Measurement method and device, computer program and lithography system

Abstract

A method, computer program, and associated apparatus for metrology are disclosed. The method includes determining whether a substrate or a portion of a substrate is subject to a process effect. The method includes: acquiring inspection data, the inspection data including a plurality of sets of measurement data associated with a structure on the substrate or a portion thereof; for example, measuring a pupil; and acquiring fingerprint data, the fingerprint data describing spatial variations of a parameter of interest. An iterative mapping of the inspection data to the fingerprint data is performed. Whether the structure is subject to a process effect is based on the degree to which the iterative mapping converges to a solution.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Application No. 62 / 821,186 filed on March 20, 2019, U.S. Application No. 62 / 828,817 filed on April 3, 2019, and European Application No. 19171061.5 filed on April 25, 2019, which are incorporated herein by reference in their entirety. Technical Field

[0003] The present invention relates to methods and apparatus useful for metrology of devices manufactured, for example, by photolithographic techniques. Background Art

[0004] A lithographic apparatus is a machine that applies a desired pattern to a substrate, typically to a target portion of a substrate. A lithographic apparatus can be used, for example, in the manufacture of an integrated circuit (IC). In this case, a pattern forming device (which is alternatively referred to as a mask or reticle) can be used to generate a circuit pattern to be formed on a single layer of an IC. The pattern can be transferred to a target portion (e.g., a portion including one or more dies) on a substrate (e.g., a silicon wafer). The transfer of the pattern is usually performed via imaging onto a layer of radiation-sensitive material (resist) disposed on the substrate. Typically, a single substrate will contain a network of adjacent target portions that are patterned continuously. In the lithographic process, it is often necessary to measure the created structure, for example, for process control and verification. Various tools for performing such measurements are known, including scanning electron microscopes that are typically used to measure critical dimensions (CDs), and special tools for measuring overlay, which is a method for measuring the accuracy of alignment of two layers in a device. Overlay can be described in terms of the degree of misalignment between two layers, for example, a reference to a measurement overlay of 1 nm can describe a situation where the two layers are misaligned by 1 nm.

[0005] Recently, various forms of scatterometers have been developed for use in the field of lithography. These devices direct a radiation beam onto a target and measure one or more properties of the scattered radiation—for example, intensity at a single reflection angle as a function of wavelength; intensity at one or more wavelengths as a function of reflection angle; or polarization as a function of reflection angle—to obtain a diffraction image or pattern from which properties of interest of the target can be determined.

[0006] In order to cause the radiation impinging on the substrate to be diffracted, an object of a specific shape is printed onto the substrate and is often referred to as a scatterometry target or simply a target. As described above, the actual shape of the scatterometry object can be determined using a cross-sectional scanning electron microscope or the like. However, this involves a lot of time, effort and dedicated equipment and is not well suited for measurements in a production environment, as separate dedicated equipment is required that is consistent with the normal equipment in, for example, a lithography cell.

[0007] The substrate may be subject to process effects and thus have one or more process fingerprints or background fingerprints, which may result in, for example, undesired and undesigned asymmetries in structures on the substrate. These process fingerprints may affect the functionality of the device directly (e.g., film thickness-dependent CDU affecting gate clock frequency) or indirectly (e.g., negatively affecting control measurements used to control and monitor the manufacturing process). For example, in an overlay control loop, it is difficult to separate these process fingerprints from the overlay.

[0008] Therefore, it is desirable to obtain more information about such process fingerprints. Summary of the invention

[0009] In a first aspect, the present invention provides a method for determining whether a substrate or a portion of a substrate is affected by a process effect, the method comprising: acquiring inspection data including multiple sets of measurement data associated with structures on the substrate or a portion thereof; acquiring fingerprint data describing spatial variations of a parameter of interest on the substrate or a portion thereof; performing an iterative mapping of the inspection data to the fingerprint data; and determining whether the substrate is affected by the process effect based on the degree to which the iterative mapping converges to a solution.

[0010] The present invention provides in a second aspect a metrology apparatus operable to perform the method of the first aspect.

[0011] The invention also provides a computer program comprising processor readable instructions which, when run on a suitable processor controlled device, cause the processor controlled device to perform the method of the first aspect, and a computer program carrier comprising such a computer program. The processor controlled device may comprise the measurement device of the second aspect.

[0012] The other features and advantages of the present invention, as well as the structure and operation of various embodiments of the present invention are described in detail below with reference to the accompanying drawings. Note that the present invention is not limited to the specific embodiments described herein. Such embodiments are presented herein for illustrative purposes only. Based on the teachings contained herein, other embodiments will be clear to those skilled in the relevant art. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Embodiments of the present invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

[0014] Figure 1 depicts the lithographic apparatus;

[0015] Figure 2 The lithographic unit is depicted;

[0016] Figure 3 The first scatterometer is depicted;

[0017] Figure 4 A second scatterometer is depicted;

[0018] Figure 5 is a flow chart depicting an example process for reconstructing a structure from scatterometer measurements; and

[0019] Figure 6 is a flow chart describing an in-device measurement method used in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] Before describing embodiments of the present invention in detail, it is helpful to present an example environment in which embodiments of the present invention may be implemented.

[0021] Figure 1 A lithographic apparatus LA is schematically depicted. The apparatus comprises an illumination optical system (illuminator) IL configured to condition a radiation beam B (e.g., UV radiation or DUV radiation), a patterning device support or support structure (e.g., mask stage) MT configured to support a patterning device (e.g., mask) MA and connected to a first positioner PM configured to accurately position the patterning device according to certain parameters, a substrate stage (e.g., wafer stage) WT configured to hold a substrate (e.g., a resist-coated wafer) W and connected to a second positioner PW configured to accurately position the substrate according to certain parameters, and a projection optical system (e.g., 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.

