Measurement method and associated measurement device

Abstract

A method of determining at least one parameter of interest relating to a structure formed in at least one corresponding layer on a substrate is disclosed, the method comprising: obtaining measured measurement data relating to a measurement of the structure; obtaining a model, the model describing the structure in accordance with a plurality of model parameters, the model parameters comprising estimated values; and performing a plurality of optimization steps in sequence based on the measured measurement data to determine the plurality of model parameters, each optimization step of the plurality of optimization steps determining a subset of the plurality of model parameters.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to European application No. 22194304.6 filed on September 7, 2022 and European application No. 22203557.8 filed on October 25, 2022, and the entire contents of these European applications are incorporated herein by reference. Technical Field

[0003] The invention relates to a metrology method and a device which can be used, for example, to determine properties of a structure on a substrate. Background Art

[0004] A lithographic apparatus is a machine configured to apply a desired pattern onto a substrate. A lithographic apparatus may be used, for example, in the manufacture of integrated circuits (ICs). A lithographic apparatus may, for example, project a pattern (also often referred to as a "design layout" or "design") at a patterning device (e.g., a mask) onto a layer of radiation-sensitive material (resist) disposed on a substrate (e.g., a wafer).

[0005] To project a pattern onto a substrate, a lithographic apparatus may use electromagnetic radiation. The wavelength of this radiation determines the minimum size of a feature that can be formed on the substrate. Typical wavelengths currently in use are 365 nm (i-line), 248 nm, 193 nm, and 13.5 nm. Compared to a lithographic apparatus using radiation having a wavelength of, for example, 193 nm, a lithographic apparatus using extreme ultraviolet (EUV) radiation having a wavelength in the range of 4 nm to 20 nm (e.g., 6.7 nm or 13.5 nm) can be used to form smaller features on a substrate.

[0006] Low k1 lithography can be used to process features with dimensions that are smaller than the classical resolution limit of the lithographic apparatus. In such a process, the resolution formula can be expressed as CD=k1×λ / NA, where λ is the wavelength of the radiation used, NA is the numerical aperture of the projection optics in the lithographic apparatus, CD is the "critical dimension" (usually the smallest feature size printed, but in this case it is the half pitch), and k1 is an empirical resolution factor. In general, the smaller the k1, the more difficult it is to reproduce a pattern on a substrate that resembles the shape and size planned by the circuit designer in order to achieve specific electrical functionality and performance. To overcome these difficulties, complex fine-tuning steps can be applied to the lithographic projection apparatus and / or the design layout. These steps include, for example, but are not limited to, optimization of NA, customized illumination schemes, use of phase-shifted patterning devices, various optimizations of the design layout such as optical proximity correction (OPC, sometimes also referred to as "optical and process correction") in the design layout, or other methods generally defined as "resolution enhancement techniques" (RET). Alternatively, a strict control loop for controlling the stability of the lithographic apparatus can be used to improve the reproduction of patterns at low k1.

[0007] During lithography, as well as other manufacturing processes, it is desirable to frequently make measurements of the resulting structures, for example for process control and verification. Various tools for making these measurements are well known, including scanning electron microscopes, which are often used to measure critical dimensions (CDs), and specialized tools for measuring overlay, which is the alignment accuracy of two layers in a device. Recently, various forms of scatterometers have been developed for use in the lithography field.

[0008] The fabrication process may be, for example, photolithography, etching, deposition, chemical mechanical planarization, oxidation, ion implantation, diffusion, or a combination of two or more of the above.

[0009] Examples of known scatterometers generally rely on the provision of a dedicated metrology target. For example, the method may require a target in the form of a simple grating that is large enough so that the measurement beam produces a spot that is smaller than the grating (i.e., the grating is underfilled). In the so-called reconstruction method, the properties of the grating can be calculated by simulating the interaction of the scattered radiation with a mathematical model of the target structure. The parameters of the model are adjusted until the simulated interaction produces a diffraction pattern that is similar to the diffraction pattern observed from a real target.

[0010] In addition to measuring feature shapes by reconstruction, such equipment can also be used to measure diffraction-based overlays, as described in the published patent application US2006066855A1. Overlay measurements based on diffraction-based overlay measurements using dark field imaging of diffraction orders enable overlay measurements of smaller targets. These targets can be smaller than the irradiation spot and can be surrounded by product structures on the wafer. Examples of dark field imaging measurements can be found in many published patent applications such as US2011102753A1 and US20120044470A. Composite grating targets can be used to measure multiple gratings in one image. Known scatterometers tend to use light in the visible or near infrared (IR) wave range, which requires that the pitch of the grating is much coarser than the actual product structure whose properties are actually concerned. Deep ultraviolet (DUV), extreme ultraviolet (EUV) or X-ray radiation with much shorter wavelengths can be used to define such product features. Unfortunately, these wavelengths are usually not available or cannot be used for measurement.

[0011] On the other hand, the size of modern product structures is so small that the size of modern product structures cannot be imaged by optical measurement techniques. Smaller features include, for example, features formed by multiple patterning processes and / or pitch multiplication. Therefore, targets for high-volume measurement often use features that are much larger than products whose overlay error or critical dimensions are the properties of interest. The measurement results are only indirectly related to the size of the real product structure and may be inaccurate because the measurement target does not suffer the same deformation under optical projection in the lithography equipment, and / or different treatments in other steps of the manufacturing process. Although scanning electron microscopy (SEM) can directly resolve these modern product structures, SEM is much more time-consuming than optical measurement. In addition, electrons cannot penetrate thicker process layers, which makes electrons less suitable for measurement applications. Other techniques such as measuring electrical properties using contact pads are also well known, but the other techniques only provide indirect evidence of the real product structure.

[0012] By reducing the wavelength of the radiation used during metrology, it may be possible to resolve smaller structures, to increase sensitivity to structural changes of structures and / or to penetrate further into the product structure. One such method of generating suitable high frequency radiation (e.g. hard X-rays, soft X-rays and / or EUV radiation) may be to use pump radiation (e.g. infrared IR radiation) to excite the production medium, thereby generating emitted radiation, optionally including higher order harmonic generation of the high frequency radiation.

[0013] Current reconstruction techniques for reconstructing the parameters of the measured structure are mathematically and computationally complex due to the number of different parameter permutations. It is often also necessary to have prior knowledge of many accurate parameters describing the measured sample, when detailed sample information may not be available. To address this problem, at least in the case of overlay measurements, other overlay measurement techniques have been developed that do not require reconstruction, such as those based on measurements of asymmetries of the target, which are typically biased to separate overlay asymmetries from other asymmetries.

[0014] It would be desirable to improve such reconstruction techniques and / or overlay measurement techniques. Summary of the invention

[0015] In a first aspect of the present invention, a method for determining at least one parameter of interest associated with a structure in at least one corresponding layer formed on a substrate is provided, the method comprising: obtaining measured measurement data associated with a measurement result of the structure; obtaining a model, the model describing the structure based on a plurality of model parameters, the model parameters including estimated values; and sequentially performing a plurality of optimization steps based on the measured measurement data to determine the plurality of model parameters, each of the plurality of optimization steps determining a subset of the plurality of model parameters.

[0016] According to a second aspect of the present invention, there is provided a computer program product comprising one or more sequences of machine readable instructions for implementing the computing steps of the method according to the first aspect of the present invention as set out above.

[0017] The present invention further provides a processing device and a measuring device comprising the computer program according to the second aspect.

[0018] These and other aspects and advantages of the apparatus and methods disclosed herein will be appreciated from a consideration of the following description of exemplary embodiments and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Embodiments will now be described by way of example with reference to the accompanying schematic diagram, in which:

[0020] - Figure 1 A schematic overview diagram depicting a lithographic apparatus;

[0021] - Figure 2 A schematic overview diagram depicting a lithography unit;

[0022] - Figure 3 A schematic diagram depicting overall lithography showing the collaboration between three key technologies for optimizing semiconductor manufacturing;

[0023] - Figure 4 schematically illustrates a scatterometry device;

[0024] - Figure 5 Schematically illustrates a transmission type scattering measurement device;

[0025] - Figure 6 depicts a schematic diagram of a metrology apparatus in which EUV and / or SXR radiation is used;

[0026] - Figure 7 A simplified schematic diagram depicting the source of irradiation;

[0027] - Figure 8 Illustration (a) shows a portion of an overlapping object of the correct size; and (b) a model of the same overlapping object including an estimated size, as used in an initial setup of the method of an embodiment;

[0028] - Fig. 9 Figure (a) Figure 8 (a) a portion of an overlapping object shown in illustration showing the correct size; and (b) a model of the same overlapping object including some estimated sizes and the sizes optimized in a first step using the method of an embodiment;

[0029] - Fig.10 Figure (a) Figure 8 (a) is a diagrammatic representation of a portion of an overlapping object showing the correct size; and (b) is a model of the same overlapping object after performing the method of an embodiment for determining overlap;

[0030] - Fig.11 A portion of the target structure on the stack is shown. Fig.11 illustrates parameters that can be reconstructed using a method according to another embodiment;

[0031] - Fig.12 (a) and Fig.12 (b) is a flowchart describing a method for determining an optimization order according to an embodiment;

[0032] - Fig.13 (a) is used to describe Fig.12 A graph of the diagonal of the covariance matrix of a plurality of exemplary parameters associated with the structure determined in the method;

[0033] - Fig.13 (b) is used to describe Fig.12 A graph of a correlation matrix of a plurality of exemplary parameters associated with the structure determined in the method; and

[0034] - Fig.13 (c) is used as a graphic representation Fig.12 A flow chart of an exemplary optimization sequence for determining a method. DETAILED DESCRIPTION

[0035] In this document, the terms "radiation" and "beam" are used to cover all types of electromagnetic radiation and particle radiation, including ultraviolet radiation (e.g., having a wavelength of 365 nm, 248 nm, 193 nm, 157 nm or 126 nm), EUV (extreme ultraviolet radiation, e.g., having a wavelength in the range of about 5 nm to 100 nm), X-ray radiation, electron beam radiation, and other particle radiation.

[0036] The terms "reticle", "mask" or "patterning device" as used herein may be broadly interpreted as referring to a general patterning device that can be used to impart a patterned cross-section to an incident radiation beam that corresponds to the pattern to be produced in a target portion of the substrate. In such contexts, the term "light valve" may also be used. In addition to typical masks (transmissive or reflective, binary, phase-shifting, hybrid, etc.), other examples of such patterning devices include programmable mirror arrays and programmable LCD arrays.

[0037] Figure 1 A lithographic apparatus LA is schematically depicted. The lithographic apparatus LA comprises an illumination system (also referred to as an illuminator) IL configured to condition a radiation beam B (e.g., UV radiation, DUV radiation, EUV radiation, or X-ray radiation), a mask support (e.g., a mask stage) T configured to support a patterning device (e.g., a mask) MA and connected to a first positioner PM configured to accurately position the patterning device MA according to certain parameters, a substrate support (e.g., a wafer stage) WT configured to hold a substrate (e.g., a wafer coated with resist) W and connected to a second positioner PW configured to accurately position the substrate support according to certain parameters, and a projection system (e.g., a refractive projection lens system) PS configured to project a pattern imparted to the radiation beam B by the patterning device MA onto a target portion C (e.g., comprising one or more dies) of the substrate W.

[0038] In operation, the illumination system IL receives a radiation beam from a radiation source SO, for example via a beam delivery system BD. The illumination system IL may include various types of optical components, such as refractive, reflective, diffractive, magnetic, electromagnetic, electrostatic and / or other types of optical components, or any combination thereof, to direct, shape and / or control the radiation. The illuminator IL may be used to condition the radiation beam B to have a desired spatial and angular intensity distribution in its cross-section at the plane of the patterning device MA.

[0039] The term "projection system" PS as used herein should be broadly interpreted as covering various types of projection systems suitable for the exposure radiation used and / or for other factors such as the use of immersion liquid or the use of a vacuum, including refractive, reflective, diffractive, catadioptric, anamorphic, magnetic, electromagnetic and / or electrostatic optical systems, or any combination thereof. Any use of the term "projection lens" herein may be considered synonymous with the more general term "projection system" PS.

[0040] The lithographic apparatus LA may be of a type in which at least a portion of the substrate may be covered by a liquid, such as water, having a relatively high refractive index, so as to fill the space between the projection system PS and the substrate W - this is also known as immersion lithography. More information on immersion techniques is given in US6952253, which is incorporated herein by reference in its entirety.

[0041] The lithographic apparatus LA may also be of a type having two or more substrate supports WT (also known as a "dual stage"). In such a "multi-stage" machine, the substrate supports WT may be used in parallel, and / or a step of preparing a substrate W for subsequent exposure may be performed on a substrate W located on one of the substrate supports WT while another substrate W on another substrate support WT is being used to expose a pattern on another substrate W.

[0042] In addition to the substrate support WT, the lithographic apparatus LA may also comprise a measurement platform. The measurement platform is arranged to hold sensors and / or cleaning means. The sensors may be arranged to measure properties of the projection system PS or properties of the radiation beam B. The measurement platform may hold a plurality of sensors. The cleaning means may be arranged to clean parts of the lithographic apparatus, for example parts of the projection system PS or parts of the system for providing immersion liquid. The measurement platform may be moved under the projection system PS when the substrate support WT is away from the projection system PS.

[0043] In operation, a radiation beam B is incident on a pattern forming device (e.g. a mask) MA held on a mask support T and is patterned by a pattern (design layout) present on the pattern forming device MA. Having traversed the mask MA, the radiation beam B passes through a projection system PS which focuses the beam onto a target portion C of the substrate W. With the aid of the second positioner PW and the position measurement system IF, the substrate support WT can be accurately moved, for example in order to position different target portions C in the path of the radiation beam B at focused and aligned positions. Similarly, the first positioner PM and possibly a further position sensor (the further position sensor not being in position) are arranged to move the substrate support WT in a manner which allows the projection system PS to focus the beam onto a target portion C of the substrate W. Figure 1The patterning device MA may be accurately positioned relative to the path of the radiation beam B using the mask alignment marks M1, M2 and the substrate alignment marks P1, P2. Although the substrate alignment marks P1, P2 occupy dedicated target portions as illustrated, the substrate alignment marks P1, P2 may be located in spaces between target portions. When the substrate alignment marks P1, P2 are located between target portions C, they are referred to as scribe line alignment marks.

