Metrology method and related apparatus

Abstract

The present disclosure relates to metrology methods and related apparatus. A method and related apparatus for determining an edge position relating to an edge of a feature comprised within an image comprising noise, such as a scanning electron microscope image, is disclosed. The method comprises determining a reference signal from the image; and determining the edge position relative to the reference signal. The reference signal can be determined from the image by applying the image to a one-dimensional low-pass filter in a direction parallel to an initial profile estimating the edge position.

Description

[0001] Cross-references to related applications

[0002] This application is a divisional application of the invention patent application with international application number PCT / EP2019 / 055714, international application date of March 7, 2019, entry into the Chinese national phase date of September 21, 2020, Chinese national application number 201980020816.1, and invention title "Measuring Method and Related Apparatus".

[0003] This application claims priority to European Patent Application No. 18163680.4, filed on March 23, 2018, the entire contents of which are incorporated herein by reference. Technical Field

[0004] This invention relates to a method and apparatus for applying patterns to a substrate during photolithography, and to the measurement thereof. Background Technology

[0005] A photolithography apparatus is a machine that applies a desired pattern onto a substrate, typically onto a target portion of the substrate. Photolithography apparatuses can be used, for example, in the fabrication of integrated circuits (ICs). In this case, a patterning device (which is alternatively called a mask or photomask) can be used to generate a circuit pattern to be formed on a single layer of the IC. This pattern can then be transferred onto a target portion (e.g., a portion comprising one or more dies) on a substrate (e.g., a silicon wafer). The transfer of the pattern is typically performed via imaging onto a layer of radiation-sensitive material (resist) disposed on the substrate. Typically, a single substrate will contain a network of consecutively patterned adjacent target portions. Known photolithography apparatuses include: so-called steppers, in which each target portion is irradiated by exposing the entire pattern onto the target portion at once; and so-called scanners, in which each target portion is irradiated by scanning the pattern with a radiation beam in a given direction (“scanning” direction) while simultaneously scanning the substrate parallel or antiparallel to that direction. A pattern can also be transferred from a patterning device onto a substrate by imprinting the pattern onto the substrate.

[0006] To monitor the photolithography process, parameters of the patterned substrate are measured. These parameters may include, for example, the overlap error between successive layers formed in or on the patterned substrate, and the critical linewidth or critical dimension (CD) of the developed photoresist. This measurement can be performed on the product substrate and / or a dedicated measurement target. Various techniques are available for measuring the microstructures formed during the photolithography process, including the use of scanning electron microscopy and various specialized tools.

[0007] During photolithography processes, such as applying patterns to a substrate or measuring such patterns, process control methods are used to monitor and control the process. These process control techniques are typically employed to obtain corrections for controlling the photolithography process. There is a desire to improve such process control methods. Summary of the Invention

[0008] In a first aspect of the invention, a method is provided for determining the edge position associated with an edge of a feature, said feature being included within an image including noise; the method includes: determining a reference signal from said image; and determining the edge position relative to said reference signal.

[0009] In a second aspect of the invention, a computing device is provided, the computing device including a processor and configured to perform the method of the first aspect.

[0010] In a third aspect of the invention, a scanning electron microscope inspection apparatus is provided, which is operable to image multiple features on a substrate and includes the computing device of the second aspect.

[0011] In a fourth aspect of the invention, a computer program is provided, the computer program including program instructions operable to perform the method of the first aspect when run on a suitable device.

[0012] Other aspects, features, and advantages of the invention, as well as the structure and operation of various embodiments thereof, are described in detail below with reference to the accompanying drawings. Note that the invention is not limited to the specific embodiments described herein. Such embodiments are presented herein for illustrative purposes only. Other embodiments will be readily apparent to those skilled in the art based on the teachings contained herein. Attached Figure Description

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

[0014] Figure 1 The image depicts a photolithography device, which, together with other equipment, forms a manufacturing facility for semiconductor devices.

[0015] Figure 2 This is a first example flowchart depicting a method according to an embodiment of the present invention;

[0016] Figure 3 The image shows a reference SEM signal after the first and second iterations of the proposed method; and

[0017] Figure 4 This is a second example flowchart depicting a method according to an embodiment of the present invention. Detailed Implementation

[0018] Before describing the embodiments of the invention in detail, it is beneficial to provide example environments in which the embodiments of the invention can be implemented.