[0022] The illumination optics system may include various types of optical components for directing, shaping, or controlling radiation, such as refractive, reflective, magnetic, electromagnetic, electrostatic, or other types of optical components, or any combination thereof.

[0023] The patterning device support holds the patterning device in a manner that depends on the orientation of the patterning device, the design of the lithographic apparatus, and other conditions (e.g., whether the patterning device is held in a vacuum environment). The patterning device support can hold the patterning device using mechanical, vacuum, electrostatic, or other clamping techniques. The patterning device support can be, for example, a frame or table that can be fixed or movable as desired. The patterning device support can ensure that the patterning device is in a desired position, for example relative to a projection system. Any use of the term "reticle" or "mask" herein may be considered synonymous with the more general term "patterning device."

[0024] The term "patterning device" as used herein should be broadly interpreted as referring to any device that can be used to impart a pattern to a cross-section of a radiation beam so as to create a pattern in a target area of ​​the substrate. It should be noted that the pattern imparted to the radiation beam may not exactly correspond to the desired pattern in the target portion of the substrate, for example if the pattern includes phase-shifting features or so called assist features. Typically, the pattern imparted to the radiation beam will correspond to a specific functional layer in a device created in the target portion such as an integrated circuit.

[0025] The patterning device may be transmissive or reflective. Examples of patterning devices include masks, programmable mirror arrays, and programmable LCD panels. Masks are well known in lithography and include mask types such as binary, alternating phase shift, and attenuated phase shift, as well as various hybrid mask types. An example of a programmable mirror array employs a matrix arrangement of small mirrors, each of which can be individually tilted to reflect an incoming radiation beam in different directions. The tilted mirrors impart a pattern in the radiation beam, which is reflected by the matrix of mirrors.

[0026] As depicted herein, the device is transmissive (eg, employing a transmissive mask). Alternatively, the device may be reflective (eg, employing a programmable mirror array of the type described above, or employing a reflective mask).

[0027] The lithographic apparatus may also 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 (e.g., water) to fill the space between the projection system and the substrate. Immersion liquid may also be applied to other spaces in the lithographic apparatus, such as between the mask and the projection system. Immersion techniques for increasing the numerical aperture of the projection system are well known in the art. The term "immersion" as used herein does not mean that structures such as the substrate must be submerged in the liquid, but only that the liquid is located between the projection system and the substrate during exposure.

[0028] refer to Figure 1, the illuminator IL receives a radiation beam from a radiation source SO. The source and the lithographic apparatus may be separate entities, for example when the source is an excimer laser. In this case, the source is not considered to form a part of the lithographic apparatus, and the radiation beam is transferred from the source SO to the illuminator IL with the help of a beam delivery system BD comprising, for example, suitable directional mirrors and / or a beam expander. In other cases, the source may be an integral part of the lithographic apparatus, for example when the source is a mercury lamp. If desired, the source SO and the illuminator IL together with the beam delivery system BD may be referred to as a radiation system.

[0029] The illuminator IL may include an adjuster AD for adjusting the angular intensity distribution of the radiation beam. Typically, at least the outer and / or inner radial extent of the intensity distribution in a pupil plane of the illuminator (commonly referred to as outer σ and inner σ, respectively) may be adjusted. Furthermore, the illuminator IL may include various other components, such as an integrator IN and a condenser CO. The illuminator may be used to adjust the radiation beam to have a desired uniformity and intensity distribution in its cross-section.

[0030] The radiation beam B is incident on a patterning device (e.g. a mask) MA held on a patterning device support (e.g. a mask table MT) and is patterned by the patterning device. After passing through the patterning device (e.g. a mask) MA, the radiation beam B passes through a projection optical system PS which focuses the beam onto a target portion C of the substrate W, thereby projecting an image of the pattern onto the target portion C. With the aid of a second positioner PW and a position sensor IF (e.g. an interferometric measurement device, a linear encoder, a 2D encoder or a capacitive sensor), the substrate table WT can be accurately moved, for example, in order to position different target portions C in the path of the radiation beam B. Similarly, the first positioner PM and the further position sensor (not shown) are arranged in a manner such that ... Figure 1 ) can be used to accurately position the patterning device (e.g., mask) MA relative to the path of the radiation beam B, for example after mechanical retrieval from a mask library or during scanning.

[0031] Mask alignment marks M1, M2 and substrate alignment marks P1, P2 can be used to align the patterning device (e.g., mask) MA and substrate W. Although the substrate alignment marks as shown occupy dedicated target portions, they can be located in the space between the target portions (these are referred to as scribe alignment marks). Similarly, in the case where more than one die is provided on the patterning device (e.g., mask) MA, the mask alignment marks can be located between the dies. Small alignment marks can also be included in the die, in the device features, in which case it is desired that the marks be as small as possible and do not require imaging or process conditions that are different from adjacent features. An alignment system for detecting alignment marks is further described below.

[0032] The lithographic apparatus LA in this example is a so-called dual-stage type, which has two substrate tables WTa, WTb and two stations (exposure station and measurement station), between which the substrate table can be exchanged. While one substrate on one substrate table is exposed at the exposure station, another substrate can be loaded onto the other substrate table at the measurement station and various preparation steps are performed. The preparation steps may include surface control of drawing the substrate using a level sensor LS and measuring the position of the alignment mark on the substrate using an alignment sensor AS. This makes it possible to significantly increase the yield of the apparatus.