[0044] like Figure 2 As shown in FIG. 1 , the lithography apparatus LA may form part of a lithography cell LC (sometimes also referred to as a lithocell or (lithography) cluster), which often also includes equipment for performing pre-exposure and post-exposure processes on a substrate W. Conventionally, these equipment include a spin coater SC for depositing a resist layer, a developer DE for developing the exposed resist, a chill plate CH and a bake plate BK, for example, for regulating the temperature of the substrate W (e.g., for regulating the solvent in the resist layer). A substrate transport device or robot RO picks up the substrate W from the input / output ports I / O1, I / O2, moves the substrate W between the different process equipment and transfers the substrate W to a feed station LB of the lithography apparatus LA. The devices in the lithography cell, which are often also collectively referred to as a track or coating and developing system, may be under the control of a track or coating and developing system control unit TCU, which itself may be controlled by a management control system SCS, which may also control the lithography apparatus LA, for example, via a lithography control unit LACU.

[0045] During a lithographic process, it is desirable to frequently measure the structures produced, for example for process control and verification. The tool used to perform such measurements may be referred to as a metrology tool MT. Different types of metrology tools MT for performing such measurements are well known, including scanning electron microscopes or various forms of scatterometer metrology tools MT. A scatterometer is a multifunctional instrument that allows measuring parameters of a lithographic process by having a sensor in or close to the pupil or a plane conjugated with the pupil of the objective of the scatterometer (measurements are usually referred to as pupil-based measurements), or by having the sensor in or close to the image plane or a plane conjugated with the image plane, in which case the measurements are usually referred to as image- or field-based measurements. Such scatterometers and related measurement techniques are further described in patent applications US20100328655, US2011102753A1, US20120044470A, US20110249244, US20110026032 or EP1,628,164A, which are incorporated herein by reference in their entirety. The aforementioned scatterometers can measure gratings using light from the hard X-ray (HXR), soft X-ray (SXR), extreme ultraviolet (EUV), visible to near infrared (IR) and IR wavelength ranges. In the case where the radiation is hard X-ray or soft X-ray, the aforementioned scatterometer can optionally be a small angle X-ray scattering measurement tool.

[0046] In order to correctly and consistently expose a substrate W exposed by the lithographic apparatus LA, it is desirable to inspect the substrate to measure properties of the patterned structure, such as overlay errors between subsequent layers, line thickness, critical dimensions (CD), shape of the structure, etc. Inspection tools and / or metrology tools (not shown) may be included in the lithography cell LC for this purpose. If errors are detected, then exposure of subsequent substrates or other processing steps to be performed on the substrate W may be adjusted, for example, particularly if the inspection is performed before other substrates W of the same batch or lot are yet to be exposed or processed.

[0047] The inspection apparatus, which may also be referred to as a metrology apparatus, is used to determine properties of a substrate W, and in particular, to determine how properties of different substrates W vary or how properties relating to different layers of the same substrate W vary from layer to layer. The inspection apparatus may alternatively be configured to identify defects on the substrate W, and may for example be part of the lithography cell LC, or may be integrated into the lithography apparatus LA, or may even be a separate device. The inspection apparatus may measure properties on a latent image (the image in the resist layer after exposure), or on a semi-latent image (the image in the resist layer after a post-exposure bake step PEB), or on a developed resist image (wherein exposed or unexposed parts of the resist have been removed), or even on an etched image (after a pattern transfer step such as etching).

[0048] In a first embodiment, the scatterometer MT is an angle-resolved scatterometer. In such a scatterometer, a reconstruction method can be applied on the measured signal to reconstruct or calculate the properties of the grating. Such a reconstruction can be caused, for example, by simulating the interaction of the scattered radiation with a mathematical model of the target structure and comparing the simulation results with the measured results. The parameters of the mathematical model are adjusted until the simulated interaction produces a diffraction pattern similar to the diffraction pattern observed from a real target.

[0049] In a second embodiment, the scatterometer MT is a spectroscopic scatterometer MT. In such a spectroscopic scatterometer MT, radiation emitted by a radiation source is directed onto a target and reflected, transmitted or scattered radiation from the target is directed to a spectrometer detector which measures the spectrum of the specularly reflected radiation (i.e. a measurement of intensity as a function of wavelength). From such data the structure or profile of the target producing the detected spectrum can be reconstructed, for example by rigorous coupled wave analysis and non-linear regression or by comparison with a library of simulated spectra.

[0050] In a third embodiment, the scatterometer MT is an ellipsometric scatterometer. An ellipsometric scatterometer allows parameters of a lithography process to be determined by measuring scattered or transmitted radiation for each polarization state. Such a metrology device emits polarized light (such as linear, circular or elliptically) by using, for example, an appropriate polarization filter in the illumination section of the metrology device. A source suitable for the metrology device may also provide polarized radiation. Various embodiments of existing ellipsometric scatterometers are described in U.S. patent applications 11 / 451,599, 11 / 708,678, 12 / 256,780, 12 / 486,449, 12 / 920,968, 12 / 922,587, 13 / 000,229, 13 / 033,135, 13 / 533,110 and 13 / 891,410, which are incorporated herein by reference in their entirety.

[0051] In one embodiment of the scatterometer MT, the scatterometer MT is adapted to measure the overlap of two misaligned gratings or periodic structures by measuring the reflection spectrum and / or an asymmetry in the detection configuration (the asymmetry being related to the extent of the overlap). The two (superimposable) grating structures may be applied in two different layers (not necessarily consecutive layers) and the two grating structures may be formed to be in approximately the same position on the wafer. The scatterometer may have a symmetrical detection configuration as described, for example, in the commonly owned patent application EP1,628,164A, so that any asymmetry is clearly distinguishable. This provides a straightforward way to measure misalignment in the gratings. Further examples of overlay errors between two layers containing a periodic structure as a target measured via asymmetry of the periodic structure may be found in PCT Patent Application Publication No. WO 2011 / 012624 or in U.S. Patent Application No. US 20160161863, which are incorporated herein by reference in their entirety.

[0052] Other parameters of interest may be focus and dose. Focus and dose may be determined simultaneously by scatterometry as described in U.S. Patent Application US2011-0249244, which is incorporated herein by reference in its entirety (or alternatively by scanning electron microscopy). A single structure may be used that has a unique combination of critical dimension and sidewall angle measurements for each point in a focus energy matrix (FEM - also referred to as a focus exposure matrix). If these unique combinations of critical dimension and sidewall angle are available, focus and dose values ​​may be uniquely determined from these measurements.

[0053] The metrology target may be a collection of composite gratings formed primarily in resist by a lithography process and also after other manufacturing processes (e.g., an etching process). The pitch and line width of the structures in the grating may rely heavily on the measurement optics (particularly, the NA of the optics) to be able to capture the diffraction orders from the metrology target. As previously indicated, the diffraction signal may be used to determine the shift between two layers (also referred to as "overlap") or may be used to reconstruct at least a portion of the original grating as produced by the lithography process. Such a reconstruction may be used to provide quality guidance for the lithography process and may be used to control at least a portion of the lithography process. The target may have smaller sub-segments configured to mimic the size of a functional portion of a design layout in the target. Due to such sub-segments, the target will behave more similarly to the functional portion of the design layout, so that the overall process parameter measurement is more preferably similar to the functional portion of the design layout. The target may be measured in an underfill mode or in an overfill mode. In the underfill mode, the measurement beam produces a spot that is smaller than the overall target. In the overfill mode, the measurement beam produces a spot that is larger than the overall target. In such an overfill mode, it is also possible to measure different targets simultaneously and thus to determine different process parameters simultaneously.

[0054] The overall measurement quality of the lithography parameters performed using a specific target is determined at least in part by the measurement options used to measure such lithography parameters. The term "substrate measurement options" may include measuring one or more parameters of the measurement itself, one or more parameters of the measured pattern, or measuring one or more parameters of the measurement itself and one or more parameters of the measured pattern. For example, if the measurement used in the substrate measurement options is a diffraction-based optical measurement, one or more of the parameters measured may include the wavelength of the radiation, the polarization of the radiation, the incident angle of the radiation relative to the substrate, the orientation of the radiation relative to the pattern on the substrate, etc. One of the criteria for selecting the measurement options may be, for example, the sensitivity of one of the measurement parameters to process changes. More examples are described in U.S. Patent Application US2016-0161863 and U.S. Patent Publication Application US 2016 / 0370717A1, which are incorporated herein by reference in their entirety.

[0055] The patterning process in the lithographic apparatus LA can be one of the most critical steps in the process, requiring a high accuracy in the dimensioning and placement of structures on the substrate W. In order to ensure this high accuracy, the three systems can be combined in a so-called "holistic" control environment, such as Figure 3 Schematically depicted. One of these systems is the lithographic apparatus LA, which is (in practice) connected to a metrology tool MT (a second system) and to a computer system CL (a third system). The key to this "holistic" environment is to optimize the cooperation between these three systems to enhance the overall process window and provide a tight control loop to ensure that the patterning performed by the lithographic apparatus LA is within the process window. The process window defines the range of process parameters (e.g. dose, focus, overlay) within which a particular manufacturing process produces a defined result (e.g. a functional semiconductor device) - perhaps the process parameters in the lithographic process or the patterning process are allowed to vary within the range.

[0056] The computer system CL can use (part of) the design layout to be patterned to predict which resolution enhancement techniques to use and perform computational lithography simulations and calculations to determine which mask layout and lithography equipment settings achieve the maximum overall process window for the patterning process (indicated by the double arrows in the first scale SC1). Figure 3 The resolution enhancement technique may be arranged to match the patterning possibilities of the lithographic apparatus LA. The computer system CL may also be used to detect where within the process window the lithographic apparatus LA is currently operating (e.g. using input from a metrology tool MET) to predict whether defects may exist (e.g. due to suboptimal processing) Figure 30 in the figure).

[0057] The metrology tool MT may provide input to the computer system CL to enable accurate simulations and predictions, and may provide feedback to the lithographic apparatus LA to identify, for example, possible drift in the calibration state of the lithographic apparatus LA (in Figure 3 SC3).

[0058] Many different forms of metrology tools MT can be provided for measuring structures produced using lithographic patterning equipment. The metrology tool MT can use electromagnetic radiation to interrogate the structure. The properties of the radiation (e.g. wavelength, bandwidth, power) may affect different measurement characteristics of the tool, wherein shorter wavelengths generally allow increased resolution. The wavelength of the radiation has an impact on the resolution achievable by the metrology tool. Therefore, in order to be able to measure structures with features having smaller dimensions, a metrology tool MT with a short wavelength radiation source is preferred.

[0059] Another way in which the wavelength of the radiation may affect the measurement characteristics is the penetration depth, and the transparency / opacity of the material to be inspected at the wavelength of the radiation. Depending on the opacity and / or penetration depth, the radiation may be used for transmission-type or reflection-type measurements. The type of measurement may affect whether information is obtained about the surface and / or the interior of the bulk of the structure / substrate. Therefore, penetration depth and opacity are another element to be considered when selecting the wavelength of radiation for the metrology tool.

[0060] In order to achieve higher resolution for the measurement of lithographically patterned structures, a measurement tool MT with a short wavelength is preferred. This may include wavelengths shorter than the visible wavelength, for example in the UV, EUV and X-ray parts of the electromagnetic spectrum. Hard X-ray methods such as transmitted small angle X-ray scattering (TSAXS) exploit the high resolution and high penetration depth of hard X-rays and can therefore operate in transmission. On the other hand, soft X-rays and EUV are not able to penetrate deeply into the target, but may induce a rich optical response in the material to be detected. This can be attributed to the optical properties of many semiconductor materials, and to the size of the structures being comparable to the detection wavelength. As a result, the EUV and / or soft X-ray measurement tool MT can operate in reflection, for example by imaging or by analyzing diffraction patterns from lithographically patterned structures.

[0061] For hard X-rays, soft X-rays, and EUV radiation, applications in high volume manufacturing (HVM) applications can be limited by the lack of available high brightness radiation sources at the required wavelengths. In the case of hard X-rays, sources commonly used in industrial applications include X-ray tubes. X-ray tubes, including advanced X-ray tubes based on, for example, liquid metal anodes or rotating anodes, can be relatively affordable and compact, but may lack the brightness required for HVM applications. There are currently high brightness X-ray sources such as synchrotron light sources (SLS) and X-ray free electron lasers (XFELs), but the size (>100 m) and high cost (multiple 100 million Euros) of high brightness X-ray sources make them prohibitively large and expensive for metrology applications. Similarly, there is a lack of availability of sufficiently bright EUV and soft X-ray radiation sources.

[0062] Figure 4 An example of a metrology device, such as a scatterometer, is depicted in FIG. The metrology device may comprise a broadband (e.g. white light) radiation projector 2 that projects radiation 5 onto a substrate W. The reflected or scattered radiation 10 is passed to a spectrometer detector 4, which measures the spectrum 6 of the specularly reflected radiation (i.e. a measurement of the intensity I in dependence on the wavelength λ). From this data, the spectrum 6 may be obtained by a processing unit PU, for example by rigorous coupled wave analysis and nonlinear regression or by comparing the spectrum with the spectrum obtained in FIG. Figure 4 The structure or profile that produced the detected spectrum is reconstructed by comparing the simulated spectrum library shown at the bottom of FIG8. Typically, for the reconstruction, the general form of the structure is well known and some parameters are assumed from knowledge of the process used to make the structure, leaving only a few parameters of the structure to be determined from the scatterometry data. Such a scatterometer can be configured as a normal incidence scatterometer or an oblique incidence scatterometer.

[0063] Figure 5 A transmission version of an example of a measurement device such as Figure 4 The transmitted radiation 11 is passed to the spectrometer detector 4, which measures the Figure 4 Spectra 6 discussed. Such a scatterometer may be configured as a normal incidence scatterometer or an oblique incidence scatterometer. Optionally, a transmission version of hard X-ray radiation having a wavelength of less than 1 nm, optionally less than 0.1 nm, optionally less than 0.01 nm is used.