[0019] Figure 1 A lithography apparatus LA is shown at position 200, as part of an industrial production facility that enables high-volume lithography manufacturing processes. In this example, the manufacturing process is adapted to produce semiconductor products (integrated circuits) on substrates such as semiconductor wafers. Those skilled in the art will understand that in variations of this process, a wide variety of products can be manufactured by processing different types of substrates. The semiconductor product is used purely as an example for production that is of significant commercial importance today.

[0020] Within the lithography apparatus (or simply "lithography tool" 200), the measurement station MEA is shown at 202, and the exposure station EXP is shown at 204. The control unit LACU is shown at 206. In this example, each substrate accesses both the measurement station and the exposure station to be patterned. For example, in an optical lithography apparatus, a projection system is used to transfer a product pattern from the patterning apparatus MA onto the substrate using an adapted radiation and projection system. This is done by forming an image of the pattern in a radiation-sensitive resist material layer.

[0021] The term "projection system" as used herein should be broadly interpreted to encompass any type of projection system, including refractive, reflective, catadioptric, magnetic, electromagnetic, and electrostatic optical systems, or any combination thereof, suitable for the exposure radiation or other factors used, such as the use of immersion liquids or vacuum. The patterning MA apparatus can be a mask or stencil that imparts a pattern to a radiation beam transmitted or reflected by the patterning apparatus. Known operating modes include step mode and scan mode. It is well known that projection systems can cooperate in various ways with support and positioning systems for substrates and patterning apparatuses to apply a desired pattern to many target portions of a substrate. Programmable patterning apparatuses can be used instead of masks with fixed patterns. Radiation may, for example, include electromagnetic radiation in the deep ultraviolet (DUV) or extreme ultraviolet (EUV) bands. This disclosure also applies to other types of lithography processes, such as imprint lithography and direct write lithography, for example, by electron beam.

[0022] The photolithography equipment control unit (LACU) controls all movements and measurements of the various actuators and sensors used to receive the substrate W and the mask MA and to perform patterning operations. The LACU also includes signal and data processing capabilities to perform desired calculations related to the operation of the apparatus. In practice, the LACU is implemented as a system of many sub-units, each handling real-time data acquisition, processing, and control of subsystems or components within the apparatus.

[0023] Before applying the pattern to the substrate at the Exposure Station (EXP), processing the substrate at the Measurement Station (MEA) allows for various fabrication steps. Fabrication steps may include using a level sensor to plot the surface height of the substrate and using an alignment sensor to measure the position of alignment marks on the substrate. Alignment marks are typically arranged in a regular grid pattern. However, due to inaccuracies in mark generation and deformation of the substrate throughout its processing, the marks deviate from the ideal grid. Therefore, in addition to measuring the substrate's position and orientation, the alignment sensor must actually measure the position of numerous marks on the substrate area in detail if the apparatus is to print product features in the correct locations with very high accuracy. The apparatus can be a so-called dual-platform type with two substrate stages, each with a positioning system controlled by a control unit (LACU). While one substrate on one stage is exposed at the Exposure Station (EXP), another substrate can be loaded onto the other substrate stage at the Measurement Station (MEA), allowing for various fabrication steps to be performed. Therefore, measuring the alignment marks is very time-consuming, and the provision of two substrate stages significantly increases the apparatus's productivity. If the position sensor IF is unable to measure the position of the substrate stage when it is at both the measurement and exposure stations, a second position sensor can be provided to enable tracking of the substrate stage position at both stations. A lithography apparatus LA can be, for example, a so-called dual-stage type with two substrate stages and two stations (exposure station and measurement station), where the substrate stages can be exchanged between the two stations.

[0024] In the production facility, apparatus 200 forms part of a "lithography cell" or "lithography cluster," and also includes a coating apparatus 208 for applying photoresist and other coatings to a substrate W for patterning via apparatus 200. On the output side of apparatus 200, a baking apparatus 210 and a developing apparatus 212 are provided for developing the exposed pattern into a physical resist pattern. Between all these apparatuses, a substrate handling system is responsible for supporting the substrates and transferring them from one apparatus to another. These apparatuses, generally referred to collectively as tracks, are under the control of a track control unit, which in turn is controlled by a management control system SCS, which also controls the lithography equipment via a lithography equipment control unit LACU. Therefore, different apparatuses can be operated to maximize productivity and processing efficiency. The management control system SCS receives recipe information R, which provides detailed definitions of the steps to be performed to form each patterned substrate.

