US-12619164-B2 - Metrology method and apparatus, computer program and lithographic system

Abstract

A method, computer program and associated apparatuses for metrology. The method includes determining a reconstruction recipe describing at least nominal values for use in a reconstruction of a parameterization describing a target. The method includes obtaining first measurement data relating to measurements of a plurality of targets on at least one substrate, the measurement data relating to one or more acquisition settings and performing an optimization by minimizing a cost function which minimizes differences between the first measurement data and simulated measurement data based on a reconstructed parameterization for each of the plurality of targets. A constraint on the cost function is imposed based on a hierarchical prior. Also disclosed is a hybrid model method comprising obtaining a coarse model operable to provide simulated coarse data; and training a data driven model to correct the simulated coarse data so as to determine simulated data for use in reconstruction.

Inventors

• Alexandru ONOSE
• Remco Dirks
• Roger Hubertus Elisabeth Clementine Bosch
• Sander Silvester Adelgondus Marie JACOBS
• Frank Jaco BUIJNSTERS
• Siebe Tjerk De Zwart
• Artur PALHA DA SILVA CLERIGO
• Nick VERHEUL

Assignees

• ASML NETHERLANDS B.V.

Dates

Publication Date

20260505

Application Date

20240418

Priority Date

20190314

Claims (20)

1. 1 . A method of constructing a hybrid model for providing simulated data for use in parameter reconstruction of a structure, the method comprising: obtaining a coarse model operable to provide simulated coarse data; and training, by a hardware computer, a data driven model so as to determine the simulated data, the data driven model configured to produce a same type of data as the simulated coarse data and the same type of data is a corrected version of the simulated coarse data.
2. 2 . The method according to claim 1 , wherein the simulated coarse data comprises simulated coarse reflectivity data.
3. 3 . The method according to claim 1 , wherein the data driven model is configured to determine corrections to combine with the simulated coarse data.
4. 4 . The method according to claim 1 , wherein the data driven model is configured to derive the simulated data from the simulated coarse data.
5. 5 . The method according to claim 1 , wherein at least the data driven model is a machine learned neural network model.
6. 6 . The method according to claim 1 , wherein the hybrid model is a machine learned neural network model and comprises a bottleneck layer having fewer nodes than one or more preceding layers.
7. 7 . The method according to claim 6 , wherein the bottleneck layer and the one or more one or more preceding layers together learn a mapping to a restricted parameter space with respect to an input parameterization.
8. 8 . The method according to claim 7 , wherein: the hybrid model comprises one or more of the preceding layers, the preceding layers comprising decorrelation layers, or the restricted parameter space represents a minimum sufficient statistic for the combination of all parameters of the input parameterization, or the hybrid model comprises one or more modeling layers subsequent to the bottleneck layer, which models the simulated data from the restricted parameter space.
9. 9 . The method according to claim 6 , further comprising training an additional model which is operable to invert and/or re-map a mapping which the bottleneck layer induces; and using the additional model to determine a correlation metric for describing a correlation between a parameter and one or more other parameters.
10. 10 . The method according to claim 1 , wherein the training step comprises performing iterations of: determining parameter value estimates for the coarse model based on measurement data and the simulated data; and updating the data driven model using the determined parameter value estimates such that the measurement data and the simulated data are a better match.
11. 11 . The method according to claim 10 , wherein the measurement data comprises intensity data relating to a plurality of targets.
12. 12 . The method according to claim 1 , further comprising using the hybrid model in a parameter reconstruction of a structure in a metrology operation.
13. 13 . A computer program product comprising a non-transitory computer readable medium having instructions therein, the instructions, when executed by a computer system, configured to cause the computer system to at least: obtain a coarse model operable to provide simulated coarse data; and train a data driven model so as to determine simulated data for use in parameter reconstruction of a structure by a hybrid model, the data driven model configured to produce a same type of data as the simulated coarse data and the same type of data is a corrected version of the simulated coarse data.
14. 14 . The computer program product of claim 13 , wherein the simulated coarse data comprises simulated coarse reflectivity data.
15. 15 . The computer program product of claim 13 , wherein the data driven model is configured to determine corrections to combine with the simulated coarse data.
16. 16 . The computer program product of claim 13 , wherein the data driven model is configured to derive the simulated data from the simulated coarse data.
17. 17 . The computer program product of claim 13 , wherein at least the data driven model is a machine learned neural network model.
18. 18 . The computer program product of claim 17 , wherein the hybrid model is a machine learned neural network model and comprises a bottleneck layer having fewer nodes than one or more preceding layers.
19. 19 . The computer program product of claim 13 , wherein the instructions configured to cause the computer system to train the data driven model are further configured to cause the computer system to perform iterations of: determination of parameter value estimates for the coarse model based on measurement data and the simulated data; and updates of the data driven model using the determined parameter value estimates such that the measurement data and the simulated data are a better match.
20. 20 . The computer program product of claim 13 , further configured to cause the computer system to use the hybrid model in a parameter reconstruction of a structure in a metrology operation.

