Method for parameter reconstruction of metrology device and associated metrology device

Abstract

Disclosed is a method comprising: obtaining measurement data relating to at least one measurement of a measurement device configured to irradiate radiation onto each of one or more structures on a substrate; decomposing the measurement data using a decomposition method to obtain a plurality of measurement data components; obtaining simulation data related to at least one simulation based on the one or more structures; decomposing the analog data using the decomposition method to obtain a plurality of analog data components; matching between at least a portion of the analog data component and at least a portion of the measured data component; and extracting a feature of the substrate based on the matching of at least a portion of the analog data component and at least a portion of the measured data component.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to EP application 22176959.9 filed on June 2, 2022 and EP application 22191645.5 filed on August 23, 2022, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present invention relates to metrology applications in the manufacture of integrated circuits. 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") of a patterning device (e.g., a mask) onto a layer of radiation-sensitive material (resist) provided 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. Lithographic apparatus using extreme ultraviolet (EUV) radiation with a wavelength in the 4 nm to 20 nm heating range (e.g., 6.7 nm or 13.5 nm) can be used to form smaller features on a substrate than lithographic apparatus using, for example, radiation with a wavelength of 193 nm.

[0006] Low k1 lithography can be used to process features with dimensions 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 radiation employed, 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 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 is similar in shape and size to that planned by the circuit designer 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. For example, these include, but are not limited to, optimization of the 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 tight control loop for controlling the stability of the lithographic apparatus can be used to improve the reproduction of patterns at low k1.

[0007] During photolithography, as well as other manufacturing processes, it is desirable to make frequent measurements of the structures being created, for example for process control and verification. Various tools are known for making such measurements, including scanning electron microscopes (commonly used to measure critical dimensions (CDs)) and specialized tools for measuring overlay, or the alignment accuracy of two layers in a device. More recently, various forms of scatterometers have been developed for use in the field of photolithography.

[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 thereof.

[0009] Examples of known scatterometers generally rely on providing a dedicated measurement target. For example, one approach may require a target in the form of a simple grating that is large enough to cause the measurement beam to generate 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 similar to that observed from a real target.

[0010] In addition to measuring feature shapes by reconstruction, diffraction-based overlay can also be measured using such a device, as described in the published patent application US2006066855A1. Diffraction-based overlay measurement using dark field imaging of diffraction orders enables overlay measurement on smaller targets. These targets can be smaller than the illumination 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, for example, US2011102753A1 and US20120044470A. Multiple gratings can be measured in one image using a composite grating target. Known scatterometers tend to use light in the visible or near infrared (IR) wave range, which requires the pitch of the grating to be much coarser than the actual product structure whose characteristics are actually of interest. Such product features can be defined using deep ultraviolet (DUV), extreme ultraviolet (EUV) or X-ray radiation with shorter wavelengths. Unfortunately, such wavelengths are often unavailable or unavailable for measurement.

[0011] On the other hand, the dimensions of modern product structures are so small that they cannot be imaged by optical metrology techniques. Small features include, for example, those formed by multiple patterning processes and / or pitch multiplication. Therefore, targets for high-volume metrology typically use features that are much larger than the product whose overlay error or critical dimensions are the characteristics of interest. The measurement results are only indirectly related to the dimensions of the real product structures and may not be accurate because the measurement targets do not suffer the same distortions under optical projection in the lithography equipment and / or different processing in other steps of the manufacturing process. Although scanning electron microscopes (SEMs) are able to directly resolve these modern product structures, SEMs are more time consuming than optical measurements. Moreover, electrons cannot penetrate thicker process layers, which makes them less suitable for metrology applications. Other techniques (such as measuring electrical properties using contact pads) are also known, but it only provides indirect evidence of the real product structure.

[0012] By reducing the wavelength of radiation used during metrology, smaller structures can be resolved to increase sensitivity to structural changes in structures and / or to penetrate further into product structures. 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 a generating medium to generate emitted radiation, optionally including high harmonic generation of the high frequency radiation.

[0013] For parameter reconstruction, two approaches are commonly used: 1) model-based reconstruction, where the entire sample (object of interest) and the measurement system are modeled to obtain a match between the observed signal (e.g., detector image) and the simulated signal; and 2) data-driven reconstruction, where the parameters of interest are inferred from the observed signal, typically using a neural network. Summary of the invention

[0014] According to a first aspect of the present disclosure, a measurement method is provided, comprising: obtaining measurement data associated with at least one measurement result of a measurement device, the measurement device being configured to irradiate radiation onto each of one or more structures on a substrate; decomposing the measurement data using a decomposition method to obtain a plurality of measurement data components; obtaining simulation data associated with at least one simulation based on the one or more structures; decomposing the simulation data using a decomposition method to obtain a plurality of simulation data components; matching at least a portion of the simulation data components with at least a portion of the measurement data components; and extracting features of the substrate based on matching at least a portion of the simulation data components with at least a portion of the measurement data components.

[0015] In another aspect of the present invention, a measurement method is provided, comprising: irradiating radiation onto a substrate; obtaining measurement data associated with at least one measurement result of each of one or more structures on the substrate; decomposing the measurement data using a decomposition method to obtain a plurality of measurement data components; obtaining simulation data associated with at least one simulation based on the one or more structures; decomposing the simulation data using a decomposition method to obtain a plurality of simulation data components; matching between at least a portion of the simulation data components and at least a portion of the measurement data components; and extracting features of the substrate.

[0016] Optionally, the one or more structures comprise vertically stacked nanosheets and / or alternating layers of different materials.

[0017] Optionally, the one or more structures include a gate-all-around (GAA) transistor. Optionally, the one or more structures include a nanosheet structure. Optionally, the nanosheet structure is included in a gate-all-around (GAA) transistor, a fork-sheet transistor and / or a complementary field effect transistor (CFET).

[0018] Optionally, the feature is a parameter of a semiconductor manufacturing process, optionally a parameter of a lithography process and / or an etching process.

[0019] Optionally, the feature comprises a lateral etch depth.

[0020] Optionally, the step of matching between at least a portion of the simulated data component and at least a portion of the measured data component may further comprise adding one or more components from both the simulated data component and the measured data component to the matching.

[0021] Optionally, the method may further include irradiating radiation onto the substrate.

[0022] Optionally, the simulation may also be performed based on the at least one measurement result.

[0023] Optionally, matching between at least a portion of the simulated data component and at least a portion of the measured data component may include an iterative process.

[0024] Optionally, matching between at least a portion of the simulated data component and at least a portion of the measured data component may include using a minimization algorithm.

[0025] Optionally, the measurement data may include diffracted radiation.

[0026] Alternatively, the diffracted radiation may have been diffracted in reflection and / or transmission by one or more structures on the substrate.

[0027] Optionally, the decomposition method may include Fourier analysis.

[0028] Optionally, deep filtering in the autocorrelation space may be performed before performing the Fourier analysis.

[0029] Optionally, the radiation may include one or more wavelengths in the range of 0.01 nm to 50 nm, optionally one or more wavelengths in the range of 0.01 nm to 20 nm, optionally one or more wavelengths in the range of 1 nm to 10 nm, and optionally one or more wavelengths in the range of 10 nm to 20 nm.

[0030] Optionally, the method may further include: determining a weight matrix based on one or more characteristics of the at least one measurement result; and applying the weight matrix to the measurement data, wherein applying the weight matrix to the measurement data adds relevance to the measurement data based on the one or more characteristics of the at least one measurement result.

[0031] Optionally, the one or more characteristics of the at least one measurement result may comprise one or more measurement parameters of the measurement data.

[0032] Optionally, the one or more measurement parameters may include overlay, leveling, profilometry, alignment, critical dimension, focus, and / or dose.

[0033] Optionally, the one or more characteristics of the at least one measurement result may comprise one or more characteristics of a measurement device.

[0034] Optionally, the one or more characteristics of the at least one measurement result may comprise one or more characteristics of the radiation used to irradiate the substrate.

[0035] Optionally, the one or more characteristics of the radiation may include wavelength, intensity distribution and / or beam shape.

[0036] Optionally, the one or more characteristics of the at least one measurement result may comprise one or more characteristics of the substrate.

[0037] Optionally, the method may further comprise applying the weight matrix to simulated data.

[0038] Optionally, the decomposition method may include: determining a covariance matrix of the measurement data; applying a weight matrix to the covariance matrix to obtain a weighted covariance matrix; performing singular value decomposition on the weighted covariance matrix; and obtaining the plurality of measurement data components based on the singular value decomposition.

[0039] According to another aspect of the present disclosure, there is provided a non-transitory computer program product including machine-readable instructions therein, which, when executed by a computer system, are configured to cause the computer system to at least cause execution of the above-described method.

[0040] According to another aspect of the present disclosure, there is provided a computer program comprising computer readable instructions operable to perform at least the processing steps of the above method.

[0041] According to another aspect of the present disclosure, there is provided a processor and an associated storage medium, the storage medium comprising the above-mentioned computer program, so that the processor is operable to perform the above-mentioned method.

[0042] According to another aspect of the present disclosure, a measurement device is provided, comprising the above-mentioned processor and an associated storage medium, so as to be operable to perform the above-mentioned method.

