Abstract: There is provided a system and method of runtime defect examination of a semiconductor specimen, comprising obtaining a first image representative of at least part of the semiconductor specimen, the first image acquired by an examination tool configured with a first focus plane; estimating whether the first image is in focus using a machine learning (ML) model, wherein the ML model is previously trained for classifying images into focused images and defocused images; upon an estimation that the first image is out of focus, performing focus calibration on the examination tool to select a second focus plane associated with an optimal focus score; and obtaining a second image acquired by the examination tool configured with the second focus plane, and estimating whether the second image is in focus using the ML model. The second image, upon being estimated as being in focus, is usable for defect examination on the specimen.

Abstract: Disclosed herein is a system for non-destructive depth-profiling of samples. The system includes: (i) an electron beam (e-beam) source for projecting e-beams at each of a plurality of landing energies on an inspected sample; (ii) an electron sensor for obtaining a measured set of electron intensities pertaining to each of the landing energies; and (iii) processing circuitry for determining a set of structural parameters, which characterizes an internal geometry and/or a composition of the inspected sample, based on the measured set of electron intensities and taking into account reference data indicative of an intended design of the inspected sample.

Abstract: Disclosed herein is a computer-based method for non-destructive depth-profiling of samples. The method includes a measurement operation and a data analysis operation. The measurement operation includes, for each of a plurality of landing energies: (i) projecting an electron beam on a sample, which penetrates the sample to a respective depth determined by the landing energy, and (ii) sensing electrons returned from the sample, thereby obtaining a respective sensed electrons data set. The data analysis operation includes generating from the sensed electrons data sets a concentration map, which characterizing at least a vertical dimension of the sample.

Abstract: A computer-based method for non-destructive z-profiling of samples. The method includes: a measurement operation and a data analysis operation. The measurement operation includes, for each of a plurality of landing energies: (i) projecting an electron beam on a sample at a respective landing energy, such that light-emitting interactions between electrons from the electron beam and the sample occur within a respective probed region of the sample, which is centered about a respective depth; and (ii) measuring the emitted light to obtain an optical emission data set of the sample. The data analysis operation includes obtaining from the measured optical emission data sets a concentration map quantifying a dependence of a concentration of a material, which the sample comprises, on at least the depth.

Abstract: Disclosed herein is a system for non-destructive depth-profiling of samples. The system includes an electron beam source, a light sensor, and processing circuitry. The electron beam source configured to project e-beams on an inspected sample at each of a plurality of landing energies, which induce X-ray emitting interactions within each of a plurality of probed regions in the inspected sample, respectively, whose depth is determined by the landing energy. The light sensor is configured to measure the emitted X-ray light to obtain optical emission data sets pertaining to each of the probed regions, respectively. The processing circuitry is configured to determine a set of structural parameters, characterizing an internal geometry and/or a composition of the inspected sample, based on the measured optical emission data sets and taking into account reference data indicative of an intended design of the inspected sample.

Abstract: There is provided a system and method of runtime defect examination of a semiconductor specimen, comprising obtaining a first image representative of at least part of the semiconductor specimen, the first image acquired by an examination tool configured with a first focus plane; estimating whether the first image is in focus using a machine learning (ML) model, wherein the ML model is previously trained for classifying images into focused images and defocused images; upon an estimation that the first image is out of focus, performing focus calibration on the examination tool to select a second focus plane associated with an optimal focus score; and obtaining a second image acquired by the examination tool configured with the second focus plane, and estimating whether the second image is in focus using the ML model. The second image, upon being estimated as being in focus, is usable for defect examination on the specimen.

Abstract: A method for x-ray based evaluation of a status of a structure of a substrate, the method may include acquiring an electron image of a region of the substrate, the region comprises the structure; acquiring an x-ray image of the structure; and evaluating the status of the structure, wherein the evaluating is based at least on a number of x-ray photons that were emitted from the structure.

Abstract: There is provided a method and a system configured to obtain an image of a one or more first areas of a semiconductor specimen acquired by an examination tool, determine data Datt informative of defectivity in the one or more first areas, determine one or more second areas of the semiconductor specimen for which presence of a defect is suspected based at least on an evolution of Datt, or of data correlated to Datt, in the one or more first areas, and select the one or more second areas for inspection by the examination tool.

Abstract: There is provided a method and a system configured to obtain an image of a one or more first areas of a semiconductor specimen acquired by an examination tool, determine data Datt informative of defectivity in the one or more first areas, determine one or more second areas of the semiconductor specimen for which presence of a defect is suspected based at least on an evolution of Datt, or of data correlated to Datt, in the one or more first areas, and select the one or more second areas for inspection by the examination tool.