Description

This application is a continuation of U.S. patent application Ser. No. 17/436,947, filed Sep. 7, 2021, which is the U.S. national phase entry of PCT Patent Application No. PCT/EP2020/054967 which was filed on Feb. 26, 2020, which claims the benefit of priority of European Patent Application No. 19162808.0, which was filed on Mar. 14, 2019, and of European Patent Application No. 19178432.1, which was filed on Jun. 5, 2019, each of the foregoing applications is incorporated herein in its entirety by reference. FIELD The present description relates to methods and apparatuses for metrology usable, for example, in the manufacture of devices by lithographic techniques. BACKGROUND A lithographic apparatus is a machine that applies a desired pattern onto a substrate, usually onto a target portion of the substrate. A lithographic apparatus can be used, for example, in the manufacture of integrated circuits (ICs). In that instance, a patterning device, which is alternatively referred to as a mask or a reticle, may be used to generate a circuit pattern to be formed on an individual layer of the IC. This pattern can be transferred onto a target portion (e.g., including part of, one, or several dies) on a substrate (e.g., a silicon wafer). Transfer of the pattern is typically via imaging onto a layer of radiation-sensitive material (resist) provided on the substrate. In general, a single substrate will contain a network of adjacent target portions that are successively patterned. In lithographic processes, it is desirable frequently to make measurements of the structures created, e.g., for process control and verification. Various tools for making such measurements are known, including scanning electron microscopes, which are often used to measure critical dimension (CD), and specialized tools to measure overlay, a measure of the accuracy of alignment of two layers in a device. Overlay may be described in terms of the degree of misalignment between the two layers, for example reference to a measured overlay of 1 nm may describe a situation where two layers are misaligned by 1 nm. Recently, various forms of scatterometers have been developed for use in the lithographic field. These devices direct a beam of radiation onto a target and measure one or more properties of the scattered radiation—e.g., intensity at a single angle of reflection as a function of wavelength; intensity at one or more wavelengths as a function of reflected angle; or polarization as a function of reflected angle—to obtain a diffraction image or pattern from which a property of interest of the target can be determined. In order that the radiation that impinges on to the substrate is diffracted, an object with a specific shape is printed on to the substrate and is often known as a scatterometry target or simply target. As mentioned above, it is possible to determine the actual shape of a scatterometry object using a cross-section scanning electron microscope and the like. However, this involves a large amount of time, effort and specialized apparatus and is less suited for measurements in a production environment because a separate specialized apparatus is required in line with normal apparatus in, for example, a lithographic cell. Determination of the property of interest may be performed by various techniques: e.g., reconstruction. Reconstruction, which is defined as the inference of a given parameterization based on measured data, uses iterative solvers to find a solution to the inverse problem (e.g., intensity via reflectivity to parametrization). RCWA is a forward model (parametrization via reflectivity to intensity) that can simulate how a model responds to the light; however, it cannot be used alone to infer the backwards parametrization. To perform such reconstructions, a profile, defining the shape of a structure being measured in terms of a number of parameters, may be used. To make the profile more robust, a reconstruction recipe describing good nominal values for parameters (representative of the data as a whole) should be chosen. It is desirable to provide a method which can help with defining a reconstruction recipe. SUMMARY In a first aspect, there is provided a method of determining a reconstruction recipe describing at least nominal values for using in a reconstruction of a parameterization describing a target; comprising: obtaining first measurement data relating to measurements of a plurality of targets on at least one substrate, said measurement data relating to one or more acquisition settings; s performing an optimization by minimizing a cost function which minimizes differences between the first measurement data and simulated measurement data based on a reconstructed parameterization for each of said plurality of targets; wherein a constraint on the cost function is imposed based on a hierarchical prior. In a second aspect, there is provided a method of constructing a hybrid model for providing simulated data for use in paramete