[0064] As an alternative to optical metrology methods, the use of hard X-rays, soft X-rays or EUV radiation has also been considered, such as radiation having at least one of the following wavelength ranges: less than 0.01 nm, less than 0.1 nm, less than 1 nm, between 0.01 nm and 100 nm, between 0.01 nm and 50 nm, between 1 nm and 50 nm, between 1 nm and 20 nm, between 5 nm and 20 nm, and between 10 nm and 20 nm. An example of a metrology tool that functions in one of the wavelength ranges presented above is transmission small angle X-ray scattering (such as T-SAXS in US 2007224518A, the contents of which are incorporated herein by reference in their entirety). Lemaillet et al. discuss profile (CD) measurements using T-SAXS in "Intercomparison between optical and X-ray scatterometry measurements of FinFET structures" (Proc. of SPIE, 2013, 8681). It should be noted that the use of a laser produced plasma (LPP) x-ray source is described in U.S. Patent Publication No. 2019 / 003988A1 and U.S. Patent Publication No. 2019 / 215940A1, which are incorporated herein by reference in their entirety. Reflectometry techniques using X-rays (GI-XRS) and extreme ultraviolet (EUV) radiation at grazing incidence can be used to measure the properties of films and stacked layers on substrates. Within the general field of reflectometry, goniometric and / or spectroscopic techniques can be applied. In goniometric, the variation of a reflected beam with different angles of incidence can be measured. On the other hand, spectroscopic reflectometry (using broadband radiation) measures the spectrum of wavelengths reflected at a given angle. For example, EUV reflectometry has been used for inspection of mask blanks prior to fabrication of reticles (patterning devices) for use in EUV lithography.

[0065] It may be that the scope of applicability is such that the use of wavelengths in, for example, the hard X-ray, soft X-ray or EUV domain is insufficient. Published patent applications US 20130304424A1 and US2014019097A1 (Bakeman et al. / KLA) describe hybrid metrology techniques in which measurements using x-rays and optical measurements with wavelengths in the range of 120 nm and 2000 nm are combined to obtain measurements of parameters such as CD. The CD measurement is obtained by coupling the x-ray mathematical model and the optical mathematical model via one or more common parts. The contents of the cited US patent applications are incorporated herein by reference in their entirety.

[0066] Figure 6 Depicted is a schematic representation of a metrology apparatus 302 in which the aforementioned radiation may be used to measure parameters of structures on a substrate. Figure 6 The metrology device 302 presented in FIG. 3 may be applicable to the hard X-ray, soft X-ray or EUV domains.

[0067] Figure 6 A schematic physical arrangement of a metrology apparatus 302 comprising a spectroscopic scatterometer optionally using hard X-rays, soft X-rays and / or EUV radiation at grazing incidence is shown, by way of example only. An alternative form of inspection apparatus may be provided in the form of an angle-resolved scatterometer which, like conventional scatterometers operating at longer wavelengths, may use radiation at normal or near normal incidence, and which may also use radiation having a direction which makes an angle greater than 1° or 2° with respect to a direction parallel to the substrate. An alternative form of inspection apparatus may be provided in the form of a transmission-type scatterometer, Figure 5 The configuration in is applied to the transmission type scatterometer.

[0068] The inspection apparatus 302 comprises a radiation source or so-called illumination source 310 , an illumination system 312 , a substrate support 316 , a detection system 318 , 398 and a metrology processing unit (MPU) 320 .

[0069] The radiation source 310 in this example is used to generate EUV, hard X-ray or soft X-ray radiation. The radiation source 310 can be based on Figure 6 The high-order harmonic generation (HHG) technology shown in the figure, and the irradiation source 310 can also be other types of irradiation sources, such as a liquid metal jet source, an inverse Compton scattering (ICS) source, a plasma channel source, a magnetic undulator source, a free electron laser (FEL) source, a compact storage ring source, a discharge-generated plasma source, a soft X-ray laser source, a rotating anode source, a solid anode source, a particle accelerator source, a microfocus source, or a laser-generated plasma source.

[0070] The HHG source can be a gas jet / nozzle source, a capillary / fiber source, or a gas chamber source.

[0071] For example, a HHG source such as Figure 6, the main components of the radiation source are a pump radiation source 330 operable to emit pump radiation and a gas delivery system 332. Optionally, the pump radiation source 330 is a laser, optionally, the pump radiation source 330 is a pulsed high-power infrared or optical laser. The pump radiation source 330 can be, for example, a fiber-based laser with an optical amplifier, which produces pulses of infrared radiation that can last, for example, less than 1 ns (1 nanosecond) per pulse, wherein the pulse repetition rate is as high as several million Hertz as required. The wavelength of the infrared radiation can be in the range of 200 nm to 10 μm, for example, about 1 μm (1 micrometer). Optionally, the laser pulses are delivered to the gas delivery system 332 as first pump radiation 340, wherein a portion of the radiation is converted to a higher frequency in the gas than the first radiation is converted into emission radiation 342. A gas supply 334 supplies a suitable gas to the gas delivery system 332, wherein the suitable gas is optionally ionized by a power supply 336. The gas delivery system 332 may be a shut-off tube.

[0072] The gas provided by the gas delivery system 332 defines a gas target, which can be a gas flow or a static volume. The gas can be, for example, air, neon (Ne), helium (He), nitrogen (N2), oxygen (O2), argon (Ar), krypton (Kr), xenon (Xe), carbon dioxide, and combinations thereof. These gases can be selectable options within the same device. The emitted radiation may include multiple wavelengths. If the emitted radiation is monochromatic, the measurement calculation (e.g., reconstruction) can be simplified, but it is easier to generate radiation with several wavelengths. The emission divergence angle of the emitted radiation can be related to the wavelength. Different wavelengths can, for example, provide different levels of contrast when imaging structures of different materials. For example, in order to inspect metal structures or silicon structures, different wavelengths can be selected as wavelengths for imaging features of (carbon-based) resists or for detecting contamination of these different materials. One or more filtering devices 344 can be provided. For example, filters such as aluminum (Al) or zirconium (Zr) films can be used to cut off fundamental IR radiation to prevent further transmission into the inspection device. A grating (not shown) can be provided to select one or more specific wavelengths from the generated wavelengths. Optionally, the illumination source includes a space configured to be evacuated and the gas delivery system is configured to provide a gas target in the space. Optionally, some or all of the beam path can be contained within a vacuum environment, keeping in mind that SXR and / or EUV radiation is absorbed when traveling in air. Various components of the radiation source 310 and illumination optics 312 can be adjustable to implement different measurement "options" within the same device. For example, different wavelengths and / or polarizations can be selected.

[0073] Depending on the material of the structure under inspection, different wavelengths can provide the desired level of penetration into the underlying layers. In order to distinguish the smallest device features and defects among the smallest device features, short wavelengths are likely to be preferred. For example, one or more wavelengths in the range of 0.01 nm to 20 nm, or alternatively in the range of 1 nm to 10 nm, or alternatively in the range of 10 nm to 20 nm can be selected. Wavelengths shorter than 5 nm can suffer from very low critical angles when reflected from materials of interest in semiconductor manufacturing. Therefore, selecting a wavelength greater than 5 nm can provide a stronger signal at higher angles of incidence. On the other hand, if the inspection task is to detect the presence of a certain material, for example to detect contamination, wavelengths up to 50 nm can be useful.

[0074] From the radiation source 310, the filtered beam 342 may enter an inspection chamber 350 in which a substrate W including a structure of interest is held by a substrate support 316 for inspection at a measurement position. The structure of interest is labeled T. Optionally, the atmosphere within the inspection chamber 350 may be maintained as a near vacuum by a vacuum pump 352 so that SXR and / or EUV radiation may pass through the atmosphere without undue attenuation. The illumination system 312 has the function of focusing the radiation into a focused beam 356 and may include, for example, a two-dimensional curved mirror or a series of two-dimensional curved mirrors, as described in the above-mentioned published U.S. patent application US2017 / 0184981A1 (the contents of US2017 / 0184981A1 are incorporated herein by reference in their entirety). Focusing is performed to achieve a circular or elliptical spot S with a diameter of less than 10 μm when projected onto the structure of interest. The substrate support 316 includes, for example, an XY translation stage and a rotation stage, by which any part of the substrate W can be brought to the focus of the beam in a desired orientation. Thus, a radiation spot S is formed on the structure of interest. Alternatively or additionally, the substrate support 316 includes, for example, a tilting stage, which can tilt the substrate W at a certain angle to control the incident angle of the focused beam on the structure of interest T.

[0075] Optionally, the illumination system 312 provides a reference radiation beam to a reference detector 314, which can be configured to measure the spectrum and / or intensity of different wavelengths in the filtered beam 342. The reference detector 314 can be configured to generate a signal 315 that is provided to the processor 320, and the filter can include information about the spectrum of the filtered beam 342 and / or the intensity of different wavelengths in the filtered beam.

[0076] The reflected radiation 360 is captured by the detector 318 and the spectrum is provided to the processor 320 for calculating the properties of the target structure T. The illumination system 312 and the detection system 318 thus form an inspection device. Such an inspection device may include a hard X-ray, soft X-ray and / or EUV spectroscopic reflectometer of the type described in US2016282282A1, the contents of which are incorporated herein by reference in their entirety.

[0077] If the target Ta has a certain periodicity, the radiation of the focused beam 356 may also be partially diffracted. The diffracted radiation 397 follows another path at a well-defined angle relative to the angle of incidence and then relative to the reflected radiation 360. Figure 6 In FIG. 3 , the diffracted radiation 397 plotted is plotted in a schematic manner, and the diffracted radiation 397 may follow many other paths other than the plotted path. The inspection device 302 may also include another detection system 398 that detects at least a portion of the diffracted radiation 397 and / or images at least a portion of the diffracted radiation 397. Figure 6 , a single further detection system 398 is drawn, but embodiments of the inspection device 302 may also include more than one further detection system 398, which are arranged at different positions to detect and / or image the diffracted radiation 397 in multiple diffraction directions. In other words, the (higher) diffraction orders of the focused radiation beam impinging on the target Ta are detected and / or imaged by one or more further detection systems 398. The one or more detection systems 398 generate a signal 399 which is provided to the metrology processor 320. The signal 399 may include information of the diffracted light 397 and / or may include an image obtained from the diffracted light 397.

[0078] To assist in the alignment and focusing of the spot S with the desired product structure, the inspection device 302 may also provide auxiliary optics that use auxiliary radiation under the control of the metrology processor 320. The metrology processor 320 may also communicate with a position controller 372 that operates the translation stage, the rotation stage, and / or the tilt stage. The processor 320 receives highly accurate feedback about the position and orientation of the substrate via sensors. The sensor 374 may include, for example, an interferometer that can give an accuracy of about a few picometers. In operation of the inspection device 302, the spectral data 382 captured by the detection system 318 is transmitted to the metrology processing unit 320.

[0079] As mentioned, an alternative form of inspection equipment uses hard X-rays, soft X-rays and / or EUV radiation at normal incidence or near normal incidence, for example to perform diffraction-based asymmetry measurements. Another alternative form of inspection equipment uses hard X-rays, soft X-rays and / or EUV radiation having a direction that is angled greater than 1° or 2° from a direction parallel to the substrate. Both types of inspection equipment can be provided in a hybrid measurement system. The performance parameters to be measured may include overlay (OVL), critical dimension (CD), focus of the lithography equipment when the lithography equipment prints the target structure, coherent diffraction imaging (CDI) and over-resolution overlay (ARO) measurement. The hard X-rays, soft X-rays and / or EUV radiation may, for example, have a wavelength of less than 100 nm, for example using radiation in the range of 5 nm to 30 nm, optionally in the range of 10 nm to 20 nm. The radiation may be narrowband or broadband in nature. The radiation may have discrete peaks in a particular wavelength band or may have a more continuous nature.

[0080] Similar to optical scatterometers used in today's production facilities, inspection device 302 can be used to measure structures in resist materials processed in a lithography cell (after-development inspection or ADI), and / or to measure structures after they have been formed in harder materials (after-etch inspection or AEI). For example, inspection device 302 can be used to inspect a substrate after it has been processed by a developer, etch, anneal, and / or other device.

[0081] The measurement tool MT, including but not limited to the scatterometer mentioned above, can use radiation from a radiation source to perform measurements. The radiation used by the measurement tool MT can be electromagnetic radiation. The radiation can be optical radiation, such as radiation in the infrared part, the visible light part and / or the ultraviolet part of the electromagnetic spectrum. The measurement tool MT can use radiation to measure or inspect the properties and aspects of the substrate, such as the pattern exposed by photolithography on the semiconductor substrate. The type and quality of the measurement can depend on several properties of the radiation used by the measurement tool MT. For example, the resolution of the electromagnetic measurement can depend on the wavelength of the radiation, wherein smaller wavelengths can measure smaller features, for example, due to diffraction limitations. In order to measure features with small sizes, it is preferred to use radiation with short wavelengths, such as EUV, hard X-ray (HXR) and / or soft X-ray (SXR) radiation, to perform the measurement. In order to perform measurements at a specific wavelength or wavelength range, the measurement tool MT needs to access a source of radiation provided at the wavelength / those wavelengths. There are different types of sources for providing radiation of different wavelengths. Depending on the (multiple) wavelengths provided by the source, different types of radiation generation methods can be used. For extreme ultraviolet (EUV) radiation (e.g., 1 nm to 100 nm) and / or soft X-ray (SXR) radiation (e.g., 0.1 nm to 20 nm, 1 nm to 20 nm, or 10 nm to 20 nm), the source can use high-order harmonic generation (HHG) or any other type of source mentioned above to obtain radiation at the desired wavelength(s).

