Apparatus and method for determining critical areas of patterned substrate

Abstract

A computer program product comprising a computer readable medium, having instructions therein, the instructions, upon execution by a computer system, configured to cause the computer system to at least: generate a surface map of a surface of one or more investigation areas, based on obtained first scans of the one or more investigation areas of a substrate; calculate values of one or more key performance indicators (KPIs) of the surface map of each investigation area; and determine outlier investigation areas for investigation via a technique which generates second scans, wherein an outlier investigation area is one which has at least one KPI not within an acceptable predetermined range of values of the KPI.

Description

APPARATUS AND METHOD FOR DETERMINING CRITICAL AREAS OF PATTERNED SUBSTRATECROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority of EP application 23186833.2 which was filed on July 20, 2023 and which is incorporated herein in its entirety by reference.TECHNICAL FIELD

[0002] The description herein relates generally to improved metrology systems and methods. More particularly, identifying critical areas of interest by employing a model configured to determine three- dimensional data of a structure patterned on a substrate using a single image (e.g., SEM image), and analysing critical areas of interest via other techniques.BACKGROUND

[0003] A lithographic projection apparatus can be used, for example, in the manufacture of integrated circuits (ICs). In such a case, a patterning device (e.g., a mask) may contain or provide a pattern corresponding to an individual layer of the IC (“design layout”), and this pattern can be transferred onto a target portion (e.g. comprising one or more dies) on a substrate (e.g., silicon wafer) that has been coated with a layer of radiation-sensitive material (“resist”), by methods such as irradiating the target portion through the pattern on the patterning device. In general, a single substrate contains a plurality of adjacent target portions to which the pattern is transferred successively by the lithographic projection apparatus, one target portion at a time. In one type of lithographic projection apparatuses, the pattern on the entire patterning device is transferred onto one target portion in one go; such an apparatus is commonly referred to as a stepper. In an alternative apparatus, commonly referred to as a step-and-scan apparatus, a projection beam scans over the patterning device in a given reference direction (the “scanning” direction) while synchronously moving the substrate parallel or anti-parallel to this reference direction. Different portions of the pattern on the patterning device are transferred to one target portion progressively. Since, in general, the lithographic projection apparatus will have a reduction ratio M (e.g., 4), the speed F at which the substrate is moved will be 1 / M times that at which the projection beam scans the patterning device. More information with regard to lithographic devices as described herein can be gleaned, for example, from US 6,046,792, incorporated herein by reference.

[0004] With the advancement of lithography and other patterning process technologies, the dimensions of functional elements have continually been reduced while the amount of the functional elements, such as transistors, per device has been steadily increased over decades. Meanwhile, the requirement of accuracy in terms of critical dimension (CD), height, etc. has become more and more stringent. Errors in shape and size of the structure may cause a problem in terms of the functioning ofthe device, including failure of the device to function or one or more electrical problems of the functioning device. Accordingly, there is a need to measure the surface profile of the three dimensional structure of the functional elements to characterize, reduce or minimize one or more of defects present on the functional elements. However, measuring three dimensional (3D) structure of the functional elements using existing metrology tools and methods is time consuming. Furthermore, metrology techniques which can measure features of the surface profile at a high enough resolution (i.e. at a resolution of the same order of magnitude as CDs or even smaller surface features) are typically destructive to the sample, and therefore can negatively affects the yield of the patterning process.

[0005] “Training procedure for scanning electron microscope 3D surface reconstruction using unsupervised domain adaptation with simulated data” Tim Houben, Thomas Huisman, Maxim Pisarenco, Fons van der Sommen, Peter de With, Journal of Micro / Nanopatterning, Materials, and Metrology, Vol, 22(3), 031208-1 discloses a data-driven method that predicts the dimensions, height and width (CD) values from 2D SEM images. According to this method, height estimate values with a mean absolute error (MAE) of 0.5 nm of structures formed on the substrate surface can be obtained.

[0006] “Depth estimation from a single SEM image using pixel-wise fine-tuning with multimodal data”, Houben, T., Huisman, T., Pisarenco, M. et al., Machine Vision and Applications 33, 56 (2022) discloses a computer-implemented method of extracting depth information of a substrate surface from SEM images using pixel-wise fine-tuning with multimodal data. According to this example, a neural network architecture, together with a tailored training procedure, leads to reasonably accurate depth predictions. The training procedure includes a weakly supervised domain adaptation stem, which is referred to as pixel-wise fine tuning. This step employs scatterometry data to address the ground-truth scarcity problem. This method is well suited for important semiconductor metrics, such as top critical dimension (CD), bottom CD and sidewall angle.

[0007] The above-mentioned computer-implemented methods of extracting depth information from a 2D SEM scan provide a reasonable estimate of the topology of the sample surface. Another benefit of these methods is that depth information can be extracted without performing destructive techniques on the substrate. However, in some instances, the resolution of these methods is too low to extract topology information at a resolution of the same order of magnitude as CDs or at better resolution.

