Abstract: A measurement apparatus and method for determining a substrate grid describing a deformation of a substrate prior to exposure of the substrate in a lithographic apparatus configured to fabricate one or more features on the substrate. Position data for a plurality of first features and/or a plurality of second features on the substrate is obtained. Asymmetry data for at least a feature of the plurality of first features and/or the plurality of second features is obtained. The substrate grid based on the position data and the asymmetry data is determined. The substrate grid and asymmetry data are passed to the lithographic apparatus for controlling at least part of an exposure process to fabricate one or more features on the substrate.

Abstract: A method for determining a correction relating to a performance metric of a semiconductor manufacturing process, the method including: obtaining a set of pre-process metrology data; processing the set of pre-process metrology data by decomposing the pre-process metrology data into one or more components which: a) correlate to the performance metric; or b) are at least partially correctable by a control process which is part of the semiconductor manufacturing process; and applying a trained model to the processed set of pre-process metrology data to determine the correction for the semiconductor manufacturing process.

Abstract: A method for determining a root cause affecting yield in a process for manufacturing devices on a substrate, the method including: obtaining yield distribution data including a distribution of a yield parameter across the substrate or part thereof; obtaining sets of metrology data, each set including a spatial variation of a process parameter over the substrate or part thereof corresponding to a different layer of the substrate; comparing the yield distribution data and metrology data based on a similarity metric describing a spatial similarity between the yield distribution data and an individual set out of the sets of the metrology data; and determining a first similar set of metrology data out of the sets of metrology data, being the first set of metrology data in terms of processing order for the corresponding layers, which is determined to be similar to the yield distribution data.

Abstract: Generating a control output for a patterning process is described. A control input is received. The control input is for controlling the patterning process. The control input includes one or more parameters used in the patterning process. The control output is generated with a trained machine learning mod& based on the control input, The machine learning model is trained with training data generated from simulation of the patterning process and/or actual process data, The training data includes 1) a plurality of training control inputs corresponding to a plurality of operational conditions of the patterning process, where the plurality of operational conditions of the patterning process are associated with operational condition specific behavior of the patterning process over time, and 2) training control outputs generated using a physical model based on the training control inputs.

Abstract: Methods for training a process model and determining ranking of simulated patterns (e.g., corresponding to hot spots). A method involves obtaining a training data set including: (i) a simulated pattern associated with a mask pattern to be printed on a substrate, (ii) inspection data of a printed pattern imaged on the substrate using the mask pattern, and (iii) measured values of a parameter of the patterning process applied during imaging of the mask pattern on the substrate; and training a machine learning model for the patterning process based on the training data set to predict a difference in a characteristic of the simulated pattern and the printed pattern. The trained machine learning model can be used for determining a ranking of hot spots. In another method a model is trained based on measurement data to predict ranking of the hot spots.

Abstract: A method for determining an etch profile is described. The method includes determining a masking layer profile. Loading information can be determined. The loading information indicates dependence of an etch rate for the masking layer profile on a quantity and pattern of material being etched. Flux information can be determined. The flux information indicates dependence of the etch rate on an intensity and a spread angle of radiation incident on the masking layer profile. Re-deposition information can be determined. The re-deposition information indicates dependence of the etch rate on an amount of material removed from the masking layer profile that is re-deposited back on the masking layer profile. An output etch profile for the layer of the wafer is determined based on the loading information, the flux information, and/or the re-deposition information.

Abstract: Methods for training a process model and determining ranking of simulated patterns (e.g., corresponding to hot spots). A method involves obtaining a training data set including: (i) a simulated pattern associated with a mask pattern to be printed on a substrate, (ii) inspection data of a printed pattern imaged on the substrate using the mask pattern, and (iii) measured values of a parameter of the patterning process applied during imaging of the mask pattern on the substrate; and training a machine learning model for the patterning process based on the training data set to predict a difference in a characteristic of the simulated pattern and the printed pattern. The trained machine learning model can be used for determining a ranking of hot spots. In another method a model is trained based on measurement data to predict ranking of the hot spots.

Abstract: Disclosed are an apparatus and a method for granulation of a blast furnace slag and recycling of waste heat. The apparatus comprises an aerosol granulation nozzle module, a flow guide, a cyclone separator and a waste heat recovery device; wherein the aerosol granulation nozzle module comprises a slag flow controller, a compressed air flow control valve, a water volume control valve and an aerosol spray gun; the flow guide is configured to fully mix the medium temperature gas and the high-temperature granular slag having a primarily solidified surface in the flow guide; and the cyclone separator is configured to separate the high-temperature granular slag and the medium-to-high-temperature gas. The present invention completes the granulation of blast furnace slag, and organically couples slag sensible heat recovery with sludge drying, thereby recovering the waste heat in the process of slag granulation and improving the efficiency of waste heat recovery and utilization.

Abstract: A method for determining an etch profile is described. The method includes determining a masking layer profile. Loading information can be determined. The loading information indicates dependence of an etch rate for the masking layer profile on a quantity and pattern of material being etched. Flux information can be determined. The flux information indicates dependence of the etch rate on an intensity and a spread angle of radiation incident on the masking layer profile. Re-deposition information can be determined. The re-deposition information indicates dependence of the etch rate on an amount of material removed from the masking layer profile that is re-deposited back on the masking layer profile. An output etch profile for the layer of the wafer is determined based on the loading information, the flux information, and/or the re-deposition information.