[0043] According to another aspect of the present disclosure, there is provided a lithography cell comprising the above-mentioned processor and an associated storage medium so as to be operable to perform the above-mentioned method. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Embodiments will now be described, by way of example only, with reference to the accompanying schematic drawings, in which:

[0045] Figure 1 A schematic overview of a lithography apparatus is depicted;

[0046] Figure 2 A schematic overview of a lithography unit is depicted;

[0047] Figure 3 Depicted is a schematic representation of overall lithography showing the collaboration between three key technologies to optimize semiconductor manufacturing;

[0048] Figure 4 schematically illustrates a scatterometry device;

[0049] Figure 5 A transmission scattering measurement apparatus is schematically illustrated;

[0050] Figure 6 depicts a schematic representation of a metrology apparatus using EUV and / or SXR radiation;

[0051] Figure 7 A simplified schematic diagram of the irradiation source is depicted;

[0052] Figure 8 A flowchart of the steps included in yet another embodiment of the present invention;

[0053] Fig. 8A Another flow chart of steps included in yet another embodiment of the present invention;

[0054] Figure 8B Another flow chart of steps included in yet another embodiment of the present invention; and

[0055] Fig. 9 Another flow chart of steps included in a metrology method taking into account a characteristic of at least one measurement result. DETAILED DESCRIPTION

[0056] 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., wavelengths of 365 nm, 248 nm, 193 nm, 157 nm or 126 nm), EUV (extreme ultraviolet radiation, e.g., wavelengths in the range of about 5 to 100 nm), X-ray radiation, electron beam radiation, and other particle radiation.

[0057] 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 created in a target portion of a substrate. In this context, the term "light valve" may also be used. In addition to classical masks (transmissive or reflective, binary, phase-shifting, hybrid, etc.), other examples of such patterning devices include programmable mirror arrays and programmable LCD arrays.

[0058] 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 table) T constructed 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 constructed to hold a substrate (e.g. a resist-coated wafer) 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.

[0059] In operation, the illumination system IL receives a radiation beam from a radiation source SO (e.g., 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, for directing, shaping and / or controlling 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.

[0060] The term "projection system" PS as used herein should be broadly understood to cover various types of projection systems, including refractive, reflective, diffractive, catadioptric, anamorphic, magnetic, electromagnetic and / or electrostatic optical systems or any combination thereof, as appropriate for the exposure radiation used and / or other factors such as the use of immersion liquid or the use of a vacuum. Any use of the term "projection lens" herein may be considered synonymous with the more general term "projection system" PS.

[0061] The lithographic apparatus LA may be of a type in which at least a portion of the substrate may be covered by a liquid having a relatively high refractive index, such as water, to fill the space between the projection system PS and the substrate W, which is also known as immersion lithography. More information on immersion techniques is given in US6952253, which is incorporated herein in its entirety by reference.

[0062] The lithographic apparatus LA may also be of a type having two or more substrate supports WT (also referred to as a "dual stage"). In such a "multi-stage" machine, the substrate supports WT may be used in parallel, and / or steps for 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.

[0063] In addition to the substrate support WT, the lithographic apparatus LA may comprise a measurement platform. The measurement platform is arranged to hold sensors and / or cleaning means. The sensors may be arranged to measure characteristics of the projection system PS or characteristics of the radiation beam B. The measurement platform may hold a plurality of sensors. The cleaning means may be arranged to clean a part of the lithographic apparatus, e.g. a part of the projection system PS or a part of a system providing an 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.

[0064] In operation, a radiation beam B is incident on a patterning device (e.g. a mask MA) and is patterned by a pattern (design layout) present on the patterning device MA, which is held on a mask support T. After traversing the mask MA, the radiation beam B passes through a projection system PS which focuses the beam onto a target portion C of a substrate W. With the aid of a second positioner PW and a 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 another position sensor (not shown) are also provided. 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 illustrated substrate alignment marks P1, P2 occupy dedicated target portions, they 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.

[0065] like Figure 2 As shown, the lithography apparatus LA may form part of a lithography cell LC, which is sometimes also referred to as a lithography cell or (lithography) cluster, which typically also includes equipment for performing pre-exposure and post-exposure processes on a substrate W. Conventionally, these 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 regulating the temperature of the substrate W (e.g. for regulating the solvent in the resist layer). A substrate handler or robot RO picks up substrates W from input / output ports I / O1, I / O2, moves them between the different process equipment, and delivers the substrates W to a feed station LB of the lithography apparatus LA. The devices in the lithography cell (also often collectively referred to as a coater-developer) may be controlled by a coater-developer control unit TCU, which itself may be controlled by a supervisory control system SCS, which may also control the lithography apparatus LA (e.g. via a lithography control unit LACU).

[0066] During the lithographic process, it is desirable to frequently measure the created structures, for example for process control and verification. The tool that performs such measurements may be referred to as a measurement tool MT. Different types of measurement tools MT for performing such measurements are known, including scanning electron microscopes or various forms of scatterometer measurement tools MT. A scatterometer is a versatile instrument that allows measurement of parameters of the lithographic process by placing a sensor in the pupil of a scatterometer objective or in a conjugate plane conjugate to the pupil (these measurements are usually referred to as pupil-based measurements) or by placing a sensor in an image plane or in a plane conjugate to the image plane (in this case, these measurements are usually referred to as image-based or field-based measurements). Such scatterometers and associated measurement techniques are further described in patent applications US20100328655, US2011102753A1, US20120044470A, US20110249244, US20110026032 or EP1,628,164A, the entire contents of which are incorporated herein by reference. The scatterometer 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 or soft X-ray, the scatterometer can optionally be a small angle X-ray scattering measurement tool.

[0067] In order for substrates W exposed by lithographic apparatus LA to be correctly and consistently exposed, it is desirable to inspect the substrates to measure characteristics of the patterned structures, such as overlay errors between subsequent layers, line thickness, critical dimensions (CD), structure shape, etc. For this purpose, inspection tools and / or metrology tools (not shown) may be included in lithography cell LC. If errors are detected, then, for example, adjustments may be made to the exposure of subsequent substrates or other processing steps to be performed on substrate W, particularly if the inspection is completed before other substrates W of the same batch or lot are still to be exposed or processed.

[0068] The inspection apparatus (which may also be referred to as a metrology apparatus) is used to determine properties of the substrate W, in particular how properties vary from one substrate to another W, or how properties associated with different layers of the same substrate W vary from layer to layer. Alternatively, the inspection apparatus may 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 stand-alone device. The inspection apparatus may measure properties on a latent image (the image in the resist layer after exposure) or a semi-latent image (the image in the resist layer after a post-exposure bake step PEB), or a developed resist image (where exposed or unexposed parts of the resist have been removed), or even on an etched image (after a pattern transfer step such as etching).

[0069] In a first embodiment, the scatterometer MT is an angle-resolved scatterometer. In such a scatterometer, reconstruction methods may be applied to the measurement signals to reconstruct or calculate the properties of the grating. For example, such a reconstruction may be performed by simulating the interaction of the scattered radiation with a mathematical model of the target structure and comparing the simulation results with the measurement results. The parameters of the mathematical model are adjusted until the simulated interaction produces a diffraction pattern similar to that observed from a real target.

[0070] 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., an intensity-side measurement as a function of wavelength). From this data, the structure or profile of the target that produced the detected spectrum can be reconstructed, for example by rigorous coupled wave analysis and nonlinear regression or by comparison with a library of simulated spectra.

[0071] In a third embodiment, the scatterometer MT is an ellipsometric scatterometer. An ellipsometric scatterometer allows the parameters of a lithographic process to be determined by measuring the scattered or transmitted radiation of each polarization state. Such a measurement device emits polarized light (such as linear, circular or elliptical) by using, for example, an appropriate polarization filter in the illumination section of the measurement device. A source suitable for the measurement 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, the entire contents of which are incorporated herein by reference.

[0072] 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 construction, the asymmetry being related to the degree of the overlap. The two (possibly overlapping) grating structures may be applied in two different layers (not necessarily consecutive layers) and may be formed at substantially the same position on the wafer. The scatterometer may have a symmetrical detection construction, such as described in the co-owned patent application EP1,628,164A, so that any asymmetry is clearly distinguishable. This provides a direct way of measuring misalignment in the grating. Other examples of measuring the overlap error between two layers containing a periodic structure as a target by means of the asymmetry of the periodic structure may be found in PCT patent application publication number WO 2011 / 012624 or in US patent application US 20160161863, the entire contents of which are incorporated herein by reference.

[0073] Other parameters of interest may be focus and dose. Focus and dose may be determined simultaneously by scatterometry (or alternatively by scanning electron microscopy), as described in U.S. Patent Application US2011-0249244, the entire contents of which are incorporated herein by reference. 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 called 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.

[0074] The metrology target may be the entirety of a composite grating, which is formed by a lithography process, primarily in resist, but may also be formed after other manufacturing processes, such as etching / etching processes. The pitch and line width of the structures in the grating may depend greatly on the measurement optics (particularly the NA of the optics) in order to be able to capture the diffraction orders from the metrology target. As indicated earlier, the diffraction signal may be used to determine the shift between two layers (also known as 'overlap'), or may be used to reconstruct at least part of the original grating produced by the lithography process. This reconstruction may be used to provide guidance on the quality of the lithography process, and may be used to control at least part of the lithography process. The target may have smaller sub-divisions, which are configured to mimic the size of a functional part of the design layout in the target. Due to the sub-divisions, the behavior of the target may be more similar to the functional part of the design layout, so that the overall process parameter measurement better resembles the functional part 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 generates a smaller spot than the overall target. In the overfill mode, the measurement beam generates a larger spot than the overall target. In this overfill mode, it is also possible to measure different targets simultaneously and thus determine different processing parameters simultaneously.

[0075] The overall measurement quality of the lithography parameters using a specific target is at least partially determined by the measurement scheme used to measure the lithography parameters. The term "substrate measurement scheme" may include one or more parameters of the measurement itself, one or more parameters of the one or more patterns being measured, or both. For example, if the measurement used in the substrate measurement scheme is an optical measurement based on diffraction, the one or more parameters measured may include the wavelength of the radiation, the polarization of the radiation, the angle of incidence of the radiation relative to the substrate, the radiation orientation relative to the pattern on the substrate, etc. One of the criteria for selecting the measurement scheme may be, for example, the sensitivity of one of the measurement parameters to process variations. More examples are described in U.S. Patent Application US2016-0161863 and published U.S. Patent Application US 2016 / 0370717A1, the entire contents of which are incorporated herein by reference.