[0033] The depicted apparatus can be used in a variety of modes including, for example, step mode or scan mode.The construction and operation of lithographic apparatus are well known to those skilled in the art and require no further description for an understanding of the present invention.

[0034] like Figure 2 As shown, the lithographic apparatus LA forms part of a lithographic system, referred to as a lithographic cell LC or a lithocell. The lithographic cell LC may also include equipment for performing pre-exposure and post-exposure processes on a substrate. Typically these include a spin coater SC for depositing a resist layer, a developer DE for developing the exposed resist, a cooling plate CH and a baking plate BK. A substrate carrier or robot RO picks up substrates from input / output ports I / O1, I / O2, moves them between different processing devices, and then transfers them to a loading station LB of the lithographic apparatus. These devices (generally referred to as tracks) are controlled by a track control unit TCU, which itself is controlled by a monitoring system SCS, which also controls the lithographic apparatus via a lithographic control unit LACU. Therefore, different devices can be operated to maximize yield and processing efficiency.

[0035] In order to correctly and consistently expose substrates exposed by the lithographic apparatus, it is necessary to inspect the exposed substrates to measure characteristics such as overlay errors between subsequent layers, line thickness, critical dimensions (CD), etc. If errors are detected, the exposure of subsequent substrates can be adjusted, especially if the inspection can be fast and rapid enough so that other substrates of the same batch are still to be exposed. In addition, substrates that have already been exposed can be stripped and reworked (to improve yield) or discarded, thereby avoiding exposures performed on substrates that are known to be defective. In the case where only some target portions of the substrate are defective, further exposures can be performed only on the good target portions.

[0036] Inspection equipment is used to determine the characteristics of a substrate, in particular how the characteristics of different substrates or different layers of the same substrate vary between layers. The inspection equipment may be integrated into the lithographic apparatus LA or the lithographic cell LC or may be a stand-alone device. In order to be able to make the fastest measurements, it is desirable that the inspection equipment measures the characteristics in the exposed resist layer immediately after exposure. However, the latent image in the resist has a very low contrast - the difference in refractive index between the part of the resist that has been exposed to radiation and the part that has not been exposed to radiation is small - and not all inspection equipment has sufficient sensitivity to make useful measurements of the latent image. Therefore, the measurement can be made after the post-exposure baking step (PEB), which is usually the first step performed on the exposed substrate and increases the contrast between the exposed and unexposed parts of the resist. At this stage, the image in the resist can be referred to as semi-latent. It is also possible to measure the developed resist image - at this point, the exposed or unexposed parts of the resist have been removed - or after a pattern transfer step such as etching. The latter possibility limits the possibility of reworking defective substrates, but can still provide useful information.

[0037] Figure 3 A scatterometer which can be used in the present invention is depicted. It comprises a broadband (white light) radiation projector 2 which projects radiation onto a substrate W. The reflected radiation is passed to a spectrometer detector 4 which measures the spectrum 10 (intensity as a function of wavelength) of the specularly reflected radiation. From this data, the structure or profile which gave rise to the detected spectrum can be reconstructed by a processing unit PU, for example by rigorous coupled wave analysis and nonlinear regression or by comparing it to Figure 3 The simulated spectral library is shown at the bottom for comparison. In general, for the reconstruction, the general form of the structure is known and some parameters are assumed based on knowledge of the fabrication process of the structure, leaving only a few parameters of the structure to be determined from the scatterometry data. This scatterometer can be configured as either a normal incidence scatterometer or an oblique incidence scatterometer.

[0038] Figure 4Another scatterometer that can be used with the present invention is shown in FIG. In this device, the radiation emitted by the radiation source 2 is collimated using a lens system 12 and transmitted through an interference filter 13 and a polarizer 17, partially reflected by a reflective surface 16, and focused onto a substrate W via a microscope objective 15, which has a high numerical aperture (NA), preferably at least 0.9, more preferably at least 0.95. An immersion scatterometer can even have a lens with a numerical aperture greater than 1. The reflected radiation is then transmitted through the partially reflective surface 16 into a detector 18 in order to detect the scatter spectrum. The detector can be located in a back-projected pupil plane 11, which is at the focal length of the lens system 15, however, the pupil plane can alternatively be re-imaged onto the detector with auxiliary optics (not shown). The pupil plane is a plane in which the radial position of the radiation defines the angle of incidence and the angular position defines the azimuth angle of the radiation. The detector is preferably a two-dimensional detector so that a two-dimensional angular scatter spectrum of a substrate target 30 can be measured. The detector 18 may be, for example, a CCD or CMOS sensor array, and may use an integration time of, for example, 40 milliseconds per frame.

[0039] The reference beam is often used, for example, to measure the intensity of the incident radiation. To this end, when the radiation beam is incident on the beam splitter 16, a portion of it is transmitted through the beam splitter as a reference beam towards the reference mirror 14. The reference beam is then projected onto a different portion of the same detector 18 or alternatively onto a different detector (not shown).

[0040] A set of interference filters 13 may be used to select wavelengths of interest in the range of, for example, 405 to 790 nm or even lower, such as 200 to 300 nm. The interference filters may be tunable, rather than comprising a set of different filters. Gratings may be used instead of interference filters.

[0041] Detector 18 can measure the intensity of scattered light of a single wavelength (or narrow wavelength range), the intensity of a single plurality of wavelengths, or the intensity integrated over the wavelength range. In addition, the detector can measure the intensity of transverse magnetic polarized light and transverse electric polarized light and / or the phase difference between transverse magnetic polarized light and transverse electric polarized light, respectively.

