Non-transitory computer readable medium and system for enhancing a patterning process

Abstract

To monitor changes in semiconductor manufacturing processes, contours with identical pattern features are identified based on SEM images. These contours are then aggregated and statistically analyzed to determine changes in the features. Some of these contours are outliers, and the aggregation and averaging of these contours "hides" these outliers. This invention describes screening outlier contours before aggregating and statistically analyzing specific outlier contours. This screening can be performed at multiple levels, such as based on individual points on contours in a set of detected contours, or based on the overall geometry of the contours in the set of detected contours.

Description

Technical Field

[0001] This invention is generally related to the screening of test data associated with semiconductor manufacturing processes. Prior Technology

[0002] In the manufacturing process of integrated circuits (ICs), the inspection of unfinished or completed circuit components is performed to ensure that they are manufactured according to the design and free of defects. Inspection systems utilizing optical microscopes or charged particle (e.g., electron) beam microscopes (such as scanning electron microscopy (SEM)) can be used. As the physical size of IC components continues to shrink and their structures become increasingly complex, the accuracy and yield of defect detection and inspection become more critical. Overall image quality particularly depends on the combination of high secondary electron and backscattered electron signal detection efficiency. Backscattered electrons have higher emission energy and escape from deeper layers of the sample; therefore, their detection may be suitable for imaging complex structures, such as buried layers, nodes, high aspect ratio trenches, or holes within 3D NAND devices. For applications such as stacking measurements, high-quality imaging and efficient collection of surface information from secondary electrons, along with information on buried layers from backscattered electrons, may be required, highlighting the need for multiple electron detectors in SEM. Although multiple electron detectors in various configurations can be used individually to maximize the collection and detection efficiency of secondary and backscattered electrons, the combined detection efficiency is still low. Therefore, the resulting image quality may not be sufficient to achieve high accuracy and high yield of defect detection and measurement for two-dimensional and three-dimensional structures. Summary of the Invention

[0003] To monitor manufacturing process variations, contours with identical pattern features can be identified based on SEM images of substrate patterns. These contours can be aggregated (e.g., stacked) and statistically analyzed to determine variations in a given feature. Some of these contours are typically outliers, and aggregation of these contours "hides" these outliers, which can lead to reduced accuracy in the measurement / characterization of specific manufacturing process characteristics, particularly critical dimensions, edge placement errors, or overlap errors. This invention describes screening outlier contours before aggregating and statistically analyzing them. This screening can be performed at multiple levels, such as based on individual points on contours in a set of detected contours, or based on the overall geometry of contours in a set of detected contours. This can enhance the accuracy of the measurement / characterization of these manufacturing process characteristics, thereby enabling more optimal adjustments to the manufacturing process to increase device yield, or potentially offering other advantages.

[0004] According to one embodiment, a method for enhancing a patterning process is provided. The method includes: receiving substrate pattern detection images; and determining contours in the substrate pattern detection images to form a set of detection contours. Determining the contours includes detecting edges of features in the substrate pattern detection images. The method further includes filtering outlier contours from the set of detection contours.

[0005] In some embodiments, once a contour is determined to contain vertices; the edges of the features detected include identifying the vertices in the determined contours; and the filtering of the outlier contours is based on the vertices.

[0006] In some embodiments, the method further includes: determining an angle formed at the vertices of the determined contours; and filtering outlier contours from the set of detected contours based on the angle.

[0007] In some embodiments, the outlier contours are filtered from the set of detected contours based on a comparison of the determined angles with an angle threshold, and determined contours at a vertex with an angle less than the angle threshold are identified as outlier contours and filtered from the set of detected contours. In some embodiments, the threshold angle is 120 degrees, 90 degrees, 60 degrees, or 45 degrees.

[0008] In some embodiments, the method further includes: determining the distance between neighboring vertices of the determined contours; and filtering the outlier contours from the set of detected contours based on the distance.

[0009] In some embodiments, the outlier contours are filtered from the set of detected contours based on a comparison of the determined distances with a distance threshold, and a determined contour whose distance exceeds the distance threshold is determined as an outlier contour and filtered from the set of detected contours.

[0010] In some embodiments, the distance threshold includes: a given multiple of an average distance between vertices or a distance greater than or less than a percentage of that average distance; or a distance corresponding to a profile edge roughness parameter.

[0011] In some embodiments, the method further includes: determining the centroid of the determined contours; and filtering the outlier contours from the set of detected contours based on a relationship between a centroid and one or more vertices of a given contour.

[0012] In some embodiments, the filtering based on the relationship includes filtering the given contour from the set of detected contours in response to one or more vertices of the given contour being at a distance from the centroid exceeding a centroid distance threshold.

[0013] In some embodiments, the centroid distance threshold includes a distance that is a multiple of one of the average distances between the vertex and the centroid, or a percentage greater than or less than one of the average distances.

[0014] In some embodiments, the method further includes: determining the centroid of the determined contours; fitting a desired contour shape to the determined contours based on the centroid; and filtering the outlier contours from the set of detected contours based on a relationship between the vertices of a fitted desired contour and a given contour.

[0015] In some embodiments, the method further includes: determining the centroid of the determined contours; fitting a circle or ellipse to the determined contours based on the centroid; and filtering the outlier contours from the set of detected contours based on a relationship between a fitted circle or ellipse and the vertices of a given contour.

[0016] In some embodiments, the filtering based on the relationship includes filtering the given contour from the set of detected contours in response to one or more vertices of the given contour being at a distance from the fitted circle or ellipse that exceeds a fitted distance threshold.

[0017] In some embodiments, the fitting distance threshold includes a distance that is a multiple of or a percentage greater than or less than one of the average distances between the vertex and the fitted circle or ellipse.

[0018] In some embodiments, the outline comprises a polygon.

[0019] In some embodiments, the substrate pattern inspection images are generated by an optical inspection system or a charged particle detection system. In some embodiments, the substrate pattern inspection images are generated by the charged particle detection system, which includes a scanning electron microscope.

[0020] In some embodiments, the method further includes determining a manufacturing variation of one of the features based on the remaining contours in the detected contour set after the filtering. In some embodiments, the manufacturing variation is configured to provide a cost function to facilitate the determination of costs associated with individual patterning process variables, and the costs associated with individual patterning process variables are configured to facilitate the optimization of a patterning process.

[0021] In some embodiments, determining contours in the substrate pattern detection images to form the detection contour set includes detecting repeating contours that span a unit cell or a 1x scale mask associated with a pattern; and wherein filtering outlier contours from the detection contour set includes filtering each contour or a portion of a contour that is associated with an outlier unit cell or an outlier scale mask.

[0022] According to another embodiment, a method is provided for electronically filtering out-of-line contours from a set of detected contours in substrate pattern detection images. The method includes receiving the substrate pattern detection images. The method includes determining contours based on the substrate pattern detection images to form the set of detected contours. Determining contours includes detecting edges of repeating features in the substrate pattern detection images. The method includes filtering the out-of-line contours from the set of detected contours, thereby leaving remaining contours in the set of detected contours after the filtering.

[0023] In some embodiments, the method includes: determining a manufacturing variation of one of the repeating features based on the remaining contours in the detected contour set after the filtering. The manufacturing variation is configured to provide a cost function to facilitate the determination of costs associated with individual patterning process variables. The costs associated with individual patterning process variables are configured to facilitate the optimization of a patterning process.

[0024] In some embodiments, determining the manufacturing variation of the repeating features includes: stacking the remaining contours in the detected contour set; and statistically analyzing the stacked remaining contours.

[0025] In some embodiments, the filtering is performed based on individual points on the contours in the set of detected contours or on the geometry of the contours in the set of detected contours. The filtering based on individual points on the contours in the set of detected contours includes determining the image contrast or noise level of a pixel location along a given contour in an image detected by a substrate pattern. The filtering based on the geometry of the contours in the set of detected contours includes determining the smoothness of a geometry of a given contour.

[0026] In some embodiments, determining contours based on the substrate pattern detection images to form the detection contour set includes detecting repeating contours that span a unit cell or a scaled-down mask associated with a pattern. Filtering the outlier contours from the detection contour set includes filtering each contour associated with an outlier unit cell or an outlier scaled-down mask.

[0027] In some embodiments, the method includes: determining a score for each contour in the set of detected contours; and filtering outlier contours from the set of detected contours based on the score. In some embodiments, the score is determined by referring to the substrate pattern detection images based on individual points on the contours in the set of detected contours or the geometry of the contours in the set of detected contours. In some embodiments, the score includes individual scores for each of a plurality of points on a contour, and the filtering is based on the individual scores. In some embodiments, the score includes a total score associated with a geometry of a contour, and the filtering is based on the total score.

[0028] In some embodiments, the substrate pattern inspection images are generated by an optical inspection system or a charged particle detection system. In some embodiments, the substrate pattern inspection images are generated by the charged particle detection system, which includes a scanning electron microscope.

[0029] According to another embodiment, a method is provided for electronically filtering out-of-line contours from a set of detected contours in substrate pattern inspection images. The method includes: receiving the substrate pattern inspection images; and determining contours based on the substrate pattern inspection images to form the set of detected contours. Determining the contours includes detecting edges of repeating features in the substrate pattern inspection images. The method includes filtering the out-of-line contours from the set of detected contours. The filtering is performed based on individual points on the contours in the set of detected contours or the geometry of the contours in the set of detected contours. The method includes determining a manufacturing variation of one of the repeating features based on the remaining contours in the set of detected contours after the filtering.

[0030] In some embodiments, the manufacturing variation is configured to provide a cost function to facilitate the determination of costs associated with individual patterning process variables. These costs associated with individual patterning process variables are configured to facilitate the optimization of a patterning process.

[0031] In some embodiments, determining the manufacturing variation of the repeating features includes: stacking the remaining contours in the detected contour set; and statistically analyzing the stacked remaining contours.

[0032] In some embodiments, the filtering based on individual points on the contours in the detection contour set includes determining the image contrast or noise level of a pixel position along a given contour in an image of a substrate pattern. In some embodiments, the filtering based on the geometry of the contours in the detection contour set includes determining the smoothness of a geometry of the given contour.

[0033] In some embodiments, determining contours based on the substrate pattern detection images to form the detection contour set includes detecting repeating contours that span a unit cell or a scaled-down mask associated with a pattern. Filtering the outlier contours from the detection contour set includes filtering each contour associated with an outlier unit cell or an outlier scaled-down mask.

[0034] In some embodiments, the method includes: determining a score for each profile in the set of detected profiles; and filtering out the outlier profiles from the set of detected profiles based on the score. The score is determined by referring to the substrate pattern detection images based on individual points on the profiles in the set of detected profiles or the geometry of the profiles in the set of detected profiles. In some embodiments, the score indicates a confidence level or a reliability.

[0035] In some embodiments, the score comprises an individual score for each of a plurality of points on a contour, and the filtering is based on the individual scores. In some embodiments, the score comprises a total score associated with one geometry of a contour, and the filtering is based on the total score.

[0036] In some embodiments, the substrate pattern inspection images are generated by an optical inspection system. In some embodiments, the substrate pattern inspection images are generated by a charged particle detection system. In some embodiments, the charged particle detection system includes a scanning electron microscope.

[0037] According to another embodiment, a non-transitory computer-readable medium having instructions thereon is provided, which, when executed by a computer, cause the computer to perform one or more of the method steps described above.

[0038] According to another embodiment, a system is provided for electronically filtering outoutlier contours from a set of detected contours in a substrate pattern detection image. The system includes one or more hardware processors configured with machine-readable instructions to perform one or more of the method steps described above.