[0025] Once the pattern has been applied in the photolithography cell and developed, the patterned substrate 220 is transferred to other processing apparatuses such as 222, 224, and 226. In a typical manufacturing facility, multiple processing steps are performed by multiple apparatuses. For illustrative purposes, in this embodiment, apparatus 222 is an etching station, and apparatus 224 performs a post-etch annealing step. Other physical and / or chemical processing steps are applied in other apparatuses such as 226. Fabricating a real device may require various types of operations, such as material deposition, modification of surface material properties (oxidation, doping, ion implantation, etc.), chemical mechanical polishing (CMP), etc. In practical applications, apparatus 226 may represent a series of different processing steps performed in one or more apparatuses. As another example, apparatuses and processing steps may be provided to achieve multiple self-aligned patternings to produce multiple smaller features based on the precursor pattern placed by the photolithography apparatus.

[0026] As is well known, the fabrication of semiconductor devices involves many repetitions of this process to construct a device structure with appropriate materials and patterns layer by layer on a substrate. Therefore, the substrate 230 arriving at the photolithographic cluster can be a newly fabricated substrate, or it can be a substrate previously processed in that cluster or entirely in another device. Similarly, depending on the required processing, substrates 232 leaving device 226 can be returned for subsequent patterning operations in the same photolithographic cluster, can be designated for patterning operations in another cluster, or they can be final products awaiting dicing and packaging.

[0027] Each layer of the product structure requires a different set of process steps, and the type of apparatus 226 used at each layer can be completely different. Furthermore, even when the processing steps to be applied by apparatus 226 are nominally the same, in large facilities, several supposedly identical machines may operate in parallel to perform step 226 on different substrates. Small setup differences or imperfections between these machines may mean that these small setup differences or imperfections affect different substrates in different ways. Even steps that are relatively common for each layer (such as etching (apparatus 222)) can be implemented by several nominally identical but parallel etching apparatuses to maximize productivity. Moreover, in practical applications, different layers require different etching processes (e.g., chemical etching, plasma etching) and special requirements (such as anisotropic etching) depending on the details of the material to be etched.

[0028] Previous and / or subsequent processes can be performed in other lithography equipment (as described above), and even in different types of lithography equipment. For example, some layers in device fabrication processes with very high requirements for parameters such as resolution and overlap can be performed in more advanced lithography tools compared to other layers with lower requirements. Therefore, some layers can be exposed in immersion lithography tools, while others can be exposed in "dry" tools. Some layers can be exposed in tools using DUV wavelengths, while others are exposed using EUV wavelength radiation.

[0029] To ensure correct and consistent exposure of substrates by photolithography equipment, it may be desirable to inspect the exposed substrates to measure characteristics such as overlap error between subsequent layers, line thickness, and critical dimension (CD). Therefore, the manufacturing facility housing the photolithography cell (LC) also includes a metrology system that receives some or all of the substrates (W) that have already been processed within the photolithography cell. The metrology results are provided directly or indirectly to the management and control system (SCS). If an error is detected, the exposure of subsequent substrates can be adjusted, especially if the metrology can be completed quickly enough so that other substrates in the same batch are still awaiting exposure. Similarly, exposed substrates can be stripped and reprocessed to increase yield, or discarded to avoid further processing of known defective substrates. In cases where only some target portions of the substrate are defective, further exposure can only be performed on the "good" target portions.