[0042] It is possible to use a broadband light source (i.e., a light source with a wide range of light frequencies or wavelengths - and therefore colors), which provides a large light spreading to allow mixing of multiple wavelengths. The multiple wavelengths in the broadband preferably each have a bandwidth of Δλ and a spacing of at least 2Δλ (i.e., twice the bandwidth). The several radiation "sources" can be different parts of an extended radiation source that have been separated using a fiber bundle. In this way, angle-resolved scatter spectra at multiple wavelengths can be measured in parallel. A 3D spectrum (wavelength and two different angles) can be measured, which contains more information than a 2D spectrum. This allows more information to be measured, thereby improving the robustness of the measurement process.

[0043] The target 30 on the substrate W may be a 1D grating which is printed such that after development the bars are formed by solid resist lines. The target 30 may be a 2D grating which is printed such that after development the grating is formed by solid resist columns or vias in the resist. The bars, columns or vias may alternatively be etched into the substrate. The pattern is sensitive to chromatic aberrations in the lithographic projection equipment, in particular the projection system PL, and the illumination symmetry and the presence of such aberrations will manifest themselves in variations in the printed grating. Therefore, scatterometry data of the printed grating are used to reconstruct the grating. Based on knowledge of the printing step and / or other scatterometry processes, parameters of the 1D grating (such as line width and shape) or parameters of the 2D grating (such as column or via width or length or shape) may be input to a reconstruction process performed by the processing unit PU.

[0044] As mentioned above, the target is on the surface of the substrate. The target is usually in the shape of a series of lines in a grating or a basic rectangular structure of a 2D array. The purpose of strict optical diffraction theory in measurement is to effectively calculate the diffraction spectrum reflected from the target. In other words, the target shape information is obtained for CD (critical dimension) uniformity and overlay or focus measurement. Overlay measurement is a measurement system in which the overlay of two targets is measured to determine whether two layers on a substrate are aligned. Focus measurement determines the focus (and / or dose) setting used when forming the target. CD uniformity is simply a measurement of the uniformity of the grating over the spectrum to determine how the exposure system of the lithography equipment works. Specifically, the CD or critical dimension is the width of the object "written" on the substrate and is the limit of what the lithography equipment can physically write on the substrate.

[0045] Using a scatterometer, such as described above in connection with modeling of a target structure such as target 30 and its diffraction properties, measurement of the shape and other parameters of the structure may be performed in a variety of ways. Figure 5An example of this process is described. A diffraction pattern based on a first estimate of the target shape (a first candidate structure) is calculated and compared to the observed diffraction pattern. The parameters of the model are then systematically changed and the diffraction is recalculated in a series of iterations to generate new candidate structures to achieve the best fit. In a variation of this process, the diffraction spectra of many different candidate structures can be pre-calculated to create a "library" of diffraction spectra. The diffraction pattern observed from the measurement target is then compared to the library of calculated spectra to find the best fit. These two approaches can be used together: a rough fit can be obtained from the library and then the best fit is found through an iterative process.

[0046] exist Figure 5 Throughout the description of , the term "diffraction image" will be used, assuming that Figure 3 or Figure 4 A diffraction image is an example of an inspection data element in the context of the present disclosure. One skilled in the art can easily adapt the teachings to different types of scatterometers, or even other types of measurement instruments.

[0047] Figure 5 The following is a flow chart that generally describes the steps of a method for measuring a target shape and / or material property. The steps are as follows and then described in more detail:

[0048] 402- measuring the diffraction image;

[0049] 403-Define model selection scheme;

[0050] 404-Estimate shape parameter θ 0 ;

[0051] 406-calculate model diffraction image;

[0052] 408 - Comparing the measured image and the calculated image;

[0053] 410-calculate value function;

[0054] 412-Generate revised shape parameter θ 1 ;

[0055] 414 - Report final shape parameters

[0056] For this description it will be assumed that the target is periodic in only 1 direction (1D structure). In practice it may be periodic in 2 directions (2D structure) and the processing will be adapted accordingly.

[0057] 402: The diffraction image of the actual target on the substrate is measured using a scatterometer such as the above. The measured diffraction image is forwarded to a computing system such as a computer. The computing system may be the processing unit PU mentioned above, or may be a separate device.

[0058] 403: Create a profile that defines a parameterized model of the target structure in terms of a number of parameters θ. For example, in a 1D periodic structure, these parameters may represent the angle of the sidewalls, the height or depth of the features, the width of the features. The properties of the target material and underlying layers are also represented by parameters such as the refractive index (at a specific wavelength present in the scattered measurement radiation beam). Specific examples are given below. Importantly, while the target structure can be defined by many parameters that describe its shape and material properties, the profile will define many of these as having fixed values, and others as variable or "floating" parameters for the purposes of the following process steps. In addition, methods will be introduced that allow parameters to vary rather than being completely independent floating parameters. In order to describe Figure 5 , considering only the variable parameters as parameters θ. The profile also defines the measurement radiation settings for a given target structure and how the parameter values ​​are estimated by fitting the inspection data to the model.

[0059] 404: By setting an initial value for the floating parameter θ 0 to estimate the model target shape. Each float parameter will be generated within a specific predetermined range, as defined in the scheme.

[0060] 406: The parameters representing the estimated shape are used together with the optical properties of the different elements of the model to calculate the scattering properties, for example using a rigorous optical diffraction method such as RCWA or any other solver equation of Maxwell. This gives an estimate or model diffraction image of the estimated target shape.

[0061] 408, 410: The measured diffraction image and the model diffraction image are then compared and their similarities and differences are used to calculate a "merit function" for the model target shape.

