Apparatus and method for determining critical region of patterned substrate

Abstract

A computer program product includes a computer readable medium having instructions therein configured, when executed by a computer system, to cause the computer system to at least: generate a surface map of a surface of one or more inspection areas of a substrate based on an obtained first scan of the one or more inspection areas; calculating values of one or more key performance indicators (KPIs) of the surface map of each check area; and determining the anomalous value verification region for verification via a technique that generates a second scan, wherein the anomalous value verification region is a region having at least one KPI that is not within an acceptable predetermined range of the value of the KPI.

Description

Cross-references to related applications

[0001] This application claims priority to EP application 23186833.2, filed on July 20, 2023, which is incorporated herein by reference in its entirety. Technical Field

[0002] The description in this paper generally relates to improved metrology systems and methods. More specifically, it involves identifying key areas of interest by employing models configured to determine the three-dimensional data of a patterned structure on a substrate using a single image (e.g., a SEM image), and analyzing these key areas of interest via other techniques. Background Technology

[0003] For example, photolithography projection equipment can be used in the fabrication of integrated circuits (ICs). In this case, a patterning apparatus (e.g., a mask) can contain or provide a pattern corresponding to an individual layer (“design layout”) of the IC, and the pattern can be transferred to a target portion (e.g., comprising one or more dies) on a substrate (e.g., a silicon wafer) coated with a layer of radiation-sensitive material (“resist”) by methods such as irradiating the target portion through the pattern on the patterning apparatus. Generally, a single substrate contains multiple adjacent target portions, and the photolithography projection equipment transfers the pattern sequentially to these multiple adjacent target portions, transferring one target portion at a time. In one type of photolithography projection equipment, the entire pattern on the patterning apparatus is transferred to a single target portion at once; this type of equipment is often referred to as a stepper. In an alternative equipment, often referred to as a step-scanning equipment, a projection beam scans over the patterning apparatus in a given reference direction (“scanning” direction) while the substrate moves synchronously parallel to or antiparallel to this reference direction. Different portions of the pattern on the patterning apparatus are gradually transferred to a single target portion. Generally, since the photolithography projection apparatus will have a reduction ratio M (e.g., 4), the substrate movement speed F will be 1 / M of the speed at which the projection beam scans the pattern forming apparatus. For example, more information about the photolithography apparatus described herein can be obtained from US 6,046,792, which is incorporated herein by reference.

[0004] With advancements in photolithography and other patterning technologies, the dimensions of functional components have continuously shrunk, while the number of functional components (such as transistors) in each device has been steadily increasing over the decades. Simultaneously, precision requirements in areas such as critical dimensions (CD) and height have become increasingly stringent. Errors in the shape and size of the structure can cause functional problems, including device malfunction or one or more electrical issues with the functional device. Therefore, it is necessary to measure the surface profile of the three-dimensional structure of functional components to characterize, reduce, or minimize one or more defects present on the functional components. However, measuring the three-dimensional (3D) structure of functional components using existing metrology tools and methods is time-consuming. Furthermore, metrology techniques capable of measuring surface profile features at sufficiently high resolution (i.e., at the same resolution as the CD or even smaller surface features) are often destructive to the sample and can therefore negatively impact the yield of the patterning process.

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

[0006] Houben, T., Huisman, T., Pisarenco, M. et al., in “Depth estimation from a single SEM image using pixel-wise fine-tuning with multimodal data” (MachineVision and Applications 33, 56 (2022)), disclose a computer-implemented method for extracting depth information of a substrate surface from an SEM image using pixel-wise fine-tuning based on multimodal data. According to this example, a neural network architecture (along with a custom training process) achieves fairly accurate depth predictions. This training process includes a weakly supervised domain adaptation stem, referred to as pixel-wise fine-tuning. This stem uses scattering measurement data to address the ground-truth deficiency. This method is well-suited for important semiconductor metrics such as top critical dimension (CD), bottom CD, and sidewall corners.

[0007] The computer-implemented methods mentioned above for extracting depth information from 2D SEM scans provide reasonable estimates of the topology of the sample surface. Another advantage of these methods is that they extract depth information without performing destructive techniques on the substrate. However, in some cases, the resolution of these methods is too low to extract topological information at a resolution on the same order of magnitude as CD or at a better resolution.

[0008] Metrological tools such as transmission electron microscopy (TEM), cross-sectional SEM (XSEM), and atomic force microscopy (AFM), which provide 3D data about a sample surface at CD resolution (or better), are destructive and time-consuming. For example, sample preparation for these techniques requires preparing a cross-section of the sample to enable accurate 3D information about the sample surface profile for measurement. Therefore, sample preparation for TEM is a time-consuming and complex process. However, accurate 3D information (e.g., height information) about the functional elements formed on the sample is often required to characterize, reduce, or minimize one or more defects in the device. For example, 3D information about defective functional elements (e.g., height information) can provide an understanding of how processing parameters of photolithography or other patterning processes affect the functional elements formed on the substrate, and therefore, can provide an understanding of how to adjust processing parameters to prevent the formation of defective functional elements on the substrate. Summary of the Invention

[0009] Therefore, a time-efficient method is needed to inspect the surface of a substrate, which can provide accurate 3D information about the structure formed on the substrate. Furthermore, a method is needed to control a photolithography apparatus so that the process formulation used to form structures on the sample surface can be adjusted to reduce or eliminate the occurrence of structural defects.