[0030] Figure 1 The diagram also illustrates a measurement device 240 provided for measuring product parameters at desired stages of the manufacturing process. A common example of a measurement station in modern lithography production facilities is a scatterometer, such as a dark-field scatterometer, an angle-resolved scatterometer, or a spectral scatterometer, which can be used to measure the properties of the developed substrate at 220 prior to etching in device 222. Using measurement device 240, it can be determined that, for example, important performance parameters (such as overlap or critical dimension (CD)) do not meet the specified accuracy requirements in the developed resist. Prior to the etching step, there is an opportunity to strip the developed resist and reprocess the substrate 220 through the lithography cluster. By making minor adjustments over time to the management control system SCS and / or control unit LACU 206, the measurement results 242 from device 240 can be used to maintain the accurate execution of patterning operations in the lithography cluster, thereby minimizing the risk of product defects and rework.

[0031] Additionally, measurement device 240 and / or other measurement devices (not shown) can be used to measure the characteristics of the processed substrates 232, 234 and the incoming substrate 230. The measurement devices can be used on the processed substrates to determine important parameters such as overlap or CD.

[0032] Another example of a measurement station is a scanning electron microscope (SEM), also known as an electron beam (electron beam) measurement device, which can complement or replace a scattering instrument. Thus, the measurement device 240 can be used alone or in addition to an electron beam or SEM measurement device. The advantage of electron beam and SEM measurement devices is that they directly measure features (i.e., they directly image the features), rather than the indirect measurement techniques used in scattering measurements (where parameter values ​​are determined based on the reconstruction and / or asymmetry of different orders of radiation diffracted by the measured structure). The main disadvantage of electron beam or SEM measurement devices is their measurement speed, which is much slower than scattering measurements, thus limiting their potential application in certain offline monitoring processes.

[0033] Critical-size scanning electron microscopy (CD-SEM) is a widely used technique for substrate and mask metrology in the semiconductor industry. In CD-SEM, low-energy electrons (<1 keV) are accelerated toward the surface and then diffuse. Secondary and / or backscattered electrons emitted by surface atoms excited by the electron beam are detected by a detector. The number of these detected electrons depends particularly on the sample morphology. This process is performed on different excitation sites on the surface (e.g., by scanning the surface with an electron beam) to obtain images. Such CD-SEM images can have nanometer-scale spatial resolution, unmatched by other metrology techniques used in the industry. However, a certain amount of noise may be present in such images.

[0034] The primary application of CD-SEM is to observe the geometric edges of features (e.g., formed on a substrate during a photolithography process). In doing so, accurately determining the geometry and location of features from noisy SEM images can be particularly challenging. In the presence of significant noise, there are many known methods to improve edge detection. One such method uses an edge detection algorithm that typically smooths / filters the image to reduce noise; however, this also affects how edges are perceived. Alternatively, more robust algorithms use a reference SEM signal obtained from a reference geometric edge, which is then fitted to the SEM image. This approach is described, for example, in T. Verduin et al., J. Micro / Nanolith. MEMS MOEMS, 13 033009 (2014) and CAMack et al., Proc. SPIE 10145, 101451R (2017), both of which are incorporated herein by reference. However, the effectiveness of these algorithms is questionable when there is a significant difference between the reference SEM signal and the actual SEM signal. Furthermore, a fixed reference value cannot address local variations in the SEM signal, which may be caused by changes in feature shape. An improved method may include extracting a reference SEM signal from comparable features by averaging the SEM signals parallel to the edges of the reference target. This technique is described in patent US 7,269,287 B2, which is incorporated herein by reference. However, this method does not address local variations in the reference SEM signal, which may be caused by local deformations of the feature. Thus, this method requires prior knowledge of the edge locations of the feature of interest.

[0035] Therefore, a method is proposed to obtain the reference SEM signal from the SEM image itself rather than from the reference image. This means that the proposed method is independent of the sample material and the SEM setup used. Thus, this method allows for local variations in the geometry / structure of the edge shape and location, and can iteratively compensate for LER (Line Edge Roughness) and SEM-induced drift in edge location.