[0008] Metrology tools which can provide 3D data about the sample surface at the CD resolution (or better), such as transmission electron microscopy (TEM), cross-sectional SEM (XSEM), atomic force microscopy (AFM), are destructive and time-consuming. For example, sample preparation for these techniques requires a cross-section of the sample to be prepared, to enable accurate 3D information about the sample surface profile to be measured. Sample preparation for TEM is thus a timeconsuming and complicated process. However, accurate 3D information (e.g., height information) about the functional elements formed on the sample is often required in order to characterize, reduce or minimize one or more of defects in the device. For example, 3D information (e.g., heightinformation) of defective functional elements can provide an insight into how the processing parameters of the lithographic or other patterning process affects the functional elements formed on the substrate, and thus, how the processing parameters can be adjusted to prevent the formation of defective functional elements on the substrate.SUMMARY

[0009] Therefore, there is a need for a time-efficient method of investigating a surface of substrate wherein accurate 3D information of the structures formed on a substrate can be provided.Furthermore, there is a need for a method of controlling a lithographic apparatus such that the recipe used to form a structure on the sample surface can be adjusted to reduce or eliminate the occurrence of defects on the structure.

[0010] According to the present disclosure there is provided a computer program product comprising a computer readable medium, having instructions therein, the instructions, upon execution by a computer system, configured to cause the computer system to at least: generate a surface map of a surface of one or more investigation areas, based on obtained first scans of the one or more investigation areas of a substrate; calculate values of one or more key performance indicators (KPIs) of the surface map of each investigation area; and determine outlier investigation areas for investigation via a technique which generates second scans of the outlier investigation areas, wherein an outlier investigation area is one which has at least one KPI not within an acceptable predetermined range of values of the KPI.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above aspects and other aspects and features will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments in conjunction with the accompanying figures, wherein:

[0012] Figure 1 shows a block diagram of various subsystems of a lithography system, according to an embodiment;

[0013] Figure 2 is a schematic diagram of a lithographic projection apparatus, according to an embodiment;

[0014] Figure 3 is a schematic diagram of another lithographic projection apparatus, according to an embodiment;

[0015] Figure 4A illustrates an example SEM image of a line having a depth of lOnm in a z- direction from a top of a substrate, according to an embodiment;

[0016] Figure 4B illustrate another example SEM image of a line having a depth of lOOnm in a z- direction from a top of a substrate, according to an embodiment;

[0017] Figure 5A illustrates a SEM image of contact holes patterned on a substrate and corresponding depth map, according to an embodiment;

[0018] Figure 5B illustrates a SEM image of a set of lines patterned on a substrate and corresponding depth map, according to an embodiment;

[0019] Figure 5C illustrates a SEM image of another set of lines patterned on a substrate and corresponding depth map, according to an embodiment;

[0020] Figure 6 illustrates the method of investigating a surface of a structure according to an embodiment of the present invention;

[0021] Figure 7 illustrates an example KPI (key performance indicator) distribution obtained for a given substrate.DETAIEED DESCRIPTION

[0022] Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the drawings. The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise represented. The implementations set forth in the following description of exemplary embodiments do not represent all implementations consistent with the invention. Instead, they are merely examples of apparatuses, systems, and methods consistent with aspects related to subject matter that may be recited in the appended claims.

[0023] An integrated circuit (IC) chip used in a device (e.g., phone, laptop, computer memory, etc.) includes complex circuit patterns. During manufacturing of such circuit patterns, images of the printed circuit patterns are captured to determine whether desired circuit patterns are printed accurately. The ultimate performance of the fabricated device is critically dependent on the accuracy of the positioning and sizing of the various features of the product structure formed via lithography and other processing steps. These features are three dimensional (3D) structures having a predetermined depth and shape at nanometer scale. The product structure made by an imperfect lithography procedure or other processing steps will result in a slightly different structure than an ideal or nominal desired structure.

[0024] In order to inspect the size of various features, three-dimensional information (e.g., height of features) can be very beneficial to ensure a feature at one layer connects to feature at another layer. However, obtaining 3D information of structures at nanometer scale is not a trivial task. In existing technology, 3D information may be obtained via tilted beam scanning electron microscope (SEM), in which two or more images targeted at the same location are needed in order to infer proper depth information. However, the use of multiple images for 3D metrology has several limitations. For example, capturing a pair of stereo images decreases a throughput of a patterning process or a metrology process due to having to switch between beam tilt angles. A proper alignment between the images is required to capture the stereo images. Processing the stereo images to determine depth information may be computationally expensive and prone to noise in the images and drifts in a metrology hardware. Thus, extraction of 3D information using existing technology can slow down thechip manufacturing and metrology process.

[0025] Various inspection techniques may be used to detect potential defects in the structures. Some inspection techniques may include generation of simulated images. Such inspection techniques or algorithms may be used to detect potential defects in the manufactured structures, to extract 2D geometric features of the manufactured structures (e.g., feature edge positions) from the inspection image, or to extract or reconstruct 3D geometric features (e.g., a height profile map) of the manufactured structures from the inspection image.

[0026] Although specific reference may be made in this text to the manufacture of ICs, it should be explicitly understood that the description herein has many other possible applications. For example, it may be employed in the manufacture of integrated optical systems, guidance and detection patterns for magnetic domain memories, liquid crystal display panels, thin film magnetic heads, etc. The skilled artisan will appreciate that, in the context of such alternative applications, any use of the terms “reticle,” “wafer,” or “die” in this text should be considered as interchangeable with the more general terms “mask,” “substrate,” and “target portion,” respectively. The substrate referred to herein may be processed, before or after exposure, in for example a track (a tool that typically applies a layer of resist to a substrate and develops the exposed resist) or a metrology or inspection tool. Where applicable, the disclosure herein may be applied to such and other substrate processing tools. Further, the substrate may be processed more than once, for example in order to create a multi-layer IC, so that the term substrate used herein may also refer to a substrate that already contains multiple processed layers.