[0062] 412: Assuming that the evaluation function indicates that the model needs improvement before it can accurately represent the actual target shape, estimate the new parameters θ 1 Etc. and iteratively feed back to step 406. Steps 406 to 412 are repeated.

[0063] To assist the search, the calculation in step 406 may also generate a partial derivative of the evaluation function to indicate that increasing or decreasing the parameter in this particular region in the parameter space will increase or decrease the sensitivity of the evaluation function. The calculation of the evaluation function and the use of the derivative are well known in the art and will not be described in detail herein.

[0064] 414: When the evaluation function indicates that the iterative process has converged to a solution with the desired accuracy, the currently estimated parameters are reported as measures of the actual target structure.

[0065] The computation time of this iterative process is mainly determined by the forward diffraction model used, that is, the estimated model diffraction image is calculated using a rigorous optical diffraction theory from the estimated target structure. If more parameters are required, there are more degrees of freedom. The computation time in principle increases with the number of degrees of freedom, however, this can be alleviated if finite differences are used to approximate the Jacobian determinant. The estimated or model diffraction image calculated at 406 can be represented in various forms. If the calculated image is represented in the same form as the measurement image generated in step 402 (e.g., spectrum, pupil image), the comparison is simplified.

[0066] The creation of the profile involves multiple refinements of the profile, wherein the physical model is gradually adjusted to best represent the inspection data. The inspection data may include inspection data elements. The inspection data elements may be images, diffraction images (if a diffraction-based scatterometer is used), spectra, or pupil images; or may be reconstruction parameter values ​​obtained from such diffraction images, etc. Each of the inspection data elements may be obtained by inspecting the corresponding target structure, for example, using a scatterometer such as described above. Each of these inspection data elements may be described by a plurality of intensity values. The adjustment is typically based on the results of the reconstruction. As described above, the reconstruction fits the model to the inspection data, thereby converting the inspection data elements into parameter values. At the beginning of the process, the reconstruction may fail because the uncertainty may be large. Therefore, it may be more efficient to reconstruct only one or a few measurements rather than the complete data set.

[0067] To make the contour more robust, the nominal parameter values ​​of the contour should be well chosen. Ideally, many target structures should be reconstructed in order to correctly estimate these nominal parameter values.

[0068] Another metrology technique includes in-die metrology (IDM). IDM is a specific metrology application that is typically used to measure post-etch device in-overlay between structures / layers in semiconductor process flows. This is achieved by measuring the "pupil", which is an angle-resolved scatterometry signal collected by a suitable metrology tool (alternatively, image plane measurements can be used). The tool focuses a spot of light onto a small sub-element of the actual product structure, rather than onto a dedicated metrology target, and collects the pupil, which includes information about the structure illuminated by the spot of light.

[0069] The information projected onto the pupil plane is "all mixed together" so that no single pixel in the pupil contains isolated information about any feature on the wafer. Figure 5 As shown, this information is "reconstructed" by building a parameterized version of the device structure and running the model through a simulation engine to generate a "simulated pupil." The optimization engine then uses the difference between the measured pupil and the simulated pupil to drive the model parameters and iteratively tries to determine what is on the wafer.

[0070] However, it is well known that such reconstruction is difficult to do correctly and takes a lot of time to set up. For most applications, the cost / benefit ratio is unbalanced (too difficult to set up, and the resulting data is sometimes problematic). In addition, it has been found that the current models used in the reconstruction (typically used to reconstruct CD) are technically insufficient to simulate the very small signals used to extract overlay information (they are optimized for symmetric parameter extraction rather than asymmetric parameter extraction). Therefore, the above-mentioned reconstruction techniques are typically only used to monitor film thickness, nk and / or CD. They do not provide a good solution to report asymmetric process parameters (e.g., 3D NAND column tilt). Due to these problems, IDM technologies (such as IDM-Overlay, used to measure overlay) use a "data-driven" solution setup method.

[0071] Asymmetric process parameters (line / pillar tilt, bottom grating asymmetry (BGA), side wall angle (SWA) asymmetry, pitch walk, etc.) are troublesome because usually nothing is designed to be asymmetric, and therefore, many metrology tools assume symmetric structures. Because these asymmetric process fingerprints are difficult to measure directly using existing strategies and tools, only the effects of these fingerprints can be detected (large residues, wafer-to-wafer overlay variations, etc.). Therefore, providing information about these process fingerprints is very valuable, even if the information is not perfect.

[0072] The iterative training process for in-die metrology consists of Figure 6 Description. This shows that the measurement data or pupil PU and the parameter fingerprint FP (or an error file derived from the fingerprint) (such as an overlay fingerprint) are fed into the estimator EST. In this context, the overlay fingerprint may include a description of the overlay on the wafer or part thereof (e.g., a field); for example, it may include a set of overlay values ​​on the substrate or field. In the first iteration, the fingerprint FP input to the estimator EST may include an initial fingerprint SFP, such as any initial estimate or guess. In the overlay example, the initial estimate may simply include the overlay value set by the scanner (i.e., an intentional bias or intentional overlay applied to aid in the measurement calculation). For other process fingerprints, the initial fingerprint SFP may come from a variety of sources, including but not limited to historically important process fingerprints, other semiconductor processing tools (e.g., etching chamber chuck heating parameters), other metrology tools (e.g., film thickness measurements).

[0073] The estimator computes one or more weight maps WM or schemes that describe the mapping of pupils PU to fingerprints FP (i.e., to overlays or other parameter values ​​of interest). For example, multiple weight maps WM may be computed for multiple pupils PU of the same structure captured under different illumination conditions (e.g., different wavelengths, polarizations, angles of incidence, etc., or combinations thereof). One or more of these pupils may be significantly different from the other pupils, even if they are acquired through measurements of the same structure.