[0039] According to another embodiment, a non-transitory computer-readable medium having instructions thereon is provided, which, when executed by a computer, cause the computer to electronically filter outoutvalue contours from a set of detected contours in a substrate pattern inspection image. Compared to a previous patterning process, this filtering is configured to enhance the determination of manufacturing variations and the optimization of the patterning process. The instructions cause operations including: receiving the substrate pattern inspection images, wherein the substrate pattern inspection images are generated by a charged particle detection system; determining contours based on the substrate pattern inspection images to form the set of detected contours, wherein determining the contours includes detecting edges of repeating features in the substrate pattern inspection images; filtering the outoutvalue contours from the set of detected contours, wherein the filtering is performed based on individual points on the contours in the set of detected contours or the geometry of the contours in the set of detected contours; and determining a manufacturing variation of one of the repeating features based on the remaining contours in the set of detected contours after the filtering. The manufacturing variation is configured to provide a cost function to facilitate the determination of costs associated with individual patterning process variables. The costs associated with individual patterning process variables are configured to optimize the patterning process.

[0040] In some embodiments, filtering based on individual points on the contours in the detection contour set includes determining the characteristics of the substrate pattern detection images, including determining the image contrast or noise level of a pixel position along a given contour in a substrate pattern detection image; and filtering based on the geometry of the contours in the detection contour set includes determining the smoothness of a geometry of the given contour.

[0041] In some embodiments, the operations further include: determining a score for each contour in the detection contour set based on individual points on the contours in the detection contour set or the geometry of the contours in the detection contour set; and filtering outlier contours from the detection contour set based on the score.

[0042] In some embodiments, determining the manufacturing variation of the repeating features includes: stacking the remaining contours in the detected contour set; and statistically analyzing the stacked remaining contours.

[0043] In some embodiments, the charged particle detection system includes a scanning electron microscope (SEM).

[0044] According to another embodiment, a non-transitory computer-readable medium having instructions thereon is provided, which, when executed by a computer, cause the computer to electronically filter out outlier contours from a set of detected contours in a substrate pattern detection image. Compared to a previous patterning process, this filtering is configured to enhance the determination of manufacturing variations in a patterning process and the optimization of the patterning process. These instructions cause operations comprising: receiving substrate pattern detection images, wherein the substrate pattern detection images are generated by a charged particle detection system; determining contours in the substrate pattern detection images to form a set of detection contours, wherein determining the contours includes detecting edges of features in the substrate pattern detection images, the edges including vertices of the contours; filtering outlier contours from the set of detection contours, wherein the filtering is performed based on the vertices; and determining a manufacturing variation of one of the features based on the remaining contours in the set of detection contours after the filtering, wherein the manufacturing variation is configured to be provided to a cost function to facilitate the determination of costs associated with individual patterning process variables, and wherein the costs associated with individual patterning process variables are configured to facilitate the optimization of the patterning process.

[0045] In some embodiments, the operations further include: determining the angle formed at the vertices of the determined contours; and filtering the outlier contours from the set of detected contours based on the angle.

[0046] In some embodiments, the operations further include: determining the distance between neighboring vertices of the determined contours; and filtering the outlier contours from the set of detected contours based on the distance.

[0047] In some embodiments, the operations further include: determining the centroid of the determined contours; fitting a circular, elliptical, or other expected contour shape to the determined contours based on the centroid; and filtering the outlier contours from the set of detected contours based on a relationship between a fitted circular, elliptical, or other expected contour shape and the vertices of a given contour.

[0048] In some embodiments, the charged particle detection system includes a scanning electron microscope.

[0049] Other advantages of embodiments of the present invention will become clear from the following description taken in conjunction with the accompanying drawings, which illustrate certain exemplary embodiments by way of description and examples. Simple Explanation of the Diagram

[0050] The accompanying drawings, which are incorporated in and form part of this specification, illustrate one or more embodiments and explain these embodiments together with the description. The embodiments will now be described by way of example only with reference to the accompanying illustrative drawings, in which corresponding reference numerals indicate corresponding parts, and in these drawings:

[0051] Figure 1 is a schematic diagram of a lithography device according to one embodiment.

[0052] Figure 2 illustrates a schematic summary of a lithography cell according to one embodiment.

[0053] Figure 3 depicts a schematic representation of an overall lithography according to one embodiment, illustrating the collaboration between three technologies used to optimize semiconductor manufacturing.

[0054] Figure 4 schematically depicts an embodiment of a charged particle (e.g., electron beam) detection device according to one embodiment.

[0055] Figure 5 schematically illustrates another embodiment of the detection device according to one embodiment.

[0056] Figure 6 illustrates a method for electronically filtering outoutlier contours from a set of detected contours in a self-substrate pattern detection image according to an embodiment.

[0057] Figure 7 illustrates a method for filtering individual points on contours in a set of detected contours according to one embodiment.

[0058] Figure 8 illustrates a filtering method based on the geometry of contours in a set of detected contours, according to one embodiment.

[0059] Figure 9 is an overview view illustrating, according to one embodiment, filtering based on the angle between vertices of a contour, filtering based on the distance between vertices of a contour, filtering based on the distance between the centroid of a contour and one or more vertices on the contour, and filtering based on the fit of a contour to a desired contour shape.

[0060] Figure 10 illustrates, according to one embodiment, the determination of angles formed at the vertices of the determined contours, and the filtering of outlier contours based on the set of self-detected contours using these angles.

[0061] Figure 11 illustrates the determination of the distance between neighboring vertices of a determined contour according to an embodiment, and the filtering of outlier contours based on the set of self-detected contours with equal distances.

[0062] Figure 12 illustrates the determination of the centroid of the determined contour according to one embodiment, and the screening of outlier contours from the self-detected contour set based on the relationship between the centroid and one or more vertices of a given contour or based on the expected contour shape fitted to the given contour by the centroid.

[0063] Figure 13 illustrates an example of several contours selected by self-substrate pattern according to an embodiment.

[0064] Figure 14 illustrates the differences between a typical procedure (direct stacking, no filtering) and stacking and analysis after filtering, according to one embodiment.

[0065] Figure 15 is a block diagram of an example computer system according to one embodiment.

[0066] Figure 16 is a schematic diagram of another lithography device according to one embodiment.

[0067] Figure 17 is a more detailed view of the components of a lithography device according to an embodiment (e.g., the lithography device illustrated in Figure 1 or Figure 16).

[0068] Figure 18 is a detailed view of the source collector module of a lithography device according to an embodiment. Implementation

[0069] Electronic devices consist of circuits formed on silicon wafers called substrates or wafers. Many circuits can be formed together as repeating patterns of features on the same silicon segment, and are called integrated circuits or ICs. The size of these circuits has been significantly reduced, allowing many circuits to be mounted on the substrate. For example, the IC chip in a smartphone can be as small as a thumbnail and still include more than 2 billion transistors, each smaller than 1 / 1000th the size of a human hair.

[0070] Manufacturing such tiny ICs is a complex, time-consuming, and expensive process that often involves hundreds of individual steps. Even an error in a single step can potentially cause defects in the finished IC, rendering it unusable. Therefore, one goal of the manufacturing process is to avoid such defects in order to maximize the number of functional ICs manufactured in the process, that is, to improve the overall yield of the process.

[0071] One aspect of improving yield is monitoring the wafer manufacturing process to ensure that a sufficient number of functional integrated circuits are being produced. One way to monitor the process is to inspect the wafer's circuit structure at different stages of its formation. This can be done using a scanning electron microscope (SEM). SEM can be used to virtually image these extremely small structures, thus obtaining an "image" of the structure. The image can be used to determine whether the structure has formed correctly and whether it is in the correct location. If the structure is defective, the process can be adjusted to make the defect less likely to reappear.

[0072] To monitor manufacturing process variations, contours with identical pattern features can be identified based on SEM images of the substrate pattern. These contours can be aggregated (e.g., stacked) and statistically analyzed to determine variations in a given feature. Some of these contours are often outliers, and aggregation of these contours "hides" these outliers, which can lead to reduced accuracy in the measurement / characterization of specific manufacturing process characteristics, particularly such as critical dimensions, edge placement errors, or overlap errors. This invention describes screening outlier contours before aggregating and statistically analyzing them. Screening can be performed at multiple levels, such as based on individual points on the contours in the detected contour set, based on the overall geometry of the contours in the detected contour set, based on the angles between the vertices of the contours, based on the distances between the vertices of the contours, based on the distance between the centroid of the contour and one or more vertices on the contour, based on the fit from the expected contour shape to the contour, or based on other information. This can enhance the accuracy of the measurement / characterization of these manufacturing process characteristics, thereby enabling more optimal adjustments to the manufacturing process to increase device yield, or may have other advantages.

[0073] Embodiments of the invention are described in detail with reference to the accompanying drawings, which are provided as illustrative examples to enable those skilled in the art to practice the invention. It is important to note that the following drawings and examples are not intended to limit the scope of the invention to a single embodiment, but rather to make other embodiments possible by means of some or all of the described or illustrated elements. Furthermore, where certain elements of the invention can be implemented partially or entirely using known components, only those portions of such known components necessary for understanding the invention will be described, and detailed descriptions of other portions of such known components will be omitted so as not to obscure the invention. Unless otherwise stated herein, it will be apparent to those skilled in the art that embodiments described as being implemented in software are not limited thereto, but may include embodiments implemented in hardware or a combination of software and hardware, and vice versa. In this specification, embodiments showing a singular number of components should not be considered limiting; rather, unless expressly stated otherwise herein, the invention is intended to cover other embodiments including a plurality of identical components, and vice versa. Furthermore, the applicant does not intend for any terminology in this specification or the claims to be relegated to an uncommon or particular meaning unless expressly stated otherwise. Additionally, this invention encompasses present and future known equivalents of known components mentioned herein by way of description.

[0074] While reference may be specifically made to the manufacture of ICs herein, it should be clearly understood that the descriptions herein have many other possible applications. For example, they can be used to manufacture integrated optical systems, guide and detect patterns for magnetic domain memory, liquid crystal display (LCD) 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 "reduced photomask," "wafer," or "die" herein is interchangeable with the more general terms "mask," "substrate," and "target portion," respectively.

[0075] In this document, the terms “radiation” and “beam” are used to cover all types of electromagnetic radiation, including ultraviolet radiation (e.g., having wavelengths of 365 nm, 248 nm, 193 nm, 157 nm or 126 nm) and extreme ultraviolet radiation (EUV, e.g., having wavelengths in the range of about 5 nm to 100 nm).

[0076] For example, the term "projection optics" as used herein should be broadly interpreted to encompass various types of optical systems, including refractive optics, reflective optics, aperture and catadioptric optics. The term "projection optics" may also include components operating according to any of these design types for collectively or individually guiding, shaping, or controlling projected radiation beams. The term "projection optics" may include any optical component in a lithography apparatus, regardless of where the optical component is positioned in the optical path of the lithography apparatus. Projection optics may include optical components for shaping, adjusting, or projecting radiation from a source before it passes through a (e.g., semiconductor) patterning device, or for shaping, adjusting, or projecting radiation after it has passed through the patterning device. Projection optics typically exclude the light source and the patterning device.

