There is provided a mask inspection system and a method of mask inspection. The method comprises: during a runtime scan of a mask of a semiconductor specimen, processing a plurality of aerial images of the mask acquired by the mask inspection system to calculate a statistic-based Edge Positioning Displacement (EPD) of a potential defect, wherein the statistic-based EPD is calculated using a Print Threshold (PT) characterizing the mask and is applied to each of the one or more acquired aerial images to calculate respective EPD of the potential defect therein; and filtering the potential defect as a runtime true defect when the calculated statistic-based EPD exceeds a predefined EPD threshold, and filtering out the potential defect as a false defect when the calculated statistic-based EPD is lower than the predefined EPD threshold. The method can further comprise after-runtime EPD-based filtering of the plurality of runtime true defects.

A method, involving computing a first intensity of a first aerial image and a second intensity of a second aerial image, the first aerial image corresponding to a first location within a resist layer and the second aerial image corresponding to a second location within the resist layer. The method further involving performing, using a resist model, a computer simulation of the resist layer to obtain a value of a parameter for a resist layer feature based on a difference between the first and second intensities or on a difference between a resist model result for the first intensity and a resist model result for the second intensity.

Provided is a method for fabricating a semiconductor device including generating an ideal image using measured contour data and fitted conventional model terms. The method further includes using the fitted conventional model terms and a mask layout to provide a conventional model aerial image. In some embodiments, the method further includes generating a plurality of mask raster images using the mask layout, where the plurality of mask raster images is generated for each measurement site of the measured contour data. In various embodiments, the method also include training a neural network to mimic the ideal image, where the generated ideal image provides a target output of the neural network, and where the conventional model aerial image and the plurality of mask raster images provide inputs to the neural network.

Embodiments described herein provide a method for cleaning contamination from sensors in a lithography tool without requiring recalibrating the lithography tool. More particularly, embodiments described herein teach cleaning the sensors using hydrogen radicals for a short period while the performance drifting is still above the drift tolerance. After a cleaning process described herein, the lithography tool can resume production without recalibration.

A method for determining an overlapping process window (OPW) of an area of interest on a portion of a design layout for a device manufacturing process for imaging the portion onto a substrate, the method including: obtaining a plurality of features in the area of interest; obtaining a plurality of values of one or more processing parameters of the device manufacturing process; determining existence of defects, probability of the existence of defects, or both in imaging the plurality of features by the device manufacturing process under each of the plurality of values; and determining the OPW of the area of interest from the existence of defects, the probability of the existence of defects, or both.

A method for calibrating a resist model. The method includes: generating a modeled resist contour of a resist structure based on a simulated aerial image of the resist structure and parameters of the resist model, and predicting a metrology contour of the resist structure from the modeled resist contour based on information of an actual resist structure obtained by a metrology device. The method includes adjusting one or more of the parameters of the resist model based on a comparison of the predicted metrology contour and an actual metrology contour of the actual resist structure obtained by the metrology device.

A method for the microlithographic production of microstructured components, includes: providing a wafer, to which a photoresist is applied at least partly; providing a mask having structures to be imaged; providing a projection exposure apparatus having an illumination unit and a projection lens; exposing the photoresist by projecting at least one part of the mask onto a region of the photoresist with the aid of the projection exposure apparatus; and ascertaining a deviation between a structure property of the structures produced on the exposed wafer from a predefined desired structure property. Ascertaining includes: determining at least one property of a light field used for exposing the photoresist applied to the wafer. The method further includes aftertreating the wafer on the basis of the ascertained deviation, and chemically developing the after treated wafer.

The invention relates to a method for examining a photolithographic mask for the extreme ultraviolet (EUV) wavelength range in a mask metrology apparatus. In this method, at least one structured region of the mask is selected, a scanner photon number in the extreme ultraviolet (EUV) wavelength range for which the mask in the lithographic production run is provided and a metrology photon number in the extreme ultraviolet (EUV) wavelength range with which the measurement is performed are determined. Next, a photon statistics examination mode is established on the basis of the scanner photon number and the metrology photon number and at least one aerial image of the at least one structured region is produced with the mask metrology apparatus.

For the qualification of a mask for microlithography, the effect of an aerial image of the mask on the wafer is ascertained by means of a simulation for predicting the wafer structures producible by means of the mask.

Provided is a method for fabricating a semiconductor device including generating an ideal image using measured contour data and fitted conventional model terms. The method further includes using the fitted conventional model terms and a mask layout to provide a conventional model aerial image. In some embodiments, the method further includes generating a plurality of mask raster images using the mask layout, where the plurality of mask raster images is generated for each measurement site of the measured contour data. In various embodiments, the method also include training a neural network to mimic the ideal image, where the generated ideal image provides a target output of the neural network, and where the conventional model aerial image and the plurality of mask raster images provide inputs to the neural network.