Disclosed herein is a computer-implemented 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.

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

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 modeling distributions of post-lithography critical dimensions includes the following steps. A plurality of aerial images of respective portions of a physical design layout of a semiconductor wafer are generated, and the plurality of aerial images are employed as training data. In the method, first and second portions of a neural network architecture are generated. The first portion includes a neural network which is shared by a plurality of output channels, and the second portion includes a plurality of neural networks, wherein each of the plurality of neural networks respectively correspond to one of the plurality of output channels. The method further includes training the first and second portions of the neural network architecture with the training data, and outputting the distributions of the post-lithography critical dimensions based on the plurality of output channels.

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 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.

For determining a focus position of a lithography mask (e.g., 5), a focus stack of a measurement region free of structures to be imaged is recorded and the speckle patterns of the recorded images are evaluated.

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 one or more design variables; determining a value of the stochastic variation, for each of the plurality of sets of values of the one or more design variables, from a distribution of the values of the characteristic for that set of values of the one or more 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 one or more design variables.

A method of modeling distributions of post-lithography critical dimensions includes the following steps. A plurality of aerial images of respective portions of a physical design layout of a semiconductor wafer are generated, and the plurality of aerial images are employed as training data. In the method, first and second portions of a neural network architecture are generated. The first portion includes a neural network which is shared by a plurality of output channels, and the second portion includes a plurality of neural networks, wherein each of the plurality of neural networks respectively correspond to one of the plurality of output channels. The method further includes training the first and second portions of the neural network architecture with the training data, and outputting the distributions of the post-lithography critical dimensions based on the plurality of output channels.

A method is provided for determining an OPC model comprising: recording an aerial image by use of a mask inspection microscope, wherein the aerial image comprises at least one segment of a mask; simulating a plurality of aerial images which comprise at least the segment, proceeding from a mask design and from predefined parameters of an optical model which is part of the OPC model, wherein the parameters differ for each of the simulated aerial images of the plurality of aerial images; determining differences between the measured aerial image and the simulated aerial images; determining those parameters for which the differences between simulated aerial image and measured aerial image are the least. In addition, a mask inspection microscope for carrying out the method is provided.