[0082] Figure 7 A simplified schematic diagram of an embodiment 600 of an illumination source 310 is shown, which may be an illumination source for high-order harmonic generation (HHG). Figure 6 One or more of the features of the illumination source in the described metrology tool may also be present in the illumination source 600, where appropriate. The illumination source 600 comprises a chamber 601 and is configured to receive pump radiation 611 having a propagation direction indicated by an arrow. The pump radiation 611 shown here is an example of pump radiation 340 from the pump radiation source 330, as shown in FIG. Figure 6. Pump radiation 611 may be directed into chamber 601 by radiation input 605, which may be a viewport optionally made of molten silicon dioxide or a similar material. Pump radiation 611 may have a Gaussian or hollow (e.g., annular) transverse cross-sectional profile and may be incident (optionally focused) on a gas flow 615 within chamber 601, the gas flow having a flow direction indicated by a second arrow. Gas flow 615 includes a small volume of a specific gas (e.g., air, neon (Ne), helium (He), nitrogen (N2), oxygen (O2), argon (Ar), krypton (Kr), xenon (Xe), carbon dioxide, and combinations thereof) with a gas pressure above a certain value, the small volume being referred to as a gas volume or gas target (e.g., a few cubic millimeters). The gas flow 615 may be a steady flow. Other media such as metal plasma (e.g., aluminum plasma) may also be used.

[0083] The gas delivery system of the irradiation source 600 is configured to provide a gas flow 615. The irradiation source 600 is configured to provide pump radiation 611 in the gas flow 615 to drive the generation of emission radiation 613. The region in which at least a large part of the emission radiation 613 is generated is called the interaction region. The interaction region may vary from tens of microns (for tightly focused pump radiation) to a few millimeters or centimeters (for moderately focused pump radiation) or even up to a few meters (for very loosely focused pump radiation). The gas delivery system is configured to provide the gas target for generating the emission radiation at the interaction region of the gas target, and optionally, the irradiation source is configured to receive the pump radiation and provide the pump radiation at the interaction region. Optionally, the gas flow 615 is provided by the gas delivery system into an evacuated or nearly evacuated space. The gas delivery system may include a gas nozzle 609, such as Figure 6 609. As shown in FIG. 601, the gas nozzle includes an opening 617 in the exit plane of the gas nozzle 609. A gas flow 615 is provided from the opening 617. A gas trap is used to confine the gas flow 615 in a certain volume by extracting the residual gas flow and maintaining a vacuum or near-vacuum atmosphere inside the chamber 601. Optionally, the gas nozzle 609 can be made of a thick-walled tube and / or a highly thermally conductive material to avoid thermal deformation due to the high-power pump radiation 611.

[0084] The dimensions of the gas nozzle 609 can conceivably also be used in scaled-up or scaled-down versions ranging from micrometer-sized nozzles to meter-sized nozzles. This wider range of sizing comes from the fact that the arrangement can be scaled so that the intensity of the pump radiation at the gas flow ends up being within a specific range that can be beneficial for the emitted radiation, which requires different sizing for different pump radiation energies that can be pulsed lasers, and the pulse energies can vary from tens of microjoules to several joules. Optionally, the gas nozzle 609 has thicker walls to reduce nozzle deformation caused by thermal expansion effects that can be detected by, for example, a camera. A gas nozzle with thicker walls can produce a stable gas volume with reduced variation. Optionally, the irradiation source includes a gas trap close to the gas nozzle to maintain the pressure of the chamber 601.

[0085] Due to the interaction of the pump radiation 611 with the gas atoms of the gas flow 615, the gas flow 615 will convert part of the pump radiation 611 into emission radiation 613, which can be Figure 6 342 is shown in FIG. 34. The central axis of the emitted radiation 613 may be co-linear with the central axis of the incident pump radiation 611. The emitted radiation 613 may have a wavelength in the X-ray or EUV range, hereinafter referred to as SXR radiation, wherein the wavelength is in the range from 0.01 nm to 100 nm, optionally in the range from 0.1 nm to 100 nm, optionally in the range from 1 nm to 100 nm, optionally in the range from 1 nm to 50 nm, optionally in the range from 2 nm to 50 nm, optionally in the range from 2 nm to 20 nm or optionally in the range from 10 nm to 20 nm.

[0086] In operation, a beam of emitted radiation 613 may pass through radiation output port 607 and may then be manipulated and directed to a substrate to be inspected for metrology measurements by illumination system 603, which may be Figure 6 6. Emitted radiation 613 may be directed (optionally focused) to a structure on a substrate.

[0087] Because air (and indeed any gas) absorbs SXR or EUV radiation to a large extent, the volume between the gas flow 615 and the wafer to be inspected can be evacuated or nearly evacuated. Since the central axis of the emitted radiation 613 can be collinear with the central axis of the incident pump radiation 611, the pump radiation 611 may need to be blocked to prevent the pump radiation 611 from passing through the radiation output 607 and entering the illumination system 603. This can be accomplished by placing Figure 6The filter device 344 shown in the figure is incorporated into the radiation output end 607, which is placed in the path of the emitted beam and is opaque or almost opaque to the pump radiation (for example, opaque or almost opaque to infrared or visible light) but at least partially transparent to the emission radiation beam. The filter can be made using zirconium or multiple materials combined in multiple layers. When the pump radiation 611 has a hollow (optionally annular) transverse cross-sectional profile, the filter can be a hollow (optionally annular) block. Optionally, the filter is not perpendicular and not parallel to the propagation direction of the emission radiation beam to have efficient pump radiation filtering. Optionally, the filter device 344 includes a hollow block and a thin film filter such as an aluminum (Al) or zirconium (Zr) surface film filter. Optionally, the filter device 344 can also include a reflector that effectively reflects the emission radiation but poorly reflects the pump radiation, or includes a metal wire mesh that effectively transmits the emission radiation but poorly transmits the pump radiation.

[0088] Methods, devices and components for obtaining emitted radiation, optionally at high-order harmonic frequencies of pump radiation are described herein. By the process, nonlinear effects are optionally used to produce HHG of radiation, optionally at harmonic frequencies of the provided pump radiation, and the generated radiation can be provided as radiation in a measurement tool MT for inspection and / or measurement of the substrate. If the pump radiation comprises short pulses (i.e., smaller cycles or periods), the generated radiation does not have to be exactly at the harmonics of the pump radiation frequency. The substrate can be a lithographically patterned substrate. The radiation obtained by the process can also be provided in a lithographic apparatus LA and / or a lithographic cell LC. The pump radiation can be pulsed radiation that can provide a high peak intensity in a short pulse train time.

[0089] The pump radiation 611 may include radiation having one or more wavelengths higher than one or more wavelengths of the emitted radiation. The pump radiation may include infrared radiation. The pump radiation may include radiation having a wavelength(s) in the range of 500 nm to 1500 nm. The pump radiation may include radiation having a wavelength(s) in the range of 800 nm to 1300 nm. The pump radiation may include radiation having a wavelength(s) in the range of 900 nm to 1300 nm. The pump radiation may be pulsed radiation. The pulsed pump radiation may include pulses having a duration in the femtosecond range.

[0090] For some embodiments, the emitted radiation, which is optionally higher order harmonic radiation, may include one or more harmonics having the wavelength of the pump radiation(s). The emitted radiation may include wavelengths in the extreme ultraviolet, soft x-ray, and / or hard x-ray portions of the electromagnetic spectrum. The emitted radiation 613 may include wavelengths in one or more of the following ranges: less than 1 nm, less than 0.1 nm, less than 0.01 nm, 0.01 nm to 100 nm, 0.1 nm to 100 nm, 0.1 nm to 50 nm, 1 nm to 50 nm, and 10 nm to 20 nm.

[0091] Radiation such as the high-order harmonic radiation described above can be provided as source radiation in the measurement tool MT. The measurement tool MT can use the source radiation to perform measurements on a substrate exposed by a lithographic device. The measurements can be used to determine one or more parameters of a structure on the substrate. Compared to using longer wavelengths (e.g., visible radiation, infrared radiation), the use of radiation at shorter wavelengths (e.g., at EUV, SXR and / or HXR wavelengths included in the wavelength range described above) can allow the measurement tool to resolve smaller features of the structure. Radiation with shorter wavelengths, such as EUV, SXR and / or HXR radiation, can also penetrate deeper into materials such as patterned substrates, which means that measurement of deeper layers on the substrate is possible. These deeper layers may not be accessible by radiation with longer wavelengths.

[0092] In the measurement tool MT, source radiation can be emitted from a radiation source and directed onto a target structure (or other structure) on a substrate. The source radiation may include EUV, SXR and / or HXR radiation. The target structure may reflect, transmit and / or diffract the source radiation incident on the target structure. The measurement tool MT may include one or more sensors for detecting diffracted radiation. For example, the measurement tool MT may include detectors for detecting positive (+1) and negative (-1) diffraction orders. The measurement tool MT may also measure specularly reflected or transmitted radiation (0th order diffracted radiation). Other sensors for measurement may be present in the measurement tool MT, for example to measure other diffraction orders (e.g., higher order diffraction).

[0093] In an exemplary lithography metrology application, HHG-generated radiation can be focused onto a target on a substrate using an optical column, which can be referred to as an illuminator, which transfers radiation from a HHG source onto the target. The HHG radiation can then be reflected from the target, detected, and processed, for example, to measure and / or infer properties of the target.

[0094] Gas target HHG configurations can be broadly divided into three separate categories: gas jet, gas chamber, and gas capillary. Figure 7An exemplary gas jet configuration is depicted, wherein a gas volume is introduced into the driving radiation laser beam. In the gas jet configuration, the interaction of the driving radiation with the solid part is kept to a minimum. The gas volume may, for example, comprise a gas flow perpendicular to the driving radiation beam, wherein the gas volume is enclosed inside a gas chamber. In a gas capillary device, the size of the capillary structure containing the gas is very small in the lateral direction, so that the capillary structure significantly affects the propagation of the driving radiation laser beam. The capillary structure may, for example, be a hollow core optical fiber, wherein the hollow core is configured to contain the gas.

[0095] The gas jet HHG configuration can provide a relative degree of freedom to shape the spatial profile of the drive radiation beam in the far field, because the spatial profile of the drive radiation beam in the far field is not subject to the defined constraints imposed by the gas capillary structure. The gas jet configuration can also have less stringent alignment tolerances. On the other hand, the gas capillary can provide an increased interaction zone of the drive radiation with the gaseous medium, which can optimize the HHG process.

[0096] In order to use HHG radiation, for example, in measurement applications, the HHG radiation is separated from the driving radiation downstream of the gas target. The separation of HHG radiation from the driving radiation may be different for the gas jet configuration and the gas capillary configuration. In both cases, the driving radiation suppression scheme may include a metal transmission filter for filtering out any remaining driving radiation from the short-wavelength radiation. However, before such a filter can be used, the intensity of the driving radiation should be significantly reduced from its intensity at the gas target to avoid damage to the filter. The methods that can be used for such intensity reduction are different for the gas jet configuration and the capillary configuration. For the gas jet HHG, due to the relative freedom of the shape and spatial profile (the spatial profile can also be referred to as spatial distribution and / or spatial frequency) of the driving radiation beam focused on the gas target, the gas jet HHG can be designed so that in the far field, the gas jet HHG has a low intensity along the direction of short-wavelength radiation propagation. This spatial separation in the far field means that the hole can be used to block the driving radiation and reduce the intensity of the driving radiation.

[0097] In contrast, in a gas capillary structure, the spatial profile of the beam as it passes through the gaseous medium can be primarily dictated by the capillary. The spatial profile of the driving radiation can be determined by the shape and material of the capillary structure. For example, where a hollow core fiber is used as the capillary structure, the shape and material of the fiber structure determine which driving radiation modes are supported to propagate through the fiber. For most standard optical fibers, the supported propagation modes produce a spatial profile where high intensities of the driving radiation overlap with high intensities of the HHG radiation. For example, the driving radiation intensity can be centered in a Gaussian or near-Gaussian profile in the far field.

[0098] In one embodiment of overlay metrology, the overlay target comprises two or more diffraction gratings (substructures) that are printed in different layers so that the overlay targets are superimposed. Some overlay metrology methods in known overlay metrology methods are based on the measurement of an overlay target with a programmed bias (intentional positional offset between the substructures / gratings) to infer the overlay. Overlay metrology methods using such biased targets can avoid the need to perform a model-based reconstruction and thus reduce the need to know the (geometric) stacking parameters (also called stacking information) between two or more gratings. For known model-based reconstruction methods, stacking information is necessary. But stacking information is often confidential and therefore not always available.

[0099] Some known metrology methods use measurement illumination in the visible wavelength band and biased targets to set up an option for overlay metrology. Similar methods can also be used at soft X-ray (SXR) wavelengths, where a biased target-based approach for training models is an option for measurement inference, optionally overlay and / or profilometry inference.

[0100] The use of targets with multiple offsets affects yield, and such methods are still affected by grating asymmetry, stack imbalance, etc. In addition, some measurement targets are printed in the scribe line, which limits accuracy and requires additional steps to infer the overlap of product structures. Other measurement methods address this latter problem, but this comes at the expense of increasing the complexity of inferring overlap from structures with product scale pitch. In addition, some measurement methods require a maximum layer spacing between gratings of about 50 nm, which further limits their applicability. One or more of the above-mentioned problems can be solved by embodiments of the present invention.

[0101] In addition to overlay, the reconstruction of the critical dimension (CD) and other geometric parameters of the printed structures becomes increasingly important for the control of the lithography scanner. Note that CD is the dimension of the structure, and in one embodiment, the structure is the space where material has been removed (eg, etched away) by the etching process.

[0102] Resist profile control can be achieved using scanning electron microscopy (SEM) or transmission electron microscopy (TEM), but such techniques are slow and can be destructive to the structure being measured. As the structures being printed become more complex (e.g., ring gates, nanoplates, fork plates), it becomes increasingly desirable to perform such geometric parameter measurements without requiring information about the actual stackup in the target or device (which is typically not available). Embodiments of the present invention provide fast and non-destructive methods for making geometric parameter measurements, especially for complex structures.