[0077] Patterned devices (e.g., semiconductors) may contain or form one or more design layouts. Computer-aided design (CAD) programs, often called electronic design automation (EDA), can be used to generate these layouts. Most CAD programs follow a predetermined set of design rules to generate functional design layouts / patterned devices. These rules are set through processing and design constraints. For example, design rules define the space tolerances between devices (such as gates, capacitors, etc.) or interconnects to ensure that these devices or lines do not interact with each other in an undesirable manner. Design rules may include or specify specific parameters, limits on the range of parameters, or other information. One or more of the design rule constraints or parameters may be referred to as "critical dimensions" (CDs). A critical dimension of a device may be defined as the minimum width of a line or via, the minimum space between two lines or two vias, or other characteristics. Therefore, CDs determine the overall size and density of the designed device. One of the goals in device fabrication is to faithfully reproduce the original design intent (via the patterned device) on the substrate.

[0078] The terms "mask" or "patterning device" as used herein can be broadly interpreted as any general patterning device that can impart a patterned cross-section to an incident radiation beam, the patterned cross-section corresponding to a pattern to be generated in a target portion of a substrate; in this context, the term "light valve" may also be used. Examples of such patterning devices, besides classic masks (transmissive or reflective; binary, phase-shifting, hybrid, etc.), include programmable mirror arrays and programmable LCD arrays.

[0079] An example of a programmable mirror array can be a matrix-addressable surface with a viscoelastic control layer and a reflective surface. The underlying principle of this device is (e.g.) that addressed regions of the reflective surface reflect incident radiation as diffracted radiation, while unaddressed regions reflect incident radiation as non-diffracted radiation. Using appropriate filters, the non-diffracted radiation can be filtered out from the reflected beam, leaving only diffracted radiation; in this way, the beam becomes patterned according to the addressing pattern of the matrix-addressable surface. Suitable electronic components can be used to perform the desired matrix addressing. An example of a programmable LCD array is given in U.S. Patent No. 5,229,872, which is incorporated herein by reference.

[0080] As used herein, the term "patterning process" generally refers to a process, as part of a lithography process, of creating an etched substrate by applying light to a specified pattern. However, a "patterning process" may also include plasma etching, as many of the features described herein can provide benefits for forming printed patterns using plasma processing.

[0081] As used in this article, the term "pattern" refers to an idealized pattern that will be etched onto a substrate.

[0082] As used herein, the term "printed pattern" refers to a solid pattern on a substrate etched based on a target pattern. Printed patterns may include, for example, grooves, channels, recesses, edges, or other two-dimensional and three-dimensional features generated by lithography.

[0083] As used herein, the terms “predictive model,” “program model,” “electronic model,” or “simulation model” (which are used interchangeably) mean a model that includes one or more models of a patterning process. For example, a model may include an optical model (e.g., modeling a lens system / projection system for delivering light in a lithography process and may include a model of the final optical image of light that becomes photoresistive), a resist model (e.g., modeling the physical effects of a resist, such as chemical effects attributable to light), an OPC model (e.g., used to fabricate a target pattern and may include sub-resolution resist features (SRAF), etc.), an etching (or etching) model (e.g., simulating the physical effects of an etching process on a printed wafer pattern), or other models.

[0084] As used in this article, the term "calibration" means to modify (e.g., improve or tune) or verify something, such as a model.

[0085] A patterning system may be a system comprising any or all of the components described above, configured to perform any or all of the operations associated with such components. For example, a patterning system may include a lithography device, a scanner, a system configured to apply or remove resist, an etching system, or another system.

[0086] For illustration, Figure 1 is a schematic diagram of a lithography projection apparatus according to one embodiment. The lithography projection apparatus may include an illumination system IL, a first object stage T, a second object stage WT, and a projection system PS. The illumination system IL can adjust the radiated beam B. In this example, the illumination system also includes a radiation source SO. The first object stage (e.g., a patterning device stage) T may have a patterning device holder for holding a patterning device MA (e.g., a magnifying mask) and is connected to a first locator to accurately position the patterning device relative to the object PS. The second object stage (e.g., a substrate stage) WT may be provided with a substrate holder for holding a substrate W (e.g., a resist-coated silicon wafer) and is connected to a second locator for accurately positioning the substrate relative to the object PS. The projection system (e.g., including a lens) PS (e.g., a refractive, reflective, or reflective-refractive optical system) can image the irradiated portion of the patterning device MA onto a target portion C (e.g., containing one or more grains) of the substrate W. For example, patterning device alignment marks M1, M2 and substrate alignment marks P1, P2 can be used to align the patterning device MA and the substrate W.

[0087] As depicted, the device can be of the transmissive type (i.e., having transmissive patterning devices). However, in general, it can also be of the reflective type, for example (having reflective patterning devices). The device can use different types of patterning devices than classic masks; examples include programmable mirror arrays or LCD matrices.

[0088] A source SO (e.g., a mercury lamp or excimer laser, LPP (laser-generated plasma) EUV source) generates a radiant beam. For example, this beam is fed directly into the illumination system (illuminator) IL, either directly or after passing through an adjustment member such as a beam expander or beam delivery system BD (containing a guide mirror, beam expander, etc.). The illuminator IL may include an adjustment member AD for setting the outer radial range or inner radial range (typically referred to as σ_outer and σ_inner, respectively) of the intensity distribution in the beam. Additionally, it typically includes various other components, such as a beam accumulator IN and a condenser CO. In this way, the beam B illuminating the patterning device MA has the desired uniformity and intensity distribution in its cross-section.

[0089] In some embodiments, the source SO may be located within the housing of the lithography device (often when the source SO is, for example, a mercury lamp), but it may also be located remotely from the lithography device. For example, the radiation beam generated by the source may be guided into the device (e.g., by means of a suitable guiding mirror). This latter scenario may be, for example, when the source SO is an excimer laser (e.g., based on KrF, ArF, or F2 laser action).

[0090] The beam B can then intercept the patterning device MA held on the patterning device stage T. Having traversed the patterning device MA, the beam B can pass through the lens PL, which focuses the beam B onto the target portion C of the substrate W. The substrate stage WT can be accurately moved, for example, to position different target portions C within the path of the beam B, by means of the second positioning member (and the interferometric measurement member IF). Similarly, the first positioning member can be used to accurately position the patterning device MA relative to the path of the beam B, for example, after mechanically picking up the patterning device MA from the patterning device library or during scanning. Generally, the movement of the stages T and WT can be achieved by using a long-stroke module (coarse positioning) and a short-stroke module (fine positioning). However, in the case of a stepper (relative to a stepping scanning tool), the patterning device stage T may be connected only to the short-stroke actuator, or it may be fixed.

[0091] The depicted tool can be used in two different modes—stepping mode and scanning mode. In stepping mode, the patterning stage T remains essentially stationary, and the entire patterning image is projected onto the target portion C in a single operation (i.e., a single "flash"). The substrate stage WT can be shifted in the x or y direction so that different target portions C can be irradiated by the beam B. In scanning mode, essentially the same applies, except that a given target portion C is not exposed in a single "flash". In practice, the patterning stage T can move at a speed v in a given direction (e.g., the "scanning direction" or the "y" direction) so that the projected beam B scans the patterning image. Simultaneously, the substrate stage WT moves at a speed V = Mv in the same or opposite directions, where M is the magnification of the lens (typically M = 1 / 4 or 1 / 5). In this way, a relatively large target portion C can be exposed without compromising resolution.

[0092] Figure 2 illustrates a schematic overview of lithography cells. As shown in Figure 2, the lithography apparatus LA can form lithography cells LC, sometimes also referred to as lithocells or lithocell clusters. These lithography cells often also include devices for performing pre-exposure and post-exposure processes on the substrate W. Conventionally, such devices include a spin coater SC configured to deposit a resist layer, a developer DE for developing the exposed resist, a cooling plate CH for regulating the temperature of the substrate W (e.g., for regulating the solvent in the resist layer), and a baking plate BK. A substrate handler or robot RO picks up the substrate W from input / output ports I / O1 and I / O2, moves the substrate W between different processing devices, and delivers the substrate W to the loading cassette LB of the lithography apparatus LA. The devices in the lithography cell, which are usually referred to as the coating and developing system, are typically under the control of the coating and developing system control unit (TCU). The TCU itself can be controlled by the supervisory control system (SCS), which can also control the lithography equipment (LA) via, for example, the lithography control unit (LACU).

[0093] To ensure accurate and consistent exposure of the substrate W (Figure 1) exposed by the lithography apparatus LA, it is necessary to inspect the substrate to measure properties of the patterned structure, such as overlap error between subsequent layers, line thickness, and critical dimension (CD). For this purpose, inspection tools (not shown) may be incorporated into the lithography cell LC. If errors are detected, adjustments can be made, for example, to the exposure of subsequent substrates or other processing steps to be performed on the substrate W, especially when inspection is performed before other substrates W in the same batch or group are exposed or processed.

[0094] The inspection device, also known as a measurement device, is used to determine the properties of a substrate W (Figure 1), and specifically to determine how the properties of different substrates W vary or how properties associated with different layers of the same substrate W vary between different layers. The inspection device can alternatively be constructed to identify defects on the substrate W and can be, for example, part of a lithography cell (LC), integrated into a lithography apparatus (LA), or even a standalone device. The inspection device can use actual substrates (e.g., charged particle-SEM images of wafer patterns) or images of actual substrates to measure properties on latent images (images in the resist layer after exposure), semi-latent images (images in the resist layer after the post-exposure baking (PEB) step), developed resist images (where the resist has been removed from exposed or unexposed portions), etched images (after a pattern transfer step such as etching), or otherwise.

[0095] Figure 3 illustrates a schematic representation of overall lithography, illustrating the collaboration between three technologies used to optimize semiconductor manufacturing. Typically, the patterning process in the lithography apparatus LA is one of the most critical steps in the process, requiring high accuracy in the sizing and placement of the structure on the substrate W (Figure 1). To ensure this high accuracy, three systems (in this example) can be combined in a so-called "overall" control environment, as schematically depicted in Figure 3. One of these systems is the lithography apparatus LA, which is (virtually) connected to a measuring instrument (e.g., a measuring tool) MT (the second system), and connected to a computer system CL (the third system). The "overall" environment can be configured to optimize the collaboration between these three systems to enhance the overall process window and provide a tight control loop, thereby ensuring that the patterning performed by the lithography apparatus LA remains within the process window. The process window defines a range of process parameters (e.g., dosage, focus, overlap) within which a particular manufacturing process produces a defined result (e.g., a functional semiconductor device)—typically allowing variations in process parameters within the lithography or patterning process.

[0096] The computer system CL can use the design layout (partially) to be patterned to predict which resolution enhancement technique to use and perform computational lithography simulations and calculations to determine which masking layout and lithography device settings achieve the maximum overall process window of the patterning procedure (described by the double arrows in the first scale SC1 in Figure 3). Typically, the resolution enhancement technique is configured to match the patterning possibilities of the lithography device LA. The computer system CL can also be used to detect where the lithography device LA is currently operating within the process window (e.g., using input from the metric tool MT) to predict whether there might be defects attributable to, for example, suboptimal processing (described by the arrow pointing to "0" in the second scale SC2 in Figure 3).

[0097] The measuring device (tool) MT can provide input to the computer system CL for accurate simulation and prediction, and can provide feedback to the lithography device LA to identify possible drifts in the calibration state of the lithography device LA (depicted by multiple arrows in the third scale SC3 in Figure 3).

[0098] In lithography, it is frequently necessary to measure the resulting structures, for example, for program control and verification. Tools used for such measurements include metrological instruments (MTs). Different types of metrological instruments (MTs) for performing such measurements are known, including scanning electron microscopes (SEMs) or various forms of scatterometer metrological instruments (MTs). In some embodiments, the metrological instrument (MT) is or includes an SEM.