[0010] According to this disclosure, a computer program product is provided, including a computer-readable medium having instructions therein, which, when executed by a computer system, are configured to cause the computer system to at least: generate a surface map of the surface of one or more check regions of a substrate based on a first scan of one or more check regions obtained therefrom; calculate values ​​of one or more key performance indicators (KPIs) for the surface map of each check region; and determine an outlier check region for check via a technique for generating a second scan of an outlier check region, wherein the outlier check region is a region having at least one KPI that is outside an acceptable predetermined value range of that KPI. Attached Figure Description

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

[0012] Figure 1 A block diagram of various subsystems of the lithography system according to an embodiment is shown;

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

[0014] Figure 3 This is a schematic diagram of another photolithography projection device according to an embodiment;

[0015] Figure 4A An example SEM image of a line with a depth of 10 nm from the top of the substrate in the z-direction, according to an embodiment, is illustrated.

[0016] Figure 4B Another example SEM image is illustrated according to an embodiment, showing a line with a depth of 100 nm from the top of the substrate in the z-direction;

[0017] Figure 5A The illustration shows an SEM image and a corresponding depth map of a patterned contact hole on a substrate according to an embodiment.

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

[0019] Figure 5CThe illustration shows an SEM image and a corresponding depth map of another set of lines patterned on a substrate according to an embodiment.

[0020] Figure 6 The illustration shows a method for inspecting structural surfaces according to an embodiment of the present invention;

[0021] Figure 7 The illustration shows an example KPI (Key Performance Indicator) distribution obtained for a given substrate. Detailed Implementation

[0022] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings, wherein the same reference numerals in different drawings denote the same or similar elements unless otherwise stated. The implementations set forth in the following description of the exemplary embodiments do not represent all implementations conforming to the invention. Rather, they are merely examples of devices, systems, and methods conforming to aspects relating to the subject matter recounted in the appended claims.

[0023] Integrated circuit (IC) chips used in devices (e.g., telephones, laptops, computer memory, etc.) comprise complex circuit patterns. During the fabrication of these circuit patterns, images of the printed circuit patterns are captured to determine if the desired circuit pattern has been accurately printed. The final performance of the manufactured device depends primarily on the accuracy of the positioning and sizing of various features of the product structure formed via photolithography and other processing steps. These features are three-dimensional (3D) structures with predetermined depths and shapes at the nanometer scale. Product structures manufactured through imperfect photolithography processes or other processing steps will result in structures that differ slightly from the ideally or nominally desired structure.

[0024] To examine the size and three-dimensional information (e.g., feature height) of various features, it is highly beneficial to ensure that features at one layer are connected to features at another layer. However, obtaining 3D information of nanoscale structures is no trivial task. In the prior art, 3D information can be obtained via tilt-beam scanning electron microscopy (SEM), which requires two or more images targeting the same location to infer appropriate depth information. However, using multiple images for 3D metrology has several limitations. For example, capturing a pair of stereo images reduces the throughput of patterning or metrology processes because the beam tilt angles must be switched. Proper alignment between images is required to capture stereo images. Processing stereo images to determine depth information can be computationally expensive and is prone to introducing noise in the images and drift in metrology hardware. Therefore, using existing techniques to extract 3D information slows down chip manufacturing and metrology processes.

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

[0026] While specific references may be made herein to the fabrication of ICs, it should be clearly understood that the description herein has many other possible applications. For example, it can be used to fabricate integrated optical systems, guide and detection patterns for magnetic domain memories, liquid crystal display panels, thin-film magnetic heads, etc. Those skilled in the art will understand that, in the context of such alternative applications, any use of the terms “mask,” “wafer,” or “die” herein should be considered interchangeable with the more general terms “mask,” “substrate,” and “target portion,” respectively. The substrate mentioned herein may be processed, for example, in a track (a tool typically used to apply a resist layer to the substrate and develop the exposed resist) or in a metrology or inspection tool before or after exposure. Where applicable, the disclosure herein can be applied to such and other substrate processing tools. Furthermore, for example, to create a multilayer IC, the substrate may be processed more than once, such that the term “substrate” as used herein may also refer to a substrate that already contains multiple processed layers.

[0027] The critical dimension (CD) of a device refers to the minimum width of a line or hole, or the minimum spacing between two lines or holes. Therefore, 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 a patterning device).

[0028] In this document, the terms “radiation” and “beam” can be used to cover all types of electromagnetic radiation, including ultraviolet radiation (e.g., with wavelengths of 365, 248, 193, 157, or 126 nm) and EUV (extreme ultraviolet radiation, e.g., with wavelengths in the range of about 5–100 nm).