[0103] Therefore, it is proposed to perform a measurement method based on N-dimensional reconstruction by decomposing the N-dimensional reconstruction (or optimization) problem into a series of M (optionally, where M < N, optionally, where M > N, optionally, where M = N) reconstruction (or optimization) problems, each of which has a lower dimension L M . Optionally, This simplifies the optimization problem after the reconstruction process and also relaxes the constraints on the prior knowledge about the target needed to start the reconstruction. As will be explained in more detail, this is possible due to the specific properties that are inherent to electromagnetic scattering at SXR wavelengths (especially for wavelengths of 2 nm to 50 nm, optionally 2 nm to 20 nm). In addition, decomposition reconstruction has advantages in terms of the total computational time of the reconstruction, since solving the complex structure in one go using an optimizer provides an infeasible problem in terms of computational time.

[0104] In one embodiment, it is proposed to perform such measurements in at least two main optimization steps. During the first optimization step (s), at least one vertical (or first direction) position parameter (e.g., a layer thickness-related parameter or an effective layer thickness-related parameter that considers the optical properties of the layer) is determined or optimized using other parameters fixed at the estimated value. It should be noted that the determination process in this context can be an optimization process, that is, the determined value after a certain step or steps (optionally one or more optimization steps) can still be different from the actual value (the actual value is the actual true value), but the determined value is closer to the actual value than the value before a certain step or steps. The estimated value can have a difference with the actual value. The difference may deviate from the actual value by up to 10% to 20%. In one embodiment, the estimated value of CD can deviate from the actual value of CD by up to 5 nm and 8 nm, 40 nm and 50 nm, respectively. In such a context, the vertical (or first direction) describes the direction perpendicular to the substrate plane, which is hereinafter labeled as the Z direction. Such a step can be implemented with high accuracy using limited prior knowledge about the target / structure. In the case where at least one vertical position parameter is optimized, one or more second optimization steps may be used to optimize one or more additional / other parameters, such as geometric parameters of interest (e.g., position parameters in at least one direction perpendicular to the vertical direction (e.g., one or two directions of the substrate plane (X direction and / or Y direction))). The one or more other parameters determined in the one or more second optimization steps include one or more substrate plane position parameters in the model parameters. The one or more substrate plane position parameters are related to one or two directions of the substrate plane. At least one vertical parameter may describe at least one position and / or thickness of a layer or structure. One or both of the first step and the second step may include multiple sub-steps for separately optimizing separate parameters or different subsets of parameters in the Z direction and the X and / or Y direction, respectively. In one embodiment, the vertical position parameter includes an optical property of a material that affects the effective optical path in the vertical direction. In one embodiment, one or more other parameters of interest include one or more of a sidewall angle, a hole or through hole angle, a tilt angle, a floor tilt or other tilt.

[0105] Thus, the method may include determining (and / or optimizing) at least one parameter of interest associated with a structure in at least one corresponding layer formed on a substrate. The method includes obtaining measured metrology data associated with a measurement result of the structure. The method also includes obtaining a model, the model describing the structure in terms of a plurality of model parameters. The model parameters include estimated values. The method also includes performing (optionally sequentially) a plurality of optimization steps based on the measured metrology data to determine (and / or optimize) a plurality of model parameters. Optionally, each of the plurality of optimization steps determines a subset of the plurality of model parameters.

[0106] In one embodiment, the step of performing a plurality of optimization steps in sequence includes performing one or more first optimization steps using the measured metrology data to determine one or more vertical position parameters in the model parameters. Optionally, the step of performing a plurality of optimization steps in sequence also includes: after performing the one or more first optimization steps, using the measured metrology data and the model having the one or more vertical position parameters determined in the one or more first optimization steps to perform one or more second optimization steps to determine one or more other model parameters in the plurality of model parameters. Optionally, the one or more other model parameters are different from the one or more vertical position parameters. The one or more first optimization steps may optimize at least one vertical position parameter (e.g., optimize only at least one vertical position parameter, so that other model parameters other than the at least one vertical position parameter are fixed during the step); the one or more second optimization steps may optimize at least one other parameter of the model parameters, such as, for example, a substrate plane position parameter of the model parameters (at least one substrate plane position parameter associated with one or two directions of the substrate plane). Optionally, the optimization order may be determined in the initial optimization order determination step, for example, based on the calculation of the covariance matrix and the correlation matrix of the model parameters.

[0107] The methods described herein may use a simulation model such as a forward model (e.g., based on a physical sensor configuration or optical configuration for capturing scattered radiation from a measured structure or target) to simulate the electromagnetic interaction between the incident field and both the target / structure and the sensor configuration. The forward model may, for example, be based on a Born approximation. Optionally, the simulation model is a model that describes the structure in terms of a plurality of model parameters. Optionally, the simulation model is part of a model that describes the structure in terms of a plurality of model parameters. Optionally, the simulation model is different from a model that describes the structure in terms of a plurality of model parameters.

[0108] The method may include comparing the measured sensor output (e.g., diffraction pattern) with the forward model output (modeled diffraction pattern) for different model parameter values ​​until the two outputs are matched (e.g., according to a matching metric). Thus, an optimization scheme may be used that varies one or more parameters of the model until an extreme value of an appropriate cost function or merit function is found. An example of such a merit function is a cross-correlation of the calculated signal with the measured signal (e.g., where the signal may be the diffraction intensity of at least one diffraction order). More details of the forward model will be described later.

[0109] Soft X-ray (SXR) metrology uses simultaneous multi-wavelength measurement illumination below 100 nm, below 70 nm, below 50 nm, below 30 nm, or below 20 nm (e.g., in a range between 10 nm and 20 nm (or any of the ranges disclosed elsewhere herein)). This inherently provides higher spatial resolution than visible light. At such SXR wavelengths (e.g., at least below 100 nm, especially for wavelengths from 2 nm to 50 nm, optionally from 2 nm to 20 nm), most materials have a refractive index close to that of a vacuum. This makes the impact of multiple scattering effects (although still present) less significant than at visible wavelengths. This allows the parameter inference problem to be decomposed into a set of simplified and independent sub-problems. Thus, while the described method is a model-based overlap acquisition, the described method deals with quasi-model-based reconstruction because the requirements on the model are very relaxed. Essentially the only prior knowledge required is very basic knowledge of the type of structure being measured (e.g., in the first example below, the only initial knowledge needed is that an overlapping target is being measured, including gratings in two or more patterned layers). The actual values ​​of the other geometrical and physical parameters of the stack (CD, permittivity) can be ignored, as they will not significantly affect the accuracy of determining the Z interfaces (the positions of the patterned layer interfaces in the Z direction including the overlapping target). In other words, the actual positions of the interfaces of the patterned layers dominate the measured signal at the SXR wavelength, so that those interfaces can be optimized or determined via a very simple representation of the stack, with all other parameters initially set to estimated values ​​(or nominal values) that can be very far from the actual values ​​(e.g., 0% to 20%, optionally 0% to 10%).

[0110] At SXR wavelengths and in contrast to measurements in the visible spectrum, the parameter space can be considered to be factored in subspaces (e.g., Z-interface subspace, overlap subspace, CD subspace, etc.) that can be considered independent of each other. A direct consequence of this approach is that the optimum for each subspace can be found in turn. This leads to a significant simplification of the optimization process. In fact, the problem can be split into a series of lower-dimensional problems that can each be solved (in turn) in a step-by-step approach, without having to optimize all parameters at the same time (i.e., searching for the maximum in an N-dimensional optimization problem). In another embodiment, each of the series of lower-dimensional problems can be solved in parallel.

[0111] The disclosed concept will first be described in the context of overlap inference. Then the method for inferring other geometric parameters will be described using the same basic concept. Figures 8 to 10 Methods according to overlapping embodiments are described.

[0112] In one embodiment, since the overlay target comprises two patterned layers (the two patterned layers are two layers comprising a diffraction grating, wherein the other layer is not patterned), it is proposed to obtain the overlay via two main consecutive steps: a first step for optimizing the position of the Z interface of the patterned layer in the stack, i.e. the position of the layer interface of the patterned layer within the stack in a direction perpendicular to the substrate plane; and a second step for determining or optimizing the overlay.

[0113] Determining the patterned layer Z interfaces may include using a forward model to find a global extremum or maximum similarity by changing the position of the search layer interface in the Z direction, maintaining all other positions fixed (e.g., all other modeled target parameters are maintained at estimated values). These interfaces may be parameterized by three undefined Z positions (i.e., including the bottom Z interface of the top grating and the undefined interface positions of the top and bottom Z interfaces of the bottom grating). The top Z interface of the top grating may be located at Z=0 by definition, so that the other three interfaces are determined relative to this topmost interface. Of course, while this simplifies the step, the method may actively find all four Z interfaces (e.g., another interface should be considered as a reference). Depending on how much prior information is available on the target, the Z interfaces may be found, for example, by employing an exhaustive search or via a local or global optimizer. If such prior knowledge is limited, a global optimizer may be preferred. An exhaustive search is a brute force method that sweeps the values ​​of the interfaces in the forward model within a given plausible range until the exhaustive search finds the optimal match between the simulated diffraction pattern and the measured diffraction pattern.

[0114] Figure 8 The illustration shows the initial position at the start of the overlap determination. Figure 8(a) shows the ground truth (actual target geometry, overlap and CD) and Figure 8 (b) illustrates the first estimate with estimated values. It can be seen that the initial estimate is inaccurate and all parameters are significantly off (i.e., CD, overlap, and Z interface). More specifically, in this illustrative example, the correct CD value is 38 nm for both the top and bottom gratings, compared to an estimated value of 32 nm. The correct overlap (offset between the two gratings) is 5 nm, compared to 0 nm in the estimated target. The target is a 1D periodicity with a pitch of 90 nm along the x-direction. Only in Figure 8 One cycle is shown in FIG.

[0115] Assume that there are two patterned layers (1D periodicity along the x-direction) that form an overlapping target. The first step can include sweeping a wide range of values ​​for each of the three interfaces for the estimated target (the bottom interface of the top grating and the top and bottom interfaces of the bottom grating). For each combination of these interfaces, a forward model is used to generate a simulated far-field signal that is then compared to the correct signal. This can be an exhaustive search, which is computationally expensive (the exhaustive search sweeps over 3D space). However, using, for example, the Born approximation, the exhaustive search is an operation that can still be performed in a short time (e.g., less than a minute using current common hardware). For a strict solver, a faster method can be used.

[0116] Fig. 9 (b) shows the situation after the Z interface of the patterned layer has been determined in the first step, and Fig. 9 (a) The actual truth value is shown again for comparison purposes and is therefore Figure 8 (a) is the same. The forward model is used to optimize these Z interfaces, where all other parameters (including CD and overlap) are fixed during the optimization. Fig. 9 (b) As can be seen, the position in the stack of the Z interface is accurately captured. CD and overlap of course do not change from the initial estimates which have been fixed, and so remain inaccurate. Thus, regardless of whether the modeled target has incorrect CD and overlap values, the Z interface is determined at the proper location. This illustrates that the model / target parameters can be viewed as independent parameter subspaces.

[0117] Once the Z interface value of the patterned layer is determined, an additional separate optimization can be performed to determine the overlap in a second step by using the forward model. In such an optimization step, the now known Z interface can be fixed at its determined position. Alternatively, all other parameters can also be fixed so that only the overlap is varied during the optimization. Alternatively, the optimization can jointly optimize the overlap and one or more other parameters (such as one or two CDs).

[0118] Overlay optimization can be achieved by laterally shifting the position of either grating within the forward model (or shifting the two gratings by different amounts) so that different overlap values ​​(position shifted top and bottom gratings) are modeled. The overlap value can in turn be determined by matching the diffraction pattern from the model with the diffraction pattern actually measured over a range of overlap values.

[0119] Fig.10 (b) shows the situation after the overlapping OV has been determined in this second step, and Fig.10 (a) The actual truth value is shown again for comparison purposes and is therefore Figure 8 (a) and Fig. 9 (a) The same. The overlap OV is accurately determined using the disclosed method, even though the CD value is still not accurately known.

[0120] What makes this approach convenient and able to work in practice is that at SXR wavelengths, the determination of the Z interface of the patterned layer is essentially insensitive to the actual grating shape (OVL, CD, etc.) and the thickness of the other layers in the stack. This is a result of the lower impact of multiple scattering effects at SXR wavelengths mentioned above. More specifically, when an SXR probe beam interacts with a structure or target (e.g., an overlapping target), the interaction between the light and the target consists of several scattering events: each point in the target is excited by the incident field and in turn produces a new field that is added to the original incident field. This new field can also propagate through other parts of the target, which are now exposed not only to the original field, but also to the field that has been generated by previous scattering events within the target. This process can continue so as to form a multiple scattering process, in which each small domain in the target substantially contributes to the modification of the total field at every other position within the target. The importance of such high-order scattering events depends on the refractive index of the materials involved, the wavelength of the light, and the size of the target. At SXR wavelengths (especially for wavelengths from 2 nm to 50 nm, optionally from 2 nm to 20 nm), although the higher order scattering events are not negligible, they are less intense than at visible wavelengths. Therefore, the exact value of the parameter in the Z direction (e.g., the thickness of the patterned layer) can be found for the thickness of the patterned layer without regard to the starting values ​​of the other parameters.

[0121] Studies have been conducted to identify a number of situations that allow monitoring the correlation of such higher scattering events ( ) (TA van der Sijs, O. El Gawhary, HP Urbach, Phys. Rev. Research 2, 013308, 2020, incorporated herein by reference). This quantity depends essentially on the permittivity contrast of the medium ( ), the size of the medium (d), and the wavelength of light (λ):

[0122]

[0123] when (strong scattering region), multiple scattering is very relevant and in fact contributes as much as the original input field. (weak scattering region), the multiple scattering effect is negligible and for or slightly greater than 1, then the multiple scattering effects are not negligible, but still represent a quadratic correction to the original input field. It can be shown that for typical sizes of devices of interest for scattering at visible wavelengths (e.g., in the range of 40 nm to a few microns) values ​​of about 10 or higher appear (thus essentially always in the strong scattering region). On the other hand, at SXR wavelengths (e.g., 2 nm to 50 nm, optionally 2 nm to 20 nm wavelengths), About 1 or slightly larger (e.g., less than 2, less than 1.8, less than 1.5, or less than 1.5, where the lower limit may be 0.1, 0.5, 0.8, or 0.9, or 1 in each case). This is a major difference in the scattering process at SXR wavelengths compared to at longer wavelengths.