[0099] In some embodiments, the metrological instrument (MT) is or includes a spectroradiometer, an elliptic scatterometer, or other light-based instruments. A spectroradiometer can be configured to direct radiation emitted by a radiation source onto a target feature of a substrate and to direct reflected or scattered radiation from the target to a spectrometer detector, which measures the spectrum of the reflected radiation (i.e., the intensity as a function of wavelength). From this data, the structure or profile of the target that generated the detected spectrum can be reconstructed, for example, by rigorous coupled wave analysis and nonlinear regression or by comparison with a simulated spectral library. An elliptic scatterometer allows for the determination of parameters of a lithography process by measuring the scattered radiation for each polarization state. Such metrological devices (MTs) emit polarized light (such as linear, circular, or elliptical) by using, for example, a suitable polarization filter in the illumination section of the metrological device. The source suitable for the metrological device can also provide polarized radiation.

[0100] As described above, the manufactured device (e.g., patterned substrate) can be inspected at various times during manufacturing. Figure 4 schematically depicts a generalized embodiment of a charged particle (electron beam) inspection device 50. In some embodiments, the inspection device 50 may be an electron beam or other charged particle inspection device (e.g., the same as or similar to a scanning electron microscope (SEM)) that produces an image of a structure (e.g., some or all of the structure of a device (such as an integrated circuit)) exposed or transferred onto a substrate. A primary electron beam 52 emitted from an electron source 54 is focused by a condenser lens 56 and then passes through a beam deflector 58, an E×B deflector 60, and an objective lens 62 to irradiate the substrate 70 on the substrate stage ST at a focal point.

[0101] When the substrate 70 is irradiated with electron beam 52, secondary electrons are generated from the substrate 70. These secondary electrons are deflected by E×B deflector 60 and detected by secondary electron detector 72. A two-dimensional electron beam image can be obtained by detecting electrons generated from the sample in synchronization with operations such as: scanning the electron beam in two dimensions by beam deflector 58 or repeatedly scanning the electron beam 52 in the X or Y direction by beam deflector 58, and continuously moving the substrate 70 in the other of the X or Y directions by substrate stage ST. Thus, in one embodiment, the electron beam detection device has a field of view for the electron beam defined by angular range, within which the electron beam can be provided by the electron beam detection device (e.g., the deflector 60 can provide the angular range of the electron beam 52). Therefore, the spatial extent of the field of view is the spatial extent that the angular range of the electron beam can irradiate on the surface (where the surface can be stationary or movable relative to the field).

[0102] As shown in Figure 4, the signal detected by the secondary electronic detector 72 can be converted into a digital signal by an analog-to-digital (A / D) converter 74, and the digital signal can be sent to the image processing system 76. In some embodiments, the image processing system 76 may have memory 78 for storing all or part of the digital image for processing by the processing unit 80. The processing unit 80 (e.g., specially designed hardware or a combination of hardware and software, or a computer-readable medium containing software) is configured to convert or process the digital image into a dataset representing the digital image. In some embodiments, the processing unit 80 is configured or programmed to perform the operations described herein (e.g., SEM detection). Additionally, the image processing system 76 may have storage medium 82 configured to store the digital image and corresponding dataset in a reference database. A display device 84 may be connected to the image processing system 76, allowing an operator to perform necessary operations of the equipment via a graphical user interface.

[0103] Figure 5 schematically illustrates another embodiment of a charged particle detection device. This device is used to detect a sample 90 (such as a patterned substrate) on a sample stage 89 and includes a charged particle beam generator 81, a condenser lens module 99, a probe forming objective module 83, a charged particle beam deflection module 88, a secondary charged particle detector module 85, an image forming module 86, or other components. The charged particle beam generator 81 generates a primary charged particle beam 91. The condenser lens module 99 focuses the generated primary charged particle beam 91. The probe forming objective module 83 focuses the focused primary charged particle beam into a charged particle beam probe 92. The charged particle beam deflection module 88 scans the surface of the region of interest on the sample 90, which is fixed to the sample stage 89, to form the charged particle beam probe 92. In some embodiments, the charged particle beam generator 81, the condenser lens module 99, and the probe forming objective module 83, or their equivalents, alternatives, or any combination thereof, together form a charged particle beam probe generator that generates a scanning charged particle beam probe 92.

[0104] Secondary charged particle detector module 85 detects secondary charged particles 93 emitted from the sample surface immediately after being bombarded by charged particle beam probe 92 (and possibly along with other reflected or scattered charged particles from the sample surface) to generate a secondary charged particle detection signal 94. An image forming module 86 (e.g., a computing device) is coupled to the secondary charged particle detector module 85 to receive the secondary charged particle detection signal 94 from the secondary charged particle detector module 85 and accordingly form at least one scanned image. In some embodiments, the secondary charged particle detector module 85 and the image forming module 86, or their equivalents, alternatives, or any combination thereof, together form an image forming apparatus that forms a scanned image from the detected secondary charged particles emitted from the sample 90 bombarded by the charged particle beam probe 92.

[0105] In some embodiments, the monitoring module 87 is coupled to the image forming module 86 of the image forming apparatus to monitor, control, etc., the patterning process, or to derive parameters for patterning process design, control, monitoring, etc., using scanned images of samples 90 received from the image forming module 86. In some embodiments, the monitoring module 87 is configured or programmed to perform the operations described herein. In some embodiments, the monitoring module 87 includes a computing device. In some embodiments, the monitoring module 87 includes a computer program configured to provide the functionality described herein. In some embodiments, the detector spot size of the electron beam in the system of FIG3 is significantly larger than, for example, a CD, such that the detector spot is large enough to allow for fast detection speed. However, the resolution may be lower due to the large detector spot.

[0106] As described above, one or more tools may be needed to generate results, for example, that can be used to design, control, monitor, etc., patterning processes. One or more tools may be provided for computationally controlling, designing, and otherwise patterning processes, such as pattern design for patterning devices (including, for example, adding sub-resolution auxiliary features or optical proximity correction), and illumination for patterning devices. Therefore, in systems used for computationally controlling, designing, and otherwise patterning-related manufacturing processes, manufacturing system components and / or programs can be described by various functional modules or models. In some embodiments, one or more electronic (e.g., mathematical, parametric, etc.) models describing one or more steps of the patterning process and / or devices may be provided. In some embodiments, one or more electronic models may be used to simulate the patterning process to simulate how the patterning process forms a patterned substrate using a design pattern provided by the patterning device.

[0107] Images from systems such as those in Figure 4 or 5 can be processed to extract dimensions, shapes, contours, or other information describing the dimensions of edges representing the structure of a semiconductor device within the image. This shape, contour, or other information can be quantified using measurements at user-defined tangents or other locations, such as edge placement error (EPE, CD, etc.). For example, this shape, contour, or other information can be used to optimize the patterning process.

[0108] In some embodiments, the optimization of a patterning process can be represented as a cost function. The optimization process may involve finding a set of parameters (design variables, program variables, etc.) that minimize the cost function of the patterning process. The cost function can have any suitable form depending on the optimization objective. For example, the cost function may be the weighted root mean square (RMS) of the deviations of certain characteristics (evaluation points) of the system from expected values ​​(e.g., ideal values) of those characteristics. The cost function may also be the maximum value of these deviations (i.e., the worst-case deviation). The term "evaluation point" should be broadly interpreted to include any characteristic of the system or manufacturing method. Due to the feasibility of implementing the system or method, the design or processing variables of the patterning process may be limited to a finite range or be interdependent. In the case of lithography equipment, constraints are often associated with the physical properties and characteristics of the hardware (such as tunability or manufacturability design rules for the patterning device). Evaluation points may include physical points in the image of the substrate, as well as non-physical characteristics such as one or more etching parameters, dosage, and focus.

[0109] In an etching system, as an example, the cost function (CF) can be expressed as follows: in There are N design variables or their values, and Can be a design variable Functions, such as those for The difference between the actual and expected values ​​of the set of design variables. In some embodiments, To and Associated weighting constants. For example, a characteristic could be the position of the edge of a pattern (e.g., multiple points on the edge forming a contour) measured at a given point on the edge. Different Can have different weights For example, if a particular edge has a narrow range of permitted positions, then the term used to represent the difference between the actual and expected positions of the edge is... Weight A higher value can be given. It can also be a function of interlayer characteristics, which in turn are design variables. The function of. Of course, Not limited to the forms in the above equations, and It can be any other suitable form.

[0110] The cost function may represent any one or more suitable characteristics of the patterning system, patterning process, lithography equipment, lithography process, or substrate, such as focus, CD, image shift, image distortion, image rotation, random variation, yield, local CD variation, program window, interlayer characteristics, or a combination thereof. In some embodiments, the cost function may include a function representing one or more characteristics of the resist image. For example, It can simply be the distance between the expected positions of one point and another in the resist image after, for example, etching or some other process (i.e., edge placement error). Variables (e.g., design variables) may include any adjustable parameters, such as adjustable parameters of the etching system, source, patterning device, projection optics, dose, focus, etc.

[0111] Parameters (such as design variables) may have constraints, which can be expressed as , where Z is the set of possible values ​​for the design variables. A possible constraint on the design variables can be imposed by the desired output rate of the lithography equipment. Without this constraint imposed by the desired output rate, optimization can result in an unrealistic set of values ​​for the design variables. Constraints should not be interpreted as necessities.

[0112] Pattern features are often determined based on images of substrate patterns. These features are used to determine various key performance indicators (KPIs), which in turn are used to monitor changes in the semiconductor manufacturing process. As an example, edge placement error (EPE) has become a common KPI for monitoring process changes. EPE includes contributions from imaging (e.g., critical dimension (CD) and pattern placement variations) and overlay.

[0113] Contours are stacked and analyzed (e.g., to determine EPE or other KPIs) to identify variations in a given feature. However, some of these contours are outliers, and the aggregation and averaging of these contours "hides" these outliers. For example, these contours may have different qualities (e.g., the image may not provide a clear, sharp view of the edges of the features used to determine the contour, and may instead be pixelated, blurred, etc.) or confidence levels (e.g., it may be completely unclear where the contour is in another image due to lower image quality, or a particular manufacturing process may have produced unusual results). Outliers can arise due to inherent differences in pattern design (some patterns are more challenging for imaging, analysis, or other extraction tasks) or for other reasons, such as process variations or tool condition offsets. If these outlier contours are included in the stack and analysis is performed based on them, the outlier contours may affect any determinations made from that stack. Using EPE as an example, EPE calculations based on stacked outlier profiles may lead to erroneous (or at least less accurate) EPE determinations and may cause unnecessary manufacturing process adjustments to certain manufacturing parameters (when the "budget" used for such adjustments may have been optimally used for other manufacturing parameters).

[0114] Advantageously, the present invention describes screening certain outlier contours, subsequently aggregating (e.g., stacking) them and analyzing them statistically. Each contour is analyzed, and in some embodiments, a score assesses how close the contour is to "ideal," and contours with scores below a threshold are screened, and the remaining contours are aggregated. Screening can be performed at multiple levels, such as based on individual points on contours in a set of detected contours, based on the overall geometry of contours in a set of detected contours, based on angles between vertices of contours, based on distances between vertices of contours, based on the centroid of a contour and the distance between one or more vertices of the contour, based on the fit from the expected contour shape to the contour, at the unit cell level (which includes a plurality of different contours with different features), or at the grain or magnification mask level.