[0029] As a brief introduction, Figure 1An exemplary photolithography projection apparatus 10A is illustrated. The main components are a radiation source 12A, which may be a deep ultraviolet excimer laser source or other types of sources, including extreme ultraviolet (EUV) sources (as discussed above, the photolithography projection apparatus itself does not need to have a radiation source), illumination optics that, for example, define partial coherence (denoted as σ), and may include optics 14A, 16Aa, and 16Ab for shaping the radiation from source 12A; a pattern forming apparatus 18A; and a transmission optics 16Ac that projects an image of a pattern from the pattern forming apparatus onto a substrate plane 22A. An adjustable filter or aperture 20A at the pupil plane of the projection optics can limit the range of the beam angle impacting the substrate plane 22A.

[0030] In a photolithography projection apparatus, a light source provides illumination (i.e., radiation) to a patterning apparatus, and projection optics guide and shape the illumination onto a substrate via the patterning apparatus. The projection optics may include at least some of elements 14A, 16Aa, 16Ab, and 16Ac. A spatial image (AI) is the distribution of radiation intensity at a substrate level. A resist layer on the substrate is exposed, and the spatial image is transferred to the resist layer as a potential “resist image” (RI). A resist image (RI) can be defined as the spatial distribution of the solubility of the resist in the resist layer. Resist models can be used to calculate the resist image based on the spatial image, examples of which can be found in U.S. Patent Application Publication No. 2009-0157360, which is incorporated herein by reference in its entirety.

[0031] Once a semiconductor chip is fabricated, measurements can be performed to determine the dimensions of the structure fabricated on the substrate. For example, inspection of the fabricated structure can be based on critical dimension (CD) measurements obtained using metrology tools such as SEM, AFM, optical tools, etc. In an embodiment, the lateral dimensions of the structure (e.g., in the x, y plane) can be extracted from the CD measurements. In addition to the lateral dimensions of the feature, measurements (e.g., CD-SEM) can also include information about the feature's 3D dimensions (e.g., height, CD values ​​at different heights, height profile, etc.). In an embodiment, the height of the feature refers to its depth in the z-direction perpendicular to the xy plane.

[0032] Figure 2 An exemplary photolithography projection apparatus is schematically depicted, which can be utilized in conjunction with the techniques described herein. The apparatus includes: - An irradiation system IL for modulating beam B. In this specific case, the irradiation system also includes a radiation source SO; - A first stage (e.g., a pattern forming device stage) MT, which is provided with a pattern forming device holder to hold a pattern forming device MA (e.g., a mask) and is connected to a first locator to accurately position the pattern forming device relative to an article PS. - A second stage (substrate stage) WT, which is equipped with a substrate holder to hold the substrate W (e.g., a resist-coated silicon wafer) and is connected to a second locator to accurately position the substrate relative to the article PS. - A projection system (“lens”) PS (e.g., a refractive, reflective, or catadioptric optical system) for imaging the irradiated portion of the pattern forming apparatus MA onto a target portion C (e.g., including one or more dies) of the substrate W.

[0033] The beam PB then intercepts the patterning apparatus MA held on the patterning apparatus stage MT. After traversing the patterning apparatus MA, the beam B passes through the lens PL, which focuses the beam B onto the target portion C of the substrate W. The substrate stage WT can be precisely moved, for example, to position different target portions C within the path of the beam PB, by means of a second positioning component (and an interferometry component IF). Similarly, the first positioning component can be used, for example, to precisely position the patterning apparatus MA relative to the path of the beam B, after it has been mechanically retrieved from the patterning apparatus library or during scanning. Typically, movement of the stages MT and WT is achieved by means of a long-stroke module (coarse positioning) and a short-stroke module (precise positioning). However, in the case of a stepper (as opposed to a stepping scanning tool), the patterning apparatus stage MT may be connected only to a short-stroke actuator, or it may be fixed.

[0034] Figure 3 Another exemplary photolithography projection device, LA, is illustrated and can be utilized in conjunction with the techniques described herein. The photolithography projection apparatus LA includes a source collector module SO, an irradiation system (irradiator) IL, a support structure (e.g., a patterning stage) MT, a substrate stage (e.g., a wafer stage) WT, the substrate stage WT being configured to hold a substrate (e.g., a resist-coated wafer) W and being connected to a second positioner PW, the second positioner PW being configured to accurately position the substrate; and a projection system (e.g., a reflective projection system) PS, which is configured to project a pattern, given a radiation beam B by the patterning apparatus MA, onto a target portion C (e.g., including one or more dies) of the substrate W, similar to... Figure 2 The device shown above.

