Abstract:
A method of determining an estimated intensity of radiation scattered by a target illuminated by a radiation source, has the following steps: obtaining and training (402) a library REFLIB of wavelength-dependent reflectivity as a function of the wavelength, target structural parameters and angle of incidence R(λ,θ,x,y); determining (408) a wide-band library (W-BLIB) of integrals of wavelength-dependent reflectivity R of the target in a Jones framework over a range of radiation source wavelengths λ; training (TRN) (410) the wide-band library; and determining (412), using the trained wide-band library, an estimated intensity (INT) of radiation scattered by the target illuminated by the radiation source.
Abstract:
A method of tuning a prediction model relating to at least one particular configuration of a manufacturing device. The method includes obtaining a function including at least a first function of first prediction model parameters associated with the at least one particular configuration, and a second function of the first prediction model parameters and second prediction model parameters associated with configurations of the manufacturing device and/or related devices other than the at least one particular configuration. Values of the first prediction model parameters are obtained based on an optimization of the function, and a prediction model is tuned according to these values of the first prediction model parameters to obtain a tuned prediction mode.
Abstract:
A method of determining edge placement error within a structure produced using a lithographic process, the method comprising the steps of: (a) receiving a substrate comprising a first structure produced using the lithographic process, the first structure comprising first and second layers, each of the layers having first areas of electrically conducting material and second areas of non-electrically conducting material; (b) receiving a target signal indicative of a first target relative position which is indicative of a target position of edges between the first areas and the second areas of the first layer relative to edges between the first areas and second areas of the second layer in the first structure during said lithographic process; (c) detecting scattered radiation while illuminating the first structure with optical radiation to obtain a first signal; and (d) ascertaining an edge placement error parameter on the basis of the first signal and the first target relative position.
Abstract:
Disclosed is a method of metrology. The method comprises illuminating a radiation onto a substrate; obtaining measurement data relating to at least one measurement of each of one or more structures on the substrate; using a Fourier-related transform to transform the measurement data into a transformed measurement data; and extracting a feature of the substrate from the transformed measurement data, or eliminating an impact of a nuisance parameter.
Abstract:
Methods and inspection apparatus and computer program products for assessing a quality of reconstruction of a value of a parameter of interest of a structure, which may be applied for example in metrology of microscopic structures. It is important the reconstruction provides a value of a parameter of interest (e.g. a CD) of the structure which is accurate as the reconstructed value is used to monitor and/or control a lithographic process. This is a way of assessing a quality of reconstruction (803) of a value of a parameter of interest of a structure which does not require the use of a scanning electron microscope, by predicting (804) values of the parameter of interest of structures using reconstructed values of parameters of structures, and by comparing (805) the predicted values of the parameter of interest and the reconstructed values of the parameter of interest.
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:
A method including obtaining measurement results of a device manufacturing process or a product thereof, obtaining sets of one or more values of one or more parameters of a distribution by fitting the distribution against the measurement results, respectively, and obtaining, using a computer, a set of one or more values of one or more hyperparameters of a hyperdistribution by fitting the hyperdistribution against the sets of values of the parameters.
Abstract:
Disclosed is method of optimizing bandwidth of measurement illumination for a measurement application, and an associated metrology apparatus. The method comprises performing a reference measurement with reference measurement illumination having a reference bandwidth and performing one or more optimization measurements, each of said one or more optimization measurements being performed with measurement illumination having a varied candidate bandwidth. The one or more optimization measurements are compared with the reference measurement; and an optimal bandwidth for the measurement application is selected based on the comparison.
Abstract:
Disclosed are a method, computer program and associated apparatuses for metrology. The method includes acquiring inspection data comprising a plurality of inspection data elements, each inspection data element having been obtained by inspection of a corresponding target structure formed using a lithographic process; and performing an unsupervised cluster analysis on said inspection data, thereby partitioning said inspection data into a plurality of clusters in accordance with a metric. In an embodiment, a cluster representative can be identified for each cluster. The cluster representative may be reconstructed and the reconstruction used to approximate the other members of the cluster.