[0076] The patterning process in the lithography apparatus LA is one of the most critical steps in the process, which requires high accuracy in dimensioning and placement of structures on the substrate W. To ensure this high accuracy, the three systems can be combined into a so-called "holistic" control environment, such as Figure 3. One of these systems is the lithography apparatus LA, which is (in practice) connected to a metrology tool MT (a second system) and 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 to provide a tight control loop to ensure that the patterning performed by the lithography apparatus LA stays within the process window. The process window defines the range of process parameters (e.g. dose, focus, overlay) within which a specific manufacturing process will produce a defined result (e.g. a functional semiconductor device), possibly within which process parameters of the lithography process or the patterning process are allowed to vary.

[0077] The computer system CL may use (a portion of) the design layout to be patterned to predict the resolution enhancement technique to be used, and perform computational lithography simulations and calculations to determine which mask layouts and lithography equipment settings achieve the maximum overall process window for the patterning process (in 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 (e.g. using input from a metrology tool MET) where within the process window the lithographic apparatus LA is currently operating, to predict whether defects may be present (e.g. due to suboptimal processing) Figure 3 ) is depicted by an arrow pointing to “0” in the second scale SC2.

[0078] 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 possible drifts, e.g. in the calibration state of the lithographic apparatus LA (in Figure 3 ) depicted by multiple arrows in a third scale SC3.

[0079] Many different forms of metrology tools MT can be provided for measuring structures created using lithographic patterning equipment. The metrology tool MT can use electromagnetic radiation to investigate the structure. The characteristics of the radiation (e.g. wavelength, bandwidth, power) may affect different measurement characteristics of the tool, with shorter wavelengths generally allowing for improved resolution. The wavelength of the radiation has an influence on the resolution that can be achieved by the metrology tool. Therefore, in order to be able to measure structures with small-sized features, a metrology tool MT with a short wavelength radiation source is preferred.

[0080] Another way in which the wavelength of the radiation may affect the characteristics of the measurement 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 measurements made in transmission or reflection. The type of measurement may affect whether information is obtained about the surface of the structure / substrate and / or the interior of the body. Therefore, penetration depth and opacity are additional factors to consider when selecting a radiation wavelength for a metrology tool.

[0081] In order to achieve higher resolution for measurements of lithographically patterned structures, a metrology tool MT with a short wavelength is preferred. This can include wavelengths shorter than the wavelength of visible light, such as wavelengths in the UV, EUV and X-ray parts of the electromagnetic spectrum. Hard X-ray methods, such as transmission small angle X-ray scattering (TSAXS), take advantage of the high resolution and high penetration depth of hard X-rays and can therefore be operated in transmission. On the other hand, soft X-rays and EUV do not penetrate as far into the target, but may induce a rich optical response in the material to be inspected. This may be due to the optical properties of many semiconductor materials and because the size of these structures is comparable to the detection wavelength. Therefore, the EUV and / or soft X-ray metrology tool MT can be operated in reflection, for example by imaging the lithographically patterned structures or by analyzing the diffraction pattern from the lithographically patterned structures.

[0082] For hard X-rays, soft X-rays, and EUV radiation, their use in high-volume manufacturing (HVM) applications may be limited by the lack of 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, for example, advanced X-ray tubes based on liquid metal anodes or rotating anodes) may be relatively affordable and compact, but may lack the brightness required for HVM applications. Currently, high-brightness X-ray sources such as synchrotron light sources (SLS) and X-ray free electron lasers (XFELs) exist, but their size (>100m) and high cost (hundreds of millions of euros) make them too large and expensive for metrology applications. Similarly, there is a lack of availability of sufficiently bright EUV and soft X-ray radiation sources.

[0083] exist Figure 4 An example of a metrology device such as a scatterometer is depicted in FIG. It may include 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 as a function of the wavelength λ). From this data, for example, by rigorous coupled wave analysis and nonlinear regression or by comparing Figure 4The structure or profile that produces the detected spectrum can be reconstructed by the processing unit PU for comparison with the library of simulated spectra shown at the bottom 8. Typically, for the reconstruction, the general form of the structure is known and some parameters are assumed by knowledge of the process by which the structure was manufactured, with 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.

[0084] exist Figure 5 Depicted in Figure 4 The transmitted radiation 11 is passed to the spectrometer detector 4, which measures the spectrum 6, such as for Figure 4 Such a scatterometer may be configured as a normal incidence scatterometer or an oblique incidence scatterometer. Optionally, a transmission version uses hard X-ray radiation with a wavelength <1 nm, optionally with a wavelength <0.01 nm, optionally with a wavelength <0.01 nm.

[0085] As an alternative to optical metrology methods, the use of hard X-rays, soft X-rays or EUV radiation is also contemplated, for example radiation having at least one of the following wavelength ranges: wavelength <0.01 nm, wavelength <0.1 nm, wavelength <1 nm, wavelength between 0.01 nm and 100 nm, wavelength between 0.01 nm and 50 nm, wavelength between 1 nm and 50 nm, wavelength between 1 nm and 20 nm, wavelength between 5 nm and 20 nm, and wavelength between 10 nm and 20 nm. An example of a metrology tool that functions in one of the wavelength ranges mentioned above is transmission small angle X-ray scattering (T-SAXS, as in US 2007224518A, the contents of which are incorporated herein by reference in their entirety). Profile (CD) measurements using T-SAXS are discussed in "Intercomparison between optical and X-rayscatterometry measurements of FinFET structures" by Lemaillet et al., SPIE Proceedings, 2013, p. 8681. It is 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, the entire contents of which are incorporated herein by reference. 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 stacks on substrates. Within the general field of reflectometry, goniometric and / or spectroscopic techniques can be applied. In goniometric, the variation of the reflected beam with different angles of incidence can be measured. On the other hand, spectroscopic reflectometers (using broadband radiation) measure the spectrum of wavelengths reflected at a given angle. For example, EUV reflectometers have been used to inspect mask blanks before manufacturing reticles (patterning devices) for EUV lithography.

[0086] It may be that the range of applications is not adequate for the use of wavelengths in, for example, the hard X-ray, soft X-ray or EUV domain. Published patent applications US 20130304424A1 and US2014019097A1 (Bakeman et al. / KLA) describe hybrid metrology techniques in which measurements using x-rays and optical measurements at wavelengths in the range of 120 nm and 2000 nm are combined to obtain measurements of parameters such as CD. The CD measurements are obtained by coupling an x-ray mathematical model and an optical mathematical model through one or more common points. The contents of the cited US patent applications are incorporated herein by reference in their entirety.

[0087] Figure 6 A schematic representation of a metrology device 302 is depicted, wherein the above-mentioned radiation may be used to measure parameters of structures on a substrate. Figure 6 The metrology device 302 proposed in may be adapted for hard X-ray, soft X-ray and / or EUV domains.

[0088] Figure 6 A schematic physical arrangement of a metrology apparatus 302 is illustrated by way of example only and includes a spectroscopic scatterometer using hard X-rays, soft X-rays and / or EUV radiation, optionally at grazing incidence. An alternative form of inspection apparatus may be provided in the form of an angle-resolved scatterometer which may use radiation at normal or near-normal incidence similar to conventional scatterometers operating at longer wavelengths and which may also use radiation directed at an angle greater than parallel to the substrate. or An alternative form of inspection equipment may be provided in the form of a transmission scatterometer, Figure 5 The configuration in applies to this inspection device.

[0089] 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 .

[0090] The illumination source 310 in this example is used for the generation of EUV, hard X-ray or soft X-ray radiation. The illumination source 310 may be based on Figure 6 The high harmonic generation (HHG) technique shown is a novel high-order harmonic generation (HHG) technique, and it can also be other types of irradiation sources, such as liquid metal jet sources, inverse Compton scattering (ICS) sources, plasma channel sources, magnetic undulator sources, free electron laser (FEL) sources, compact storage ring sources, discharge-generated plasma sources, soft X-ray laser sources, rotating anode sources, solid anode sources, particle accelerator sources, microfocus sources, or laser-generated plasma sources.

[0091] The HHG source can be a solid source with a solid medium for HHG generation, a gas jet / nozzle source, a capillary / fiber optic source, or a gas chamber source.

[0092] For example, for HHC sources, Figure 6As shown, 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, and 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, and when required, the infrared radiation pulses produced can last, for example, less than 1 ns (1 nanosecond) per pulse, with a pulse repetition rate of up to several megahertz. The wavelength of the infrared radiation can be in the range of 200 nm to 10µm, for example in the region of 1 μm (1 micrometer). Optionally, the laser pulses are delivered to the gas delivery system 332 as the first pump radiation 340, wherein in the gas, a portion of the radiation is converted to a higher frequency than the first radiation to become the emitted radiation 342. The gas source 334 delivers a suitable gas to the gas delivery system 332, wherein the gas is optionally ionized by a power supply 336. The gas delivery system 332 can be a cutting tube.

[0093] The gas provided by the gas delivery system 332 defines a gas target or 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 can be selectable options within the same device. The emitted radiation can contain multiple wavelengths. If the emitted radiation is monochromatic, then the measurement calculations (e.g., reconstruction) can be simplified, but it is easier to generate radiation with multiple wavelengths. The emission divergence angle of the emitted radiation can be wavelength-dependent. For example, when imaging structures of different materials, different wavelengths can provide different levels of contrast. For example, for the inspection of metal structures or silicon structures, different wavelengths can be selected as wavelengths for imaging features of (carbon-based) resists, or wavelengths for detecting contamination of such 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 fundamental frequency 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 those generated. 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, given that SXR and / or EUV radiation is absorbed when propagating in air, some or all of the beam path may be contained within a vacuum environment. Various components of the radiation source 310 and illumination optics 312 may be adjustable to enable different measurement 'regimens' within the same device. For example, different wavelengths and / or polarizations may be selectable.