[0036] pass Figure 2 The flowchart is used to illustrate the proposed method. Figure 2 (a) A representation including a CD-SEM image showing a portion of a feature 300 (e.g., a line) having an edge 305 defined by a feature 30 from an adjacent groove 310. The edge 305 has roughness (i.e., LER) and additional noise. The method includes first making an initial estimate of the edge location. In this particular example, as Figure 2As shown in (b), the estimated location is simply a straight line roughly at the edge location to be detected, defined as the initial contour 315 (represented by the white dashed line). However, the initial estimate can include any contour; for example, a rough outline of the edge. Figure 2 In the next step shown in (c), a region including either side of the initial profile 315 is selected, and this region is plotted in space with respect to the transformation of the profile (i.e., a curve parallel to the profile perpendicular to the profile). This region can be, for example, a few nanometers (e.g., less than 10 nm or less than 5 nm) of either side of the profile 315. In this particular example, because the initial profile 315 is a straight line, Figure 2 (c) The diagram (roughly) follows the edge contour to be defined. Figure 2 (d) shows the result of the next step, where a one-dimensional low-pass filter, such as Gaussian smoothing or Gaussian blur, is applied in a direction parallel to the contour. It should be understood that the Gaussian filter is applied parallel to all points along the contour; that is, the direction of the blur varies with the direction of the contour (e.g., when it is not a straight line). Since the filter is applied only parallel to the contour, the blur perpendicular to the actual edge is minimal. The result of this step is used as a reference SEM signal 320. More specifically, it is assumed that... Figure 2 (d) The columns of the reference SEM signal 320 are local reference SEM signals. For example, the width of each column can be defined as the pixel width of the image. Figure 2 (d) The columns of the smoothed reference SEM signal were fitted / matched to Figure 2 (c) corresponding columns, thus allowing only the offset of the height difference between corresponding columns. Thus, this step includes column-by-column processing at (noisy) imaging edges. Figure 2 (c) with smooth (filtered) edges Figure 2 (d) is a one-dimensional cross-correlation between the columns, where each column is aligned (matched) perpendicular to the contour 315. Because a local reference is used for contrast, possible variations in edge shape are also considered, and the obtained column height offset represents the displacement due to LER, and as... Figure 2 As shown in (e), the updated contour 325 is used to define the line profile. In this way, the updated line profile is based on a reference SEM signal 320 determined from the actual image. Figure 2 As can be seen in (f), the updated line profile (white dashed line) has a much higher matching degree than the actual feature edge 305.

[0037] Determining edge contours (matched contours) solely based on the reference SEM signal 320 from the image itself may be undesirable. Therefore, in one embodiment, an additional thresholding step can be performed on the fitted / matched contour immediately after the matching step (matching the reference SEM signal 320 with the initial contour 315) to obtain a thresholded contour. The thresholded contour can represent a better estimate of the determined edge location than the matched (unthresholded) contour. This thresholding step can be performed for each individual column of the matched reference SEM signal. Thresholding may include determining a threshold (e.g., a grayscale value) that defines the edge location (according to any suitable criterion), and using said threshold, based on the matched contour... Figure 2 (c) The same matched image Figure 2 (d) Further define the edges. The concept of thresholding is well-known in the field of edge detection and will not be described in detail here. Thresholding may also include scaling the minimum and maximum values ​​of each column proportionally to prevent thresholding artifacts, which typically occur when normalizing the minimum and maximum values ​​in the region with varying SEM signal intensities. The result is an updated contour 325, determined by a combination of matching a reference SEM signal 320 obtained from the actual image and a thresholding step from the matched image.

[0038] In one embodiment, the method can be performed iteratively to improve edge contour estimation. This involves repeating the steps described above using the updated contour 325 as the input contour (initial contour). The steps for the second iteration are as follows: Figure 2 As shown in (g)-2(i). Figure 2 (g) is a graph of the region that has changed around the updated contour 325 (i.e., a curve that is parallel to the contour but perpendicular to it). Since the contour now closely matches the edge contour, the curve shows a near-horizontal change. Figure 2 (h) is the smoothed reference SEM signal after applying Gaussian smoothing parallel to the updated contour 325. Finally, after the matching step and any thresholding, the second iteration updated contour 330 is obtained, as shown. Figure 2 As shown in (i).

[0039] The comparison of the first iteration profile 325 with the initial profile 315 includes measurements of the LER, while the comparison of the second iteration profile 330 with the first iteration profile 325 shows that, apart from a few minor trends, most of it is random noise. In fact, the first iteration profile 325 and the second iteration profile 330 look very similar, with the latter showing only a slight improvement over the former. However, as... Figure 3As shown in the graph, this improvement is not necessarily negligible. This is an example of the reference SEM signal after the first iteration 340 and the second iteration 350. As can be seen from the graph, there are some differences between the two iterations. It can be expected that the reference SEM signal of the first iteration 340 will have some "rounding" near the maximum and minimum values ​​compared to the second iteration 350.