Abstract: A roller treatment process and a treatment device suitable for a total-amount steel slag treatment. The treatment process is: firstly, a slag tank (2) with the molten slag is tightly held by a slag tank tilting mechanism and moved to a slag inlet position, the slag tank (2) is tilted to pour the molten slag with good fluidity into a rotary roller device (5) through a feeding chute (51), so that a roller treatment is achieved; secondly, when the steel slag (3) without fluidity in the slag tank (2) cannot flow out, a slag removal machine (4) is used for pushing the high-viscosity slag or the solid slag into the roller device (5); and thirdly, the slag tank (2) is reversed by a large angle tilting to make the slag at the bottom of the tank drop into the roller device (5), so that the total-amount steel slag treatment of the same roller device (5) is achieved.

Abstract: A method includes projecting an illumination beam of radiation onto a metrology target on a substrate, detecting radiation reflected from the metrology target on the substrate, and determining a characteristic of a feature on the substrate based on the detected radiation, wherein a polarization state of the detected radiation is controllably selected to optimize a quality of the detected radiation.

Abstract: A method, system and program for determining a position of a feature referenced to a substrate. The method includes measuring a position of the feature, receiving an intended placement of the feature and determining an estimate of a placement error based on knowledge of a relative position of a first reference feature referenced to a first layer on a substrate with respect to a second reference feature referenced to a second layer on a substrate. The updated position may be used to position the layer of the substrate having the feature, or another layer of the substrate, or another layer of another substrate.

Abstract: There is disclosed a method of measuring a process parameter for a manufacturing process involving lithography. In a disclosed arrangement the method comprises performing first and second measurements of overlay error in a region on a substrate, and obtaining a measure of the process parameter based on the first and second measurements of overlay error. The first measurement of overlay error is designed to be more sensitive to a perturbation in the process parameter than the second measurement of overlay error by a known amount.

Abstract: A method for determining a stack configuration for a substrate subjected to a patterning process. The method includes obtaining (i) measurement data of a stack configuration with location information on a printed substrate, (ii) a substrate model configured to predict a stack characteristic based on a location of the substrate, and (iii) a stack map including a plurality of stack configurations based on the substrate model. The method iteratively determines values of model parameters of the substrate model based on a fitting between the measurement data and the plurality of stack configurations of the stack map, and predicts an optimum stack configuration at a particular location based on the substrate model using the values of the model parameters.

Abstract: Methods for training a process model and determining ranking of simulated patterns (e.g., corresponding to hot spots). A method involves obtaining a training data set including: (i) a simulated pattern associated with a mask pattern to be printed on a substrate, (ii) inspection data of a printed pattern imaged on the substrate using the mask pattern, and (iii) measured values of a parameter of the patterning process applied during imaging of the mask pattern on the substrate; and training a machine learning model for the patterning process based on the training data set to predict a difference in a characteristic of the simulated pattern and the printed pattern. The trained machine learning model can be used for determining a ranking of hot spots. In another method a model is trained based on measurement data to predict ranking of the hot spots.

Abstract: A method of hot spot ranking for a patterning process. The method includes obtaining (i) a set of hot spots of a patterning process, (ii) measured values of one or more parameters of the patterning process corresponding to the set of hot spots, and (ii) simulated values of the one or more parameters of the patterning process corresponding to the set of hot spots; determining a measurement feedback based on the measured values and the simulated values of the one or more parameters of the patterning process; and determining, via simulation of a process model of the patterning process, a ranking of a hot spot within the set of hot spots based on the measurement feedback.

Abstract: Grouping image patterns to determine wafer behavior in a patterning process with a trained machine learning model is described. The described operations include converting, based on the trained machine learning model, one or more patterning process images including the image patterns into feature vectors. The feature vectors correspond to the image patterns. The described operations include grouping, based on the trained machine learning model, feature vectors with features indicative of image patterns that cause matching wafer and/or wafer defect behavior in the patterning process. The one or more patterning process images include aerial images, resist images, and/or other images. The grouped feature vectors may be used to: detect potential patterning defects on a wafer during a lithography manufacturability check as part of optical proximity correction, adjust a mask layout design, and/or generate a gauge line/defect candidate list, among other uses.

Abstract: A method for determining a correction relating to a performance metric of a semiconductor manufacturing process, the method including: obtaining a set of pre-process metrology data; processing the set of pre-process metrology data by decomposing the pre-process metrology data into one or more components which: a) correlate to the performance metric; or b) are at least partially correctable by a control process which is part of the semiconductor manufacturing process; and applying a trained model to the processed set of pre-process metrology data to determine the correction for the semiconductor manufacturing process.

Abstract: A method for predicting yield relating to a process of manufacturing semiconductor devices on a substrate, the method including: obtaining a trained first model which translates modeled parameters into a yield parameter, the modeled parameters including: a) a geometrical parameter associated with one or more selected from: a geometric characteristic, dimension or position of a device element manufactured by the process and b) a trained free parameter; obtaining process parameter data including data regarding a process parameter characterizing the process; converting the process parameter data into values of the geometrical parameter; and predicting the yield parameter using the trained first model and the values of the geometrical parameter.

Abstract: A method for analyzing a process, the method including obtaining a multi-dimensional probability density function representing an expected distribution of values for a plurality of process parameters; obtaining a performance function relating values of the process parameters to a performance metric of the process; and using the performance function to map the probability density function to a performance probability function having the process parameters as arguments.