[0094] Depending on the material of the structure being inspected, different wavelengths may provide the desired level of penetration into the lower layers. In order to distinguish the smallest device features and defects in the smallest device features, short wavelengths may be preferred. For example, one or more wavelengths in the range of 0.01nm to 20nm, or optionally in the range of 1nm to 10nm, or optionally in the range of 10 to 20nm can be selected. When reflected from materials of interest in semiconductor manufacturing, wavelengths shorter than 5nm may be affected by very low critical angles. Therefore, selecting a wavelength greater than 5nm can provide a stronger signal at a higher angle of incidence. On the other hand, if the inspection task is to detect the presence of a certain material, such as to detect contamination, a wavelength of up to 50nm may be useful.

[0095] The filtered beam 342 from the radiation source 310 can enter the inspection chamber 350, where the substrate W including the structure of interest is held by the substrate support 316 for inspection at the measurement position. The structure of interest is marked as T. Optionally, the atmosphere within the inspection chamber 350 can be maintained by the vacuum pump 352 not close to vacuum, so that the SXR and / or EUV radiation can pass through the environment without excessive attenuation. The irradiation system 312 has the function of focusing the radiation into a focused beam 356, and can include, for example, a two-dimensional curved mirror or a series of one-dimensional curved mirrors, as described in the published US patent application US2017 / 0184981A1 (the contents of which are incorporated herein by reference in their entirety), as mentioned above. When projected onto the structure of interest, focusing is performed to achieve a circular or elliptical spot S with a diameter of less than 10 μm. 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 to be in a desired orientation. Thus, the radiation spot S is formed on the structure of interest. Alternatively or additionally, the substrate support 316 includes, for example, a tilting platform, which can tilt the substrate W at a certain angle to control the angle of incidence of the focused beam on the structure T of interest.

[0096] Optionally, illumination system 312 provides a reference radiation beam to reference detector 314, which can be configured to measure the spectrum and / or intensity of different wavelengths in filtered beam 342. Reference detector 314 can be configured to generate signal 315, which is provided to processor 320, and the filter can include information about the spectrum of filtered beam 342 and / or the intensity of different wavelengths in the filtered beam.

[0097] 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. The 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.

[0098] 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, followed by the reflected radiation 360. Figure 6 In FIG. 3 , the plotted diffracted radiation 397 is plotted in a schematic manner, and the diffracted radiation 397 may follow many other paths than the plotted path. The inspection device 302 may also include an additional detection system 398 that detects and / or images at least a portion of the diffracted radiation 397. Figure 6 , a single further detection system 398 is depicted, 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.

[0099] 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 measurement processor 320. The measurement processor 320 may also communicate with a position controller 372 that operates a translation platform, a rotation and / or a tilt platform. 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, which may give accuracy in the picometer region. In the operation of the inspection device 302, the spectral data 382 captured by the detection system 318 is delivered to the measurement processing unit 320.

[0100] As mentioned, an alternative form of inspection apparatus uses hard X-rays, soft X-rays and / or EUV radiation, optionally at normal incidence or near normal incidence, for example to perform diffraction-based asymmetry measurements. Another alternative form of inspection apparatus uses hard X-rays, soft X-rays and / or EUV radiation, whose direction is greater than 1 / 20° from parallel to the substrate. or . Two types of inspection equipment may be provided in a hybrid metrology system. Performance parameters to be measured may include overlay (OVL), critical dimension (CD), focal length of the lithography equipment when the lithography equipment prints a target structure, coherent diffraction imaging (CDI) and resolution overlay (ARO) metrology. The hard X-ray, soft X-ray 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 specific wavelength bands, or may be of a more continuous nature.

[0101] Like 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 (development inspection or post-ADI), and / or to measure structures after they are formed in harder materials (etching inspection or post-AEI). For example, inspection device 302 can be used to inspect a substrate after it has been processed by a development device, an etching device, an annealing device, and / or other devices.

[0102] 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, visible and / or ultraviolet parts of the electromagnetic spectrum. The measurement tool MT can use radiation to measure or inspect the characteristics and various aspects of the substrate, such as a lithography exposure pattern on a semiconductor substrate. The type and quality of the measurement may depend on several characteristics of the radiation used by the measurement tool MT. For example, the resolution of the electromagnetic measurement may depend on the wavelength of the radiation, for example, due to the diffraction limit, a smaller wavelength can measure smaller features. In order to measure features with small sizes, it may be preferable to use radiation with a short wavelength, 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 that provides radiation of that / those (multiple) 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 10 nm), the source may use high harmonic generation (HHG) or any other type of source mentioned above to obtain radiation of the desired wavelength(s).

[0103] 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 harmonic generation (HHG). Figure 6 One or more features of the illumination sources in the described metrology tool may also be present in the illumination source 600 as 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 As shown. Pump radiation 611 can be directed into chamber 601 through radiation input device 605, which can be a viewport, optionally made of fused silica or similar material. Pump radiation 611 can have a Gaussian or hollow (e.g., annular) cross-sectional profile and can be incident (optionally, focused) on a gas flow 615 within chamber 601, which has 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), which is called a gas slug or gas target (e.g., a few cubic millimeters), where the gas pressure is above a specific value. Gas flow 615 can be a steady flow. Other media, such as metal plasma (e.g., aluminum plasma) can also be used.

[0104] 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 majority of the emission radiation 613 is generated is referred to as the interaction region. The interaction region may vary from tens of microns (for tightly focused pump radiation) to several millimeters or centimeters (for moderately focused pump radiation), or even up to several meters (for very loosely focused pump radiation). The gas delivery system is configured to provide a gas target for generating 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 As shown, the gas nozzle 609 includes an opening 617 located in the exit plane of the gas nozzle 609. A gas flow 615 is provided from the opening 617. The gas trap is used to confine the gas flow 615 within a certain volume by extracting the residual gas flow and maintaining a vacuum or near-vacuum atmosphere within the chamber 601. Optionally, the gas nozzle 609 can be made of a thick-walled tube and / or a high thermal conductivity material to avoid thermal deformation caused by the high-power pump radiation 611.

[0105] It is conceivable that the size of the gas nozzle 609 can also be used in scaled-up or scaled-down versions from micrometer-sized nozzles to meter-sized nozzles. This wide range of sizing comes from the fact that the setup can be scaled so that the pump radiation intensity at the gas flow ends up in a specific range, which may be beneficial for emitted radiation that needs to be sized differently for different pump radiation energies, which can be pulsed lasers and the pulse energies can vary from tens of microjoules to joules. Optionally, the gas nozzle 609 has thicker walls to reduce nozzle deformation caused by thermal expansion effects, which can be detected by, for example, a camera. A gas nozzle with thicker walls can produce a stable gas cloud with reduced variations. Optionally, the irradiation source includes a gas trap close to the gas nozzle to maintain the pressure of the chamber 601.

[0106] Due to the interaction of the pump radiation 611 with the gas atoms of the gas flow 615, the gas flow 615 may convert a portion of the pump radiation 611 into emission radiation 613, which may be Figure 6 An example of emitted radiation 342 is shown. 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, 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, or optionally in the range from 10 nm to 20 nm.

[0107] In operation, the beam of emitted radiation 613 may pass through the radiation output device 607 and may then be Figure 6 The illumination system 603 of the example of the illumination system 312 in FIG. 6 is manipulated and directed to a substrate to be inspected for metrology measurements. The emitted radiation 613 may be directed (optionally, focused) to structures on the substrate.

[0108] Because air (and indeed any gas) absorbs SXR or EUV radiation significantly, the volume or space between the gas flow 615 and the wafer to be inspected may be evacuated or nearly evacuated. Since the central axis of the emitted radiation 613 may be collinear with the central axis of the incident pump radiation 611, the pump radiation 611 may need to be blocked to prevent it from passing through the radiation output device 607 and entering the illumination system 603. This may be accomplished by placing Figure 6The filter device 344 shown is incorporated into the radiation output device 607, which is placed in the emission beam path 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 of a variety of materials such as zirconium or a multilayer combination. When the pump radiation 611 has a hollow (optionally, annular) cross-sectional profile, the filter can be a hollow (optionally, annular) block. Optionally, the filter is not perpendicular to and 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 diaphragm filter, such as an aluminum (Al) or zirconium (Zr) thin diaphragm filter. Optionally, the filter device 344 may also include a reflector that efficiently reflects the emission radiation but poorly reflects the pump radiation, or a wire mesh that efficiently transmits the emission radiation but poorly transmits the pump radiation.

[0109] Methods, apparatus and assemblies for obtaining emitted radiation, optionally at high harmonic frequencies of pump radiation, are described herein. The radiation output by the process (optionally using nonlinear effects to generate HHG radiation at harmonic frequencies of the provided pump radiation) can be provided as radiation in a metrology tool MT for inspection and / or measurement of a substrate. If the pump radiation comprises short pulses (i.e., a few cycles), the generated radiation is not necessarily exactly at harmonics of the pump radiation frequency. The substrate may be a lithographically patterned substrate. The radiation obtained by the process may also be provided in a lithographic apparatus LA and / or a lithographic cell LC. The pump radiation may be pulsed radiation, which may provide high peak intensity in a short burst time.

[0110] 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.

[0111] For some embodiments, the emitted radiation (optionally, high harmonic radiation) may include one or more harmonics of the pump radiation wavelength(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 a range less than 1 nm, a range less than 0.1 nm, a range less than 0.01 nm, a range of 0.01 nm to 100 nm, a range of 0.1 nm to 100 nm, a range of 0.1 nm to 50 nm, a range of 1 nm to 50 nm, and a range of 10 nm to 20 nm.

[0112] Radiation (such as the above-mentioned high-harmonic radiation) can be provided as source radiation in the measurement tool MT. The measurement tool MT can use the source radiation to perform measurements on the substrate exposed by the lithographic device. The measurement can be used to determine one or more parameters of the structure on the substrate. Compared with the use of longer wavelengths (such as visible radiation, infrared radiation), the use of shorter wavelength radiation (such as EUV, SXR and / or HXR wavelengths included in the above-mentioned wavelength range) 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 deep into materials such as patterned substrates, which means that deeper layers on the substrate can be measured. Radiation with longer wavelengths may not be able to reach these deeper layers.