[0027] A critical dimension (CD) of a device refers the smallest width of a line or hole or the smallest space between two lines or two holes. Thus, the CD determines the overall size and density of the designed device. Of course, one of the goals in device fabrication is to faithfully reproduce the original design intent on the substrate (via the patterning device).

[0028] In the present document, the terms “radiation” and “beam” may be used to encompass all types of electromagnetic radiation, including ultraviolet radiation (e.g. with a wavelength of 365, 248, 193, 157 or 126 nm) and EUV (extreme ultra-violet radiation, e.g. having a wavelength in the range of about 5-100 nm).

[0029] As a brief introduction, Figure 1 illustrates an exemplary lithographic projection apparatus 10A. Major components are a radiation source 12A, which may be a deep-ultraviolet excimer laser source or other type of source including an extreme ultra violet (EUV) source (as discussed above, the lithographic projection apparatus itself need not have the radiation source), illumination optics which, e.g., define the partial coherence (denoted as sigma) and which may include optics 14A, 16Aa and 16Ab that shape radiation from the source 12A; a patterning device 18 A; and transmission optics 16Ac that project an image of the patterning device pattern onto a substrate plane 22A. An adjustable filter or aperture 20A at the pupil plane of the projection optics may restrict the range of beam angles that impinge on the substrate plane 22A.

[0030] In a lithographic projection apparatus, a source provides illumination (i.e. radiation) to a patterning device and projection optics direct and shape the illumination, via the patterning device, onto a substrate. The projection optics may include at least some of the components 14A, 16Aa, 16Ab and 16Ac. An aerial image (Al) is the radiation intensity distribution at substrate level. A resist layer on the substrate is exposed and the aerial image is transferred to the resist layer as a latent “resist image” (RI) therein. The resist image (RI) can be defined as a spatial distribution of solubility of the resist in the resist layer. A resist model can be used to calculate the resist image from the aerial image, an example of which can be found in U.S. Patent Application Publication No. US 2009-0157360, the disclosure of which is hereby incorporated by reference in its entirety.

[0031] Once a semiconductor chip is manufactured, measurements may be performed to determine dimension of structures fabricated on the substrate. For example, inspection of the fabricated structure may be based on critical dimension (CD) measurements obtained using a metrology tool (e.g., SEMs, AFM, optical tool, etc). In an embodiment, lateral dimensions (e.g., in x, y plane) of the structure may be extracted from the CD measurements. Besides the lateral dimensions of features, the measurements (e.g., CD-SEM) may also include information on 3D information (e.g., a height, CD values at different heights, height profile, etc.) of the features. In an embodiment, the height of the feature refers to a depth in a z-direction perpendicular to the x-y plane.

[0032] Figure 2 schematically depicts an exemplary lithographic projection apparatus in conjunction with the techniques described herein can be utilized. The apparatus comprises:- an illumination system IL, to condition a beam B of radiation. In this particular case, the illumination system also comprises a radiation source SO;- a first object table (e.g., patterning device table) MT provided with a patterning device holder to hold a patterning device MA (e.g., a reticle), and connected to a first positioner to accurately position the patterning device with respect to item PS;- a second object table (substrate table) WT provided with a substrate holder to hold a substrate W (e.g., a resist coated silicon wafer), and connected to a second positioner to accurately position the substrate with respect to item PS;- a projection system (“lens”) PS (e.g., a refractive, catoptric or catadioptric optical system) to image an irradiated portion of the patterning device MA onto a target portion C (e.g., comprising one or more dies) of the substrate W.

[0033] The beam PB subsequently intercepts the patterning device MA, which is held on a patterning device table MT. Having traversed the patterning device MA, the beam B passes through the lens PL, which focuses the beam B onto a target portion C of the substrate W. With the aid of the second positioning means (and interferometric measuring means IF), the substrate table WT can be moved accurately, e.g. so as to position different target portions C in the path of the beam PB. Similarly, the first positioning means can be used to accurately position the patterning device MA with respect to the path of the beam B, e.g., after mechanical retrieval of the patterning device MAfrom a patterning device library, or during a scan. In general, movement of the object tables MT, WT will be realized with the aid of a long-stroke module (coarse positioning) and a short-stroke module (fine positioning). However, in the case of a stepper (as opposed to a step-and-scan tool) the patterning device table MT may just be connected to a short stroke actuator, or may be fixed.

[0034] Figure 3 schematically depicts another exemplary lithographic projection apparatus LA in conjunction with the techniques described herein can be utilized.The lithographic projection apparatus LA comprises a source collector module SO, an illumination system (illuminator) IL, a support structure (e.g. a patterning device table) MT, a substrate table (e.g. a wafer table) 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; and a projection system (e.g. a reflective projection system) PS configured to project a pattern imparted to the radiation beam B by patterning device MA onto a target portion C (e.g. comprising one or more dies) of the substrate W, similar to the apparatus shown on Figure 2.