[0115] Figure 6 illustrates an exemplary method 600 for electronically filtering out-of-line profiles from a set of detected profiles in a substrate pattern inspection image. Compared to a previous patterning process, this filtering is configured to enhance the determination of manufacturing variations in a patterning process and the optimization of the patterning process. Method 600 includes: receiving (operation 602) a substrate pattern inspection image; determining (operation 604) profiles based on the substrate pattern inspection image to form a set of detected profiles; filtering (operation 606) out-of-line profiles from the set of detected profiles; determining (operation 608) manufacturing variations of repeating features; and providing (operation 610) the manufacturing variations to a cost function to facilitate the determination of costs associated with individual patterning process variables or other operations.

[0116] In some embodiments, non-transitory computer-readable media storage instructions, when executed by a computer, cause the computer to perform one or more of operations 602 to 610 or other operations. The operation of method 600 is intended to be illustrative. In some embodiments, method 600 may be implemented with one or more additional operations not described or without one or more of the operations discussed. For example, operation 610 or other operations may be optional. Furthermore, the order in which the operations of method 600 are illustrated in FIG. 6 and described herein is not intended to be limiting.

[0117] At operation 602, substrate pattern detection images are received. These substrate pattern detection images are generated by an optical detection system, a charged particle detection system, an electronic model, or other systems. The optical detection system may be, for example, a scatterometer (as described above) or other optical detection systems. The charged particle detection system may be, or include, for example, a scanning electron microscope (e.g., shown in Figures 4 and 5 and as described above) or other charged particle detection systems such as a multi-beam charged particle detection system. The electronic model may be any electronic model that generates a representation of the substrate pattern.

[0118] The substrate pattern inspection image may include information describing the geometry of the contours in the pattern or information related to the geometry. The geometry of the contours in the pattern may, for example, be a two-dimensional geometry. The received substrate pattern inspection image includes data describing the characteristics of the contours (e.g., XY-dimensional data points, mathematical equations describing the geometry, etc.), processing parameters associated with the contours, or other data. The image may further include 3D information, such as information about features embedded in one or more sublayers, data from previous inspections of previous layers, information selected and input by the user operating the inspection system to generate the inspection image, or other information.

[0119] The substrate pattern detection image can be received electronically from one or more other parts of the system of the present invention (e.g., from different processors, or from different parts of a single processor), from a remote computing system not associated with the system of the present invention, from an (optical or charged particle) detection system, or from other sources. The substrate pattern detection image can be received wirelessly or via wire, via portable storage media, or from other sources. The substrate pattern detection image can be uploaded or downloaded from another source (such as cloud storage), or otherwise received.

[0120] At operation 604, a contour is determined based on the substrate pattern detection image. Determining the contour includes detecting the edges of features in the substrate pattern detection image. Features may be repeating or non-repeating. In some embodiments, the contour includes a set of points indicating the location of boundaries or edges around pattern features in an image (e.g., an SEM image). Detecting the edges of such features may involve detecting these points using localized (e.g., pixel or other localization indicators) image properties, electronic files (e.g., GDS files), or other sources or information specifying the edge location or other dimensions of the pattern. Image properties may include contrast, sharpness, color, noise, or other properties or information. As a non-limiting example, the edges of features may be detected based on strong contrast between similar or dissimilar colors in a given image (e.g., which would indicate sharp edges). As another non-limiting example, using SEM imagery, localized contour point detection can lead to the identification of several candidate edge points near a location on a pattern feature. In this case, the sharpest point can be selected as the most probable edge point (i.e., a single sharp edge is easier to locate with less uncertainty than several sharp edges in the same window). For example, the probability can be generated based on a local probability model and can be quantified as a KPI.

[0121] In some embodiments, operation 604 includes detecting contours that span the same repeating feature across a unit cell or scale-down mask associated with, for example, a pattern (e.g., a patterning device as described above). Determined contours for the same repeating feature can form a set of detected contours.

[0122] At operation 606, outlier contours are filtered from the self-detected contour set, leaving remaining contours in the detected contour set after filtering. The filtering is performed based on: individual points on the contours in the detected contour set, the geometry of the contours in the detected contour set, the angles between the vertices of the contours, the distances between the vertices of the contours, the distance between the centroid of the contour and one or more vertices on the contour, the fit from the expected contour shape to the contour, or other information. In some embodiments, filtering outlier contours from the self-detected contour set includes filtering each contour associated with, for example, an outlier unit cell or an outlier magnification mask.

[0123] As a non-limiting example, point-based KPIs (e.g., EPEs) can be determined for contours detected based on local grayscale contrast information in an image. Sharp edges with strong contrast in an image are easily located with less uncertainty than blurred edges. Each point in the contour can be associated with a KPI value that provides point-level confidence information. Points located along sharp edges in such images include contours configured to remain in the set of detected contours (e.g., such that any KPI values ​​determined for points along this contour are used in future calculations), while points located along blurred edges include contours configured to be filtered (e.g., such that any KPI values ​​determined for points along this contour are filtered and not used in future calculations).

[0124] As another non-limiting example, the geometry of a contour can be used to determine feature (polygon) level KPIs. In this example, if the detected edges of a feature form a contour with sharp peaks or multiple disconnected regions (or other unusual or unexpected overall shapes of the feature) (as two possible examples), the contour can be filtered from the set of detected contours (e.g., so that any feature level KPI values ​​determined using this contour are filtered and not used in future calculations). These concepts can be extended to include unit cell level filtering, grain or magnification mask level filtering, or other filtering. Furthermore, these concepts can be extended to include, for example, soft filtering / kernel density estimation of percentage contours using KPIs.

[0125] Figures 7 and 8 illustrate additional details related to point-based filtering (Figure 7) and geometry-based filtering (Figure 8). Figure 7 illustrates filtering for repeating feature 704 based on individual points 700 on contours 702 in the set of detected contours. In some embodiments, this includes determining the image contrast, color, sharpness, or noise level of the pixel location at point 700 on contour 702 in the image 706 along the substrate pattern. Figure 7 illustrates a heatmap 708 of point 700 on contour 702. In heatmap 708, darker tones indicate that contour 702 is actually at a lower confidence level at a given point 700 (e.g., determined based on image contrast, color, sharpness, noise, etc.), and lighter tones indicate a higher confidence level. Heatmap 708 indicates portions of contour 702 associated with lower confidence levels 710 and higher confidence levels 712. In practice, using the system and method of this invention, the portion of the self-detected contour set associated with the lower confidence level 710 is filtered out, while the portion of the contour 702 associated with the higher confidence level 712 is kept in the detected contour set.

[0126] Figure 8 illustrates the filtering of geometry 800, 802 of contours 804, 806 in the detected contour set for (repeating) feature 808. Figure 8 shows examples of contours 804, 806 of two individual contact holes (repeating feature 808). In some embodiments, filtering based on geometry 800, 802 of contours 804, 806 in the detected contour set includes determining the smoothness of geometry 800, 802 of a given contour 804, 806. Here, for example, contour 804 has a relatively smooth geometry, while contour 806 has a shape deviation 807. Contour 806 is affected by shading, which appears to cause deviation 807. The geometry-based KPI will likely have different values ​​for contours 804 and 806. In practice, using the system and method of the present invention to enhance the accuracy of geometry-based KPIs determined from the detected contour set of feature 808 (or for other reasons), contour 806 is filtered from the detected contour set, while contour 804 remains in the detected contour set.

[0127] Figure 9 provides an overview view illustrating filtering based on angle 902 between vertices 904, 906, and 908 of contour 910; filtering based on distances 914 and 916 between vertices (918 and 920, and 920 and 922) of contour 924; filtering based on distance 932 between the centroid 934 of contour 936 and one or more vertices 938 on contour 936; and filtering based on fitting the expected contour shape 942 to contour 936. In some embodiments, the pattern feature comprises a polygon. In other words, the pattern feature may be multi-sided or multi-edge. The contour comprises the sides or edges (or portions of the sides or edges) of a polygon. The contour may be, or include, for example, multiple segments of lines, curves, or other shapes. Multiple segments of lines, curves, etc., may have junctions between segments. The vertices of the contour may be located at junctions or other intersections of segments of lines or curves. Filtering outlier contours may be based at least in part on these vertices, as described below.

[0128] Referring to Figure 9 and filter 900, the angles (e.g., angle 902) of neighboring segments at each vertex 904, 906, 908 can be determined, and if the angle exceeds a given threshold (e.g., 90 degrees), then profile 910 can filter the corresponding feature (or at least a portion of the feature). Referring to filter 912, the distances 914 and 916 between neighboring vertices 918, 920, 922 (or other points on profile 924) can be determined, and if the distance exceeds a given threshold (e.g., a specific distance or program parameter requirement, such as edge roughness, as an example), then profile 924 can filter the corresponding feature (or at least a portion of the feature). Referring to filters 930 and 940, the centroid 934 can be determined, and then the expected feature shape (e.g., a circle or an ellipse) can be fitted to the contour. The fit can be checked for outlier vertices or other points on the contour 936 (e.g., by checking the distance from the centroid 934 to the position on the contour 936, checking the difference between the expected fit and the actual fit, etc.). Each of these examples is discussed in more detail below.

[0129] Figure 10 illustrates the determination of angles 1000 to 1016 at vertices 1020 to 1056 of the determined contours 1060, 1062, and 1064, and the filtering of outlier contours 1060 and 1062 based on the self-detected contour set (formed by grouping contours 1060, 1062, and 1064) at angles 1000 to 1016. In this example, contour 1064 is retained. The filtering of outlier contours 1060 and 1062 from the self-detected contour set is based on a comparison between the determined angles 1000 to 1016 and angle thresholds. Thresholds can be determined by the user through input or selection via a user interface (e.g., as described below), automatically by the processor (as described herein) based on (current or historical) dimensional data or other information, or in other ways. For example, users can set thresholds based on experience, processing data, etc., to ensure that the filtering program does not over- or under-filter contours based on these angles. Contours whose vertices have angles exceeding an angle threshold are identified as outlier contours and are filtered from the set of detected contours. For example, contours whose vertices have angles less than an angle threshold are identified as outlier contours and are filtered from the set of detected contours. Such threshold angles can be 120 degrees, 90 degrees, 60 degrees, or 45 degrees, or other angles. As another example, contours whose vertices have angles greater than an angle threshold are identified as outlier contours and are filtered from the set of detected contours. Such threshold angles can be 150 degrees, 160 degrees, 170 degrees, or 180 degrees, or other angles. It should be noted that, as described herein, in some embodiments, only portions of contours with angles exceeding the threshold angle may be filtered, while the rest of the contour may be retained, or the entire contour may be filtered.

[0130] Figure 11 illustrates the determination of distances 1100 to 1114 between neighboring vertices 1120 to 1150 of the determined contours 1160, 1162, and 1164, and the filtering of outlier contours 1160 and 1162 from the self-detected contour set (formed by grouping contours 1160, 1162, and 1164) based on distances 1100 to 1114. In some embodiments, the filtering of outlier contours 1160 and 1162 from the self-detected contour set is based on a comparison of the determined distances 1100 to 1114 with a distance threshold. Determined contours whose distances exceed the distance threshold are determined as outlier contours and are filtered by the self-detected contour set. In some embodiments, the distance threshold includes specific values, distances that are a given multiple of the average distance between vertices (e.g., 2×, 5×, 10×, etc.) or a percentage greater than or less than that average distance (e.g., 150%, 200%, etc.); or distances that correspond to, for example, contour edge roughness parameters or other program parameters. In some embodiments, the type or value of the threshold may be determined by the user through input or selection made via a user interface (e.g., as described below), automatically by the processor (as described herein) based on (current or historical) dimensional data or other information, or may be determined in other ways. For example, the user may set thresholds based on experience, processing data, etc., to ensure that the filtering procedure does not over- or under-filter contours based on these distances. It should be noted that, as described herein, in some embodiments, only portions of contours that are beyond the threshold distance may be filtered, while the rest of the contour is retained, or the entire contour may be filtered.