[0124] In addition to this, when the target comprises layers with a periodic pattern, then the periodicity of the target in those layers also imposes a periodicity on the final field scattered by the target. Since the scattered field is periodic, when the scattered field propagates away from the target and reaches the detector, it is split into discrete parts (diffraction orders) propagating in different corresponding directions in space. In SXR metrology tools, it is possible to detect some of these diffraction orders separately (e.g., so that the +1 order is separated from the -1 order and / or the 2nd order equally). The combination of multiple scattering with a periodic target leads to the fact that the signal of a specific diffraction order measured on the detector (e.g., the first order) is not only caused by the layer comprising the periodic structure, but also by other layers in the stack. How strongly these different layers interfere with the main signal from the periodic layer depends on whether the multiple scattering is actually strong (e.g., consisting of (multiple) visible range wavelengths) or just strong (e.g., consisting of (multiple) SXR range wavelengths). When using SXR radiation, in particular radiation having a wavelength from 2 nm to 50 nm, optionally from 2 nm to 20 nm, the effect of multiple scattering is present but not so strong as to cause the signals received from different parts of the target to become too entangled.

[0125] These considerations make it possible to neglect the influence of inaccuracies on the thickness of the unpatterned layer below the grating when trying to acquire, for example, the height of a diffraction grating (this is because the unpatterned layer below or above the patterned layer can only contribute to the creation of signals exclusively in diffraction orders via multiple scattering). As explained later, this physical property combined with a suitable choice of the evaluation function enables a step-by-step reconstruction of the target starting from, for example, the height of the patterned layer to be performed, since the height of the patterned layer is the parameter that determines the largest part of the measured signal.

[0126] Thus, when trying to obtain the height of a diffraction grating (or a layer comprising a diffraction grating), it is possible to first ignore the effects of inaccuracies on other parameters (such as CD and / or thickness of other unpatterned layers). This physical property makes it possible to decompose the reconstruction problem as described, for example to perform a step-by-step reconstruction of the height / Z position and overlay of the target.

[0127] Another more general method will be described to reconstruct or determine other parameters of interest of a structure or target according to the concepts disclosed herein. The specific case described in this embodiment is merely an example and the method can be extended to other use cases. In this embodiment, the structure or target is assumed to include a 1D periodic grating on top of multiple stacked layers. During the first step of reconstruction, at least one vertical position parameter determined is the height of the grating (e.g., the height of one grating feature). This step can be implemented with high accuracy using limited prior knowledge about the target below and / or above the stack. After determining the height, one or more other parameters, such as the thickness of the layer below the target and / or the grating CD, can be reconstructed depending on the type of target and / or the required accuracy.

[0128] Fig.11 Schematic diagram of an exemplary target feature (in resist) on a certain number of layers. The main parameters of interest describe the shape of the target feature, namely the feature height hfea, the top CD TCD and the bottom CD BCD. All other geometric shapes present can also be determined (for example, layer heights hL1 to hL4 and silicon height hSi). The method may include obtaining the shape of the target feature by first determining the grating height and then (optionally) determining one or more of the heights hL1 to hL4 of the layers L1 to L4 (for example, at least the height hL4 of the top layer L4, which is directly below the patterned layer). In another embodiment, layer L4 is directly above the patterned layer, in which case the method described herein can still be applied. This step may then be followed by a step of determining the CD TCD, BCD of the feature.

[0129] As described above, a simulation model or a forward model may be used (e.g. based on a rigorous electromagnetic solver). Using the measured sensor output (e.g. diffraction pattern), an optimization scheme may be set up that simply varies the height hfea of ​​the grating in the model until an appropriate extremum of the cost function is found. No accurate prior knowledge of the target is required at such a stage; it may be roughly estimated at Fig.11 All other geometries marked in . Since the optimization of this step is only for a single parameter value, an exhaustive search can be used (e.g., a brute force method that sweeps values ​​of height within a given plausible range until an exhaustive search finds the best match between the simulated diffraction pattern and the measured diffraction pattern). If there is no prior knowledge available at all, a global optimizer that obtains the height may be preferred. It should be noted that the feature height actually depends on the position of the top and bottom of the feature; however, as in the overlap example, the position of the top of the feature can be defined as zero.

[0130] Once the grating height value hfea is determined, the next parameter to be acquired may be the thickness / height of at least one of the layers (eg, the topmost layer height hL4). In this example, the height of layer L4 (eg, bottom anti-reflective coating BARC) layer may be determined in this step.

[0131] Optionally, after performing this step to determine the thickness of layer L4 (and / or other layers), the first step may be repeated to improve the reconstruction of the target height hfea based on the new knowledge of the layer height(s). Since the measured signal is dominated by the target characteristic parameter, it may be desirable to improve the determination of the target height hfea before moving on to another parameter.

[0132] Once the target height is determined, at least one other parameter such as a substrate plane position parameter can be determined, for example, at least one dimension of the structure in one or two directions in the substrate plane (e.g., at least one CD). In particular, in this example, the top CD TCD and the bottom CD BCD can then be determined. This is only possible after the height of the grating has been determined with good accuracy. Such a step can use a forward model as already described, in which the feature height is fixed at the determined value. Additional other parameters may include one or more of any parameters related to the angle or tilt of the structure or a portion thereof, for example, a side wall angle, a hole or via angle, a tilt angle, a floor tilt or other tilt.

[0133] If necessary, the thickness of one or more other layers and / or one or more other parameters can be optimized in a further optimization step. In one embodiment, the further optimization step is to determine one or more model parameters in the model parameters that were not determined in the one or more first optimization steps and / or the one or more second optimization steps.

[0134] The physical reason why this method enables a good reconstruction of the height in the initial step is that the shape of the merit function (which is highly non-linear) is mostly determined by the height value hfea. In other words, the actual positions of the interfaces of the patterned layer dominate the measured signal at the SXR wavelength, so that those interfaces can be determined by a very simple representation of the stack, where all other parameters are initially set to estimated values ​​that can be very far from the actual values ​​(by up to 10%).

[0135] The above embodiments can generally be summarized as a method for reconstructing or optimizing a complex object (or other structure) comprising the following steps:

[0136] a) Determine the height of the patterned layer.

[0137] At this stage, the exact value of CD, the layer thickness of the unpatterned layer, can be set to an estimate that does not need to be accurate. The acquisition of height is a search in 1D space, so the acquisition of height can be performed quickly. The acquisition of height can be implemented by a large number of searches, a global optimizer using a forward solver based on the first Born approximation, or a global optimizer using a rigorous electromagnetic solver.

[0138] b) Obtain the thickness of any anti-reflective coating, when present.

[0139] c) Obtain the thickness of other (eg, thicker) layers.

[0140] At SXR wavelengths, materials do not show very large differences in refractive index. For example, the maximum difference (absolute value) in permittivity between silicon and resist is 0.07, while the maximum (absolute) value of permittivity for silicon is 1.034. This means that at In the expression for , the thickness d of the layer actually determines which parameter (e.g., which layer) contributes most to the multiple scattering correction of the signal and thus which parameter is best determined next. Therefore, it is possible that only one or more of the thicker layers (e.g., above a threshold thickness) are determined in this step, and / or such a step determines the layers in order of thickness (the thickest layer is determined first).

[0141] d) Obtain any other desired parameters, e.g., CD, SWA, corner rounding, etc.

[0142] Multiple iterations of these steps a) to d) may be performed until the desired accuracy is reached. This may be done in iterations of the entire method and / or in iterations of a subset of these steps.

[0143] Some targets have special features, such as so-called pitch walk targets or overlap targets. Both overlap and pitch walk impose a strong signature on the measured signal, since the measured signal destroys the usual symmetry of the system. Therefore, the method for such targets may comprise an additional step between step a) and step b), in which overlap or pitch walk (as required) is determined. Of course, as has been described in terms of an overlap target, it is possible that step c) and step d) are then omitted, since overlap / pitch walk are the parameters of interest.

[0144] For complex targets, such as nanoplates, where there are substructures within the height of the grating, the target can be reconstructed first by ignoring the presence of the substructures and acquiring only the total height of the features (e.g., fins). After acquiring the height (e.g., step a)) and other layers underneath, the acquisition of the thickness and CD of the nanoplate can be performed.

[0145] For the optimization step, these can be based on the use of a suitable merit function. It has been observed that when the first Bonn approximation is used for electromagnetic scattering or a strict solver (e.g., based on the integral equation of the so-called Lippmann-Schwinger equation directly solving) at the SXR wavelength, the effect of the height change on the measured signal is structurally different from the change caused by the CD change. The change in height mainly causes the change of the main frequency of the observed signal and the later shift, wherein the scale (amplitude) remains roughly unchanged. In contrast, the change in CD mainly causes the change of the scale of the measured signal (amplitude change), thereby causing the negligible change of the main frequency of the signal or its later shift. Due to this observation, a merit function that is only sensitive to the main frequency and lateral shift can be used to obtain the value of height instead of CD. A suitable merit function with this property can be based on the degree of correlation between two signals (e.g., measured and simulated signal / diffraction pattern); This can be determined as the normalized inner product between the two signals. When CD is determined, a different merit function may be used that is sensitive to CD (ie, sensitive to scale / amplitude) and largely insensitive to height (ie, low or negligible sensitivity to the dominant frequencies and their shifts).

[0146] Therefore, the evaluation function for a particular step of the method can be selected based on the parameters determined at the particular step of the method. Therefore, ideally, different evaluation functions can be selected for different parameters, so that the selected evaluation function is only sensitive to changes in the specific parameters that are optimized (at the particular step) and is insensitive to other parameters.

[0147] A merit function that is most sensitive to shift or frequency changes but insensitive to scaling will mostly pick up height changes (z-direction changes or vertical position parameter changes).Thus, the choice of merit function can potentially reduce the number of iterations required by the optimizer to obtain a successful reconstruction.

[0148] Therefore, the evaluation function for each optimization in one or more first optimization steps may include a higher sensitivity and / or maximized sensitivity to the main frequencies and lateral shifts of the measured measurement data and / or simulated measurement data, and a lower sensitivity and / or minimized sensitivity to the scale of the measured measurement data and / or simulated measurement data.

[0149] Similarly, the evaluation function for each optimization in one or more second optimization steps may include a higher sensitivity and / or maximized sensitivity to the scale of the measured measurement data and / or the simulated measurement data, and a lower sensitivity and / or minimized sensitivity to the main frequencies and lateral shifts of the measured measurement data and / or the simulated measurement data.

[0150] In an embodiment, one or more of the optimization steps may be regularized, for example, by adding a regularization term to one or more of the evaluation functions used. By adding a regularization term to the evaluation function, the N-dimensional problem may be automatically decomposed into multiple sub-problems, thereby sweeping the layers from top to bottom as the iterations increase. The purpose of this regularization is to automate the acquisition strategy described above. For example, the regularization term Can be defined as:

[0151]

[0152] Among them, P is the parameter set to be inferred, p i is the value of parameter p at iteration i and where c p (i) is a coefficient whose value can be dynamically adjusted at each iteration. For example, the scaling or ruler coefficient c p One possible approach for (i) could be:

[0153]

[0154] where α p 、i p , l p is a regularization hyperparameter. This particular regularization term penalizes changes in the parameter p outside a certain window based on the number of iterations, which allows the optimizer to weakly decompose the N-dimensional problem into lower L-dimensional problems based on the physical properties of the SXR scatterometer data without any user input. mDimension problem.

[0155] This regularization represents a specific case, but any regularization for the value of parameter p added as a restriction of the parameter space, as a penalty term, or as a physical restriction is within the scope of the invention. Any method of estimating the hyperparameters of such regularization, as well as preprocessing or iterative methods, is also within the scope of the invention.

[0156] It is proposed to reconstruct complex geometries by comparing the measured results with the simulated results. To perform this scheme, a coarse model of the target is required (the coarse model is initially defined in the proposed method and improved at each optimization step of the reconstruction process) and an electromagnetic solver. Rigorous electromagnetic solvers can be time consuming. On the other hand, models based on simpler descriptions of the physical interaction between the probe and the target can be fast, but less accurate.

[0157] The forward model is described to a large extent according to an approximation based on a solver of the Born approximation, wherein the multiple scattering effects are ignored. As explained above, a single scattering event contributes to a large extent to the signal from the height of the patterned layer. This means that for this height parameter (the strongest component contributing to the measured signal), the strict and time-consuming Maxwell solver can be replaced by a simpler Born approximation solver. Since the shape of the evaluation function is expected to be mostly defined by the height of the patterned layer, and the height is mostly encoded in the first scattering event, it can be expected that a good estimate of the height can be obtained using this simple solver. More generally, the accuracy (and complexity) of the Maxwell solver can be adapted to the parameters currently concerned.

[0158] Thus, any suitable electromagnetic solver (also called rigorous solver) may be used. Electromagnetic solvers differ in the way they solve Maxwell's equations and the use cases for which they may be suitable. For example, rigorous coupled wave analysis (RCWA) may be used to solve Maxwell's equations for periodic structures and uses expansions of fields and permittivity in Fourier series. Other solvers use numerical approximations of the finite element method (FEM) or modal methods (e.g., finite domain time domain FDTD).

[0159] In an embodiment, it is proposed to use a solver that solves Maxwell's equations in their integral form (the integral form is basically called the vector Lipmann-Schwinger equations). However, in principle, which solver is used does not affect the results of the method disclosed herein; only the speed of reconstruction and its numerical accuracy and therefore any solver can be used.

[0160] Another aspect that can be configured is the optimizer used. Depending on the purpose of a particular optimization step, different choices of optimizers can improve a particular step in terms of speed and / or accuracy. The optimizer can be a local or global optimizer. An example of a global optimizer is a Bayesian optimizer.