[0040] The previous examples demonstrated how to obtain a (local) reference SEM signal from a SEM image and how to iteratively use this concept to compensate for severe LER. As supplementary examples, the method is applied to very different experimental CD-SEM images, such as... Figure 4 As shown. Figure 4 (a) shows the experimentally measured resist line 400, which has a more complex shape, and the estimated initial profile 410 is shown as a dashed line. This initial profile 410 is... Figure 4 This is shown more clearly in (b). For the more complex outline 410, Figure 4 (c) and (d) show the relationship with Figure 2 (c) and (d) follow the same steps. Although contour 410 is of arbitrary shape, the image can essentially be unfolded (deformed) along that contour. During this unfolding process, the local normals of the contour are interpolated and sampled. Figure 4 Near the center of (c), the changing SEM signal 420 can be clearly observed. This changing SEM signal 420 corresponds to... Figure 4 The resist line considered in (a) is at its tip, caused by the profile 410 not properly following the geometry at that tip. In this example, averaging parallel to the entire profile may result in an incorrect reference SEM signal at the resist line tip, and may also be incorrect for the entire resist line. However, it is desirable to average over some existing noise. Therefore, averaging in the direction parallel to the profile... Figure 4 (c) (e.g., σ = 5 nm) Apply Gaussian blur to obtain Figure 4 (d) Reference SEM signal 430. It can be observed that the reference SEM signal 430 has an improved signal-to-noise ratio, while the tip region remains clearly distinguishable. Again, no filtering is performed in the vertical direction. Again, it is assumed... Figure 4 Column (d) is the local reference SEM signal, and it is fitted to... Figure 4 (c) uses the same columns, thus allowing only the height difference between columns to be offset (i.e., the cross-correlation between columns of the two images), in order to redefine the contour. The acquired column height offset represents displacement and, in combination with the edge position thresholds in (c) (optionally), they provide the edge positions shown as the new contour 440. Figure 4 (e) and Figure 4 (f)) better matches the actual contour of feature 400.

[0041] By fitting the signal using spline functions, noise in the reference SEM signal can be further reduced, thereby preserving as much noise-free SEM signal as possible. This is particularly important when performing the method iteratively, as the profile may also begin to align with the noise present in the reference SEM signal.

[0042] In summary, the proposed method for detecting geometric edges described herein acquires a reference signal (e.g., a reference edge or reference SEM signal) from the actual image (e.g., an actual SEM image) at the edge location being detected by unfolding an initial contour. This is an improvement on methods such as those described in US 7,269,287B2, which acquire a reference SEM signal from a reference target and is only applicable to linear and cylindrical symmetric features. In cases of severe LER and / or inaccurate initially guessed contours, this method can be used iteratively in a sense that optimization can be derived from the reference SEM signal. Determining the local reference SEM signal by Gaussian blur parallel to the contour is robust to material variations and changes in SEM settings, especially when the blur length is equal to or less than the beam width used in the SEM hardware. Similarly, the reference SEM signal is acquired from the region surrounding the initial contour, whereas in US 7,269,287, a so-called white band is used, which is not always well defined or even nonexistent. It is believed that this method can locate edges at sub-pixel resolution with greater accuracy and precision compared to thresholding methods that might typically be used, especially in the presence of severe LER.

[0043] The proposed method is highly flexible; while many CD-SEM analysis tools can only be applied to a single use case with well-defined geometry, these methods allow for the analysis of features of arbitrarily complex shapes. This is particularly important for edge placement error metrics.

[0044] The embodiments may be further described using the following terms:

[0045] 1. A method for determining the edge location associated with the edge of a feature, said feature being included within an image comprising noise; said method comprising:

[0046] Determine a reference signal from the image; and

[0047] The edge position is determined relative to the reference signal.

[0048] 2. The method according to Clause 1, wherein the image has been acquired using a scanning electron microscope.

[0049] 3. The method according to clause 1 or 2, wherein determining the reference signal from the image comprises:

[0050] A one-dimensional low-pass filter is applied to the image in a direction parallel to the initial contour to obtain a filtered image including the reference signal.