[0035] As described herein, the apparatus LA is reflective (e.g., employing a reflective patterning device). It should be noted that because most materials are absorbent in the EUV wavelength range, the patterning device can have multilayer reflectors, such as multilayer stacks comprising molybdenum and silicon. In one example, the multilayer stacked reflector has 40 layers of molybdenum and silicon pairs, where the thickness of each layer is a quarter wavelength. Even smaller wavelengths can be produced using X-ray lithography. Because most materials are absorbent at both EUV and X-ray wavelengths, the patterned sheets of absorbing material on the morphology of the patterning device (e.g., a TaN absorber on top of a multilayer reflector) define where features will be printed (positive resist) or not printed (negative resist).

[0036] See Figure 3 The irradiator IL receives an extreme ultraviolet (EUV) radiation beam from the source collector module SO. Methods for generating EUV radiation include, but are not limited to, converting a material into a plasma state having at least one element (e.g., xenon, lithium, or tin) with one or more emission lines in the EUV range. In such a method, commonly referred to as laser plasma (“LPP”), the plasma can be generated by irradiating a fuel (such as a droplet, stream, or cluster of material having spectral emission elements) 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. For example, when a CO2 laser is used to provide the laser beam for fuel excitation, the laser and the source collector module can be separate entities.

[0037] In this case, the laser is not considered part of the lithography apparatus, and the radiation beam is delivered from the laser to the source collector module by means of a beam delivery system, for example, including suitable guide mirrors and / or beam expanders. In other cases, the source can be a component of the source collector module; for example, when the source is a discharge-generated plasma EUV generator, it is often referred to as a DPP source.

[0038] A photolithography (LA) apparatus can be configured to pattern a substrate according to a process recipe, which defines the processing parameters for the patterning step. This process recipe affects the patterned structure formed on the substrate, and is therefore selected to achieve the desired patterned structure. The process recipe can depend on several factors, such as the pattern on the patterning apparatus, the desired CD (discrete core) to be achieved, and the substrate material.

[0039] The surface of a patterned substrate can be examined using scanning techniques to obtain information about its surface profile. For example, a scanning electron microscope (SEM) can be used to examine the surface.

[0040] Figure 4A and Figure 4BThe illustration shows an example of two different SEM images of lines with the same CD but different heights. Figure 4A The diagram illustrates a shape with z-direction (perpendicular to) Figure 4A and Figure 4B SEM image I1 of a line at a depth of 10 nm from the top of the substrate (the plane), while Figure 4B Another SEM image, I2, is illustrated, showing a line with a depth of 100 nm from the top of the substrate in the z-direction. These figures illustrate how depth affects the information captured in a SEM image, meaning that some information about the height of the substrate surface can be extracted from a 2D SEM image.

[0041] For many issues related to semiconductor manufacturing, there may be a correlation between inputs (e.g., process formulations) and outputs (e.g., patterns on patterned substrates), but this correlation may be too complex for humans to model or identify.

[0042] According to embodiments, it is possible to obtain estimated 3D data (such as depth information) of the structure of a patterned substrate from a 2D metrological image via a computing component. In one example, this can be accomplished by a system comprising: an image capturing device such as a scanning electron microscope (SEM) having electron beam optics configured to capture images of a patterned substrate; and one or more processors including a trained model stored in memory, capable of receiving the captured images and executing the model to determine depth information from the captured images. The one or more processors may be configured to input the captured images of the patterned substrate into the trained model, which is configured to generate depth-related data based on a single image; and to extract depth information from the captured images by executing the trained model. The 2D metrological image can be obtained by any suitable scanning technique and does not necessarily have to be obtained via an SEM.

[0043] There are no particular limitations on the model configured to generate data for estimating the depth information of the structure of the patterned substrate, and it can be determined based on several examples.

[0044] An example of performing this method includes: determining the depth information of the structure formed on the substrate based on a single SEM image of the structure. This depth information is determined using a convolutional neural network (CNN) trained to simulate the parallax effect present in pairs of stereo SEM images. This determination is performed by a computer system that executes the following steps: A single SEM image of a patterned structure on a substrate is obtained using an SEM tool; The SEM image is fed into the CNN to predict the disparity data associated with that SEM image. The CNN is trained through the following steps: A stereo pair of SEM images of a patterned substrate is obtained using an SEM tool. The stereo pair includes a first SEM image obtained under a first electron beam tilt setting of the SEM tool and a second SEM image obtained under a second electron beam tilt setting of the SEM tool. Using a CNN, disparity data between the first SEM image and the second SEM image is generated; The disparity data is combined with the second SEM image to generate a reconstructed image of the first SEM image; and Compare the reconstructed image with the first SEM image; and Based on the predicted parallax data, depth information associated with the patterned structure on the substrate is generated.

[0045] The implementation of this method is described in detail in WO2022128373, which is incorporated herein by reference in its entirety. WO2022128373 provides several other examples that can be used to generate models associated with depth information of a patterned structure on a substrate, and these examples can be used in accordance with the methods of this disclosure. According to these methods, it is possible to extract 3D information from a first scan using advanced modeling techniques based on deep neural networks.