[0113] In the measurement tool MT, source radiation may 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 a detector for detecting positive (+1) and negative (-1) first 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 for measuring other diffraction orders (e.g., higher diffraction orders).

[0114] In an example 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 transmits radiation from the HHG source to 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.

[0115] Gas target HHG configurations can be roughly divided into three separate categories: gas jet, gas chamber, and gas capillary. Figure 7An example gas jet configuration is depicted, in which a gas slug 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 slug may, for example, comprise a gas flow perpendicular to the driving radiation beam, the gas slug being enclosed in a gas chamber. In a gas capillary arrangement, the dimensions of the capillary structure holding the gas are small in the lateral direction, so that it significantly affects the propagation of the driving radiation laser beam. The capillary structure may, for example, be a hollow core fiber, in which the hollow core is configured to contain the gas.

[0116] The gas jet HHG configuration may provide a relative degree of freedom to shape the spatial profile of the driving radiation beam in the far field, since it is not subject to the constraints imposed by the gas capillary structure. The gas jet configuration may also have less stringent alignment tolerances. On the other hand, the gas capillary may provide an increased interaction zone of the driving radiation and the gaseous medium, which may optimize the HHG process.

[0117] In order to use HHG radiation, for example, in metrology applications, the HHG radiation is separated from the driving radiation downstream of the gas target. The separation of HHG and driving radiation may be different for gas jet and gas capillary configurations. In both cases, the driving radiation suppression scheme can 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 damaging the filter. The methods that can be used for this intensity reduction vary depending on the gas jet and capillary configurations. For gas jet HHG, due to the relative freedom of the shape and spatial profile (also referred to as spatial distribution and / or spatial frequency) of the driving radiation beam focused on the gas target, this can be designed so that the driving radiation beam has low intensity in the far field along the direction of propagation of the short wavelength radiation. This spatial separation in the far field means that an aperture can be used to block the driving radiation and reduce its intensity.

[0118] In contrast, in a gas capillary structure, the spatial profile of the beam as it passes through the gaseous medium may be largely determined by the capillary. The spatial profile of the driven radiation may be determined by the shape and material of the capillary structure. For example, in the case of a hollow core fiber being used as the capillary structure, the shape and material of the fiber structure determine which driven radiation modes are supported to propagate through the fiber. For most standard fibers, the supported propagation modes will produce a spatial profile where high intensity driven radiation overlaps with high intensity HHG radiation. For example, the driven radiation intensity may be centered on a Gaussian or near-Gaussian profile in the far field.

[0119] The above-mentioned apparatus may be used to obtain a signal for parameter inference using the method embodiments mentioned above and / or below, and / or to cause the execution of the method embodiments.

[0120] For model-based reconstruction, knowledge of the complete measurement system may need to be obtained via calibration measurements. These may include offline calibration of static system parameters as well as online calibration of different parameters. These parameters may be used together with sample parameters for forward modeling. These sample parameters are floating in forward modeling, some of which may be parameters of interest (POIs) such as overlay after development and / or after etching, focus, CD, (3D) edge placement error (EPE), sidewall angle, tilt angle, etch depth, height, thickness and (3D) profile measurement, while others may be nuisance parameters. The goal may be to make the simulated signal consistent with the measured signal to obtain a POI that best describes the measurement results. This approach has the following disadvantages:

[0121] A great deal of system knowledge is required to achieve agreement between simulation and measurement;

[0122] Fluctuating system parameters need to be monitored and included in the forward model;

[0123] Depends on optimization, for complex structures like nanosheets, many sample parameters (POI and interference) must be floating.

[0124] For data-driven approaches, large (convolutional) neural networks (CNNs) or variational autoencoders (VAEs) have to be trained. After training, they can interpret the measured input signals and report POIs. To be able to account for complex sample structures and measurement settings, both CNNs and VAEs may require many layers with a large number of nodes. Therefore, the number of weights that need to be trained can easily reach 10 6 . For training to be successful, a large amount of labeled reference data needs to be available. This is inherently difficult, as simple simulations are often not sufficient, as they do not cover the expected range of variations in the measurement system or sample structure. Moreover, labeled data in the form of, for example, TEM images is expensive and time-consuming. Training with suboptimal reference data may lead to incorrect POI inferences, which are difficult to detect due to the black-box nature of CNNs and VAEs. It is also possible to try to train a neural network or VAE with simulated data to supplement the available labeled reference data, but in this case the challenge of sensor calibration arises again.

[0125] In order to relax the requirement of model-based reconstruction to know the measurement system and the sample structure with very high accuracy, the following hybrid model-based reconstruction method can be used, which is based on the model-based method:

[0126] • In the sample set that can be measured, one or more POIs may be perturbed (this may also be through natural variations, such as spatial fingerprints on the substrate).

[0127] Decomposing the measurement data (e.g. detector images) using a decomposition method that identifies the main variations in the data set, such as by principal component analysis (PCA) or independent component analysis (ICA). More information on decomposition methods is given in patent application EP21209747.1, which is incorporated herein in its entirety by reference.

[0128] o Fourier analysis can also be used to access or obtain principal components, for example by deep filtering in the autocorrelation space before performing the decomposition. More information on deep filtering in the autocorrelation space technique is given in WO2021121906A1, which is incorporated herein in its entirety by reference.

[0129] o Each PCA / ICA component is associated with a change in the measured data (perturbation of the POI). If multiple parameters in the dataset vary in a correlated way, PCA may not perfectly split the signal (i.e., component 1 = groove etch, component 2 = hard mask height). But this may be acceptable. The principal components can always pick out the main changes in the signal, which may be easier to match to simulations than the original detector images.

[0130] The simulation is performed on a nominal stackup that matches the measured stackup (and optionally the measured stackup and sensor characteristics), which optionally may be actually known. The simulation may be performed using the input (geometric) parameters of the simulation. The simulation may only roughly represent the real system. For example, details such as edge rounding or line edge roughness may be missing in the simulation. Sensor characteristics (such as the spectral weighting of the illumination beam) may also be only roughly approximated in the simulation. However, the same major variations (perturbations) may be present in the simulated data set as in the measured data.

[0131] • The decomposition may be applied in the same or similar way as on the measured data (ie PCA or ICA). Note that this is in contrast to some other uses described, for example, in EP2020621A1 where PCA components derived from simulations are also used for data reduction in the measured data.

[0132] • A cost function may be calculated. The cost function may reflect the difference in projection of each individual component (optionally a PCA component) as a function of the parameter perturbation between the measured and simulated cases.

[0133]

[0134] where i is the index of the PCA component and j is the index of the perturbation. Additional weighting functions can be applied to speed up convergence.

[0135] A suitable minimization algorithm can update the input (geometric) parameters of the simulation and the process can be iterated. Thus, this optimization only looks for major changes in the measurement set, rather than a perfect match between the simulation and the detected raw data.

[0136] An iterative reconstruction procedure may be followed. In one example, at least a portion of the PCA / ICA components, such as the first or first few components, are included in the optimization. Once the model has roughly converged, one or more additional components may be added to the cost function and increasing detail may be added to the simulated samples to obtain an increasingly detailed match between the model and the measurements.

[0137] One embodiment is to use the above method to measure the lateral etch depth of a gate-all-around (GAA) transistor. GAA is an improved transistor structure in which the gate contacts the channel from all sides. GAA devices can use vertically stacked nanosheets, which are composed of individual horizontal sheets, and all sides of the vertically stacked nanosheets are surrounded by gate material. A superlattice with alternating layers of different materials (such as Si and SiGe layers) forms the basis of the nanosheet. The key step includes a channel release etch to remove the sacrificial layer, i.e., the SiGe layer. GAA can pose significant measurement challenges. An example is to measure different GAA structures with different lateral etch depths caused by different etch times. SXR is sensitive to etch depth and is able to monitor / control individual nanosheets, while most traditional techniques can only give average characteristics. SXR can also be used for hard mask height measurement, and individual etch depths can be observed with good sensitivity and parameter decorrelation.

[0138] One embodiment is to use the above method to measure the lateral etching depth of vertically stacked nanosheets and / or alternating layers with different materials. The vertically stacked nanosheets and / or alternating layers with different materials can be part of a gate-all-around (GAA) transistor, a fork-sheet transistor (also called a fork-sheet FET), and / or a complementary field effect transistor (CFET). These structures (GAA, fork-sheet transistors, and / or CFETs) may have similar profiles, which is challenging for visible light measurements. The embodiments mentioned in the present disclosure that are applicable to GAA can also be applicable to fork-sheet transistors and / or CFETs. In a fork-sheet transistor, both nFETs and pFETs can be integrated into the same structure, where dielectric walls can separate the nFETs and pFETs. CFETs may be considered a more complex version of GAA. Compared to GAA, in CFETs, nFET lines and pFET lines can be stacked on each other to reduce the active area footprint.

[0139] Embodiments may rely on variations in POIs, which may be the case where they are parameters that need to be monitored. In order to set up a rough nominal model and determine which parameters vary predominantly in the measured data, access to a limited reference data set (such as TEM or cross-sectional SEM images of actual structures) may help to pinpoint relevant varying parameters to be included in the modeling, while static or random deviations may be ignored in the modeling.

[0140] An advantage of this approach is that no detailed knowledge or monitoring of the measurement system is required, since we can assume that fluctuations due to slightly wrong setup may only appear in the higher PCA / ICA components and these are not processed further. Similar arguments apply to small sample structural deviations, which do not have to be included in the parameter sweep as they are in the full model-based reconstruction. In contrast to fully data-driven approaches, large training sets with sufficient variation are not required. Model-based reconstruction via decomposition method trend fitting enables the use of coarse models and minimal calibration of, for example, sensors.

