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.

There is provided a mask inspection system and a method of mask inspection. The method comprises: detecting, by the inspection tool, a runtime defect at a defect location on a mask of a semiconductor specimen during runtime scan of the mask, and acquiring, by the inspection tool after runtime and based on the defect location, a plurality sets of aerial images of the runtime defect corresponding to a plurality of focus states throughout a focus process window, each set of aerial images acquired at a respective focus state. The method further comprises for each set of aerial images, calculating a statistic-based EPD value of the runtime defect, thereby giving rise to a plurality of statistic-based EPD values each corresponding to a respective focus state, and determining whether the runtime defect is a true defect based on the plurality of statistic-based EPD values.

The inventive concepts provide a method of providing a stochastic prediction system. The method includes extracting contours of patterns corresponding to a first design layout from a plurality of scanning electron microscope (SEM) images, respectively, generating a first contour histogram image based on the contours, and training a stochastic prediction model by using the first contour histogram image as an output, and by using the first design layout and a first resist image, a first aerial image, a first slope map, a first density map, and/or a first photo map corresponding to the first design layout as inputs, in which the stochastic prediction model comprises a cycle generative adversarial network (GAN).

A method of determining a relationship between a stochastic variation of a characteristic of an aerial image or a resist image and one or more design variables, the method including: measuring values of the characteristic from a plurality of aerial images and/or resist images for each of a plurality of sets of values of the design variables; determining a value of the stochastic variation, for each of the plurality of sets of values of the design variables, from a distribution of the values of the characteristic for that set of values of the design variables; and determining the relationship by fitting one or more parameters from the values of the stochastic variation and the plurality of sets of values of the design variables.

A method of determining a relationship between a stochastic variation of a characteristic of an aerial image or a resist image and one or more design variables, the method including: measuring values of the characteristic from a plurality of aerial images and/or resist images for each of a plurality of sets of values of the design variables; determining a value of the stochastic variation, for each of the plurality of sets of values of the design variables, from a distribution of the values of the characteristic for that set of values of the design variables; and determining the relationship by fitting one or more parameters from the values of the stochastic variation and the plurality of sets of values of the design variables.

A defect prediction method for a device manufacturing process involving processing one or more patterns onto a substrate, the method including: determining values of one or more processing parameters under which the one or more patterns are processed; and determining or predicting, using the values of the one or more processing parameters, an existence, a probability of existence, a characteristic, and/or a combination selected from the foregoing, of a defect resulting from production of the one or more patterns with the device manufacturing process.

The present invention refers to a method for performing an aerial image simulation of a photolithographic mask which comprises the following steps: (a) modifying an optical radiation distribution at a patterned surface of the photolithographic mask, depending on at least one first arrangement of pixels to be generated in the photolithographic mask; and (b) performing the aerial image simulation of the photolithographic mask by using the generated modified optical radiation distribution.

Apparatuses and methods for metrology on devices using fast marching level sets are disclosed herein. An example method at least includes initiating a fast marching level set seed on an image, propagating a fast marching level set curve from the fast marching level set seed to locate boundaries of a plurality of regions of interest within the image, and performing metrology on the regions of interest based in part on the boundaries.

A defect prediction method for a device manufacturing process involving processing one or more patterns onto a substrate, the method including; determining values of one or more processing parameters under which the one or more patterns are processed; and determining or predicting, using the values of the one or more processing parameters, an existence, a probability of existence, a characteristic, and/or a combination selected from the foregoing, of a defect resulting from production of the one or more patterns with the device manufacturing process.

A wafer holding device (200, 415) is configured to hold a wafer (205, 416) during operation of a microlithographic projection exposure apparatus and includes at least one sensor that is positionable in different rotational positions.