[0131] Figure 12 illustrates the determination of the centroids 1200, 1202, and 1204 of the determined contours 1206, 1208, and 1210, and the filtering of outlier contours 1206 and 1208 from the detected contour set (formed by grouping contours 1206, 1208, and 1210) based on the relationship between the centroids 1200, 1202, and 1204 and one or more vertices 1220 to 1234 of the given contour, or the expected contour shapes 1240, 1242, and 1244 fitted to the given contour. In some embodiments, the relationship-based filtering includes filtering the given contour (e.g., contours 1206 or 1208) from the detected contour set in response to one or more vertices (e.g., vertices 1220, 1222, 1224, and 1228) of the given contour (e.g., contour 1206 or 1208) being more than a centroid distance threshold. The centroid distance threshold includes, for example, a distance that is a given multiple of the average distance between the vertex and the centroid, or a percentage greater than or less than one of the average distances, or another distance.

[0132] In some embodiments, filtering includes determining the centroids 1200, 1202, and 1204 of the determined contours 1206, 1208, and 1210; fitting expected contour shapes 1240, 1242, and 1244 to the determined contours 1206, 1208, and 1210 based on the centroids 1200, 1202, and 1204; and filtering outlier contours 1206 and 1208 from the detected contour set based on the relationship between the fitted expected contour shapes 1240 and 1242 (in this example) and the vertices of a given contour (e.g., 1220, 1222, and 1224 for expected contour shape 1240, and 1228 for expected contour shape 1242). In some embodiments, the expected contour shape includes, for example, a circle, an ellipse, or other shapes. In some embodiments, relationship-based filtering includes filtering the given contour from the detected contour set in response to one or more vertices of the given contour being at a distance from the fitted circle or ellipse exceeding a fitting distance threshold. In other words, a contour can be filtered if its vertices are not on or near the fitted expected contour shape. In some embodiments, the fitting distance threshold includes a distance that is a multiple of or a percentage greater than or less than one of the average distances between the vertex and the fitted circle or ellipse.

[0133] Similar to other thresholds described above, centroid distance thresholds and fitting distance thresholds can be determined by the user through input or selection via a user interface (e.g., as described below), automatically by the processor (as described herein) based on (current or historical) dimensional data or other information, or otherwise. For example, users can set thresholds based on experience, data processing, etc., to ensure that the filtering procedure does not over- or under-filter contours based on these distances. It should be noted that, as described herein, in some embodiments, only portions of contours exceeding the threshold distance can be filtered, while the rest of the contour is retained, or the entire contour can be filtered.

[0134] Figure 13 shows an example of several contours 1300 selected from the substrate pattern 1302. Figure 13 also illustrates several unselected (acceptable) contours 1304. Contours 1300 are selected (or not selected) based on one or more of the operations described above. For example, as shown in Figure 13, some contours 1306 are selected based on the angle between vertices, some contours 1308 are selected based on the distance between vertices, some contours 1310 are selected based on the centroid of some contours and the fitted expected contour, and some contours are selected based on a combination of two or more of these factors.

[0135] Returning to Figure 6, in some embodiments, operation 606 includes determining a score for each contour in the detection contour set; and filtering outlier contours from the detection contour set based on the score. The score can be determined based on individual points on the contours in the detection contour set, the geometry of the contours in the detection contour set, or other information. In some embodiments, the score includes individual scores for each of a plurality of points on a contour, and the filtering is based on these individual scores. In some embodiments, the score includes a total score associated with one geometry of a contour, and the filtering is based on the total score.

[0136] In some embodiments, each contour in the set of detected contours (e.g., as described in one or more of the examples above) is analyzed and a score is assigned to that contour. In some embodiments, the score assesses the reliability of the location of the detected contour (e.g., as described above) or the confidence level of various KPIs calculated for a specific location on the contour. For example, the score may be a statistical confidence value. A statistical confidence value may be used for a specific KPI at a specific point-level location (such as EPE or CD, as two examples) (but this may also be applied at the feature geometry level, unit cell level, or grain or magnification mask level).

[0137] In some embodiments, the score assesses the closeness of a profile to an "ideal" profile. The ideal profile may be a target feature design, the expected shape of a feature given previous manufacturing steps, or other information, or may be determined based on it. The ideal profile may be specified by a user (e.g., by uploading or downloading electronic files, labeling a specific profile as "ideal," specifying the dimension or location of the profile or points along the profile, etc.), generated by an electronic model, determined by measurements taken during the manufacturing process, determined based on similarity to a substrate pattern image, or otherwise obtained. Closeness to the ideal profile may refer to the difference between a measurement obtained at a specific location associated with the profile and the expected or permissible range used for that measurement (e.g., closeness may also be determined based on a (known or unknown) relationship between the profile and a substrate pattern image), the amount by which a given profile deviates from the expected or permissible geometry, or other measures of closeness.

[0138] In some embodiments, contours whose scores exceed (e.g., are higher or lower than a threshold value depending on the configuration of the system or method of the present invention) are filtered, and the remaining contours are aggregated (e.g., stacked). The threshold value may be determined based on the target feature design, the expected shape of the feature given previous manufacturing processing steps, or other information. The threshold value may be specified by the user (e.g., by uploading or downloading electronic files; typing a specific threshold value using a user interface (described below); specifying the dimension or location associated with the threshold value, etc.); generated by an electronic model; determined by measurements from the manufacturing process; determined based on SEM image-related metrics or user input (such as gradient sensitivity, peak dispersion tolerance, etc.); or determined in other ways.

[0139] At operation 608, a manufacturing change of the repeating feature is determined. The manufacturing change of the repeating feature may be determined based on the remaining contours in the detected contour set after filtering. In some embodiments, determining the manufacturing change of the repeating features includes: stacking the remaining contours in the detected contour set; and statistically analyzing the stacked remaining contours.

[0140] Figure 14 illustrates the difference between a typical procedure 1400 (see arrows indicating direct stacking without filtering, e.g., arrows advancing directly from profile 1408 to analysis 1404 described below) and a stack 1402 and analysis 1404 with filtering 1406 as described herein. In stack 1402 and analysis 1404, with filtering 1406, profiles 1408 from repeating features 1410 on substrate 1411 are grouped together. The user can select and set thresholds for filtering 1406, as described above. For example, if a geometry (polygon) level KPI threshold is set, profiles whose geometry (polygon) KPI exceeds the threshold are removed during stacking 1402 and subsequent analysis 1404 steps, either sequentially or otherwise. Similarly, profiles from unit cells or the entire field of view (e.g., grain or magnified mask level images) can be filtered. Figure 14 illustrates percentage profiles 1412 and 1414 (1%, 50%, 99%) calculated based on stacked unfiltered and filtered profiles, respectively. Using filter 1406, less reliable profile 1408 is filtered, and percentage profile 1414 is enhanced relative to percentage profile 1412. In other words, as shown in Figure 14, percentage profile 1414 is more regularly shaped (e.g., smoother, more rounded) compared to percentage profile 1412.

[0141] As further evidence of the effectiveness of the system and method of the present invention, in use, the above screening operation tends to screen approximately 2% to 3% of the contours in a given substrate pattern. This 2% to 3% screening ratio remains reasonably constant across rounds and across the relative positions within the substrate pattern. Encouragingly, if the image exclusion boundary used for screening (and again remains reasonably constant across rounds and positions) is maintained, this 2% to 3% screening ratio decreases to approximately 1% to 2%.

[0142] Returning to Figure 6, at operation 610, manufacturing variations or other information are provided to the cost function to facilitate the determination of costs associated with individual patterning process variables or metrics. These costs associated with individual patterning process variables or metrics are configured to facilitate the optimization of the patterning process. In some embodiments, the costs associated with individual patterning process variables or metrics are configured to be provided to an optimizer to facilitate (e.g., co-)optimization of the patterning system (e.g., a lithography device, as shown in Figure 1) or other semiconductor manufacturing processes or systems. Generally, the optimizer is a computer algorithm that finds the minimum value of a given cost function. The optimizer may be a gradient-based nonlinear optimizer configured to co-determine, for example, multiple patterning process variables, patterning system setpoints, or other information. The optimizer may be formed by one or more processors configured to balance different possible program variables (e.g., each within its own permissible range) with manufacturing capabilities or with costs associated with different metrics (e.g., critical dimensions associated with the patterning process, pattern placement error, edge placement error, critical dimension asymmetry, optimal focus shift, defect count, or other metrics).

[0143] Figure 15 is a diagram of an example computer system CS that can be used in one or more of the operations described herein. The computer system CS includes a bus BS or other communication mechanism for transmitting information, and a processor PRO (or multiple processors) coupled to the bus BS to process the information. The computer system CS also includes main memory MM, such as random access memory (RAM) or other dynamic storage devices, coupled to the bus BS for storing information and instructions to be executed by the processor PRO. The main memory MM can also be used to store temporary variables or other intermediate information during instruction execution by the processor PRO. The computer system CS further includes read-only memory (ROM) or other static storage devices coupled to the bus BS for storing static information and instructions for the processor PRO. A storage device SD, such as a magnetic disk or optical disk, is provided and coupled to the bus BS for storing information and instructions.

[0144] The computer system CS can be coupled via bus BS to a display DS for displaying information to the computer user, such as a cathode ray tube (CRT), flat panel, or touch panel display. Input devices ID, including alphanumeric keys and other keys, are coupled to bus BS to transmit information and command selections to the processor PRO. Another type of user input device is a cursor controller CC, such as a mouse, trackball, or cursor direction keys, used to transmit directional information and command selections to the processor PRO and to control cursor movement on the display DS. This input device typically has two degrees of freedom in two axes (a first axis (e.g., x) and a second axis (e.g., y)), allowing the device to be positioned in a plane. Touch panel (screen) displays can also be used as input devices.

[0145] In some embodiments, portions of one or more methods described herein may be executed by a computer system CS in response to a processor PRO executing one or more sequences of one or more instructions contained in main memory MM. These instructions may be read from another computer-readable medium (such as a storage device SD) into main memory MM. Execution of the instruction sequence included in main memory MM causes processor PRO to perform the program steps (operations) described herein. One or more processors in a multiprocessor arrangement may also be used to execute the instruction sequence contained in main memory MM. In some embodiments, a hard-wired circuit system may be used instead of or in combination with software instructions. Therefore, the description herein is not limited to any particular combination of hardware circuitry and software.

[0146] As used herein, the term "computer-readable media" means any media that participates in providing instructions to a processor (PRO) for execution. This media can take many forms, including (but not limited to) non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical discs or magnetic disks, such as storage devices (SD cards). Volatile media include dynamic memory, such as main memory (MM). Transmission media include coaxial cables, copper wires, and optical fibers, including conductors containing buses (BS). Transmission media can also take the form of sound waves or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Computer-readable media can be non-transitory, such as floppy disks, flexible magnetic disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, DVDs, any other optical media, punch cards, paper tapes, any other physical media with a perforated pattern, RAM, PROMs and EPROMs, FLASH-EPROMs, any other memory chips or cartridges. Non-transitory computer-readable media may have machine-readable instructions recorded thereon. These instructions, when executed by a computer, may perform any of the operations described herein. Temporary computer-readable media may include, for example, a carrier wave or other means of propagating electromagnetic signals.