Abstract: A method for x-ray based evaluation of a status of a structure of a substrate, the method may include acquiring an electron image of a region of the substrate, the region comprises the structure; acquiring an x-ray image of the structure; and evaluating the status of the structure, wherein the evaluating is based at least on a number of x-ray photons that were emitted from the structure.

Abstract: A method for determining a defect material element, the method includes (a) acquiring, by a charged particle beam system and by applying a spectroscopy process, an electromagnetic emission spectrum of a part of a defect; (b) acquiring, by the charged particle beam system, a backscattered electron (BSE) image of an area that includes the defect; and (c) determining a defect material element. The determining of the defect material element includes: determining whether an ambiguity exists in the electromagnetic emission spectrum, and resolving the ambiguity based on the BSE image, when it is determined that the ambiguity exists.

Abstract: A method for x-ray based evaluation of a status of a structure of a substrate, the method may include acquiring an electron image of a region of the substrate, the region comprises the structure; acquiring an x-ray image of the structure; and evaluating the status of the structure, wherein the evaluating is based at least on a number of x-ray photons that were emitted from the structure.

Abstract: A method for detecting voids in a metal line of a semiconductor device die includes: scanning an electron beam upon a selected location on the die containing the metal line; determine gray levels in an image produced by collected electrons of the electron beam backscattered from the selected location on the die; and identifying one or more voids in the metal line based on differences between the gray levels in the image.

Abstract: A method for x-ray based evaluation of a status of a structure of a substrate, the method may include acquiring an electron image of a region of the substrate, the region comprises the structure; acquiring an x-ray image of the structure; and evaluating the status of the structure, wherein the evaluating is based at least on a number of x-ray photons that were emitted from the structure.

Abstract: A method for determining a defect material element, the method includes (a) acquiring, by a charged particle beam system and by applying a spectroscopy process, an electromagnetic emission spectrum of a part of a defect; (b) acquiring, by the charged particle beam system, a backscattered electron (BSE) image of an area that includes the defect; and (c) determining a defect material element. The determining of the defect material element includes: determining whether an ambiguity exists in the electromagnetic emission spectrum, and resolving the ambiguity based on the BSE image, when it is determined that the ambiguity exists.

Abstract: A method for detecting crystal defects includes scanning a first FOV on a first sample using a charged particle beam with a plurality of different tilt angles. BSE emitted from the first sample are detected and a first image of the first FOV is created. A first area within the first image is identified where signals from the BSE are lower than other areas of the first image. A second FOV on a second sample is scanned using approximately the same tilt angles or deflections as those used to scan the first area. The BSE emitted from the second sample are detected and a second image of the second FOV is created. Crystal defects within the second sample are identified by identifying areas within the second image where signals from the BSE are different than other areas of the second image.

Abstract: A method for detecting crystal defects includes scanning a first FOV on a first sample using a charged particle beam with a plurality of different tilt angles. BSE emitted from the first sample are detected and a first image of the first FOV is created. A first area within the first image is identified where signals from the BSE are lower than other areas of the first image. A second FOV on a second sample is scanned using approximately the same tilt angles or deflections as those used to scan the first area. The BSE emitted from the second sample are detected and a second image of the second FOV is created. Crystal defects within the second sample are identified by identifying areas within the second image where signals from the BSE are different than other areas of the second image.

Abstract: A method for detecting voids in a metal line of a semiconductor device die includes: scanning an electron beam upon a selected location on the die containing the metal line; determine gray levels in an image produced by collected electrons of the electron beam backscattered from the selected location on the die; and identifying one or more voids in the metal line based on differences between the gray levels in the image.

Abstract: A system, computer readable medium and a method for material analysis, the method may include (i) receiving or generating (a) an estimated composition of a microscopic element; wherein the estimated composition is responsive to an energy spectrum of, at least, the microscopic element; wherein the energy spectrum is obtained by an energy dispersive X-ray (EDX) detector; additional information related to, at least, the microscopic element, wherein the additional information is not obtained by the energy dispersive X-ray detector; and (ii) resolving an ambiguity in the estimated composition in response to the additional information, wherein the ambiguity occurs when the energy spectrum comprises a predefined energy peak that is attributed to a predefined material of ambiguous EDX composition determination.

Abstract: An inspection system that includes charged particle optics that irradiate a bottom of a hole with a charged particle beam propagated along an optical axis, an energy dispersive x-ray detector and a processor. The x-ray detector detects x-ray photons emitted from the bottom of the hole and generates detection signals indicative of the x-ray photons. The processor processes the detection signals to provide an estimate of the bottom of the hole.