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 apparatus for directing onto a substrate a radiation beam emitted as a result of high harmonic generation (HHG). A drive radiation source provides drive radiation to an interaction region which contains a medium for HHG generation. The radiation generated by HHG may comprise a plurality of soft X-ray (SXR) wavelengths. Different HHG generated wavelengths have a different divergence angles. The apparatus further comprises an optical system which focusses the different HHG wavelengths to different focal planes, because of the different divergence angles. The apparatus further comprises a substrate support for holding the substrate. One or more of the drive radiation source, interaction region, optical system and substrate support can control the position of the substrate relative to the plurality of focal planes of the HHG radiation.
Abstract:
Methods for calibrating metrology apparatuses and determining a parameter of interest are disclosed. In one arrangement, training data is provided that comprises detected representations of scattered radiation detected by each of plural metrology apparatuses. An encoder encodes each detected representation to provide an encoded representation, and a decoder generates a synthetic detected representation from the respective encoded representation. A classifier estimates from which metrology apparatus originates each encoded representation or each synthetic detected representation. The training data is used to simultaneously perform, in an adversarial relationship relative to each other, a first machine learning process involving the encoder or decoder and a second machine learning process involving the classifier.
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:
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.
Abstract:
Methods and apparatus for estimating an unknown value of at least one of a plurality of sets of data, each set of data comprising a plurality of values indicative of radiation diffracted and/or reflected and/or scattered by one or more features fabricated in or on a substrate, wherein the plurality of sets of data comprises at least one known value, and wherein at least one of the plurality of sets of data comprises an unknown value, the apparatus comprising a processor to estimate the unknown value of the at least one set of data based on: the known values of the plurality of sets of data; a first condition between two or more values within a set of data of the plurality of sets of data; and a second condition between two or more values being part of different sets of data of the plurality of the sets of data.
Abstract:
Disclosed herein is a method comprising: obtaining measurement results of a device manufacturing 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; 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:
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:
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 comprises one or more parameters used in the patterning process. The control output is generated with a trained machine learning model 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 comprises 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.