[0147] Various forms of computer-readable media may involve carrying one or more sequences of machine-readable instructions to a processor PRO for execution. For example, initially, these instructions may be carried on a disk of a remote computer. The remote computer may load the instructions into its dynamic memory and transmit them via a data transfer device (DATA) over a telephone line. The DATA at the computer system CS local end may receive data over the telephone line and convert the data into an infrared signal using an infrared transmitter. An infrared detector coupled to a bus BS may receive the data carried in the infrared signal and place the data on the bus BS. The bus BS carries the data to main memory MM, from which the processor PRO fetches and executes the instructions. The instructions received from main memory MM may, as appropriate, be stored on a storage device SD before or after execution by the processor PRO.

[0148] The computer system CS may also include a communication interface CI coupled to a bus BS. The communication interface CI provides bidirectional data communication coupling to a network connection NDL connected to a local area network (LAN). For example, the communication interface CI may be an integrated Services Digital Network (ISDN) card or modem to provide data communication connectivity with a corresponding type of telephone line. As another example, the communication interface CI may be a local area network (LAN) card to provide data communication connectivity with a compatible LAN. Wireless connectivity may also be implemented. In any of these implementations, the communication interface CI transmits and receives electrical, electromagnetic, or optical signals carrying digital data streams representing various types of information.

[0149] Network Connectivity (NDL) typically provides data communication with other data devices via one or more networks. For example, a Network Connectivity (NDL) can provide a connection to a host computer (HC) via a Local Area Network (LAN). This may include providing data communication services via a global packet data communication network (now commonly referred to as the "Internet"). A Local Area Network (LAN) (Internet) can use electrical, electromagnetic, or optical signals carrying digital data streams. Signals through various networks and signals on the Network Connectivity (NDL) via the Communication Interface (CI) are exemplary carrier forms for transmitting information, carrying digital data to and from the computer system (CS).

[0150] A computer system (CS) can send and receive messages (including code) via a network, a network data link (NDL), and a communication interface (CI). In an Internet example, a host computer (HC) can transmit requested code for an application via an Internet connection (INT), a network data link (NDL), a local area network (LAN), and a communication interface (CI). For example, a downloaded application may provide all or part of the methods described herein. The received code may be executed by the processor (PRO) upon receipt and / or stored in a storage device (SD) or other non-volatile storage for later execution. In this way, the computer system (CS) obtains application code in carrier-based form.

[0151] Figure 16 is a schematic diagram of another lithography projection apparatus (LPA) that can be used to facilitate one or more of the operations described herein. The LPA shown in Figure 16 is similar to or the same as the apparatus shown in Figure 1. The LPA may include a source collector module SO, an illumination system (illuminator) IL configured to modulate a radiation beam B (e.g., EUV radiation), a patterning stage T, a substrate stage WT, and a projection system PS. The patterning stage T may be configured to support a patterning device (e.g., a mask or magnifying glass) MA and connected to a first positioner PM configured to accurately position the patterning device. The substrate stage (e.g., a wafer stage) WT may be configured to hold a substrate (e.g., a resist-coated wafer) W and connected to a second positioner PW configured to accurately position the substrate. The projection system (e.g., a reflective projection system) PS may be configured to project a pattern imparted to the radiation beam B by the patterning device MA onto a target portion C (e.g., containing one or more dies) of the substrate W.

[0152] As shown in this example, the LPA can be of the reflective type (e.g., using a reflective patterning device). It should be noted that because most materials are absorptive in the EUV wavelength range, the patterning device can have a multilayer reflector comprising, for example, multiple stacks of molybdenum and silicon. In one example, the multilayer reflector has 40 pairs of molybdenum and silicon layers, each layer being a quarter wavelength thick. X-ray lithography can be used to produce even smaller wavelengths. Because most materials are absorptive at both EUV and X-ray wavelengths, the patterned absorbent material segments on the topography of the patterning device (e.g., a TaN absorber on top of a multilayer reflector) will define features that are either printed (positive resist) or unprinted (negative resist).

[0153] The illuminator IL can receive 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 having at least one element (e.g., xenon, lithium, or tin) into a plasma state using one or more emission spectra within the EUV range. In one such method (often referred to as laser-generated plasma (“LPP”), plasma can be generated by irradiating fuel (such as droplets, streams, or clusters of material having an emitting element) with a laser beam. The source collector module SO may be part of an EUV radiation system including a laser (not shown in Figure 16) used to provide the laser beam for exciting the fuel. The resulting plasma emits output radiation (e.g., EUV radiation), which is collected using a radiation collector disposed within 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 may be separate entities. In this example, the laser lithography apparatus may not be considered part of the laser, and the radiated beam may be delivered from the laser to the source collector module by means of a beam delivery system including, for example, a suitable guide mirror or beam expander. In other examples, such as when the source is a discharge-generating plasma EUV generator (commonly referred to as a DPP source), the source may be an integral part of the source collector module.

[0154] An illuminator IL may include an adjuster for adjusting the angular intensity distribution of the radiated beam. Typically, this adjuster can adjust at least the outer radial range or the inner radial range (often referred to as σ_outer and σ_inner, respectively) of the intensity distribution in the pupil plane of the illuminator. Additionally, the illuminator IL may include various other components, such as faceted field mirror devices and faceted pupil mirror devices. The illuminator can be used to adjust the radiated beam to achieve a desired uniformity and intensity distribution across its cross-section.

[0155] A radiated beam B may be incident on a patterning device (e.g., a mask) MA held by a patterning device stage T, and patterned by the patterning device. After reflection from the patterning device (e.g., the mask) MA, the radiated beam B passes through a projection system PS, which focuses the beam onto a target portion C of a substrate W. The substrate stage WT can be accurately moved (e.g., to position different target portions C within the path of the radiated beam B) by means of a second locator PW and a position sensor PS2 (e.g., an interferometric measuring device, a linear encoder, or a capacitive sensor). Similarly, a first locator PM and another position sensor PS1 can be used to accurately position the patterning device (e.g., the mask) MA relative to the path of the radiated beam B. Patterning device alignment marks M1, M2 and substrate alignment marks P1, P2 can be used to align the patterning device (e.g., the mask) MA and the substrate W.

[0156] The described apparatus LPA can be used in at least one of the following modes: step mode, scan mode, and static mode. In step mode, the patterning stage T and substrate stage WT are kept substantially stationary while the entire pattern to be applied to the radiation beam is projected onto the target portion C in one pass (e.g., single static exposure). The substrate stage WT is then shifted in the X or Y direction to expose different target portions C. In scan mode, the patterning stage T and substrate stage WT are scanned simultaneously while the pattern to be applied to the radiation beam is projected onto the target portion C (i.e., single dynamic exposure). The speed and direction of the substrate stage WT relative to the patterning stage T can be determined by the magnification (reduction) and image inversion characteristics of the projection system PS. In static mode, the patterning stage T is kept substantially stationary while the pattern to be applied to the radiation beam is projected onto the target portion C, thereby holding the programmable patterning apparatus in place, and the substrate stage WT is moved or scanned. In this mode, a pulsed radiation source is typically used, and the programmable patterning device is updated as needed after each movement of the substrate stage WT or between successive radiation pulses during a scan. This operating mode can be readily applied to maskless lithography using programmable patterning devices, such as programmable mirror arrays of the type mentioned above.

[0157] Figure 17 is a more detailed view of the lithography apparatus shown in Figure 16 (or Figure 1). As shown in Figure 17, the LPA may include a source collector module SO, an illumination system IL, and a projection system PS. The source collector module SO is configured to maintain a vacuum environment within the enclosure structure 220 of the source collector module SO. An EUV radiation emitting plasma 210 can be formed by generating a plasma source through discharge. EUV radiation can be generated by a gas or vapor (e.g., Xe gas, Li vapor, or Sn vapor), wherein a thermal plasma 210 is generated to emit radiation in the EUV range of the electromagnetic spectrum. The thermal plasma 210 is generated, for example, by a discharge that generates at least partially ionized plasma. For efficient radiation generation, Xe, Li, Sn vapor, or any other suitable gas or vapor with a partial pressure of 10 Pa may be required. In some embodiments, an excited tin (Sn) plasma is provided to generate EUV radiation.

[0158] Radiation emitted by the thermoplasm 210 is transmitted from the source chamber 211 to the collector chamber 212 via a gas barrier or contaminant trap 230 (also referred to in some cases as a contaminant barrier or foil trap) positioned in or behind an opening in the source chamber 211. The contaminant trap 230 may include a channel structure. The contaminant trap 230 may also include a gas barrier, or a combination of a gas barrier and a channel structure. The contaminant trap or contaminant barrier trap 230 (described below) also includes a channel structure. The collector chamber 211 may include a radiation collector CO, which may be a so-called grazing incidence collector. The radiation collector CO has an upstream radiation collector side 251 and a downstream radiation collector side 252. Radiation traversing the collector CO may be reflected from the grating spectral filter 240 to be focused onto a virtual source point IF along the optical axis indicated by the line "O". The virtual source point IF is often referred to as the intermediate focus, and the source collector module is configured such that the intermediate focus IF is located at or near the opening 221 in the closed structure 220. The virtual source point IF is an image of the radiative emission plasma 210.

[0159] Subsequently, radiation traverses the illumination system IL, which may include a faceted field mirror device 22 and a faceted pupil mirror device 24. The faceted field mirror device and the faceted pupil mirror device are configured to provide the desired angular distribution of the radiation beam 21 at the patterning device MA, and the desired uniformity of radiation intensity at the patterning device MA. After the radiation beam 21 is reflected at the patterning device MA held by the patterning device stage T, a patterned beam 26 is formed, and the patterned beam 26 is imaged onto the substrate W held by the substrate stage WT via the projection system PS through reflective elements 28 and 30. More devices than those shown may typically exist in the illumination optics unit IL and the projection system PS. Depending on, for example, the type of lithography equipment, a grating filter 240 may be present. Additionally, more mirrors than those shown in the figures may exist; for example, the projection system PS may have 1 to 6 additional reflective elements than those shown in Figure 17.

[0160] The collector optics CO illustrated in Figure 17 are depicted as a nested collector with grazing incidence reflectors 253, 254, and 255, serving as just one example of a collector (or collector mirror). The grazing incidence reflectors 253, 254, and 255 are arranged symmetrically about the optical axis O, and this type of collector optics CO can be used in conjunction with a discharge-generating plasma source, often referred to as a DPP source.

[0161] Figure 18 is a detailed view of the source-collector module SO of the lithography projection apparatus LPA (shown in the previous figure). The source-collector module SO may be part of the LPP radiation system. The laser LA may be configured to deposit laser energy onto a fuel such as xenon (Xe), tin (Sn), or lithium (Li), thereby generating a highly ionized plasma 210 with an electron temperature of tens of electron volts. High-energy radiation generated during the de-excitation and recombination of this plasma is emitted from the plasma, collected by near-normal incident collector optics CO, and focused onto an opening 221 in the enclosure structure 220.