[0046] Figure 5A , Figure 5B and Figure 5C The illustration shows different SEM images I3, I4, and I5, and the corresponding depths ma extracted from the SEM images using the method described in WO2022128373. It can be seen that the estimation provides a reasonable level of detail for the depth information. This level of detail is sufficient to identify regions within them that are likely to contain defective functional elements or where defective functional elements are certain to exist.

[0047] Another example of depth information extraction includes a data-driven approach that provides predictions of size, height, and width (CD) values ​​based on 2D SEM images. This method allows for the acquisition of height estimates for structures formed on a substrate surface with a mean absolute error (MAE) of 0.5 nm.

[0048] In another example, depth information of the substrate surface can be extracted from SEM images using pixel-by-pixel fine-tuning with multimodal data. According to this example, a neural network architecture, along with a customized training process, achieves accurate depth predictions. This training process includes a weakly supervised adaptive step, referred to as pixel-by-pixel fine-tuning. This step uses scattering measurement data to address the scarcity of ground truth. This method is well-suited for important semiconductor metrics such as top critical dimension (CD), bottom CD, and sidewall corners.

[0049] However, the resolution of 3D profiles obtained from 2D images is not high enough for substrate surface analysis in some cases. For example, the model may not be as accurate as 3D metrology techniques (e.g., TEM) in predicting parameters such as sidewall angles and fillet treatments.

[0050] According to embodiments of the present invention, a method for verifying the surface of a structure formed on a substrate is provided. Figure 6 The method is schematically illustrated above. This method can be executed by a computer-readable medium having instructions that, when executed by a computer system, are configured to cause the computer system to perform a method that examines the surface of a structure formed on a substrate.

[0051] According to this method, the following steps are performed. Multiple verification regions A1 are selected (step S100) for evaluation via a first scanning technique (e.g., a first metrology technique). The verification region may correspond to a target portion on the substrate; however, it may cover a smaller or larger area. Images of the verification regions A1 are obtained via the first scanning technique (step S200). Then, a surface map is generated based on each of the obtained images (step S300). At least one key performance indicator (KPI) is calculated for each surface map (S400). It is determined whether each calculated KPI falls within an acceptable predetermined range. If a KPI falls outside the acceptable predetermined range, the corresponding region (outlier verification region A2) is selected for further verification (step S500). The outlier verification region is further verified using a second scanning technique (e.g., a second metrology technique) (step S600).

[0052] The surface map obtained in step S300 may contain height information of the surface of each verification area. This surface map can be obtained by any suitable calculation method (such as the method described in WO2022128373). However, possible methods are not limited to this, and it can be any other machine learning model that can be trained to generate the surface map based on the 2D image obtained via the first scanning technique.

[0053] Surface maps can be generated by neural networks.

[0054] The criteria for selecting multiple verification regions A1 are not particularly limited. Preferably, the total area covered by the verification regions A1 is greater than 0% and less than 100% of the total area of ​​the substrate surface on which structures are formed. More preferably, the total area covered by the verification regions A1 is in the range of 25% or more and 75% or less of the total area of ​​the substrate surface on which structures are formed. In another example, all areas of the substrate on which structures are formed (i.e., the target portion) are selected for verification. This allows for the generation of a first scan for each verification region, thus providing a more detailed analysis of the substrate surface; however, this is time-consuming. In yet another example, approximately half of the area of ​​the substrate on which structures are formed is selected as the verification region. In this example, verification regions A1 are randomly selected. In another example, verification regions A1 may be selected in a chess-like arrangement (e.g., Figure 6 (As shown above). According to this arrangement, selecting the region for verification provides a representative sample of the entire substrate surface, and the time required for the first scan is less than the time required to scan most of the surface. In another example, the selected verification region A1 can cover a portion of the substrate surface in a non-uniform manner; for example, the verification region can be concentrated in a specific area of ​​the substrate. This can be advantageous when it is known that a specific area of ​​the surface has the potential to contain defective functional elements, which will be selected for further verification. Alternatively, some verification regions can be selected to cover specific areas of the surface of particular interest (i.e., areas with the potential to contain defective functional elements), and other verification regions can be selected randomly.

[0055] The first scanning technique typically does not include clearly detailed depth information about the sample. However, the first scanning technique is preferably a relatively fast and non-destructive technique, meaning that a relatively rapid scan can be performed on a large portion of the sample. For example, the first scanning technique could be a scanning electron microscope (SEM), in which case the first scan obtained by the first scanning technique is an SEM image. If it is desired to extract depth information via the method described in WO2022128373 (in step S300), SEM can be selected as the first scanning technique. According to this method, high-resolution depth information can be extracted from a 2D SEM image.

[0056] A surface map containing the height information (3D information) of the first scan is generated. This is accomplished via a computer-implemented method, such as a convolutional neural network (CNN) as described in WO2022128373. The method described in WO2022128373 targets the determination of 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 methods for extracting depth information from other scanning techniques.