[0074] Once the weight maps WM are calculated, they are combined with the pupil PU and averaged to obtain the average fingerprint AFP. This will be iteratively fed back to the estimator as the updated fingerprint UFP. This operation is performed until convergence to produce the final weight map and fingerprint, or until a predefined number of iterations are performed without achieving convergence. As shown in the figure, in each iteration, instead of feeding the original average fingerprint directly, a modeling step MOD can be performed. The modeling step can calculate the fingerprint residual RES based on the difference in the yield of the average fingerprint AFP and the fingerprint PFP used in the previous iteration (for example, the scanner setting overlay SFP of the first iteration). This residual RES is used to update the model UPD to determine the updated fingerprint UFP. The updated fingerprint UFP serves as the fingerprint FP for the next iteration.

[0075] A correction may be applied to the fingerprint FP in one or more iterations (e.g., each iteration), which accounts for the error between the true fingerprint on the wafer and the fingerprint SFP / FP used for that iteration. Such a correction may include a wafer background fingerprint correction WBFC, and may be performed, for example, by the estimator EST or as part of a modeling step MOD. The correction may operate by dynamically modifying the fingerprint FP according to the model to improve the results, and may therefore be viewed as a mechanism to compensate for a "non-perfect" initial estimate of the fingerprint SFP. For example, the correction may use principal component analysis (PCA) on the raw pupil, since the wafer background fingerprint tends to be orthogonal to the overlay fingerprint being sought. Therefore, the estimator EST may include a correction mechanism that enables the fingerprint FP to float according to some model parameters (e.g., using an initial fingerprint SFP and updating based on a WBFC mechanism).

[0076] It has been determined that the foregoing method including the WBFC step converges to a solution faster (fewer iterations) if the initial estimate is closer to the actual fingerprint (solution) rather than further away. It has also been shown that process fingerprints that generate a large amount of signal in the pupil plane require a lower accuracy of the initial fingerprint estimate SFP. Taking this to the extreme, the inventors have observed that inputting a random fingerprint (random overlay value) as Figure 6The initial estimation of the SFP by the iterative solver may lead to convergence of the solution. In other words, if the process fingerprint can be described by the model parameters in the correction (e.g., WBFC) mechanism and if the signal strength is strong enough, meaningful process fingerprint information can be extracted without prior knowledge of what the fingerprint looks like.

[0077] The inventors speculate that this observation can be used to obtain useful data about process effects. By inputting a "background fingerprint" or "process fingerprint". That is, a fingerprint describing something other than overlay, it can be determined whether the fingerprint is present in (or described by) the measured pupil data. In addition, an estimate of the extent to which such a fingerprint exists (e.g., its dominance in the data) and / or the location of a specifically identified fingerprint / process effect can be determined. The input process fingerprint can be any known fingerprint (or its composition), such as seen in historical data and / or polynomials, such as Zernike polynomials, Legendre polynomials, or Bessel functions. The fingerprint can describe, for example, a wafer effect (wafer fingerprint) and / or a field effect (field fingerprint). The iterative process and the corrections therein tend to ensure convergence of the principal components of the fingerprint. In the case where the input is not overlay, the first principal component may correspond to something other than overlay, in particular a wafer background effect or a process effect.

[0078] In an embodiment, it is proposed to construct a library that includes many examples of such fingerprints and their components. Since the knowledge required for generating the underlying background fingerprint of interest required for wafer mapping does not need to be complete, but only a close enough initial estimate or guess, such a library fingerprint can be input into Figure 6 The process (i.e., as an initial fingerprint SFP) is used to see which converges and how fast. It is expected that the main components in the process fingerprint library do not contain complete fingerprints, but only initial "seeds". The degree of convergence of the pupil mapping estimator EST indicates the strength of the input signal present on the wafer. If the input "fingerprint seed" is closely related to the actual fingerprint on the wafer, the estimator will converge to a good solution quickly; conversely, if the input fingerprint seed is not related to any actual parameters on the wafer, the pupil mapping result will be poor. By "injecting" the signal (process fingerprint) into the pupil mapping tool and seeing which converge, process fingerprint information can be extracted without any prior knowledge of the process fingerprint.

[0079] Such a library may include many examples such as one, some, or all of the following:

[0080] Zernike polynomials (chip fingerprint);

[0081] Bessel function (chip fingerprint);

[0082] Legendre polynomials (field fingerprints);

[0083] Principal components from PCA analysis of various measurement sources;

[0084] Known process fingerprints;

[0085] Historical craft fingerprints;

[0086] Prediction of process fingerprints;

[0087] Any random pattern or value.

[0088] Currently, in any case, large databases comprising metrology data from multiple sources (scanners / etchers / thin film deposition / scatterometry etc.) are being built, and it is suggested that such databases could be used to build the proposed library.

[0089] Once built, the library fingerprint can be injected into Figure 6 In the process described, a wafer mapping / scheme associated with these library fingerprints is found; for example, as part of a brute force search in which each library fingerprint is input. This search method may include the setting of convergence criteria. The convergence criteria may be set based on the number of iterations / convergence speed and / or the residual size. For example, a threshold may be set for one or both of these convergence criteria. For example, a library fingerprint may be considered to exist when the processing loop converges on the wafer mapping within a threshold maximum number of iterations and / or results in a minimum residual value (e.g., in terms of an average value over the wafer or a portion thereof, such as per field). Such a process may be done in parallel with IDM or other measurements, for example, using the same pupil. Thus, no additional measurement work is required.

