Abstract: A method of generating a computational model for determining one or more aberration coefficients of a charged particle beam focused by a focusing lens towards a specimen is described. The method includes obtaining a plurality of input data associated with a plurality of beam spot information for a plurality of defocus settings; obtaining a plurality of one or more aberrations coefficients associated with corresponding input data of the plurality of input data; providing a training data set including the plurality of aberrations coefficients and the corresponding input data; and producing or generating the computational model by machine learning from the training data set. The computational model is also used for determining one or more aberration coefficients.

Abstract: A method and system for optimizing a metrology algorithm used by an inspection tool for inspecting predetermined sites of a semiconductor wafer during fabrication so as to allow repetitive and consistent inspection for multiple sites of the wafer by both a single inspection tool of a given type using the metrology algorithm and also across a fleet of different inspection tools of the same type using the metrology algorithm. An aggregate loss function is computed from a sum of component loss functions. In one aspect, each component loss function is amplified by a non-linear function that applies a positive gain for in-range measurements and for out-of-range measurements, applies a steep penalty that swamps any cumulative gains associated with other component loss functions. In another aspect, distribution-based metrics are used to measure similarity between two distributions of measurements for multiple locations across two different tools.

Abstract: There is provided a metrology system and method. The method includes obtaining a set of tool parameters selected from multiple tool parameters characterizing the examination tool, varying a value of each tool parameter from the set a number of times, giving rise to a plurality of tool settings corresponding to a plurality of combinations of varying values of the set of tool parameters, configuring an examination tool with each given tool setting of the plurality of tool settings; and in response to receiving, from the examination tool, a plurality of sets of images corresponding to the plurality of tool settings and representing expected tool variations over time in a single tool or between different tools, optimizing a metrology algorithm using the plurality of sets of images so as to meet at least one metrology metric including tool matching.

Abstract: An electron beam spot shape reconstruction unit that includes a processing circuit and a memory unit. The processing circuit is configured to reconstruct a shape of an electron beam spot by (i) obtaining multiple groups of images of circular targets of a sample, wherein different groups of images of the multiple groups of images are associated with different polar angles; (ii) processing at least two of the multiple groups of images to determine first-axis edge width information and second-axis edge width information; and (iii) reconstructing the electron beam spot shape based on the first-axis edge width information and second-axis edge width information.

Abstract: An improved technique for determining height difference in patterns provided on semiconductor wafers uses real measurements (e.g., measurements from SEM images) and a height difference determination model. In one version of the model, a measurable variable of the model is expressed in terms of a function of a change in depth of shadow (i.e. relative brightness), wherein the depth of shadow depends on the height difference as well as width difference between two features on a semiconductor wafer. In another version of the model, the measurable variable is expressed in terms of a function of a change of a measured distance between two characteristic points on the real image of a periodic structure with respect to a change in a tilt angle of a scanning electron beam.

Abstract: An improved technique for determining height difference in patterns provided on semiconductor wafers uses real measurements (e.g., measurements from SEM images) and a height difference determination model. In one version of the model, a measurable variable of the model is expressed in terms of a function of a change in depth of shadow (i.e. relative brightness), wherein the depth of shadow depends on the height difference as well as width difference between two features on a semiconductor wafer. In another version of the model, the measurable variable is expressed in terms of a function of a change of a measured distance between two characteristic points on the real image of a periodic structure with respect to a change in a tilt angle of a scanning electron beam.

Abstract: An improved technique for determining height difference in patterns provided on semiconductor wafers uses real measurements (e.g., measurements from SEM images) and a height difference determination model. In one version of the model, a measurable variable of the model is expressed in terms of a function of a change in depth of shadow (i.e. relative brightness), wherein the depth of shadow depends on the height difference as well as width difference between two features on a semiconductor wafer. In another version of the model, the measurable variable is expressed in terms of a function of a change of a measured distance between two characteristic points on the real image of a periodic structure with respect to a change in a tilt angle of a scanning electron beam.

Abstract: A method for evaluating an object, the method may include acquiring, by a charged particle beam system, an image of an area of a reference object, wherein the area includes multiple instances of a structure of interest, and the structure of interest is of a nanometric scale; determining multiple types of attributes from the image; reducing a number of the attributes to provide reduced attribute information; generating guidelines, based on the reduced attribute information and on reference data, for evaluating the reduced attribute information; and evaluating an actual object by implementing the guidelines.

Abstract: A method for evaluating an object, the method may include acquiring, by a charged particle beam system, an image of an area of a reference object, wherein the area includes multiple instances of a structure of interest, and the structure of interest is of a nanometric scale; determining multiple types of attributes from the image; reducing a number of the attributes to provide reduced attribute information; generating guidelines, based on the reduced attribute information and on reference data, for evaluating the reduced attribute information; and evaluating an actual object by implementing the guidelines.

Abstract: An improved technique for determining height difference in patterns provided on semiconductor wafers uses real measurements (e.g., measurements from SEM images) and a height difference determination model. In one version of the model, a measurable variable of the model is expressed in terms of a function of a change in depth of shadow (i.e. relative brightness), wherein the depth of shadow depends on the height difference as well as width difference between two features on a semiconductor wafer. In another version of the model, the measurable variable is expressed in terms of a function of a change of a measured distance between two characteristic points on the real image of a periodic structure with respect to a change in a tilt angle of a scanning electron beam.

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.