A method for training a patterning process model, the patterning process model configured to predict a pattern that will be formed by a patterning process. The method involves obtaining an image data associated with a desired pattern, a measured pattern of the substrate, a first model including a first set of parameters, and a machine learning model including a second set of parameters; and iteratively determining values of the first set of parameters and the second set of parameters to train the patterning process model. An iteration involves executing, using the image data, the first model and the machine learning model to cooperatively predict a printed pattern of the substrate; and modifying the values of the first set of parameters and the second set of parameters such that a difference between the measured pattern and the predicted pattern is reduced.

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 to determine a performance indicator indicative of alignment performance of a processed substrate. The method includes obtaining measurement data including a plurality of measured position values of alignment marks on the substrate and calculating a positional deviation between each measured position value and a respective expected position value. These positional deviations are used to determine a directional derivative between the alignment marks, and the directional derivatives are used to determine at least one directional derivative performance indicator.

A method where deviations of a characteristic of an image simulated by two different process models or deviations of the characteristic simulated by a process model and measured by a metrology tool, are used for various purposes such as to reduce the calibration time, improve the accuracy of the model, and improve the overall manufacturing process.

A comprehensive inspection device for an EUV exposure process includes: a light generation unit configured to generate EUV light; a splitter configured to split the EUV light into first EUV light and second EUV light; an optical characteristic evaluation unit configured to detect reflectance and transmittance of the pellicle and reflectance of the object by measuring an intensity of the first EUV light, which has been transmitted through the pellicle, reflected from an object, and re-transmitted through the pellicle, and an intensity of the first EUV light, which has been directly reflected from the object without the pellicle; and an imaging inspection unit configured to inspect imaging performance of a mask by focusing the second EUV light, which has been reflected and diffracted from the mask, through an objective lens, and then collecting the focused second EUV light to obtain an aerial region image.

A metrology system includes an imaging system. The imaging system may include an objective lens. The metrology system may include one or more detectors. The metrology system may include an objective positioning stage structurally coupled to the objective lens and configured to adjust a focal plane of at least one of the one or more detectors via movement along an optical axis of the metrology system. The metrology system may include one or more proximity sensors configured to measure lateral positions of a stage element as the objective positioning stage moves along the optical axis. The metrology system may be configured to determine a metrology measurement associated with a target on a sample using the images and lateral positions of the stage element when implementing a metrology recipe.

A system of measuring an image of a pattern in a high NA scanning-type extreme ultra-violet (EUV) mask is disclosed. The system may include a light source generating an EUV light; an toroidal mirror; an flat mirror allowing light, which is reflected by the toroidal mirror, to be incident into the mask; an beam splitter; a light detection part; an anamorphic zone-plate lens focusing a transmitted portion of a light emitted from the beam splitter on the mask; a stage; and an anamorphic photo sensor, which is configured to measure an energy of a reflected portion of the coherent EUV light, is composed of a detector array, and has different sizes from each other in horizontal and vertical directions of an incidence surface of the detector array.

Disclosed is a method of determining a complex-valued field relating to a sample measured using an imaging system. The method comprises obtaining image data relating to a series of images of the sample, imaged at an image plane of the imaging system, and for which at least two different modulation functions are imposed in a Fourier plane of the imaging system; and determining the complex-valued field from the imaging data based on the imposed modulation functions.

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.

A method for training a machine learning model configured to predict values of a physical characteristic associated with a substrate and for use in adjusting a patterning process. The method involves obtaining a reference image; determining a first set of model parameter values of the machine learning model such that a first cost function is reduced from an initial value of the cost function obtained using an initial set of model parameter values, where the first cost function is a difference between the reference image and an image generated via the machine learning model; and training, using the first set of model parameter values, the machine learning model such that a combination of the first cost function and a second cost function is iteratively reduced, the second cost function representing a difference between measured values and predicted values.