[0090] The strongest signals found when searching the process fingerprint library can be reported in parallel with any overlay (or other parameter of interest) values. These can be tracked and used to determine control strategies or corrections, and / or determine root causes (e.g., setting up A / B testing). This can be particularly valuable for asymmetric process fingerprints, as typically nothing is designed to be asymmetric, so any observed asymmetry indicates a detrimental parameter that needs to be controlled / corrected.

[0091] In a specific use case, for 3D structures such as 3D NAND, it has been observed that one source of process asymmetry (process effect) is pillar tilt, and that this pillar tilt directly corresponds to the overlay on the product. Therefore, the proposed method can be used to provide a feedback mechanism for controlling the pillar tilt (e.g., by injecting a process fingerprint or a component thereof related to the pillar tilt), the result of which can also be used as a metric for overlay and, therefore, for monitoring / controlling overlay on the product.

[0092] Thus, a concept is described that is able to extract the largest sources of process asymmetry and map them to wafer locations without any initial knowledge of the type of process fingerprints that may be present on the wafer and without the need for additional metrology (i.e., based on the same pupil as the metrology performed as part of control or monitoring actions). It can also provide knowledge of structures on the wafer that are beyond the expected model domain.

[0093] Although already based on Figure 6 The iterative method of describing this method is described, but it should be understood that, for example Figure 5 The model-based OCD calculation described in can also extract the same information, and therefore such methods fall within the scope of the present disclosure.

[0094] In association with a physical grating structure of a target structure as implemented on a substrate and a patterning device, an embodiment may include a computer program comprising one or more sequences of machine-readable instructions describing a method of measuring a target structure on a substrate and / or analyzing the measurements to obtain information about a lithography process. The computer program may be, for example, Figure 3 or Figure 4 Unit PU and / or Figure 2 A data storage medium (e.g. a semiconductor memory, a magnetic disk or an optical disk) in which such a computer program is stored may also be provided. Figure 3 or Figure 4 Where existing metrology equipment of the type shown is already in production and / or in use, the present invention may be implemented by providing an updated computer program product to cause a processor to execute the methods as described above and in the claims.

[0095] Further embodiments of the invention are disclosed in the following list of numbered clauses:

[0096] 1. A method of determining whether a substrate or a portion of a substrate is subject to a process effect, the method comprising:

[0097] acquiring inspection data comprising a plurality of sets of measurement data associated with structures on the substrate or the substrate portion;

[0098] acquiring fingerprint data describing spatial variations of a parameter of interest on the substrate or on a portion of the substrate;

[0099] performing an iterative mapping of the inspection data to the fingerprint data; and

[0100] Whether the substrate is subject to process effects is determined based on the degree to which the iterative mapping converges to a solution.

[0101] 2. The method of clause 1, wherein the plurality of sets of measurement data comprises a plurality of sets of measurement data associated with a plurality of different locations on the substrate.

[0102] 3. A method according to clause 2, wherein the plurality of sets of measurement data comprises a plurality of sets of measurement data acquired from some or each of the plurality of different locations on the substrate using different acquisition setups from a plurality of acquisition setups.

[0103] 4. A method according to clause 1, 2 or 3, wherein each set of measurement data comprises angularly resolved intensity values.

[0104] 5. A method according to any preceding clause, wherein the fingerprint data is related to a fingerprint of a known process effect.

[0105] 6. A method according to any preceding clause, wherein the fingerprint data has been obtained from previous measurement data, the previous measurement data being related to at least one previous measurement of the known process effect.

[0106] 7. A method according to clause 6, wherein the fingerprint data comprises principal components of the previous measurement data.

[0107] 8. A method according to any preceding clause, wherein the fingerprint data comprises polynomial data.

[0108] 9. The method of clause 8, wherein the polynomial data comprises Zernike polynomials, Legendre polynomials, or Bessel functions.

[0109] 10. A method according to any preceding clause, wherein the fingerprint data comprises substrate fingerprint data describing spatial variations of the parameter of interest across the substrate.

[0110] 11. A method according to any preceding clause, wherein the fingerprint data comprises field fingerprint data describing spatial variations of the parameter of interest over a field.

[0111] 12. A method according to any preceding clause, wherein the iterative mapping produces one or more weight maps for each iteration.

[0112] 13. A method according to any preceding clause, wherein the step of performing iterative mapping comprises computing a correction for a wafer background fingerprint, said correction correcting for fingerprint contributors other than said parameter of interest.

[0113] 14. The method of clause 13, wherein the correction of the wafer background fingerprint comprises performing a compositional analysis.

[0114] 15. The method of clause 14, wherein the correction of the wafer background fingerprint comprises removing one or more principal components other than the first principal component when determining an updated fingerprint for each iteration of the iterative mapping.

[0115] 16. A method according to any preceding clause, wherein the determining whether the structure is subject to process effects is based on a convergence criterion.

[0116] 17. A method according to any preceding clause, comprising determining one or more values ​​of the parameter of interest based on the inspection data.

[0117] 18. A method according to any preceding clause, wherein the fingerprint data is obtained from a library of multiple sets of fingerprint data.

[0118] 19. A method according to clause 18, wherein the multiple sets of fingerprint data include multiple examples of one, some or all of the following: Zernike polynomials, Bessel functions, Legendre polynomials, principal components from principal component analysis of measurement data associated with one or more measurement sources, known process fingerprints, historical process fingerprints, and predicted, random values ​​of process fingerprints.

[0119] 20. A method according to clause 18 or 19, comprising repeating the method for each fingerprint data in a different set of fingerprint data in the plurality of sets of fingerprint data.