[0035] As here depicted, the apparatus LA is of a reflective type (e.g. employing a reflective patterning device). It is to be noted that because most materials are absorptive within the EUV wavelength range, the patterning device may have multilayer reflectors comprising, for example, a multi-stack of Molybdenum and Silicon. In one example, the multi-stack reflector has a 40 layer pairs of Molybdenum and Silicon where the thickness of each layer is a quarter wavelength. Even smaller wavelengths may be produced with X-ray lithography. Since most material is absorptive at EUV and x-ray wavelengths, a thin piece of patterned absorbing material on the patterning device topography (e.g., a TaN absorber on top of the multi-layer reflector) defines where features would print (positive resist) or not print (negative resist).

[0036] Referring to Figure 3, the illuminator IL receives an extreme ultra violet radiation beam from the source collector module SO. Methods to produce EUV radiation include, but are not necessarily limited to, converting a material into a plasma state that has at least one element, e.g., xenon, lithium or tin, with one or more emission lines in the EUV range. In one such method, often termed laser produced plasma ("LPP") the plasma can be produced by irradiating a fuel, such as a droplet, stream or cluster of material having the line-emitting element, with a laser beam. The resulting plasma emits output radiation, e.g., EUV radiation, which is collected using a radiation collector, disposed in the source collector module. The laser and the source collector module may be separate entities, for example when a CO2 laser is used to provide the laser beam for fuel excitation.

[0037] In such cases, the laser is not considered to form part of the lithographic apparatus and the radiation beam is passed from the laser to the source collector module with the aid of a beam delivery system comprising, for example, suitable directing mirrors and / or a beam expander. In other cases the source may be an integral part of the source collector module, for example when the source is a discharge produced plasma EUV generator, often termed as a DPP source.

[0038] A lithographic apparatus LA may be configured to pattern a substrate according to a recipe,wherein the recipe defines the processing parameters of the patterning step. The recipe affects the patterned structure which is formed on the substrate, so the recipe is selected such that the desired patterned structure is achieved. The recipe may depend on a number of factors such as the pattern on the patterning device, the CD to be achieved, the material of the substrate, etc.

[0039] The surface of the patterned substrate may be investigated by a scanning technique, to obtain information on the surface profile. For example, the surface may be investigated by scanning electron microscopy (SEM).

[0040] Figures 4A and 4B illustrate examples of two different SEM images of a line having same CDs but different heights. Figure 4A illustrates a SEM image II of a line having a depth of lOnm in z- direction (perpendicular to the plane of the figures 4A and 4B) from a top of a substrate and Figure 4B illustrate another SEM image 12 of a line having a depth of lOOnm in z-direction from a top of a substrate. These figures show that depth has an effect on the information captured in the SEM images, meaning that some information about the substrate surface height can be extracted from 2D SEM images.

[0041] For many problems related to semiconductor manufacturing there may be a correlation between input (for example, the recipe) and output (for example, the pattern on the patterned substrate), but the correlation may be too complex for humans to model or identify.

[0042] According to an embodiment, it is possible to obtain an estimate 3D data, such as depth information, of a structure of a patterned substrate from 2D metrology images via computational means. In an example, this can be done by a system which includes an image capture device such as a scanning electron microscope (SEM) having an electron beam optics configured to capture an image of a patterned substrate; and one or more processors including a trained model stored in a memory that can receive the captured image and execute the model to determine depth information form the captured image. The one or more processors can be configured to input the captured image of the patterned substrate to a trained model configured to generate depth related data from a single image; and extract depth information from the captured image by executing the trained model. The 2D metrology images can be obtained by any appropriate scanning technique, and are not necessarily obtained by SEM.

[0043] The model which is configured to generate data for estimating depth information of a structure of a patterned substrate is not particularly limited, and may be determined according to a number of examples.

[0044] One example of performing this method includes determining depth information of a structure formed on a substrate based on a single SEM image of the structure, the depth information being determined using a convolutional neural network (CNN) trained to mimic a parallax effect present in pairs of stereo SEM images. The determination is performed by a computer system which performs steps of: receiving, via a SEM tool, a single SEM image of a structure patterned on a substrate;inputting the SEM image to the CNN to predict disparity data associated with the SEM image, the CNN being trained by: obtaining, via the SEM tool, a stereo pair of SEM images of a patterned substrate, the stereo pair including a first SEM image obtained at a first e-beam tilt setting of the SEM tool, and a second SEM image obtained at a second e-beam tilt setting of the SEM tool; generating, using the CNN, disparity data between the first SEM image and the second SEM image; combining the disparity data with the second SEM image to generate a reconstructed image of the first SEM image; and comparing the reconstructed image and the first SEM image; and generating, based on the predicted disparity data, depth information associated with the structure patterned on the substrate.

[0045] Implementation of this method is described in detail in WO2022128373, which document is hereby incorporated in its entirety by reference. WO2022128373 provides a number of other examples of models which can be used for generating depth information associated with the structure patterned on the substrate, which may be used according with the method according to this disclosure. According to these methods, it is possible to extract 3D information from first scans, using advanced modelling techniques based on deep neural networks.