[0141] In one embodiment, one approach is to fit the model only to measurements of the PCA coefficient behavior (ignoring mismatches between components to avoid tedious calibration).

[0142] Our results show that this method achieves excellent agreement between measurement and simulation and allows reconstruction of etch depth per measurement point and sample. The resulting fluctuations are within the expected range. The required height changes in the simulation are also consistent with the expected fluctuations on the wafer. Agreement between the simulation and the second PCA component is also observed. Both of the original methods (model-based reconstruction and data-driven reconstruction) cannot handle the problem of lack of system knowledge and limited reference data.

[0143] exist Figure 8 An example of such a hybrid model-based reconstruction method is shown in . The method includes step 801, in which radiation is irradiated onto a substrate and measurement data is measured with one or more detectors. The measurement data is associated with at least one measurement result for each of one or more structures on the substrate. The one or more structures may be perturbed structures (e.g., perturbations of one or more POIs). In the example of measuring the lateral etch depth of a GAA structure, the perturbation may be different lateral etch depths caused by different etching times. For example, the data may be measured at a pupil or image plane of the device. Additionally and / or alternatively, the data may be measured in a plane different from the pupil plane and the image plane.

[0144] The method comprises step 803 of decomposing the measurement data using a decomposition method to obtain a plurality of measurement data components. The decomposition method identifies the main changes in the measurement data. For example, the decomposition method is principal component analysis (PCA) or independent component analysis (ICA). Optionally, Fourier analysis may also be used to access or obtain the principal components, for example by deep filtering in the autocorrelation space before performing the decomposition. Each PCA / ICA component is associated with a change in the measurement data (i.e., a perturbation of one or more POIs).

[0145] The method comprises a step 805 of projecting at least a part of the components of the measurement data generated by the decomposition method, for example the first or first few components, onto the detector signal.

[0146] The method comprises step 811 of inputting nominal parameters and performing simulation on a nominal stackup. The nominal parameters may comprise stackup parameters and / or device (eg sensor, detector and optics) parameters.

[0147] The method includes step 813, step 813 based on the one or more structures to obtain simulation data related to at least one simulation. In step 813, multiple perturbed stacks (e.g., perturbations of one or more POIs) can be simulated to obtain simulation data. In the example of measuring the lateral etch depth of the GAA structure, the perturbation may be different lateral etch depths. In one example, the simulation is further performed based on at least one measurement result.

[0148] The method comprises step 815, step 815 uses a decomposition method to decompose the simulation data to obtain a plurality of simulation data components. The decomposition can be performed in the same or similar manner as in step 803.

[0149] The method includes step 817, which projects at least a portion of the components of the analog data generated by the decomposition method (e.g., the first or first several components) onto the detector signals. Step 817 can be performed in the same or similar manner as in step 805. Optionally, step 817 is to project, for example, at least a portion of the PCA components (e.g., the first or first several PCA components) onto the four detector signals.

[0150] The method comprises a step of calculating a cost function 807. The cost function may reflect the difference in projection of each individual component as a function of the parameter perturbation between the measured data and the simulated data.

[0151] The method includes step 809 of obtaining an average projection of each parameter perturbation. In the example of measuring the lateral etching depth of the GAA structure, step 807 may obtain an average projection of each lateral etching depth.

[0152] The method comprises a step 819 of shifting and / or scaling the components to achieve an optimal match between the results of the simulated data and the measured data.

[0153] The method includes step 810 of matching between at least a portion of the simulated data components (eg, a first or first few simulated data components) and at least a portion of the measured data components.

[0154] The method comprises a step 823 of evaluating the least squares cost of the fit, optionally evaluating the least squares cost of the fit at 4 points.

[0155] The method comprises a step 821 of updating the input (geometric) parameters for the simulation using a suitable minimization algorithm. The process may be iterated via an iterative reconstruction procedure, for example feeding the result of step 823 to step 813.

[0156] exist Figure 8 After the step of inferring / extracting the characteristics of the substrate. The characteristics may be POI. The characteristics may be parameters of the manufacturing process mentioned above, optionally, parameters of the semiconductor manufacturing process, optionally, parameters of the lithography process and / or etching process. The characteristics of the substrate include parameters / characteristics of the structures on the substrate.

[0157] exist Figure 8 In the embodiment, all steps are optional, and at least a part of the steps can be selected for measurement application. For example, one or more of steps 809, 810, 811 and 819 can be optional and can be skipped.

[0158] Fig. 8A An example method of such a hybrid model-based reconstruction is shown, with optional steps removed. As an alternative to step 801 of irradiating radiation onto a substrate to obtain measurement data, the measurement data may be obtained by other means. The method includes step 803 of decomposing the measurement data using a decomposition method to obtain a plurality of measurement data components. Simulation data is also obtained 813, and the simulation data is decomposed 815, as described above. In step 807, a cost function is calculated for the measurement data components and the simulation data components so as to match at least a portion of the simulation data components with at least a portion of the measurement data components. The matching may be included in step 807 and need not be performed in step 808. Figure 8 The matching is performed in a separate step 810 of . Based on the cost function, the simulation data can be shifted and scaled to achieve a closer match.

[0159] Figure 8B A further exemplary method is shown which includes some method steps which can be used as Figure 8 and8A In step 802, measurement data is obtained. The measurement data is decomposed according to the above step 803. As explained above, simulation data is obtained 813 in a similar manner and decomposed 815. A cost function is determined 807 and used to update (e.g., scale and / or shift) the simulation data to achieve matching. Then, the updated simulation data can be provided as the obtained data, and the updated simulation data can be decomposed for further matching. This can be an iterative process. Once the matching process is completed, one or more features of the substrate and / or the structure on the substrate can be extracted 825 based on the matched simulation data.

[0160] The process of calculating the cost function and updating the simulated data components to match the simulated data components with the measured data components (ie, by reducing the cost function) can be an iterative process, such as by Figure 8 , 8A and as depicted by the arrows in 8B. The matching process may include using a minimization algorithm to reduce the size of the cost function by adapting / scaling the simulation data.

[0161] In one embodiment, the radiation used to obtain the measurement data (measurement radiation) may include one or both of hard X-ray or soft X-ray radiation. That is, the measurement radiation may include one or more wavelengths in the range of 0.01 nm to 50 nm, optionally one or more wavelengths in the range of 0.01 nm to 20 nm, optionally one or more wavelengths in the range of 1 nm to 10 nm, and optionally one or more wavelengths in the range of 10 nm to 20 nm. For the above method, the advantages of using HXR and / or SXR compared to light with longer wavelengths (such as visible light) are: 1. For light with shorter wavelengths, the signal information is richer because there is a propagation order, which helps to obtain higher sensitivity; 2. HXR and / or SXR signals have excellent decorrelation, and HXR and / or SXR signals do not work well in the visible light region.

[0162] The measurement data may include diffracted radiation captured by one or more detectors. The radiation may have been diffracted by a structure on the substrate to be measured. The radiation may have been diffracted in reflection, in transmission, or in reflection and transmission. In some examples, the structure may be a measurement target. The structure may include one or more diffraction gratings.

[0163] One embodiment may include a computer-readable recording medium having stored thereon a computer program product, the computer program product including machine-readable instructions for causing a processor to perform the control steps of the above-mentioned method. One embodiment may include a non-transitory computer program product including machine-readable instructions therein, which, when executed by a computer system, are configured to cause the computer system to at least cause the execution of the above-mentioned method. One embodiment may include a computer program, the computer program comprising one or more machine-readable instruction sequences, which describe an optical measurement method, a POI inference method, a model-based reconstruction method, and / or a method of analyzing measurements to obtain information about a lithography process or other manufacturing process. One embodiment may include a computer code, the computer code comprising one or more machine-readable instruction or data sequences describing the method. The method may be implemented, for example, in a computer program. Figure 6 The unit MPU and / or Figure 3 The computer program or code is executed in the control unit CL. A data storage medium (such as a semiconductor memory, a magnetic disk or an optical disk, etc.) in which such a computer program or code is stored may also be provided. Figure 6 In the case where a device (of the type shown) is already in production and / or in use, an embodiment of the present invention may be implemented by providing an updated computer program product for causing a processor to perform one or more of the methods described herein. The computer program or code may optionally be arranged to control an optical system, a substrate support, etc., to perform a method of measuring parameters of a lithography process or other manufacturing process on a suitable plurality of targets. The computer program or code may update the lithography or other manufacturing process and / or measurement scheme to measure other substrates. The computer program or code may be arranged to (directly or indirectly) control a lithography apparatus or other manufacturing equipment (e.g., an etcher) to pattern and process other substrates.

[0164] The illumination source may be arranged, for example, in the metrology apparatus MT, the inspection apparatus, the lithographic apparatus LA and / or the lithocell LC.

[0165] The characteristics of the emitted radiation used to perform the measurement may affect the quality of the measurement results 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 the source provide radiation with characteristics that result in high quality measurements.

[0166] For the sake of clarity, the method mentioned in EP2020621A1 is as follows Fig.10 As shown in the flowchart. Fig.11An embodiment of the present invention is also shown as a second flow chart for comparison only. In an embodiment of the present invention, when the same decomposition method is used on different data, different data components, i.e., different decomposition results, are obtained. The decomposition result is defined as the result of applying the decomposition method to the data. In EP2020621A1, the decomposition of the measured spectrum (i.e., the measurement data) is based on the principal components (i.e., the data components) of the calculated spectrum (i.e., the simulation data), rather than decomposing the measured spectrum and the calculated spectrum independently of each other. In EP2020621A1, the same data components, i.e., the same decomposition results, are obtained, which means that there is no mismatch / difference between the simulated data components and the measured data components. There is a significant difference between the method mentioned in EP2020621A1 and an embodiment of the present invention: an embodiment of the present invention allows the measurement results to be matched with an inaccurate model by absorbing errors in the decomposition components of the simulation data.