[0120] 21. The method of clause 20, comprising setting a convergence criterion for determining whether the structure is subject to process effects based on the number of iterations and / or the size of the residual.

[0121] 22. A method according to clause 21, comprising reporting the strongest one or more sets of fingerprint data according to the convergence criteria.

[0122] 23. A method according to clause 22, comprising determining a control strategy or correction, and / or determining a root cause of the strongest one or more sets of fingerprint data.

[0123] 24. A method according to any one of clauses 18 to 23, comprising an initial step of constructing the library.

[0124] 25. A method according to any preceding clause, wherein the parameter of interest is overlay.

[0125] 26. A method according to any preceding clause, wherein each set of measurement data relates to a pupil plane.

[0126] 27. A metrology apparatus operable to perform a method according to any one of clauses 1 to 26.

[0127] 28. A measuring device according to clause 27, comprising:

[0128] a support for a substrate having a plurality of target structures thereon;

[0129] an optical system for measuring each target structure; and

[0130] A processor arranged to perform a method according to any of clauses 1 to 26.

[0131] 29. A computer program comprising processor readable instructions which, when executed on a suitable processor controlled device, cause the processor controlled device to perform the method according to any of clauses 1 to 26.

[0132] 30. A computer program carrier comprising a computer program according to clause 29.

[0133] Although specific reference has been made above to the use of embodiments of the invention in the context of optical lithography, it will be appreciated that the invention may be used in other applications, such as imprint lithography, and is not limited to optical lithography where the context permits. In imprint lithography, the topography in the patterning device defines the pattern produced on the substrate. The topography of the patterning device may be pressed into a layer of resist provided to the substrate, and the resist is then cured by applying electromagnetic radiation, heat, pressure, or a combination thereof. After the resist has cured, the patterning device is removed from the resist, leaving a pattern therein.

[0134] As used herein, the terms "radiation" and "beam" encompass all types of electromagnetic radiation, including ultraviolet (UV) radiation (e.g., having a wavelength of or about 365, 355, 248, 193, 157, or 126 nm) and extreme ultraviolet (EUV) radiation (e.g., having a wavelength in the range of 5 to 20 nm), as well as particle beams (such as ion beams or electron beams).

[0135] The term "lens," where the context permits, may refer to any one or combination of various types of optical components, including refractive, reflective, magnetic, electromagnetic, and electrostatic optical components.

[0136] The foregoing description of the specific embodiments will fully reveal the general characteristics of the present invention so that others can easily modify and / or adapt the various applications of these specific embodiments by applying the knowledge within the art without departing from the general concept of the present invention, without undue experimentation. Therefore, based on the teachings and guidance presented herein, such adaptations and modifications are intended to be within the meaning and scope of the equivalents of the disclosed embodiments. It should be understood that the wording or terminology herein is for the purpose of description by example, rather than restrictive, so that the terms or wording of this specification will be interpreted by those skilled in the art based on the teachings and guidance.

[0137] The breadth and scope of the present invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.

Claims

1. A method of determining whether a substrate or a portion of a substrate is subject to a process effect, the method comprising: acquiring inspection data comprising a plurality of sets of measurement data associated with structures on the substrate or the substrate portion; acquiring fingerprint data describing spatial variations of a parameter of interest on the substrate or on a portion of the substrate; performing an iterative mapping of the inspection data to the fingerprint data; as well as Whether the substrate is subject to process effects is determined based on the degree to which the iterative mapping converges to a solution. 2 . The method of claim 1 , wherein the plurality of sets of measurement data comprises a plurality of sets of measurement data associated with a plurality of different locations on the substrate.

3. A method according to claim 2, wherein the plurality of sets of measurement data comprises a plurality of sets of measurement data acquired from some or each of the plurality of different locations on the substrate using different acquisition settings from a plurality of acquisition settings.

4. A method according to claim 1, 2 or 3, wherein each set of measurement data comprises angularly resolved intensity values. The method of claim 1 , wherein the fingerprint data is related to a fingerprint of a known process effect.

6. The method of claim 5, wherein the fingerprint data has been obtained from previous measurement data, the previous measurement data being related to at least one previous measurement of the known process effect. The method of claim 1 , wherein the iterative mapping generates one or more weight maps for each iteration.

8. The method of claim 1, wherein performing iterative mapping comprises computing a correction for a wafer background fingerprint that corrects for fingerprint contributors other than the parameter of interest.

9. The method according to claim 1, wherein the fingerprint data is obtained from a library of multiple sets of fingerprint data.

10. The method of claim 9, wherein the plurality of sets of fingerprint data include multiple examples of one, some, or all of the following: Zernike polynomials, Bessel functions, Legendre polynomials, principal components from principal component analysis of measurement data associated with one or more measurement sources, known process fingerprints, historical process fingerprints, and predicted, random values ​​of process fingerprints.

11. A method according to claim 9 or 10, comprising repeating the method for each fingerprint data in a different set of fingerprint data in the plurality of sets of fingerprint data.

12. The method of claim 11, comprising setting a convergence criterion for determining whether the structure is subject to process effects based on the number of iterations and / or the size of the residual.

13. A method according to claim 12, comprising reporting the strongest one or more sets of fingerprint data according to the convergence criteria.

14. A method according to claim 13, comprising determining a control strategy or correction, and / or determining a root cause for the strongest one or more sets of fingerprint data.

15. The method of claim 1, wherein the parameters of interest are overlaid and each set of measurement data is associated with a pupil plane.

16. A computer program comprising processor readable instructions which, when executed on a suitable processor controlled device, cause the processor controlled device to perform the method according to claim 1.

17. A computer program carrier comprising the computer program according to claim 16.

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