[0046] Figures 5A, 5B and 5C illustrate different SEM images 13, 14, and 15, and the corresponding depth ma, extracted from the SEM image by the method described in WO2022128373. As can be seen, the estimate provides a reasonable level of detail of the depth information. This level of detail may be sufficient to identify the areas which are likely to or definitely have defective functional elements.

[0047] Another example of depth information extraction includes a data-driven method that predicts the dimensions, height and width (CD) values from 2D SEM images is provided. According to this method, height estimate values with a mean absolute error (MAE) of 0.5 nm of structures formed on the substrate surface can be obtained.

[0048] In another example, depth information of a substrate surface can be extracted from SEM images using pixel-wise fine-tuning with multimodal data. According to this example, a neural network architecture, together with a tailored training procedure, leads to accurate depth predictions. The training procedure includes a weakly supervised domain adaptation stem, which is referred to as pixel-wise fine tuning. This step employs scatterometry data to address the ground-truth scarcity problem. This method is well suited for important semiconductor metrics, such as top critical dimension (CD), bottom CD and sidewall angle.

[0049] However, the resolution of the 3D profile obtained from 2D images is in some instances not high enough for analysis of the substrate surface. For example, the model may not be as accurate as 3D metrology techniques (such as TEM for example) at predicting parameters such as sidewall angleand corner rounding.

[0050] According to embodiment of the present invention, a method of investigating a surface of a structure formed on a substrate is provided. This method is schematically illustrated on Figure 6. This method may be executed by a computer readable medium, having instructions therein, the instructions, upon execution by a computer system, configured to cause the computer system to perform a method of investigating a surface of a structure formed on a substrate.

[0051] According to this method, the following steps are performed. A plurality of investigation areas Al are selected (step S100) for assessment via a first scanning technique (e.g. a first metrology technique). An investigation area may correspond to a target portion on the substrate, however, it may cover a smaller or larger area. Images of investigation areas Al are obtained via the first scanning technique (step S200). A surface map is then generated based on each of the obtained images (step S300). At least one key performance indicator is calculated for each surface map (S400). A determination is made as to whether each calculated key performance indicator (KPI) falls within an acceptable predetermined range of the KPI. If a KPI falls outside the acceptable predetermined range, the corresponding area (the outlier investigation area A2) is selected for further investigation (step S500). The outlier investigation areas are investigated further by a second scanning technique (step S600) (e.g. a second metrology technique).

[0052] The surface map obtained in step S300 may contain height information of the surface of each investigation areas. The surface map may be obtained by any suitable computational methods, such as the method described in WO2022128373. However, the possible methods are not limited thereto, and may be any other machine learning model which may be trained to generate surface maps based on 2D images obtained via a first scanning technique.

[0053] The surface map may be generated by a neural network.

[0054] The criteria for selecting a plurality of investigation areas Al is not particularly limited.The total area covered by the investigation areas Al is preferably more than 0% of the total area of the substrate surface having structures formed thereon, and is 100% or less than the total area of the substrate surface having structures formed thereon. More preferably, the total area covered by the investigation areas Al is in the range 25 % or more than and 75% or less than of the total area of the substrate surface having structures formed thereon. In another example, all areas of the substrate having a structure formed thereon (i.e. the target portions) are selected for investigation. This enables a first scan to be generated for each investigation area, providing a more detailed analysis of the substrate surface, however it is time-consuming. In a further another example, roughly half of the areas of the substrate having a structure formed thereon are selected as investigation areas. In an example, investigation areas Al are selected randomly. In another example, the investigation areas Al may be selected to be in a chess-like arrangement (as shown on Figure 6). According to this arrangement, the areas selected for investigation provide a representative sample of the whole substrate surface, and less time is occupied by obtaining the first scans than when most of the surfaceis scanned. In another example, the investigation areas Al selected may cover part of the substrate surface in a non-uniform manner, e.g. the investigation areas may be concentrated in a particular region of the substrate. This may be advantageous when it is known that a particular region of the surface is likely to have defective functional elements, which are to be selected for further investigation. Alternatively, some investigation areas may be selected to cover particular regions of the surface which is of particular interest (i.e. regions which are likely to have defective functional elements) and other investigation areas are selected randomly.

[0055] The first scanning technique is typically one which does not include obvious detailed depth information about the sample. However, the first scanning technique is preferably a relatively fast and a non-destructive technique, meaning that a relatively quick scan of large proportion of areas of the sample can be performed. For example, the first scanning technique may be scanning electron microscopy (SEM), in which case the first scans obtained by the first scanning technique are SEM images. SEM may be selected as the first scanning technique, if it is intended to extract depth information via the method described in WO2022128373 (in step S300). According to this method, high-resolution depth information can be extracted from a 2D SEM image.

[0056] A surface map containing height information (3D information) of the first scans is generated. This is done via a computer-implemented method, such as the convolutional neural network (CNN) as described in WO2022128373. The method described in WO2022128373 is directed to determining depth information of a structure formed on a substrate based on a single scanning electron microscope (“SEM”) image of the structure, however, it is possible to provide a method which extract depth information from other scanning techniques.

[0057] Next, a value of one or more key performance indicators (KPIs) of the surface map of each investigation area Al is calculated.

[0058] The key performance indicator is a parameter of the substrate surface which can be used as an indicator of whether a functional element of the substrate surface is defective.