[0167] A signal (such as an image) can be decomposed using a singular value decomposition (SVD) of the covariance matrix. SVD can decouple the relevant parts of the signal from the noise and / or interference parameters present in the signal or image. The principal components of the SVD (i.e., the components associated with the largest singular values) can be assigned to the parts of the signal that explain the largest signal variations. For example, if the main variation in the design of the measurement is the overlap (OVL) variation, the first (largest) component of the SVD is likely to involve variations associated with the overlap OVL. This may be because overlap may be the largest factor in signal variation. This first component can then be used to infer knowledge about the signal or image. SVD can also be used to clean up the signal.

[0168] Although SVD is a powerful tool, it does not use a priori information. For example, one source of a priori information about the image is the relationship between the detector pixels. SVD can consider the correlation of all pixels on the detector that provide the signal to be decomposed. The correlation between pixels can be considered regardless of the position of these pixels relative to each other. However, in reality, some pixels on the detector may have a higher correlation with some pixels than other pixels (such as neighboring pixels, pixels along a periodically repeating pattern of the relevant frequency, etc.). It is expected to improve the SVD method by considering physical information about the signal. Therefore, this paper proposes to consider only the elements of the covariance matrix whose relationship is expected based on the physical characteristics of the measurement setup. As a simple example, one can only observe pixels in the recorded diffraction pattern that may be associated with peaks of a known spectrum of the applied radiation source. It can be assumed that any pixels that are not associated with these peaks only record noise and can be discarded a priori in the decomposition.

[0169] An example case where a priori information can be used to determine correlation is as follows: An example soft X-ray (SXR) tool can expose a target using multiple wavelengths simultaneously. Such multi-wavelength exposure may be possible, for example, due to the use of a HHG source, which produces a series of high-order harmonics of the drive laser between about 12 nm and 18 nm, thereby producing an information-rich signal. Therefore, broadband SXR radiation can provide depth information in a single shot. The radiation from the source can be diffracted from the target structure, where the diffraction angle can depend on the wavelength of the radiation. Due to this wavelength-dependent positioning, the spot observed on the (pixelated) detector can be related to the harmonic and the wavelength. A pair of diffraction orders associated with a specific wavelength is only related to each other, but not to other diffracted radiation. In addition, diffraction signals associated with different harmonics (i.e., wavelengths) should not be correlated. Therefore, considering such physics-based information can be beneficial in decomposing the signal incident on the full detector. More generally, a priori information about the detector, radiation, structure, substrate and / or any other characteristics of the setup may provide advantages when taken into account when performing the decomposition.

[0170] A method for adapting a singular value decomposition of measurement data using a weight matrix representing physical information related to a metrology setup used to obtain the measurement data is described herein. The physical information may also be referred to as a characteristic of at least one measurement result.

[0171] Fig. 9 A method is depicted, including obtaining 902 measurement data associated with at least one measurement result made by a measurement device configured to irradiate radiation onto each of one or more structures on a substrate. In step 904, a weight matrix may be determined based on one or more characteristics of the at least one measurement result. The weight matrix may be applied 906 to the measurement data. Applying the weight matrix to the measurement data may add correlation to the measurement data based on one or more characteristics of the at least one measurement result. In step 908, the measurement data is decomposed using a decomposition method (as set forth above) to obtain a plurality of measurement data components. Simulation data associated with at least one simulation based on the one or more structures is obtained 910, and the simulation data is decomposed 912 using the same decomposition method as the measurement data to obtain a plurality of simulation data components. In step 914, a matching process between at least a portion of the simulation data component and at least a portion of the measurement data component is performed. Once at least a portion of the simulation data component matches at least a portion of the measurement data component, features of the substrate may be extracted 916 based on the match. The features may be extracted based on the matched simulation data components and / or based on the simulation data.

[0172] The characteristics of the at least one measurement result may include one or more measurement parameters of measurement data related to a structure on a substrate. One or more measurement parameters of the measurement data may include, for example, coherence information, overlap, leveling, profilometry, alignment, critical dimension, dose, and / or polarization for radiation parameters. One or more measurement parameters may include, for example, one or more characteristics of a measurement device. One or more measurement parameters may include, for example, one or more characteristics of radiation for irradiating the substrate. One or more characteristics of the radiation may include, for example, wavelength, intensity distribution, and / or beam shape. One or more measurement parameters may include, for example, one or more characteristics of the substrate.

[0173] After applying the weight matrix to the measurement data as described above, the weight matrix may also be applied to the simulation data in a corresponding manner. That is, the weight matrix may be applied to the simulation data before decomposing the simulation data.

[0174] Applying the weight matrix to the singular value decomposition may include: determining the covariance matrix of the measurement data, and applying the weight matrix to the covariance matrix to obtain a weighted covariance matrix. Then, the singular value decomposition may be performed on the weighted covariance matrix so as to obtain a plurality of measurement data components based on the singular value decomposition.

[0175] An example singular value decomposition considering one or more characteristics of the measurement result may be performed as follows:

[0176] 1) , where X = measurement data, = average value

[0177] The data is centered around the average value by Z,

[0178] 2)

[0179] Create a covariance matrix C from the centered data

[0180] 3) = the nearest PD(C·W)

[0181] Determine the weight matrix W and apply it to the covariance matrix. Select the nearest positive definite matrix as .

[0182] 4)

[0183] Perform a singular value decomposition on .

[0184] 5) , where K < N, and N = size(U)

[0185] Select K eigenvectors

[0186] 6)

[0187] Define the components of the decomposition based on the selected eigenvectors.

[0188] In the example decomposition above, step 3 adds weighting based on the weight matrix above.

[0189] Extracting features 825, 916 based on matched simulated data and / or simulated data components may be based on one or more global and / or local optimization algorithms. This may be based, for example, on an algorithm for minimizing a cost function to achieve matching of measured and simulated data components.

[0190] In an example implementation, overlapping measurement data may be obtained, and simulated overlapping data may be obtained. The simulated data may be obtained by a simulation that has been set to represent the measurement results by which the measurement data was obtained. The measurement data may include diffracted radiation captured by one or more detectors. Accordingly, the simulation may output simulated diffraction images, which may form the simulated data. In an example arrangement, the diffraction pattern may form a single line (e.g., a horizontal line) along which different diffraction orders are placed side by side. In some instances, the line may be curved. The curvature of the line may be gentle, such that the diffraction pattern maintains a dominant direction.

[0191] The simulated data can be preprocessed using standard methods. In one example preprocessing step, the two-dimensional (2D) simulated diffraction image can be reduced to one-dimensional (1D) data. For example, this can be achieved by summing the 2D diffraction image along a vertical axis. The sum can be the intensity of the diffracted radiation along the vertical direction of the 2D image. In this context, vertical can be understood as in a direction perpendicular to the dominant direction (horizontal) that forms the diffraction pattern.

[0192] In a 1D representation, there may be peaks. Based on physical knowledge of diffraction patterns, different peaks may be associated with different wavelengths and / or wavelength ranges. If a singular value decomposition is performed on a 1D image, then every pixel / data point of the 1D representation will be considered to be correlated with every other pixel / data point in the 1D representation. This means that the decomposition will be performed based on the covariance between every pixel in the signal. However, based on physical knowledge of diffraction patterns, we know that some areas are correlated while other areas are not (for example, pairs of positive and negative diffraction orders on opposite sides of the zero diffraction order may be correlated). It may be preferable to perform a decomposition that only considers correlations between pixels that may be correlated.

[0193] In order to use the physical knowledge about the diffraction pattern, a weight matrix can be determined that mathematically captures the relationship between the related orders along the position in the 1D representation. This weight matrix can be applied to the covariance matrix, for example, by a dot product. The rest of the SVD process can be performed as usual. The resulting decomposition can take into account the position-based correlation of the diffraction orders.

[0194] One possible advantage of adding prior knowledge of the diffraction signal to the decomposition method is that the interpretation of the data can be facilitated. Adding prior knowledge and the resulting interpretability as proposed herein can allow selection of only the signal portions corresponding to one or more points of interest in the data. This can aid in denoising the data. Additionally and / or alternatively, this can aid in analyzing small-scale effects, for example, by suppressing large changes visible in the data and focusing on one or more smaller changes of interest. For example, large changes may be changes associated with changes in interference parameters.

[0195] The improved relevance of the weighted covariance can mean that more meaningful inferences can be performed on the simplified / cleaned signal. The resulting improved decomposition can be used for signal interpretation. For example, the components that contribute the most can provide an indication of which effects are the strongest. These can be called principal components. These principal components can be (mathematically) inverted back to detector space, where they can be interpretable. Based on this interpretation, inferences can be performed on features of the measurement data, such as feature extraction.

[0196] Further embodiments are disclosed in the subsequent numbered clauses:

[0197] 1. A measurement method, comprising:

[0198] obtaining measurement data associated with at least one measurement result of a measurement device configured to irradiate radiation onto each of one or more structures on the substrate;

[0199] decomposing the measurement data using a decomposition method to obtain a plurality of measurement data components;

[0200] obtaining simulation data associated with at least one simulation based on the one or more structures;

[0201] decomposing the simulation data using a decomposition method to obtain a plurality of simulation data components;

[0202] matching between at least a portion of the simulated data component and at least a portion of the measured data component; and

[0203] A feature of the substrate is extracted based on a match of at least a portion of the simulated data component and at least a portion of the measured data component.

[0204] 2. The metrology method of clause 1, wherein the one or more substrates comprise vertically stacked alternating layers of different materials.

[0205] 3. The metrology method of clause 1 or 2, wherein the one or more structures comprise a nanosheet structure; and optionally, wherein the nanosheet structure is included in a gate-all-around (GAA) transistor, a fork-sheet transistor and / or a complementary field effect transistor (CFET).