[0057] Next, calculate the values ​​of one or more key performance indicators (KPIs) for the surface map of each audit area A1.

[0058] Key performance indicators are parameters of the substrate surface, which can be used as indicators of whether there are defects in the functional components of the substrate surface.

[0059] For example, a KPI can be an indicator of whether the characteristics of region A1 meet the accuracy requirements of the structure's critical dimension (CD). In this case, the KPI could be either the structure's critical dimension (CD) or the local CD uniformity (LCDU) associated with the structure.

[0060] In another example, a KPI could be an indicator of the texture of the area A1 being checked. For example, one or more KPIs could be line edge roughness (LER) associated with a structure, line width roughness (LWR) associated with a line spacing pattern, or contact edge roughness associated with a contact hole.

[0061] In another example, the KPI could be the defect rate associated with the structure.

[0062] In another example, a KPI can be a measure of a random event, such as random edge placement error (SEPE), deviation of the profile from the mean, or random variation associated with the geometry of the structure.

[0063] In another example, a KPI can be associated with other parameters of the surface that are related to the sample height profile. For example, a KPI could be sidewall angle (SWA), corner rounding, or 3D edge placement error. These examples of KPIs (and others that contain information about the height of the sample surface) are associated with parameters typically obtained through 3D metrology techniques such as TEM and AFM.

[0064] If the KPIs for a specific audit area fall outside the predetermined range, that audit area may have defective functional components. The acceptable range of KPIs depends on the tolerance of the structure to be formed on the surface.

[0065] Identify the audit area containing KPIs that fall outside the predetermined range. The area containing at least one KPI that is not within the acceptable predetermined range of KPI values ​​is outlier audit area A2.

[0066] Figure 7 This shows the surface height (z) and average value (z) for a given set of inspection areas. mean The example distribution of deviation values ​​is shown below. This example distribution shows that the proportion of KPI values ​​is outside the acceptable range. The verification area corresponding to these KPIs is selected as outlier verification area A2 for further verification via a second scanning technique.

[0067] The second scanning technique was used to obtain the second scan of the outlier check area A2.

[0068] The second scanning technique can be a high-resolution technique that includes 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 to these and can be any suitable metrology technique that extracts detailed height information about the sample.

[0069] According to this method, a second scanning technique is performed only on areas that may contain defective functional components. This second scanning technique includes height information about the sample. By calculating KPIs for each verification area based on the surface map, it is possible to identify areas that are likely of the most interest and should be verified by the second scanning technique. If the calculated KPIs involve 3D parameters of the surface (typically obtained through 3D metrology), areas on the sample surface with 3D features that require further verification by the second scanning technique can be accurately predicted.

[0070] Therefore, time-consuming scanning techniques (e.g., second scanning techniques) and / or destructive techniques are often performed only on the region of interest (outlier region A2), thus improving the efficiency of substrate surface inspection. According to the present invention, it is unnecessary to perform time-consuming and / or destructive techniques (such as second scanning techniques) on the entire sample surface. The second scanning technique is performed only on the region of interest identified by the method according to the present invention, and regions not containing defective functional elements remain intact.

[0071] The method according to the invention can be used to control the processing parameters of a photolithography apparatus, as described below. A structure can be formed on a substrate using a photolithography apparatus and a processing apparatus according to a process formulation. The process formulation can indicate the processing parameters (e.g., dosage, focus, resist properties, etc.) that the substrate should be processed. The surface inspection method according to the invention is performed to check the depth information of the structure formed on the substrate and identify regions on the substrate with defective functional elements (i.e., regions with KPIs that do not fall within acceptable thresholds). Then, when another substrate is processed by the photolithography apparatus, the processing parameters of the process formulation (e.g., dosage, focus, resist properties, etc.) can be adjusted so that defects in the functional elements are prevented or eliminated. This method can be performed iteratively to obtain processing parameters that produce a minimum number of outlier inspection regions, or preferably, processing parameters that eliminate outlier inspection regions.

[0072] A system may be provided comprising a photolithography apparatus configured to form a structure on the surface of a substrate, a first scanning apparatus, a second scanning apparatus, and a control unit. The control unit may be configured to cause the system to execute methods for controlling processing parameters of the photolithography apparatus. Therefore, the control unit enables the system to iteratively optimize the process formulation, thereby reducing or eliminating defects in the structure formed on the surface.

[0073] According to the present invention, a computer program product comprising a computer-readable medium may be provided. The computer-readable medium contains instructions therein, which, when executed by a computer system, are configured to cause the computer system to at least: generate a surface map of the surface of one or more check regions of a substrate based on a first scan of one or more check regions obtained therefrom; calculate values ​​of one or more key performance indicators (KPIs) for the surface map of each check region; and determine an outlier check region for check via a technique for generating an outlier check region, wherein the outlier check region is a region having at least one KPI that is outside an acceptable predetermined value range of that KPI. The second scan may be generated by a second scanning technique.