[0059] For example, the KPI may be an indicator whether the features of investigation area Al fulfil the requirement of accuracy in terms of critical dimension (CD) of a structure. In this case, the KPI may be one of the critical dimension (CD) of a structure, local CD uniformity (LCDU) associated with the structure.

[0060] In a further example, the KPI may be an indicator of the texture of the investigation area Al. For example, one of more KPIs may be line edge roughness (LER) associated with the structure, line width roughness (LWR) related to line space patterns, contact edge roughness related to contact holes.

[0061] In a further example, the KPI may be a defect rate associated with the structure,

[0062] In a further example, the KPI may be a measure of stochastic events, for example the KPI may be stochastic edge placement error (SEPE), deviation of the profile from the mean, or stochastic variations associated with geometry of the structure.

[0063] In a further example, the KPI may be associated with other parameters of the surface which are related to the sample height profile. For example, the KPI may be side wall angle (SWA), corner rounding, 3D edge placement error. These examples of KPIs (as well as other KPIs which contain height information of the sample surface) are associated with parameters that are typically obtained by 3D metrology techniques, such as TEM, AFM, etc.

[0064] If a KPI of a particular investigation area falls outside a predetermined range, said investigation area is likely to have a defective functional element. The acceptable range of KPIs depends on the tolerance of the structure to be formed on the surface.

[0065] A determination is made of the investigation areas having a KPI which fall outside a predetermined range. Areas having at least one KPI not within an acceptable predetermined range of values of the KPI are the outlier investigation areas A2.

[0066] Figure 7 shows an example of a distribution of the values of the deviations of the surface height (z) from the mean (zmean) for a given set of investigation areas. As it can be seen from this example distribution, a proportion of the KPI values are not within the acceptable range. The investigation areas corresponding to these KPIs are selected as outlier investigation areas A2 for further investigation via the second scanning technique.

[0067] The second scanning technique is used to obtain second scans of outlier investigation areas A2.

[0068] The second scanning technique may be a high-resolution technique which contains height information about the substrate surface. For example, the second scanning technique can be one of transmission electron microscopy (TEM), cross-sectional scanning electron microscopy (XSEM), or atomic force microscopy (AFM). The second scanning technique is not limited thereto, and can be any appropriate metrology technique which extract detailed height information about the sample.

[0069] According to this method, the second scanning technique which contains height information about the sample is only performed on areas which are likely to have defective functional elements.By calculating a KPI for each investigation area based on a surface map, it is possible to identify areas which are likely to be of most interest and should be investigated by a second scanning technique. If the calculated KPI relates to a 3D parameter of the surface, which is typically obtained by a 3D metrology technique, it is possible to accurately predict the areas of the sample surface which have 3D features that require further investigation by a second scanning technique.

[0070] Therefore, scanning techniques (e.g. the second scanning technique) which tend to be timeconsuming and / or destructive techniques are carried out only on the regions of interest (outlier areas A2), and efficiency of the substrate surface investigation is improved. According to the present invention, it is not necessary to carry out time-consuming and / or destructive techniques (such as the second scanning technique) on the whole sample surface. The second scanning technique is only performed on regions of interest, identified by a method according to the present invention, and areas which do not contain defective functional elements remain intact.

[0071] The method according to the present invention may be used to control the processing parameters of a lithographic apparatus, as follows. A structure may be formed on a substrate by the lithographic apparatus and a processing apparatus according to a recipe. The recipe may indicate processing parameters under which the substrate is processed (e.g., dose, focus, resist characteristics, etc.). The surface investigation method according to the present invention is carried out, to investigate the depth information of a structure formed on a substrate, and identify areas of the substrate which have defective functional elements (i.e. areas with a KPI that does not fall within an acceptable threshold). The processing parameters of the recipe (e.g., dose, focus, resist characteristics, etc.) may then be adjusted such that the defects of the functional elements are prevented or eliminated, when a further substrate is processed by the lithographic apparatus. This method may be performed iteratively to obtain the processing parameters which produce the least number of outlier investigation areas, or preferably, the processing parameter under which outlier investigation areas are eliminated.

[0072] A system comprising a lithographic apparatus configured to form structures on the surface of a substrate, a first scanning technique apparatus, second scanning technique apparatus and a control unit may be provided. The control unit may be configured to cause the system to perform the method for controlling the processing parameters of a lithographic apparatus. Accordingly, the control unit causes the system to iteratively optimise the recipe, such that defects of the structure formed on a surface are reduced or eliminated.

[0073] According to the present invention, a computer program product comprising a computer readable medium may be provided. The computer readable medium has instructions therein, the instructions, upon execution by a computer system, configured to cause the computer system to at least: generate a surface map of a surface of each investigation area, based on obtained first scans of a plurality of investigation areas of a substrate; calculate values of one or more key performance indicators (KPIs) of the surface map of each investigation area; and determine outlier investigation areas for further investigation via a technique which generates second scans of the outlier investigation areas, wherein an outlier investigation area is one which has at least one KPI not within an acceptable predetermined range of values of the KPI. The second scans may be generated by a second scanning technique.