[0206] 4. A metrology method according to any preceding clause, wherein the characteristic is a parameter of a semiconductor manufacturing process, optionally, the characteristic is a parameter of a lithography process and / or an etching process.

[0207] 5. A metrology method according to any preceding clause, wherein the feature comprises a lateral etch depth.

[0208] 6. A metrology method according to any preceding clause, wherein the step of matching between at least a portion of the analog data component and at least a portion of the measured data component further comprises: adding one or more components from both the analog data component and the measured data component to the matching.

[0209] 7. A method of measurement according to any preceding clause, further comprising:

[0210] Radiation is directed onto the substrate.

[0211] 8. A method according to any of the preceding clauses, wherein the simulation is further performed based on the at least one measurement result.

[0212] 9. A method according to any preceding clause, wherein the matching between at least a portion of the simulated data component and at least a portion of the measured data component comprises an iterative process.

[0213] 10. A method according to any preceding clause, wherein matching between at least a portion of the simulated data component and at least a portion of the measured data component comprises using a minimization algorithm.

[0214] 11. A method according to any of the preceding clauses, wherein the measurement data includes diffracted radiation.

[0215] 12. A method according to clause 11, wherein the diffracted radiation has been diffracted in reflection and / or in transmission by the one or more structures on the substrate.

[0216] 13. A method according to any of the preceding clauses, wherein the decomposition method includes Fourier analysis.

[0217] 14. A method according to any of the preceding clauses, wherein deep filtering in the autocorrelation space is performed before performing the Fourier analysis.

[0218] 15. A method according to any of the preceding clauses, wherein the radiation comprises one or more wavelengths in the range of 0.01 nm to 50 nm, optionally one or more wavelengths in the range of 0.01 nm to 20 nm, optionally one or more wavelengths in the range of 1 nm to 10 nm, and optionally one or more wavelengths in the range of 10 nm to 20 nm.

[0219] 16. A method according to any preceding clause, further comprising:

[0220] determining a weight matrix based on one or more characteristics of the at least one measurement; and

[0221] A weight matrix is ​​applied to the measurement data, wherein applying the weight matrix to the measurement data adds relevance to the measurement data based on the one or more characteristics of the at least one measurement result.

[0222] 17. A method according to clause 16, wherein the one or more characteristics of the at least one measurement result comprise one or more measurement parameters of measurement data.

[0223] 18. The method of clause 17, wherein the one or more measurement parameters include overlay, leveling, profilometry, alignment, critical dimension, focus and / or dose.

[0224] 19. A method according to any of clauses 16 to 18, wherein the one or more characteristics of the at least one measurement result comprise one or more characteristics of a measuring device.

[0225] 20. A method according to any of clauses 16 to 19, wherein the one or more characteristics of the at least one measurement result comprise one or more characteristics of radiation used to irradiate the substrate.

[0226] 21. A method according to clause 20, wherein the one or more characteristics of the radiation include wavelength, intensity distribution and / or beam shape.

[0227] 22. A method according to any of clauses 16 to 21, wherein the one or more characteristics of the at least one measurement result include one or more characteristics of the substrate.

[0228] 23. A method according to any one of clauses 16 to 22, further comprising:

[0229] Apply the weight matrix to the simulated data.

[0230] 24. The method of any one of clauses 16 to 23, wherein the decomposing method comprises:

[0231] Determine the covariance matrix of the measurement data;

[0232] Applying the weight matrix to the covariance matrix to obtain a weighted covariance matrix;

[0233] performing a singular value decomposition on the weighted covariance matrix; and

[0234] A plurality of measurement data components are obtained based on singular value decomposition.

[0235] 25. A measurement method, comprising:

[0236] directing radiation onto the substrate;

[0237] obtaining measurement data associated with at least one measurement result for each of one or more structures on the substrate;

[0238] decomposing the measurement data using a decomposition method to obtain a plurality of measurement data components;

[0239] obtaining simulation data associated with at least one simulation based on the one or more structures;

[0240] decomposing the simulation data using a decomposition method to obtain a plurality of simulation data components;

[0241] matching between at least a portion of the simulated data component and at least a portion of the measured data component; and

[0242] Features of the substrate are extracted based on the simulation data.

[0243] 26. A non-transitory computer program product comprising machine-readable instructions therein, which, when executed by a computer system, are configured to cause the computer system to at least cause performance of the method of any one of clauses 1 to 25.

[0244] 27. A processor and associated storage medium, the storage medium comprising the non-transitory computer program of clause 26, such that the processor is operable to perform the method of any one of clauses 1 to 25.

[0245] 28. A measurement device comprising the processor of clause 27 and associated storage medium so as to be operable to perform the method of any one of clauses 1 to 25.

[0246] 29. A lithocell comprising a processor as claimed in clause 27 and associated storage medium so as to be operable to perform the method as claimed in any one of clauses 1 to 25.

[0247] Although specific reference may be made herein to the use of lithographic apparatus in the manufacture of ICs, it should be understood that the lithographic apparatus described herein may have other applications. Possible other applications include the manufacture of integrated optical systems, flat panel displays, liquid crystal displays (LCDs), thin film magnetic heads, guidance and detection patterns for magnetic domain memories, and the like.

[0248] Although embodiments may be specifically referenced herein 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 objects such as wafers (or other substrates) or masks (or other pattern forming devices). These apparatuses may generally be referred to as lithographic tools. Such lithographic tools may use vacuum conditions or ambient (non-vacuum) conditions.

[0249] Although embodiments may be specifically referenced herein in the context of inspection or measurement 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 objects such as a wafer (or other substrate) or a mask (or other pattern forming device). The term "measurement equipment" (or "inspection equipment") may also refer to an inspection equipment or an inspection system (e.g., a measurement equipment or a measurement 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 specific portion of the structure, or the presence of an unwanted structure on the substrate.

[0250] Although specific reference may have been made above to the use of embodiments in the context of optical lithography, it will be appreciated that the invention is not limited to optical lithography and may be used in other applications, such as imprint lithography, where the context permits.

[0251] While the targets or target structures (more generally, structures on a substrate) described above are metrology target structures that are specifically designed and formed for measurement purposes, in other embodiments, the characteristic of interest may be measured on one or more structures that are a functional part of a device formed on a substrate. Many devices have a regular, grating-like structure. The terms "structure," "target grating," and "target structure" as used herein do not require that the structure has been provided specifically for the measurement being performed. Further, 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 lithographic 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 of similar size to the non-target structures.

[0252] Although specific embodiments have been described above, it will be appreciated that the present invention may be practiced in a manner other than as described. The above description is intended to be illustrative, not restrictive. Therefore, it will be apparent to those skilled in the art that modifications may be made to the described invention without departing from the scope of the claims set forth below.

[0253] 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 structures on a substrate or 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 wafer. 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 or wafer.

[0254] Although specific reference is made to HXR, SXR and EUV electromagnetic radiation, it will be appreciated that the invention may be practiced with all electromagnetic radiation, including radio waves, microwaves, infrared, (visible) light, ultraviolet, X-rays and gamma rays, where the context permits.

[0255] Although specific embodiments have been described above, it will be appreciated that one or more features of one embodiment may also be present in a different embodiment, and features of two or more different embodiments may also be combined together.

Claims

1. A measurement method, comprising: obtaining measurement data associated with at least one measurement result of a measurement device configured to irradiate radiation onto each of one or more structures on the substrate; Decomposing the measurement data using a decomposition method to obtain a plurality of measurement data components; obtaining simulation data associated with at least one simulation based on the one or more structures; decomposing the analog data using the decomposition method to obtain a plurality of analog data components, wherein there is a mismatch between the analog data components and the measured data components; matching between at least a portion of the simulated data component and at least a portion of the measured data component; as well as A feature of the substrate is extracted based on the matching of at least a portion of the simulation data component and at least a portion of the measurement data component.

2. The measuring method according to claim 1, wherein: The one or more substrates include vertically stacked alternating layers of different materials.

3. The measuring method according to claim 1 or 2, wherein: The one or more structures include a nanosheet structure; and optionally, wherein the nanosheet structure is included within a gate-all-around (GAA) transistor, a forksheet transistor, and / or a complementary field effect transistor (CFET).

4. A measurement method according to any preceding claim, wherein: The characteristics are parameters of a semiconductor manufacturing process, optionally parameters of a lithography process and / or an etching process.

5. A measurement method according to any preceding claim, wherein: The feature includes a lateral etch depth.

6. A method according to any one of the preceding claims, wherein: The simulation is further performed based on the at least one measurement result.

7. A method according to any one of the preceding claims, wherein: The matching between at least a portion of the simulated data component and at least a portion of the measured data component comprises an iterative process.

8. A method according to any one of the preceding claims, wherein: The matching between at least a portion of the simulated data component and at least a portion of the measured data component comprises using a minimization algorithm.

9. A method according to any one of the preceding claims, wherein: The measurement data comprises diffracted radiation.

10. The method according to any one of the preceding claims, wherein: The decomposition method includes Fourier analysis.

11. A method according to any one of the preceding claims, wherein: A deep filtering in the autocorrelation space is performed before performing the Fourier analysis.

12. The method according to any preceding claim, further comprising: determining a weight matrix based on one or more characteristics of the at least one measurement; as well as The weight matrix is ​​applied to the measurement data, wherein applying the weight matrix to the measurement data adds relevance to the measurement data based on the one or more characteristics of the at least one measurement result.

13. A non-transitory computer program product, comprising machine-readable instructions therein, which, when executed by a computer system, are configured to cause the computer system to at least cause the execution of the method of any one of claims 1 to 12.

14. A processor and associated storage medium, the storage medium comprising the non-transitory computer program of claim 13, enabling the processor to operate to perform the method of any one of claims 1 to 12.

15. A measurement device comprising the processor of claim 14 and an associated storage medium, so as to be operable to perform the method of any one of claims 1 to 12.

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