Disclosed are a method of forming an optical proximity correction (OPC) model and/or a method of fabricating a semiconductor device using the same. The method of forming the OPC model may include obtaining a scanning electron microscope (SEM) image, which is an average image of a plurality of images taken using one or more scanning electron microscopes, and a graphic data system (GDS) image, which is obtained by imaging a designed layout, aligning the SEM image and the GDS image, performing an image filtering process on the SEM image, extracting a contour from the SEM image, and verifying the contour. The verifying of the contour may be performed using a genetic algorithm. Variables in the genetic algorithm may include first parameters related to the image alignment process, second parameters related to the image filtering process, and third parameters related to a critical dimension (CD) measurement process.

The preferred embodiments are directed to a metrology method used, for example, in recess analysis in semiconductor fabrication that includes using atomic force microscopy (AFM) data of a sample having an array of 2D-periodic features to generate a sample image, and calculating a periodicity of the features. The method identifies the peaks in the periodicity to determine a feature period and a lattice angle, and constructs a lattice mask that is registered to the image to perform an alignment calculation. The mask is offset, and alignment calculation made, to optimize cost.

A method for process analysis includes acquiring first inspection data, using a first inspection modality, with respect to a substrate having multiple instances of a predefined pattern of features formed thereon using different, respective sets of process parameters. Characteristics of defects identified in the first inspection data are processed so as to select a first set of defect locations in which the first inspection data are indicative of an influence of the process parameters on the defects. Second inspection data are acquired, using a second inspection modality having a finer resolution than the first inspection modality, of the substrate at the locations in the first set. The defects appearing in the second inspection data are analyzed so as to select, from within the first set of the locations, a second set of the locations in which the second inspection data are indicative of an optimal range of the process parameters.

A method for electron beam-induced processing of a defect of a microlithographic photomask, including the steps of: a) providing an activating electron beam at a first acceleration voltage (EHT1) and a process gas in the region of a defect of the photomask for the purpose of repairing the defect, and b) producing at least one image of the photomask, in which the region of the defect is captured at least in part, by providing an electron beam at at least one second acceleration voltage (e.g., EHT2, EHT3, EHT4) which differs from the first acceleration voltage (EHT1), for the purpose of determining a quality of the repaired defect.

A method for process analysis includes acquiring first inspection data, using a first inspection modality, with respect to a substrate having multiple instances of a predefined pattern of features formed thereon using different, respective sets of process parameters. Characteristics of defects identified in the first inspection data are processed so as to select a first set of defect locations in which the first inspection data are indicative of an influence of the process parameters on the defects. Second inspection data are acquired, using a second inspection modality having a finer resolution than the first inspection modality, of the substrate at the locations in the first set. The defects appearing in the second inspection data are analyzed so as to select, from within the first set of the locations, a second set of the locations in which the second inspection data are indicative of an optimal range of the process parameters.

A system and method are disclosed for generating metrology measurements with second sub-system such as an optical sub-system. The method may include performing a training and a run-time operation. The training may include receiving first metrology data for device features from the first metrology sub-system (e.g., optical); generating first metrology measurements (e.g., critical dimensions, etc.); binning the device features into two or more device bins based on the first metrology measurements; and identifying representative metrology targets for the two or more device bins based on distributions of the first metrology measurements. The run-time operation may include receiving run-time metrology data (e.g., optical) of the representative metrology targets; and generating run-time metrology measurements based on the run-time metrology data.

A method for improving a process model for a patterning process, the method including obtaining a) a measured contour from an image capture device, and b) a simulated contour generated from a simulation of the process model. The method also includes aligning the measured contour with the simulated contour by determining an offset between the measured contour and the simulated contour. The process model is calibrated to reduce a difference, computed based on the determined offset, between the simulated contour and the measured contour.

A method for process analysis includes acquiring first inspection data, using a first inspection modality, with respect to a substrate having multiple instances of a predefined pattern of features formed thereon using different, respective sets of process parameters. Characteristics of defects identified in the first inspection data are processed so as to select a first set of defect locations in which the first inspection data are indicative of an influence of the process parameters on the defects. Second inspection data are acquired, using a second inspection modality having a finer resolution than the first inspection modality, of the substrate at the locations in the first set. The defects appearing in the second inspection data are analyzed so as to select, from within the first set of the locations, a second set of the locations in which the second inspection data are indicative of an optimal range of the process parameters.

Since a mask check wafer can utilize a different process than a production wafer, a high-contrast illumination setting with lower pupil fill ratio (PFR) that leads to a reduction of the productivity of the scanner can be utilized. By selecting a high-contrast illumination setting, which is different than that used on a production wafer, an improved ratio of particle printability to stochastic defects can be achieved. In combination, or instead higher dose resist can be utilized. This allows longer exposure of the wafer, such that the impact of photon shot noise is reduced, also resulting in an improved ratio of particle printability to stochastic defects. As a result, the particle printability can be enhanced further without leading to an excessive amount of stochastic defects. Because of this, the number of sites, and therefore the throughput, of a charged particle inspection and analysis can be significantly improved.

To monitor semiconductor manufacturing process variation, contours of identical pattern features are determined based on SEM images, and the contours are aggregated and statistically analyzed to determine the variation of the feature. Some of the contours are outliers, and the aggregation and averaging of the contours hides these outliers. The present disclosure describes filtering certain outlier contours before they are aggregated and statistically analyzed. The filtering can be performed at multiple levels, such as based on individual points on the contours in the set of inspection contours, or based on overall geometrical shapes of the contours in the set of inspection contours.