[0074] The concepts disclosed herein may simulate or mathematically model any generic imaging system for imaging sub wavelength features, and may be especially useful with emerging imaging technologies capable of producing increasingly shorter wavelengths. Emerging technologies already in use include EUV (extreme ultra violet), DUV lithography that is capable of producing a 193nm wavelength with the use of an ArF laser, and even a 157nm wavelength with the use of a Fluorine laser. Moreover, EUV lithography is capable of producing wavelengths within a range of 20-5nm by using a synchrotron or by hitting a material (either solid or a plasma) with high energy electrons in order to produce photons within this range.

[0075] While the concepts disclosed herein may be used for imaging on a substrate such as a silicon wafer, it shall be understood that the disclosed concepts may be used with any type of lithographic imaging systems, e.g., those used for imaging on substrates other than silicon wafers.

[0076] Without limiting the scope of the present disclosure, some embodiments may be described in the context of providing detectors and detection methods in systems utilizing electron beams. However, the disclosure is not so limited. Other types of charged particle beams may be similarly applied. Furthermore, systems and methods for detection may be used in other imaging systems, such as optical imaging, photon detection, x-ray detection, ion detection, etc.

[0077] As used herein, unless specifically stated otherwise, the term “or” encompasses all possible combinations, except where infeasible. For example, if it is stated that a database can include A or B, then, unless specifically stated otherwise or infeasible, the database can include A, or B, or A and B. As a second example, if it is stated that a database can include A, B, or C, then, unless specifically stated otherwise or infeasible, the database can include A, or B, or C, or A and B, or A and C, or B and C, or A and B and C.

[0078] Embodiments are further described using the following clauses:1. A method of investigating a surface of a structure formed on a substrate, comprising the steps of: selecting a plurality of investigation areas on the substrate; obtaining, via a first scanning technique, first scans of the plurality of investigation areas; generating a surface map of the surface of each investigation area, based on the obtained first scans; calculating values of one or more key performance indicators (KPIs) of the surface map of each investigation area; determining outlier investigation areas, wherein an outlier investigation area is one which has at least one KPI not within an acceptable predetermined range of values of the KPI; and obtaining, via a second scanning technique, second scans of the outlier investigation areas.2. The method of clause 1, wherein the surface map contains height information of the surface of each investigation area.3. The method of clauses 1 or 2, wherein the first scanning technique is scanning electron microscopy (SEM) and the first scan is an SEM image.4. The method of any of the preceding clauses, wherein the surface map of each investigation area is generated by a neural network.5. The method of any of the preceding clauses, wherein the second scanning technique is transmission electron microscopy and the second scans are transmission electron microscopy images.6. The method of any of the preceding clauses, wherein the second scanning technique is cross- sectional scanning electron microscopy and the second scans are cross-sectional scanning electron microscopy images.7. The method of any of the preceding clauses, wherein the second scanning technique is atomic force microscopy and the second scan is a topographical scan of the substrate surface.8. The method of any of the preceding clauses, wherein one or more KPIs are selected from the group consisting of: critical dimension (CD) of a structure, local CD uniformity (LCDU) associated with the structure; line edge roughness (LER) associated with the structure; line width roughness (LWR) related to line space patterns, and contact edge roughness related to contact holes.9. The method of any of the preceding clauses, wherein one or more KPIs are selected from the group consisting of: side wall angle (SWA), corner rounding, and 3D edge placement error.10. The method of any of the preceding clauses, wherein one or more KPIs are defect rates associated with the structure.11. The method of any of the preceding clauses, wherein one or more KPIs are one of: stochastic edge placement error (SEPE), deviations of the surface height from the surface height mean or stochastic variations associated with geometry of the structure.12. The method according to any of the preceding clauses wherein the investigation areas are selected such that the sum of investigation areas is in the range of 25% or more than and 75% or less than the total area of the substrate on which structures are formed.13. The method according to any of the preceding clauses, wherein selecting a plurality of investigation areas comprises: randomly selecting areas of the substrate.14. The method according to any of the preceding clauses, wherein selecting a plurality of investigation areas comprises or further comprises: selecting areas of the substrate which are known to have a higher likelihood of having an outlier investigation area disposed thereon than other regions of the surface of the substrate.15. The method of any of the preceding clauses, wherein the structure is formed on a substrate via lithography.16. A method of manufacturing devices on substrates, comprising the steps of: forming a structure on a substrate using a lithographic apparatus and a processing apparatus according to a recipe; investigating the surface of the structure formed on the substrate according to the method of any one of the preceding clauses; adjusting a parameter of the recipe based on information obtained from the investigation; and forming a structure on a further substrate using a lithographic apparatus and a processing apparatus according to the adjusted recipe.17. A system comprising: a lithographic apparatus configured to pattern the surface of a substrate, a first scanning technique apparatus, second scanning technique apparatus and a control unit, wherein the control unit is configured to cause the system to perform the method of clause 14.18. A computer program product comprising a computer readable medium, having instructions therein, the instructions, upon execution by a computer system, configured to cause the computer system to at least: generate a surface map of a surface of one or more investigation areas, based on obtained first scans of the one or more investigation areas of a substrate; calculate values of one or more key performance indicators (KPIs) of the surface map of each investigation area; and determine outlier investigation areas for investigation via a technique which generates second scans of the outlier investigation areas, wherein an outlier investigation area is one which has at least one KPI not within an acceptable predetermined range of values of the KPI.19. The computer program product of clause 18, wherein the surface map contains height information of the surface of each investigation area.20. The computer program product of any one of clauses 18 or 19, wherein the first scans are SEM images obtained via scanning electron microscopy.21. The computer program product of any one of clauses 18 to 20, wherein the surface map of each investigation area is generated by a neural network.22. The computer program product of any one of clauses 18 to 21, wherein the second scans are transmission electron microscopy images obtained via transmission electron microscopy.23. The computer program product of any one of clauses 18 to 22, wherein second scans are cross-sectional scanning electron microscopy images obtained via cross-sectional scanning electron microscopy.24. The computer program product of any one of clauses 18 to 23, wherein the second scans are topographical scans of the substrate surface obtained via atomic force microscopy.25. The computer program product of any one of clauses 18 to 24, wherein one or more KPIs are selected from the group consisting of: critical dimension (CD) of a structure, local CD uniformity (LCDU) associated with the structure; line edge roughness (LER) associated with the structure; line width roughness (LWR) related to line space patterns, and contact edge roughness related to contact holes.26. The computer program product of any one of clauses 18 to 25, wherein one or more KPIs are selected from the group consisting of: side wall angle (SWA), corner rounding, and 3D edge placement error.27. The computer program product of any one of clauses 18 to 26, wherein one or more KPIs are defect rates associated with the structure.28. The computer program product of any one of clauses 18 to 27, wherein one or more KPIs are one of: stochastic edge placement error (SEPE), deviations of the surface height from the surface height mean or stochastic variations associated with geometry of the structure.29. The computer program product of any one of clauses 18 to 28, wherein the investigation areas are selected such that the sum of investigation areas is in the range of 25% or more than and 75% or less than the total area of the substrate on which structures are formed.30. The computer program product of any one of clauses 18 to 29, wherein the plurality of investigation areas are selected randomly.31. The computer program product of any one of clauses 18 to 30, wherein the plurality of investigation areas are selected based on a determination that the investigation areas are known to have a higher likelihood of having an outlier investigation area disposed thereon than other regions of the surface of the substrate.32. The computer program product of any one of clauses 18 to 31, wherein the structure is formed on a substrate via lithography.33. A method of selecting regions for inspection via a second metrology technique, comprising the steps of: densely sampling a surface of a substrate via a first scanning technique to obtain first scans of sampled areas; analysing the first scans obtained during the dense sampling via a neural network to extract depth information about the sampled areas; and selecting one or more of the sampled areas for sampling via a second scanning technique, based on a determination that the depth information extracted by the neural network does not fall within a predetermined range of one or more key performance indicators, wherein the second scanning technique is configured to obtain depth information at a greater resolution than the first scanning technique.