[0161] A common option is an optimizer based on the Levenberg-Marquardt algorithm. Depending on the step-by-step reconstruction strategy, and specifically on the purpose of the steps, the choice of optimizer can improve the reconstruction. For example, if there is uncertainty in the initial estimate and the interval of certain parameters of interest during one of the first reconstruction steps, the landscape is likely not to be a convex landscape. With this part of the reconstruction strategy, the landscape is narrowed down to the interval so that the landscape includes a convex domain. Therefore, a global optimizer (such as a Bayesian optimizer (see below)) can be used to explore the landscape. Global optimization in a restricted 1D space is still a relatively fast operation. In a later stage of the reconstruction strategy, when the bounds of the parameters of interest are narrowed down to define a convex problem, a local optimizer can be used to accurately reconstruct the parameters of interest.

[0162] As mentioned above, in order to reduce the computation time, it is optionally proposed to replace the exhaustive search by adaptive sampling of the merit function based on Bayesian optimization. An important precondition for both methods to converge is the existence of a unique global extremum (i.e., no rocking curve characteristics), which has been observed to be correct in all models when the swept range is below the pitch size of the structure. Bayesian optimization uses a global optimizer (Bayesian optimizer) to find the global optimum (minimum or maximum) of the merit function. The optimizer can operate in the following way:

[0163] 1) First, the merit function is calculated for several values ​​(e.g., initial estimates) of the parameter(s) to be taken at the step. This results in a merit function with a global minimum that will be found to be sampled at the chosen parameters.

[0164] 2) The merit function is modeled as a Gaussian process and the several values ​​measured at step 1) are used to determine the next point in parameter space where it is most convenient to observe the value of the merit function. This is determined by choosing a value that increases the level of information about the shape of the function.

[0165] 3) Based on step 2), the evaluation function is updated with the new value observed; steps 2) and 3) are repeated until the global minimum is found.

[0166] In many of the above embodiments, physical intuition is used to decide the optimization order that describes which subset of parameters or subspaces are optimized at each optimization step and in which order. This means that the method is user-dependent and requires the experience of the measurement engineers involved. Although the guidelines that can be generally applied are described above, for example, optimizing the height / vertical model parameters first in one or more first optimization steps before optimizing other model parameters, the optimal order is not always obvious. For example, for complex geometries, it may be difficult to determine the appropriate optimization order. Even for relatively simple geometries, better performance can be obtained by optimizing specific "other model parameters" (such as CD in a first optimization (e.g., in a joint optimization with vertical model parameters or otherwise)) and / or optimizing the vertical model parameters in a specific order.

[0167] To solve this problem, an optimization order determination method is proposed. The optimization order determination method automatically decomposes the N-dimensional reconstruction problem into L M The order of M reconstruction problems of dimension , where .

[0168] Fig.12 (a) is a flow chart describing a method for determining an optimization order according to an embodiment. Such an embodiment may determine the optimization order based on calculations of covariance matrices and correlation matrices of model parameters (e.g., based on initial values ​​for these model parameters, and derivatives determined with respect to these model parameters (e.g., in the case of linear approximation)).

[0169] Step 1200 may include determining a covariance matrix and a correlation matrix. This step may be decomposed into Fig.12 (b). At step 1201, derivatives may be determined, for example, using a simulation model or a forward model as already described. For example, to calculate derivatives with respect to a selected model parameter ("or parameter of interest (POI)"), a signal obtained using a structure defined by initial values ​​of the model parameters is simulated. and introducing small changes in the chosen model parameters (e.g., less than 100 pm or approximately 50 pm) The derivative with respect to the parameter POI of interest can be obtained using forward numerical differentiation :

[0170]

[0171] This is merely an exemplary derivative determination method. Another method may include determining the finite first order derivative as a symmetric finite derivative:

[0172]

[0173] Another alternative could be to use automatic differentiation in the computational model to compute the derivatives, or any kernel-based method: for example, J = h × signal.

[0174] At step 1202 , these derivatives may be determined with respect to each model parameter and then stacked in a matrix J (Jacobian), for example, having a size or dimension defined by the number of model parameters×the number of pixels.

[0175] Covariance matrix C cov Then, it can be determined, for example, from the following formula (step 1203):

[0176]

[0177] The matrix is the inverse of the matrix representing the autocorrelation of the noise in the signal. For uncorrelated noise (eg, photon pulse noise or camera readout noise), the noise of each pixel can be defined by the noise model for that particular pixel.

[0178] At step 1204, the correlation matrix can be obtained from the covariance matrix by rescaling (e.g., according to the following formula):

[0179]

[0180] Once the correlation matrix and the covariance matrix are calculated, they can be used to separate the model parameters into subsets (subspaces), for example, by performing steps 1210 to 1280 .

[0181] Step 1210 may include determining measurement repeatability indicator data for model parameters under the assumption of noise introduced in the model. For example, such measurement repeatability indicator data may be determined from the diagonal elements of the covariance matrix, since these diagonal elements correspond to measurement repeatability.

[0182] At step 1220, the repeatability of each model parameter can be scaled using a characteristic quantity for that model parameter. Good candidates for characteristic quantities are: variations due to the manufacturing process and / or specific target values ​​for the manufacturing process. Both the unscaled repeatability determined at step 1210 and the rescaled repeatability determined in this step are examples of measurement repeatability indicators.

[0183] At step 1230, the model parameters may be sorted or ranked according to the rescaling repeatability determined at step 1220 to obtain ranked model parameters. Model parameters with lower rescaling repeatability tend to have lower correlated noise and larger range of variation. These model parameters may tend to be vertical model parameters such as the height of the structure.

[0184] At step 1240, regularization techniques may be applied to treat model parameters with higher rescaled repeatability because the problem becomes pathological when the rescaled repeatability is high. Different regularization techniques may mitigate or solve this problem. The proposed regularization technique may include excluding all model parameters with higher rescaled repeatability. This is a specific exemplary regularization technique (space restriction), but any suitable current state-of-the-art regularization technique may be used. An appropriate threshold rescaled repeatability value or threshold repeatability index value may be used, for example, to separate model parameters into those parameters with higher model rescaled repeatability and lower rescaled repeatability.

[0185] At step 1250, from the remaining model parameters (e.g., model parameters having repeatability index values ​​below a threshold repeatability index value), a model parameter having the lowest correlation repeatability index (e.g., rescaled repeatability) may be selected, and the correlation of such selected model parameter with other model parameters may be considered, for example, using the correlation matrix determined at step 1200. Any of the other model parameters having the lowest repeatability index value or correlation coefficient can be grouped together with the selected model parameter. In this way, the first group (i.e., the model parameter having the lowest repeatability index value and any correlation parameter grouped together with the model parameter) can be defined as and used as the first optimized subset of model parameters (i.e., the first subset of model parameters to be optimized).

[0186] At step 1260, it is determined whether there are any more model parameters that have not been assigned a group. If there are Y, the model parameter with the next lowest associated repeatability index is selected and step 1250 is repeated to determine the next optimized subset of model parameters. This is repeated until all model parameters (e.g., except for the model parameters excluded at step 1240) have been grouped.

[0187] At step 1270, the structure may be restructured according to the determined optimization order.

[0188] The method described above can be interpreted in a more rigorous manner in a Bayesian framework (e.g., as already described) using prior and posterior distributions. In such an embodiment, the characteristic quantity used in step 1220 can be interpreted as the prior uncertainty and the repeatability index can describe the variance of the probability distribution. This form allows a rigorous analytical interpretation of the regularization described in step 1240.

[0189] The covariance matrix and correlation matrix may optionally be recomputed for spaces more suitable for reconstruction (eg, new subspaces may be determined by diagonalizing the covariance matrix and correlation matrix); these new subspaces may include linear combinations of model parameters that are less correlated with each other.

[0190] Step 1250 describes a specific algorithm for determining connectivity of the graph. This is merely an exemplary method and any suitable method for determining connectivity in a matrix may be used, including graph-based methods.

[0191] The method described above considers only the problem model parameters, but in case other quantities are important for the reconstruction, they can be added to the problem. For example, if one parameter of the sensor model is important for the target reconstruction, but the calibration of one parameter of the sensor model is not accurate enough, then such parameter can be indicative of the model parameter and included in the process, thereby relaxing the specification on the parameter calibration.

[0192] This embodiment uses a linear approximation to handle the crosstalk between model parameters. A possible extension of this embodiment may take into account nonlinear crosstalk. Nonlinear crosstalk may be quantified and used in this method in any suitable manner (e.g., calculating Hansen or sampling the entire parameter space to obtain crosstalk between model parameters).

[0193] refer to Fig.13 A specific example is described. The structure can be described by ten model parameters including four vertical model parameters or heights H1, H2, H3, H4, an over-etch model parameter OE and five CD model parameters CD1, CD2, CD3, CD4, CD5. Fig.13 (a) shows the repeatability value REP for each of the model parameters, for example as described by the diagonal of the covariance matrix. Fig.13 (b) shows the correlation matrix for these model parameters. Assuming an exemplary correlation threshold of 0.3 , then the model parameters can be grouped and sorted into Fig.13 The optimization sequence shown in (c), where each box includes one or more model parameters to be optimized at each optimization step.

[0194] As expected, the model parameters to be optimized first include height. However, this approach also tells that parameter CD1 should be optimized in advance (i.e. combined with parameter H2), and thus captures the importance of the correlation between these two parameters. It may be considered that this correlation is fundamental to the success of the reconstruction, because reconstructing these parameters separately leads to a reconstruction error of about the (relatively high) sensitivity of CD1 to H2. Similarly, this approach tells that parameter CD4 should be optimized together with parameter H4.

[0195] Embodiments may include a computer program comprising one or more sequences of machine-readable instructions describing a method of optical metrology and / or a method of analyzing measurements to obtain information about a lithography process. Embodiments may include computer program code comprising one or more sequences of machine-readable instructions or data describing a method. For example, such a computer program or program code may be in Figure 6 The unit MPU and / or Figure 3 A data storage medium (e.g., semiconductor memory, magnetic disk, or optical disk, etc.) storing such a computer program or program code may also be provided. In an existing measuring device (e.g., Figure 6 In the event that a computer program product (of the type shown in Figure 1) is already in production and / or in use, embodiments of the invention may be implemented by providing an updated computer program product that causes a processor to perform one or more of the methods described herein. The computer program or program code may optionally be arranged to control an optical system, a substrate support, etc. to perform a method of measuring parameters of a lithographic process with respect to a suitable plurality of targets. The computer program or program code may update the lithography and / or metrology options for measurement of additional substrates. The computer program or program code may be arranged to (directly or indirectly) control the lithographic apparatus for patterning and processing of additional substrates.

[0196] The above embodiments have one or more of the following advantages over currently existing model-based methods, including:

[0197] The first step of determining parameters related to the Z direction (e.g., stack thickness) can be performed with high accuracy using limited prior knowledge;

[0198] The problem can be split into a series of lower dimensional problems that can each be solved independently, rather than having to optimize all parameters at the same time, which greatly simplifies the optimization process.

[0199] The method can be implemented directly on the device, for example, the method can be implemented on the device for OVL measurement.

[0200] The illumination source may be arranged, for example, in a metrology apparatus MT, an inspection apparatus, a lithographic apparatus LA and / or a lithocell LC.

[0201] The properties of the emitted radiation used to perform the measurement may affect the quality of the measurement obtained. For example, the shape and size of the lateral beam profile (cross-section) of the radiation beam, the intensity of the radiation, the power spectral density of the radiation, etc. may affect the measurement performed by the radiation. Therefore, it is beneficial to have a source that provides radiation with properties that result in high quality measurements.

[0202] Further embodiments are disclosed in the subsequent numbered aspects:

[0203] 1. A method of determining at least one parameter of interest associated with a structure formed in at least one corresponding layer on a substrate, the method comprising:

[0204] obtaining measured metrology data associated with a measurement result of the structure;

[0205] obtaining a model that describes the structure in terms of a plurality of model parameters, the model parameters comprising estimated values; and

[0206] A plurality of optimization steps are sequentially performed based on the measured metrology data to determine the plurality of model parameters, each of the plurality of optimization steps determining a subset of the plurality of model parameters.

[0207] 2. The method according to aspect 1, wherein the step of performing a plurality of optimization steps in sequence comprises:

[0208] performing one or more first optimization steps using the measured metrology data to determine one or more vertical position parameters in the model parameters; and

[0209] After performing the one or more first optimization steps, one or more second optimization steps are performed using the measured measurement data and the model having the one or more vertical position parameters determined in the one or more first optimization steps to determine one or more other model parameters among the multiple model parameters, wherein the one or more other model parameters are different from the one or more vertical position parameters.

[0210] 3. The method according to aspect 2, wherein the step of performing the one or more first optimization steps optimizes only the one or more vertical position parameters.

[0211] 4. The method according to aspect 2 or 3, comprising: performing the one or more first optimization steps separately, except that the one or more vertical position parameters are optimized and the model parameters are fixed.

[0212] 5. A method according to any one of aspects 2 to 4, wherein the one or more other parameters determined in the one or more second optimization steps include one or more substrate plane position parameters in the model parameters, and the one or more substrate plane position parameters are related to one or two directions of the substrate plane.

[0213] 6. The method according to any one of aspects 2 to 5 comprises performing the one or more second optimization steps respectively, wherein, except for the one or more other parameters determined in the one or more second optimization steps, the model parameters are fixed and the one or more vertical position parameters are fixed at the values ​​determined in the one or more first optimization steps.

[0214] 7. A method according to any one of aspects 2 to 6, wherein each of the one or more first optimization steps and the one or more second optimization steps includes: comparing the measured measurement data with the simulated measurement data obtained by simulating the measurement of the structure defined by the model.

[0215] 8. The method according to clause 7, wherein each optimization step comprises: maximizing the similarity between the measured metrology data and the simulated metrology data.

[0216] 9. A method according to clause 7 or 8, wherein the simulation uses a simulation model operable to simulate electromagnetic interactions between incident fields and the structure and sensor configuration defined by the model.

[0217] 10. The method of clause 9, wherein the sensor configuration is based on a physical sensor configuration used to obtain the measured metrology data.

[0218] 11. A method according to aspect 9 or 10, wherein the simulation model comprises a forward model.

[0219] 12. The method according to clause 9, 10 or 11, wherein the simulation model is based on a Born approximation and / or a rigorous solver.

[0220] 13. The method according to any one of aspects 7 to 12, wherein each optimization step uses a corresponding evaluation function.