[0074] The concepts disclosed herein can be used to simulate or mathematically model any general imaging system for imaging sub-wavelength features, and are particularly applicable to emerging imaging technologies capable of generating increasingly shorter wavelengths. Emerging technologies already in use include EUV (Extreme Ultraviolet) and DUV lithography, which can generate wavelengths of 193 nm using ArF lasers and even 157 nm using fluorine lasers. Furthermore, EUV lithography can generate wavelengths in the 20–5 nm range by using synchrotrons or by bombarding materials (solid-state or plasma) with high-energy electrons, thereby producing photons within this range.

[0075] While the concepts disclosed herein can be used for imaging on substrates such as silicon wafers, it should be understood that the disclosed concepts can be used with any type of lithography imaging system (e.g., a lithography imaging system for imaging on substrates other than silicon wafers).

[0076] Some embodiments are described within the context of providing detectors and detection methods in systems utilizing electron beams without limiting the scope of this disclosure. However, the invention is not limited thereto. Other types of charged particle beams can be similarly applied. Furthermore, the systems and methods used for detection can be used in other imaging systems, such as optical imaging, photon detection, X-ray detection, ion detection, etc.

[0077] As used herein, unless otherwise specifically stated, the term "or" covers all possible combinations, unless impractical. For example, if a database is declared to include A or B, then unless specifically stated or impractical, the database may include A or B or A and B. As a second example, if a database is declared to include A, B, or C, then unless specifically stated or impractical, the database may include A or B or C, or may include A and B, or may include A and C, or may include B and C, or may include A and B and C.

[0078] The embodiments are further described using the following terms: 1. A method for inspecting the surface of a structure formed on a substrate, comprising the following steps: Select multiple inspection areas on the substrate; A first scan of the multiple verification areas is obtained using a first scanning technique; Based on the obtained first scan, a surface map of the surface of each verification area is generated; Calculate the values ​​of one or more key performance indicators (KPIs) for the surface map of each audited area; Determine an outlier checking region, wherein the outlier checking region is a region having at least one KPI that is outside the acceptable predetermined value range of the KPI; and A second scan of the outlier verification area is obtained through a second scanning technique. 2. The method according to Clause 1, wherein the surface map contains height information of the surface of each verification area. 3. The method according to Clause 1 or 2, wherein the first scanning technique is a scanning electron microscope (SEM), and the first scan is an SEM image. 4. The method according to any one of the preceding clauses, wherein the surface map of each verification area is generated by a neural network. 5. The method according to any one of the preceding clauses, wherein the second scanning technique is a transmission electron microscope, and the second scan is a transmission electron microscope image. 6. The method according to any one of the preceding clauses, wherein the second scanning technique is a cross-sectional scanning electron microscope, and the second scan is a cross-sectional scanning electron microscope image. 7. The method according to any one of the preceding clauses, wherein the second scanning technique is atomic force microscopy, and the second scan is a topographic scan of the substrate surface. 8. The method according to any one of the preceding clauses, wherein the one or more KPIs are selected from the group consisting of: critical dimension (CD) of the structure, local CD uniformity (LCDU) associated with the structure; line edge roughness (LER) associated with the structure; line width roughness (LWR) associated with the line spacing pattern; and contact edge roughness associated with the contact hole. 9. The method according to any one of the preceding clauses, wherein the one or more KPIs are selected from the group consisting of: sidewall angle (SWA), corner rounding, and 3D edge placement error. 10. The method according to any one of the preceding clauses, wherein the one or more KPIs are defect rates associated with the structure. 11. The method according to any one of the preceding clauses, wherein said one or more KPIs is one of the following: random edge placement error (SEPE), deviation of surface height from the average surface height, or random variation associated with the geometry of said structure. 12. The method according to any one of the preceding clauses, wherein the verification area is selected such that the sum of the verification areas is within the range of 25% or more and 75% or less of the total area on which the structure is formed. 13. The method according to any one of the preceding clauses, wherein selecting the plurality of verification areas includes: Randomly select a region of the substrate. 14. The method according to any one of the preceding clauses, wherein selecting the plurality of verification areas includes or further includes: The selected region of the substrate is known to have a higher probability of having an outlier check area compared to other regions of the substrate surface. 15. The method according to any one of the preceding clauses, wherein the structure is formed on a substrate via photolithography. 16. A method of fabricating an apparatus on a substrate, comprising the steps of: According to the process formulation, a structure is formed on the substrate using photolithography equipment and processing equipment; The surface of the structure formed on the substrate is examined according to the method described in any one of the preceding clauses; Based on the information obtained from the verification, the parameters of the process formulation are adjusted; and According to the adjusted process formulation, a structure is formed on another substrate using photolithography equipment and processing equipment. 17. A system comprising: a photolithography apparatus configured to pattern a surface of a substrate, a first scanning apparatus, a second scanning apparatus, and a control unit, wherein the control unit is configured to cause the system to perform the method described in accordance with Clause 14. 18. A computer program product comprising a computer-readable medium having instructions therein, the instructions being configured, when executed by a computer system, to cause the computer system to at least: Based on the first scan of one or more inspection areas of the substrate, a surface map of the surface of the one or more inspection areas is generated; Calculate the values ​​of one or more key performance indicators (KPIs) for the surface map of each verification area; and The outlier check area is determined by a technique of generating an outlier check area for check purposes, wherein the outlier check area is an area having at least one KPI that is not within the acceptable predetermined value range of the KPI. 19. The computer program product according to Clause 18, wherein the surface map contains height information of the surface of each verification area. 20. The computer program product according to any one of Clauses 18 or 19, wherein the first scan is a SEM image obtained via a scanning electron microscope. 21. The computer program product according to any one of Clauses 18 to 20, wherein the surface map of each verification region is generated by a neural network. 22. The computer program product according to any one of clauses 18 to 21, wherein the second scan is a transmission electron microscope image obtained via a transmission electron microscope. 23. The computer program product according to any one of clauses 18 to 22, wherein the second scan is a cross-sectional scanning electron microscope image obtained via a cross-sectional scanning electron microscope. 24. The computer program product according to any one of Clauses 18 to 23, wherein the second scan is a topographic scan of the substrate surface obtained via atomic force microscopy. 25. A computer program product according to any one of Clauses 18 to 24, wherein said one or more KPIs are selected from the group consisting of: critical dimensions (CD) of the structure, local CD uniformity (LCDU) associated with said structure; line edge roughness (LER) associated with said structure; line width roughness (LWR) associated with line spacing pattern; and contact edge roughness associated with contact hole. 26. A computer program product according to any one of Clauses 18 to 25, wherein said one or more KPIs are selected from the group consisting of: sidewall angle (SWA), corner rounding, and 3D edge placement error. 27. A computer program product according to any one of Clauses 18 to 26, wherein one or more KPIs are defect rates associated with the structure. 28. A computer program product according to any one of Clauses 18 to 27, wherein said one or more KPIs are one of the following: random edge placement error (SEPE), deviation of surface height from the average surface height, or random variation associated with the geometry of said structure. 29. The computer program product according to any one of Clauses 18 to 28, wherein the verification area is selected such that the sum of the verification areas is within the range of 25% or more and 75% or less of the total area on which the structure is formed. 30. The computer program product according to any one of Clauses 18 to 29, wherein the plurality of verification areas are randomly selected. 31. A computer program product according to any one of clauses 18 to 30, wherein the plurality of verification regions are selected based on the determination that the verification regions are more likely to have outlier verification regions thereon than other regions of the surface of the substrate. 32. The computer program product according to any one of Clauses 18 to 31, wherein the structure is formed on a substrate via photolithography. 33. A method for selecting an area for inspection via a second metrological technique, comprising the following steps: The surface of the substrate is densely sampled using a first scanning technique to obtain a first scan of the sampled area; The first scan obtained during dense sampling is analyzed via a neural network to extract depth information about the sampled region; and Based on the determination that the depth information extracted by the neural network does not fall within a predetermined range of one or more key performance indicators, one or more regions in the sampled area are selected for sampling via a second scanning technique. The second scanning technique is configured to obtain depth information at a higher resolution than the first scanning technique.