[0079] The descriptions above are intended to be illustrative, not limiting. Thus, it will be apparent to one skilled in the art that modifications may be made as described without departing from the scope of the claims set out below.

Claims

CLAIMS1. A computer program product comprising a computer readable medium, having instructions therein, the instructions, upon execution by a computer system, configured to cause the computer system to at least: generate a surface map of a surface of one or more investigation areas, based on obtained first scans of the one or more investigation areas of a substrate; calculate values of one or more key performance indicators (KPIs) of the surface map of each investigation area; and determine outlier investigation areas for investigation via a technique which generates second scans of the outlier investigation areas, wherein an outlier investigation area is one which has at least one KPI not within an acceptable predetermined range of values of the KPI.

2. The computer program product of claim 1, wherein the surface map contains height information of the surface of each investigation area.

3. The computer program product of claim 1, wherein the first scans are SEM images obtained via scanning electron microscopy.

4. The computer program product of claim 1, wherein the surface map of each investigation area is generated by a neural network.

5. The computer program product of claim 1, wherein the second scans are transmission electron microscopy images obtained via transmission electron microscopy.

6. The computer program product of claim 1, wherein second scans are cross-sectional scanning electron microscopy images obtained via cross-sectional scanning electron microscopy.

7. The computer program product of claim 1, wherein the second scans are topographical scans of the substrate surface obtained via atomic force microscopy.

8. The computer program product of claim 1, wherein one or more KPIs are selected from the group consisting of: critical dimension (CD) of a structure, local CD uniformity (LCDU) associated with the structure; line edge roughness (LER) associated with the structure; line width roughness (LWR) related to line space patterns, and contact edge roughness related to contact holes.

9. The computer program product of claim 1, wherein one or more KPIs are selected from the group consisting of: side wall angle (SWA), corner rounding, and 3D edge placement error.

10. The computer program product of claim 1, wherein one or more KPIs are defect rates associated with the structure.

11. The computer program product of claim 1, wherein one or more KPIs are one of: stochastic edge placement error (SEPE), deviations of the surface height from the surface height mean or stochastic variations associated with geometry of the structure.

12. The computer program product of claim 1, wherein the investigation areas are selected such that the sum of investigation areas is in the range of 25% or more than and 75% or less than the total area of the substrate on which structures are formed.

13. The computer program product of claim 1, wherein the plurality of investigation areas are selected randomly.

14. The computer program product of claim 1, wherein the plurality of investigation areas are selected based on a determination that the investigation areas are known to have a higher likelihood of having an outlier investigation area disposed thereon than other regions of the surface of the substrate.

15. The computer program product of claim 1, wherein the structure is formed on a substrate via lithography.

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