[0051] 4. The method according to Clause 3, comprising the step of: estimating the initial contour such that the initial contour includes an initial estimate of the edge being determined.

[0052] 5. The method according to clause 3 or 4, wherein the one-dimensional low-pass filter includes a one-dimensional Gaussian blur.

[0053] 6. The method according to Clause 5, wherein the blur length of the Gaussian blur is equal to or less than the beam width of the beam used to acquire the image.

[0054] 7. The method according to any one of clauses 3 to 6, wherein before applying the one-dimensional low-pass filter, the method comprises: transforming at least a portion of the image including the edge to obtain the transformed image in a transform space including a first dimension defined as parallel to the initial contour and a second dimension defined as perpendicular to the initial contour.

[0055] 8. The method according to Clause 7, wherein only regions less than 10 nm on either side of the initial contour are transformed to obtain the transformed image.

[0056] 9. The method according to clause 7 or 8, comprising: matching one of the filtered image and the transformed image with the other of the filtered image and the transformed image to determine a matched contour, the matched contour including a matched estimate of the edge location.

[0057] 10. The method according to Clause 9, wherein the matching comprises: matching the filtered image with the transformed image on a column-by-column basis.

[0058] 11. The method according to Clause 10, wherein each column is defined by the width of the pixels of the image.

[0059] 12. The method according to clause 10 or 11, wherein the matching includes cross-correlation of columns of the filtered image with corresponding columns of the transformed image.

[0060] 13. The method according to Clause 12, wherein the cross-correlation comprises: offsetting the columns relative to each other only along the column direction to obtain the best match between the filtered image and the transformed image.

[0061] 14. The method according to any one of clauses 9 to 13, comprising: an additional thresholding step for obtaining a thresholded profile of the matched profile based on a threshold defining the edge location, the thresholded profile including a thresholded estimate of the edge location.

[0062] 15. The method according to any one of clauses 9 to 14, comprising: iteratively performing the method by using the matched contour or thresholded contour as the initial contour for each additional iteration of the method.

[0063] 16. The method according to any one of the preceding clauses, wherein the image is acquired for measuring parameters related to the feature, thereby monitoring the performance of the photolithography process used to form the feature.

[0064] 17. The method according to Clause 16, wherein the parameters include one or more of the following: critical dimension, line edge roughness, line width roughness, local critical dimension uniformity, and edge placement error.

[0065] 18. The method according to any one of the preceding clauses, comprising: performing a further noise reduction step on the reference signal by fitting a spline function to the reference signal that maximizes the signal-to-noise ratio.

[0066] 19. A computing device comprising a processor and configured to perform the method according to any one of the preceding clauses.

[0067] 20. A scanning electron microscope inspection apparatus operable to image one or more features on a substrate, and comprising a computing device as described in Clause 19.

[0068] 21. A computer program comprising program instructions operable to perform the method according to any one of clauses 1 to 18 when run on a suitable device.

[0069] 22. A non-transitory computer program carrier, comprising the computer program as described in Clause 21.

[0070] The terms “radiation” and “beam” used in lithography equipment encompass all types of electromagnetic radiation, including ultraviolet (UV) radiation (e.g., wavelengths of or about 365, 355, 248, 193, 157, or 126 nm) and extreme ultraviolet (EUV) radiation (e.g., wavelengths in the range of 5–20 nm), as well as particle beams, such as ion beams or electron beams.

[0071] Where the context allows, the term “lens” can refer to any one or a combination of various types of optical elements, including refractive, reflective, magnetic, electromagnetic, and electrostatic optical components.

[0072] The foregoing description of specific embodiments will so fully reveal the general characteristics of the invention that others can readily modify such specific embodiments and / or adapt them to various applications by applying the knowledge of those skilled in the art without departing from the general concept of the invention, without excessive experimentation. Therefore, based on the teachings and guidance set forth herein, such adaptations and modifications are intended to be within the meaning and scope of equivalents of the disclosed embodiments. It should be understood that the wording or terminology used herein is for illustrative purposes and not for limiting purposes, and that the terminology or terminology of this specification should be interpreted by those skilled in the art based on the teachings and guidance.