[0079] The above description is intended to illustrate and not limit. Therefore, it will be apparent to those skilled in the art that modifications can be made as described without departing from the scope of the claims set forth below.

Claims

1. A computer program product comprising a computer readable medium having instructions therein, which when executed by a computer system, are configured to cause the computer system to at least: generate a surface map of a surface of one or more inspection regions of a substrate based on a first scan of the one or more inspection regions obtained; calculate a value of one or more key performance indicators (KPIs) of the surface map of each inspection region; and determine an outlier inspection region for inspection via a technique of generating a second scan of the outlier inspection region, wherein the outlier inspection region is a region having at least one KPI that is not within an acceptable predetermined range of values of the KPIs.

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

3. The computer program product of claim 1, wherein the first scan is a SEM image obtained via a scanning electron microscope.

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

5. The computer program product of claim 1, wherein the second scan is a transmission electron microscope image obtained via a transmission electron microscope.

6. The computer program product of claim 1, wherein the second scan is a cross-sectional scanning electron microscope image obtained via a cross-sectional scanning electron microscope.

7. The computer program product of claim 1, wherein the second scan is a topography scan of the substrate surface obtained via an atomic force microscope.

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

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

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

11. The computer program product of claim 1, wherein the one or more KPIs is one of: a random edge placement error (SEPE), a deviation of a surface height from a surface height average, or a random variation associated with a geometry of the structure.

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

13. The computer program product of claim 1, wherein the plurality of inspection regions are selected randomly. ​ 14. The computer program product of claim 1, wherein the plurality of check regions are selected based on a determination that the check regions have a greater likelihood of having an outlier check region disposed thereon as compared to other regions of the surface of the substrate.

15. The computer program product of claim 1, wherein the structures are formed on a substrate via photolithography.

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