[0221] 14. A method according to clause 13, comprising selecting the merit function for each optimization step based on the model parameters optimized at the optimization step.

[0222] 15. A method according to aspect 13 or 14, wherein the evaluation function used for each optimization in the one or more first optimization steps includes a higher sensitivity and / or a maximized sensitivity to the main frequencies and lateral shifts of the measured measurement data and / or the simulated measurement data, and a lower sensitivity and / or a minimized sensitivity to the scale of the measured measurement data and / or the simulated measurement data.

[0223] 16. A method according to aspects 13, 14 or 15, wherein the merit function for each optimization in the one or more first optimization steps is based on a degree of correlation between the measured metrology data and the simulated metrology data.

[0224] 17. A method according to any one of aspects 13 to 16, wherein the evaluation function used for each optimization in the one or more second optimization steps includes a higher sensitivity and / or a maximized sensitivity to the scale of the measured measurement data and / or the simulated measurement data, and a lower sensitivity and / or a minimized sensitivity to the main frequency and lateral shift of the measured measurement data and / or the simulated measurement data.

[0225] 18. The method according to any of aspects 13 to 17, comprising: selecting an optimizer for one or more of the optimization steps in dependence on the purpose of the optimization step.

[0226] 19. The method according to any one of aspects 13 to 18, wherein one or more of the optimization steps uses an optimizer based on the Levenberg-Marquardt algorithm.

[0227] 20. The method according to any one of aspects 13 to 19, wherein one or more of the optimization steps comprises adaptive sampling of the merit function based on Bayesian optimization.

[0228] 21. A method according to any one of aspects 13 to 20, comprising regularizing one or more of the optimisation steps by applying at least one regularisation term to one or more of the merit functions.

[0229] 22. A method according to aspect 21, wherein the at least one regularization term penalizes variations of the model parameters being optimized outside a certain window according to the number of iterations.

[0230] 23. A method according to any one of clauses 7 to 22, wherein each of the measured metrology data and the simulated metrology data comprises diffraction pattern intensity or amplitude data.

[0231] 24. A method according to any one of aspects 2 to 23, wherein:

[0232] The structure includes overlapping targets of substructures contained in corresponding layers of two or more patterned layers;

[0233] The one or more vertical position parameters include at least the corresponding position of each undefined interface of the two patterned layers; and

[0234] The one or more further parameters determined in the one or more second optimization steps comprise overlap in one or two directions of the substrate plane.

[0235] 25. The method of clause 24, wherein the undefined interfaces include all interfaces of the two or more patterned layers except a topmost interface, such that the undefined interfaces are determined relative to the topmost interface.

[0236] 26. A method according to any one of aspects 2 to 23, wherein:

[0237] The one or more vertical position parameters include a height of the structure; and

[0238] The one or more other parameters determined in the one or more second optimization steps include at least one dimension of the structure in one or two directions in the plane of the substrate.

[0239] 27. The method of clause 26, wherein the one or more other parameters determined in the one or more second optimization steps include at least one critical dimension of a feature of the structure.

[0240] 28. The method of clause 26 or 27, wherein the one or more other parameters determined in the one or more second optimization steps include: a top critical dimension at a top of the feature and a bottom critical dimension at a bottom of the feature.

[0241] 29. A method according to aspects 26, 27 or 28, wherein the one or more other parameters determined in the one or more second optimization steps include: one or more of: a side wall angle of any wall of the structure, a measurement of the floor inclination of the structure and / or a measurement of any other inclination of the structure.

[0242] 30. A method according to any one of aspects 26 to 29, wherein the one or more vertical position parameters further comprise: the height of one or more of the layers below or above the structure.

[0243] 31. The method of clause 30, wherein performing one or more first optimization steps comprises:

[0244] determining said height of said structure;

[0245] determining the height of one or more of the layers directly below or above the structure; and

[0246] The step of determining the height of the structure is repeated using the determined heights of one or more of the layers directly below or above the structure.

[0247] 32. The method according to any one of aspects 2 to 31 comprises: performing one or more additional optimization steps to determine one or more model parameters of the model parameters that were not determined in the one or more first optimization steps and / or the one or more second optimization steps.

[0248] 33. A method according to any preceding aspect, comprising: an initial step of determining an optimization order describing the order of the plurality of optimization steps.

[0249] 34. A method according to clause 33, comprising: determining a measurement repeatability indicator for each or a subset of the model parameters in the presence of noise; and

[0250] The optimization order is determined from the measurement repeatability index.

[0251] 35. The method according to aspect 34, comprising:

[0252] determining a covariance matrix describing the covariance of the model parameters in the presence of noise; and

[0253] The measurement repeatability index for each of the model parameters is determined based on the diagonal of the covariance matrix.

[0254] 36. The method according to aspect 35, comprising:

[0255] determining the derivative with respect to each model parameter; and

[0256] The covariance matrix is ​​determined based on the derivatives.

[0257] 37. The method according to any one of aspects 34 to 36, comprising: ranking the model parameters according to the measurement repeatability index to obtain ranked model parameters.

[0258] 38. The method of clause 37, comprising: applying regularization to model parameters having higher measurement repeatability indicators.

[0259] 39. The method of clause 38, wherein the regularization comprises excluding from the grading those model parameters having higher measurement repeatability indicators.

[0260] 40. A method according to any one of aspects 37 to 39, comprising:

[0261] selecting the model parameters having the lowest measurement repeatability index;

[0262] determining a correlation value between the selected model parameter and other hierarchical model parameters;

[0263] grouping any model parameters having a relevance value above a relevance threshold with the selected model parameters to obtain a group of said model parameters; and

[0264] These steps are repeated until all hierarchical model parameters are grouped, the optimization order comprising the groups sorted according to the measurement repeatability index of the corresponding selected model parameter of each group.

[0265] 41. A method according to clause 40, comprising determining a correlation matrix from the covariance matrix.

[0266] 42. The method according to any preceding aspect, wherein the measured metrology data have been obtained using a measuring illumination having a wavelength of less than 100 nm.

[0267] 43. The method according to any preceding aspect, wherein the measured metrology data have been obtained using a measuring illumination having a wavelength of less than 50 nm.

[0268] 44. A method according to clause 43, wherein the measured metrology data has been obtained using a measuring illumination comprising a wavelength from 2 nm to 50 nm.

[0269] 45. A method according to any one of clauses 1 to 43, wherein the measured metrology data have been obtained using a measuring illumination having a wavelength of less than 20 nm.

[0270] 46. ​​The method according to any preceding aspect, wherein the measured metrology data have been obtained using a measuring illumination comprising a wavelength from 2 nm to 20 nm.

[0271] 47. A method according to any preceding aspect, wherein the number of optimization steps in the plurality of optimization steps comprises a smaller number than the number of model parameters in the plurality of model parameters.

[0272] 48. A method according to any preceding aspect, comprising: performing measurements of the structure to obtain the measured metrology data.

[0273] 49. A computer program comprising program instructions operable to perform a method according to any preceding aspect when run on a suitable apparatus.

[0274] 50. A non-transitory computer program carrier comprising a computer program according to clause 49.

[0275] 51. A processing device, the processing device comprising:

[0276] a computer program carrier comprising a computer program according to clause 50; and

[0277] A processor is operable to execute the computer program.

[0278] 52. A measurement device comprising the processing device according to aspect 51.

[0279] 53. A metrology device operable to perform the method of clause 48.

[0280] 54. A metrology device according to aspect 52 or 53, comprising a scatterometer.

[0281] Although specific reference may be made herein to the use of lithographic apparatus in IC manufacturing, it should be understood that the lithographic apparatus described herein may have other applications. Possible other applications include the manufacture of integrated optical systems, guidance and detection patterns for magnetic domain memories, flat panel displays, liquid crystal displays (LCDs), thin film magnetic heads, etc.

[0282] Although specific reference may be made herein to embodiments in the context of lithographic apparatus, embodiments may be used in other apparatus. Embodiments may form part of mask inspection apparatus, metrology apparatus, or any apparatus that measures or processes an object such as a wafer (or other substrate) or a mask (or other pattern forming device). These apparatuses may generally be referred to as lithographic tools. Such lithographic tools may use vacuum conditions or ambient (non-vacuum) conditions.

[0283] Although specific reference may be made herein to embodiments in the context of inspection or metrology equipment, embodiments may be used in other equipment. Embodiments may form part of a mask inspection equipment, a lithography equipment, or any equipment that measures or processes an object such as a wafer (or other substrate) or a mask (or other pattern forming device). The term "metrology equipment" (or "inspection equipment") may also refer to an inspection equipment or an inspection system (or a metrology equipment or a metrology system). For example, an inspection equipment including an embodiment may be used to detect defects in a substrate or defects in a structure on a substrate. In such embodiments, the characteristic of interest of a structure on a substrate may be related to a defect in the structure, the absence of a particular portion of the structure, or the presence of an unwanted structure on the substrate.

[0284] Although the above may refer specifically to the use of embodiments in the context of optical lithography, it will be appreciated that the invention may be used in other applications (eg, imprint lithography) and is not limited to optical lithography where the context permits.

[0285] While the targets or target structures (more generally, structures on a substrate) described above are metrology target structures specifically designed and formed for the purpose of measurement, in other embodiments, the property of interest may be measured for one or more structures that are functional parts of a device formed on a substrate. Many devices have regular grating-like structures. The terms "structure," "target grating," and "target structure" as used herein do not necessarily require that the structure be provided specifically for the measurement being performed. In addition, the pitch of the metrology target may be close to the resolution limit of the optical system of the scatterometer or may be smaller, but may be much larger than the size of a typical non-target structure (optionally a product structure) made by a lithography process in the target portion C. In practice, the lines and / or spaces of the overlapping gratings within the target structure may be made to include smaller structures that are similar in size to the non-target structures.

[0286] Although specific embodiments have been described above, it will be appreciated that the present invention may be practiced in other ways than those described. The above description is intended to be illustrative, not restrictive. Thus, it will be appreciated by those skilled in the art that the described invention may be modified without departing from the scope of the claims set forth below.

[0287] Although specific reference is made to "measurement equipment / tool / system" or "inspection equipment / tool / system", these terms may refer to the same or similar types of tools, equipment, or systems. For example, an inspection or measurement equipment including embodiments of the present invention may be used to determine characteristics of a structure on a substrate or on a wafer. For example, an inspection equipment or measurement equipment including embodiments of the present invention may be used to detect defects in a substrate or defects in a structure on a substrate or on a wafer. In such embodiments, the characteristic of interest of a structure on a substrate may relate to a defect in the structure, the absence of a particular portion of a structure, or the presence of an unwanted structure on the substrate or on the wafer.

[0288] Although specific reference is made to SXR electromagnetic radiation, it will be appreciated that the invention may be practiced where the context permits with all electromagnetic radiation, including radio waves, microwaves, infrared, (visible) light, ultraviolet, EUV, HXR and gamma rays.

[0289] Although specific embodiments have been described above, it will be appreciated that one or more of the features in one embodiment may also be present in a different embodiment, and that features from two or more different embodiments may also be combined.

Claims

1. A method of determining at least one parameter of interest associated with a structure formed in at least one corresponding layer on a substrate, the method comprising: obtaining measured metrology data related to the measurement results of the structure; obtaining a model that describes the structure in terms of a plurality of model parameters, the model parameters comprising estimated values; and A plurality of optimization steps are sequentially performed based on the measured metrology data to determine the plurality of model parameters, each of the plurality of optimization steps determining a subset of the plurality of model parameters.

2. The method according to claim 1, wherein: The steps to perform multiple optimization steps in sequence include: performing one or more first optimization steps using the measured metrology data to determine one or more vertical position parameters in the model parameters; and After performing the one or more first optimization steps, one or more second optimization steps are performed using the measured measurement data and the model having the one or more vertical position parameters determined in the one or more first optimization steps to determine one or more other model parameters among the multiple model parameters, wherein the one or more other model parameters are different from the one or more vertical position parameters.

3. The method according to claim 2, comprising: The one or more first optimization steps are respectively performed, wherein the model parameters are fixed except for the one or more vertical position parameters being optimized.

4. The method according to claim 2 or 3, wherein: The one or more other parameters determined in the one or more second optimization steps include: one or more substrate plane position parameters among the model parameters, the one or more substrate plane position parameters being related to one or two directions of the substrate plane.

5. The method according to any one of claims 2 to 4, comprising: The one or more second optimization steps are respectively performed, wherein the model parameters are fixed except for the one or more other parameters determined in the one or more second optimization steps.

6. The method according to any one of claims 2 to 5, wherein: Each of the one or more first optimization steps and the one or more second optimization steps respectively comprises comparing the measured metrology data with simulated metrology data obtained by simulation of measurements on the structure defined by the model.

7. The method according to claim 6, wherein: The simulation uses a simulation model operable to simulate electromagnetic interactions between an incident field and the structure defined by the model and a sensor configuration, wherein optionally the sensor configuration is based on a physical sensor configuration used to obtain the measured metrology data.

8. The method according to claim 7, wherein: The simulation model includes a forward model.

9. The method according to claim 7 or 8, wherein: The simulation model is based on the Born approximation and / or a rigorous solver.

10. The method according to any one of claims 2 to 9, wherein: The one or more vertical position parameters include a height of the structure; as well as The one or more other parameters determined in the one or more second optimization steps include at least one dimension of the structure in one or two directions in the plane of the substrate.

11. The method according to any one of claims 2 to 10, comprising: One or more further optimization steps are performed to determine one or more of the model parameters which were not determined in the one or more first optimization steps and / or the one or more second optimization steps.

12. A method according to any preceding claim, wherein: Said measured metrology data have been obtained using a measuring illumination comprising illumination having a wavelength of 2 nm to 50 nm, optionally 2 nm to 20 nm.

13. A method according to any preceding claim, wherein: The number of optimization steps in the plurality of optimization steps comprises a number smaller than the number of model parameters in the plurality of model parameters.

14. A computer program comprising program instructions operable to perform a method according to any preceding claim when run on a suitable device.

15. A metrology device operable to perform a method according to any one of claims 1 to 13.

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