[0073] The breadth and scope of this invention should not be limited by any of the exemplary embodiments described above, but should be defined only by the appended claims and their equivalents.

Claims

1. A method for determining the edge location associated with the edge of a feature, said feature being included within an inspection image comprising noise, said method comprising: A reference signal is determined from the inspection image by applying a one-dimensional low-pass filter in a direction parallel to the initial contour. as well as Edge locations are determined based on matched edge contours and threshold characteristics of the inspected image, wherein the matched edge contours include matched estimates of the edge locations based on the reference signal.

2. The method of claim 1, further comprising generating a filtered image including the reference signal.

3. The method of claim 2, further comprising estimating an initial contour such that the initial contour includes an initial estimate of the edge being determined.

4. The method according to claim 3, further comprising: Before applying the one-dimensional low-pass filter, at least a portion of the inspected image of the edge is transformed to obtain a transformed image in a transform space comprising a first dimension substantially parallel to the initial contour and a second dimension defined as perpendicular to the initial contour.

5. The method according to claim 4, further comprising: One of the filtered image and the transformed image is matched with the other of the filtered image and the transformed image to determine the matched edge contour.

6. The method of claim 5, wherein matching comprises: The filtered image is matched against the transformed image on a column-by-column basis, wherein each column is defined by the width of the pixels in the examined image.

7. The method of claim 6, wherein the matching further comprises: The cross-correlation between the columns of the filtered image and the corresponding columns of the transformed image.

8. The method according to claim 1, further comprising: Determine the threshold characteristics of each column of the matched reference signal associated with the matched edge contour, the threshold characteristics including a threshold gray value.

9. The method of claim 8, wherein the threshold characteristic includes scaling the minimum and maximum gray values ​​for each column of the matched reference signal.

10. The method of claim 5, further comprising: The filtered image and the transformed image are iteratively matched by performing at least one additional iteration using the matched edge contour as the initial contour for each additional iteration.

11. The method of claim 1, wherein the image is acquired for measuring parameters related to the feature, thereby monitoring the performance of the photolithography process used to form the feature.

12. The method of claim 11, wherein the parameter includes one of the following: critical dimension, line edge roughness, line width roughness, local critical dimension uniformity, or edge placement error.

13. An apparatus comprising: The processor is configured to execute a set of instructions to cause the device to perform: A reference signal is determined from the image by applying a one-dimensional low-pass filter to the image in a direction parallel to the initial contour. as well as Edge locations are determined based on matched edge contours and threshold characteristics of the inspected image, wherein the matched edge contours include matched estimates of the edge locations based on the reference signal.

14. The apparatus of claim 13, wherein the processor is configured to execute the set of instructions to cause the apparatus to further perform: generating a filtered image including the reference signal.

15. The apparatus of claim 14, wherein the processor is configured to execute the set of instructions to cause the apparatus to further perform: The initial contour is estimated such that it includes an initial estimate of the edges being determined; and Before applying the one-dimensional low-pass filter, the transformation includes at least a portion of the inspected image with features to obtain a transformed image in a transformation space that includes a first dimension substantially parallel to the initial contour.

16. The apparatus of claim 15, wherein the processor is configured to execute the set of instructions to cause the apparatus to further perform: One of the filtered image and the transformed image is matched with the other of the filtered image and the transformed image to determine the matched contour, wherein the matching includes: The filtered image is matched against the transformed image on a column-by-column basis, wherein each column is defined by the width of the pixels in the examined image.

17. The apparatus of claim 13, wherein the processor is configured to execute the set of instructions to cause the apparatus to further perform: determining a threshold characteristic for each column of a matched reference signal associated with the matched edge contour, the threshold characteristic including a threshold grayscale value.

18. A non-transitory computer-readable medium storing a set of instructions executable by a processor of a device to cause the device to perform a method for determining edge locations associated with edges of features included within an inspection image comprising noise, the method comprising: A reference signal is determined from the inspection image by applying a one-dimensional low-pass filter in a direction parallel to the initial contour. as well as Edge locations are determined based on matched edge contours and threshold characteristics of the inspected image, wherein the matched edge contours include matched estimates of the edge locations based on the reference signal.

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