data of the patterned structure, determining a measured Lamellae position of at least one measured TEM image in the TEM image data from a best fit condition between the measured and simulated data, and generating output data indicative of the simulated TEM image data corresponding to the best fit condition to thereby enable determination therefrom of the one or more parameters of the structure.

The present invention relates to a method of detecting an edge (or a contour line) of a pattern, which is formed on a workpiece (e.g., a wafer or a mask) for use in manufacturing of semiconductor, from an image generated by a scanning electron microscope. The pattern-edge detection method includes: generating an objective image of a target pattern formed on a workpiece; generating a feature vector representing features of each pixel constituting the objective image; inputting the feature vector to a model constructed by machine learning; outputting, from the model, a determination result indicating whether the pixel having the feature vector is an edge pixel or a non-edge pixel; and connecting a plurality of pixels, each having a feature vector that has obtained a determination result indicating an edge pixel, with a line to generate a virtual edge.

The invention provides a sample observation system including a scanning electron microscope and a calculator. The calculator: (1) acquires a plurality of images captured by the scanning electron microscope; (2) acquires, from the plurality of images, a learning defect image including a defect portion and a learning reference image not including the defect portion; (3) calculates estimation processing parameters by using the learning defect image and the learning reference image; (4) acquires an inspection defect image including a defect portion; and (5) estimates a pseudo reference image by using the estimation processing parameters and the inspection defect image.

Disclosed herein are scientific instrument support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a method for determining sample location and associated stage coordinates by a microscope at least comprises acquiring, with a navigation camera, an image of a plurality of samples loaded on a fixture, the image being of low resolution at a field of view that includes the fixture and all samples of the plurality of samples, analyzing the image with a trained model to identify the plurality of samples, based on the analysis, associating each sample with a location on the fixture, based on the location on the fixture of each sample, associating separate stage coordinate information with each sample of the plurality of samples loaded on the fixture, and translating a stage holding the fixture to first stage coordinates based on the associated stage coordinate information of a first sample of the plurality of samples.

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.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining an artificial neural network architecture corresponding to a sub-graph of a synaptic connectivity graph. In one aspect, there is provided a method comprising: obtaining data defining a graph representing synaptic connectivity between neurons in a brain of a biological organism; determining, for each node in the graph, a respective set of one or more node features characterizing a structure of the graph relative to the node; identifying a sub-graph of the graph, comprising selecting a proper subset of the nodes in the graph for inclusion in the sub-graph based on the node features of the nodes in the graph; and determining an artificial neural network architecture corresponding to the sub-graph of the graph.

An inspection tool comprises an imaging system configured to image a portion of a semiconductor substrate. The inspection tool may further comprise an image analysis system configured to obtain an image of a structure on the semiconductor substrate from the imaging system, encode the image of the structure into a latent space thereby forming a first encoding. the image analysis system may subtract an artifact vector, representative of an artifact in the image, from the encoding thereby forming a second encoding; and decode the second encoding to obtain a decoded image.

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 data processing device for detecting defects in sample image data generated by a charged particle assessment system, the device comprising: a first processing module configured to receive a sample image datastream from the charged particle assessment system, the sample image datastream comprising an ordered series of data points representing an image of the sample, and to apply a first defect detection test to select a subset of the sample image datastream as first selected data, wherein the first defect detection test is a localised test which is performed in parallel with receipt of the sample image datastream; and a second processing module configured to receive the first selected data and to apply a second defect detection test to select a subset of the first selected data as second selected data.

There is provided an analyzer apparatus capable of generating crisp scanned images. In the analyzer apparatus, a sample is scanned with a probe such that a first signal and a second signal are emitted from the sample. The analyzer apparatus comprises: a first detector for detecting the first signal and producing a first detector signal; a second detector for detecting the second signal and producing a second detector signal; and an image processing unit operating (i) to produce a first scanned image and a second scanned image from the first detector signal and the second detector signal, respectively, (ii) to create a filter based on the second scanned image having a higher signal-to-noise ratio than that of the first scanned image, and (iii) to apply the filter to the first scanned image.