[0162] Various embodiments are disclosed in the subsequent list of numbered items: 1. A method comprising: receiving substrate pattern detection images; determining contours based on the substrate pattern detection images to form a set of detection contours, wherein determining the contours comprises: detecting edges of repeating features in the substrate pattern detection images; and filtering outlier contours from the set of detection contours, thereby leaving residual contours in the set of detection contours after the filtering. 2. The method of any of the preceding clauses further comprises determining, based on the remaining contours in the detected contour set after the filtering, that one of the repeating features is modified. (1) 3. The method of any of the preceding clauses, wherein the manufacturing variation is configured to provide a cost function to facilitate the determination of costs associated with individual patterning process variables, and wherein such costs associated with individual patterning process variables are configured to facilitate the optimization of a patterning process. (2) 4. The method of any of the preceding clauses, wherein determining the manufacturing variation of the repeating features comprises: stacking the remaining contours in the detected contour set; and statistically analyzing the stacked remaining contours. (2) 5. The method of any of the preceding clauses, wherein the filtering is performed based on individual points on the contours in the detection contour set or on the geometry of the contours in the detection contour set. (1) 6. The method of any of the preceding clauses, wherein the filtering based on individual points on the contours in the detection contour set includes determining the image contrast or noise level of the pixel position of a given contour in an image detected along a substrate pattern. (5) 7. The method of any of the preceding clauses, wherein the filtering based on the geometry of the contours in the detection contour set includes determining the smoothness of a geometry of a given contour. (5) 8. The method of any of the preceding clauses, wherein determining the contours in the substrate pattern detection images to form the detection contour set includes detecting repeating contours that span a unit cell or a 1x scale mask associated with a pattern; and wherein filtering the outlier contours from the detection contour set includes filtering each contour associated with an outlier unit cell or an outlier scale mask. (1) 9. The method of any of the preceding clauses further comprises: determining a score for each contour in the set of detected contours; and filtering out the outlier contours from the set of detected contours based on the score. (1) 10. The method of any of the preceding clauses, wherein the score is determined by referring to the substrate pattern detection image based on individual points on the contours in the detection contour set or the geometry of the contours in the detection contour set. (9) 11. The method of any of the preceding clauses, wherein the score comprises an individual score for each of a plurality of points on a contour, and wherein the screening is based on such individual scores. (9) 12. The method of any of the preceding clauses, wherein the score comprises a total score associated with one geometry of a contour, and wherein the screening is based on the total score. (9) 13. The method of any of the preceding clauses, wherein the substrate pattern detection images are generated by an optical detection system or a charged particle detection system. (1) 14. The method of any of the preceding clauses, wherein the substrate pattern detection images are generated by the charged particle detection system, and wherein the charged particle detection system includes a scanning electron microscope. (13) 15. A non-transitory computer-readable medium having instructions thereon, which, when executed by a computer, cause the computer to perform the method as described in any of the preceding clauses. (1) 16. A system comprising one or more processors configured by machine-readable instructions to: receive substrate pattern detection images; determine contours based on the substrate pattern detection images to form a set of detection contours, wherein the determined contours include edges of detecting repeating features in the substrate pattern detection images; and filter outlier contours from the set of detection contours, thereby leaving residual contours in the set of detection contours after the filtering. 17. A system as described in any of the preceding clauses, wherein the one or more processors are further configured to determine a variation in one of the repeating features based on the remaining contours in the detected contour set after the filtering. (16) 18. A system as described in any of the preceding clauses, wherein the manufacturing variation is configured to provide a cost function to facilitate the determination of costs associated with individual patterning process variables, and wherein such costs associated with individual patterning process variables are configured to facilitate the optimization of a patterning process. (17) 19. A system as described in any of the preceding clauses, wherein determining the manufacturing variation of the repeating features comprises: stacking the remaining contours in the detected contour set; and statistically analyzing the stacked remaining contours. (17) 20. A system as described in any of the preceding clauses, wherein the filtering is performed based on individual points on the contours in the set of detected contours or on the geometry of the contours in the set of detected contours. (16) 21. A system as described in any of the preceding clauses, wherein the filtering based on individual points on the contours in the set of detected contours includes determining the image contrast or noise level of the pixel position of a given contour in an image detected along a substrate pattern. (20) 22. A system as described in any of the preceding clauses, wherein the filtering based on the geometry of the contours in the set of detected contours includes determining the smoothness of a geometry of a given contour. (20) 23. A system as described in any of the preceding clauses, wherein determining the contours in the substrate pattern detection images to form the detection contour set includes detecting repeating contours that span a unit cell or a 1x scale mask associated with a pattern; and wherein filtering the outlier contours from the detection contour set includes filtering each contour associated with an outlier unit cell or an outlier scale mask. (16) 24. A system as described in any of the preceding clauses, wherein the one or more processors are further configured to: determine a score for each profile in the set of detected profiles; and filter out the outlier profiles from the set of detected profiles based on the score. (16) 25. A system as described in any of the preceding clauses, wherein the score is determined by referring to the substrate pattern detection image based on individual points on the contours in the detection contour set or the geometry of the contours in the detection contour set. (24) 26. A system as described in any of the preceding clauses, wherein the score comprises an individual score for each of a plurality of points on a contour, and wherein the filtering is based on such individual scores. (24) 27. A system as described in any of the preceding clauses, wherein the score comprises a total score associated with one geometry of a contour, and wherein the filtering is based on the total score. (24) 28. The system of any of the preceding clauses, wherein the substrate pattern detection images are generated by an optical detection system or a charged particle detection system. (16) 29. A system as described in any of the preceding clauses, wherein the substrate pattern detection images are generated by the charged particle detection system. (28) 30. A system as described in any of the preceding clauses, wherein the charged particle detection system includes a scanning electron microscope. (29) 31. A non-transitory computer-readable medium having instructions thereon, the instructions causing a computer to perform operations when executed, the operations comprising: receiving substrate pattern detection images; determining contours based on the substrate pattern detection images to form a set of detection contours, wherein determining the contours includes detecting edges of repeating features in the substrate pattern detection images; and filtering outlier contours from the set of detection contours, thereby leaving residual contours in the set of detection contours after the filtering. 32. As in any of the preceding clauses, such operations further include determining a variation in one of the repeating features based on the remaining contours in the detected contour set after the filtering. (31) 33. The media as described in any of the preceding clauses, wherein the manufacturing variation is configured to provide a cost function to facilitate the determination of costs associated with individual patterning process variables, and wherein such costs associated with individual patterning process variables are configured to facilitate the optimization of a patterning process. (32) 34. The media as described in any of the preceding clauses, wherein determining the manufacturing variation of the repeating features comprises: stacking the remaining contours in the detected contour set; and statistically analyzing the stacked remaining contours. (32) 35. Media as described in any of the preceding clauses, wherein the filtering is performed based on individual points on the contours in the detection contour set or on the geometry of the contours in the detection contour set. (31) 36. The media as described in any of the preceding clauses, wherein the filtering based on individual points on the contours in the detection contour set includes determining the image contrast or noise level of the pixel position of a given contour in an image detected along a substrate pattern. (35) 37. The medium as described in any of the preceding clauses, wherein the filtering based on the geometry of the contours in the detected contour set includes determining the smoothness of a geometry of a given contour. (35) 38. The media as described in any of the preceding clauses, wherein determining the contours in the substrate pattern detection images to form the detection contour set includes detecting repeating contours that span a unit cell or a 1x scale mask associated with a pattern; and wherein filtering the outlier contours from the detection contour set includes filtering each contour associated with an outlier unit cell or an outlier scale mask. (31) 39. As in any of the preceding clauses, such operations further include: determining a score for each contour in the detected contour set; and filtering out the outlier contours from the detected contour set based on the score. (31) 40. The media as described in any of the preceding clauses, wherein the score is determined by referring to the substrate pattern detection image based on individual points on the contours in the detection contour set or the geometry of the contours in the detection contour set. (39) 41. Media as described in any of the preceding clauses, wherein the score comprises an individual score for each of a plurality of points on a contour, and wherein the screening is based on such individual scores. (39) 42. Media as described in any of the preceding clauses, wherein the score comprises a total score associated with one geometry of a contour, and wherein the screening is based on the total score. (39) 43. The media as described in any of the preceding clauses, wherein the substrate pattern inspection images are generated by an optical inspection system or a charged particle detection system. (31) 44. The media as described in any of the preceding clauses, wherein the substrate pattern detection images are generated by the charged particle detection system. (43) 45. The medium as described in any of the preceding clauses, wherein the charged particle detection system includes a scanning electron microscope. (44) 46. ​​A method for electronically filtering out-of-line contours from a set of detected contours in substrate pattern inspection images, the method comprising: receiving the substrate pattern inspection images; determining contours based on the substrate pattern inspection images to form the set of detected contours, wherein the determined contours include edges of detected repeating features in the substrate pattern inspection images; filtering the out-of-line contours from the set of detected contours, wherein the filtering is performed based on individual points on the contours in the set of detected contours or the geometry of the contours in the set of detected contours; and determining a manufacturing change of one of the repeating features based on the remaining contours in the set of detected contours after the filtering. 47. The method of any of the preceding clauses, wherein the manufacturing variation is configured to provide a cost function to facilitate the determination of costs associated with individual patterning process variables, and wherein such costs associated with individual patterning process variables are configured to facilitate the optimization of a patterning process. (46) 48. The method of any of the preceding clauses, wherein determining the manufacturing variation of the repeating features comprises: stacking the remaining contours in the detected contour set; and statistically analyzing the stacked remaining contours. (46) 49. The method of any of the preceding clauses, wherein the filtering based on individual points on the contours in the set of detected contours includes determining the image contrast or noise level of the pixel position of a given contour in an image detected along a substrate pattern. (46) 50. The method of any of the preceding clauses, wherein the filtering based on the geometry of the contours in the detected contour set includes determining the smoothness of a geometry of the given contour. (46) 51. The method of any of the preceding clauses, wherein determining the contours in the substrate pattern detection images to form the detection contour set includes detecting repeating contours that span a unit cell or a 1x scale mask associated with a pattern; and wherein filtering the outlier contours from the detection contour set includes filtering each contour associated with an outlier unit cell or an outlier scale mask. (46) 52. The method of any of the preceding clauses further comprises: determining a score for each contour in the set of detected contours; and filtering out the outlier contours from the set of detected contours based on the score. (46) 53. The method of any of the preceding clauses, wherein the score is determined by referring to the substrate pattern detection image based on individual points on the contours in the detection contour set or the geometry of the contours in the detection contour set. (52) 54. The method of any of the preceding clauses, wherein the score comprises an individual score for each of a plurality of points on a contour, and wherein the screening is based on such individual scores. (52) 55. The method of any of the preceding clauses, wherein the score comprises a total score associated with one geometry of a contour, and wherein the screening is based on the total score. (52) 56. The method as described in any of the preceding clauses, wherein the score indicates a level of trust or a degree of reliability. (52) 57. The method of any of the preceding clauses, wherein the substrate pattern detection images are generated by an optical detection system. (46) 58. The method of any of the preceding clauses, wherein the optical detection system is a scatterometer. (57) 59. The method of any of the preceding clauses, wherein the substrate pattern detection images are generated by a charged particle detection system. (46) 60. The method of any of the preceding clauses, wherein the charged particle detection system comprises a scanning electron microscope. (59) 61. A non-transitory computer-readable medium having instructions thereon, the instructions, when executed by a computer, causing the computer to electronically filter out-of-line contours from a set of detection contours in a substrate pattern detection image, the instructions causing operations including: receiving the substrate pattern detection image; determining contours based on the substrate pattern detection image to form the set of detection contours, wherein determining the contours includes detecting edges of repeating features in the substrate pattern detection image; filtering the out-of-line contours from the set of detection contours, wherein the filtering is performed based on individual points on the contours in the set of detection contours or the geometry of the contours in the set of detection contours; and determining a variation in one of the repeating features based on the remaining contours in the set of detection contours after the filtering. 62. The media as described in any of the preceding clauses, wherein the manufacturing variation is configured to provide a cost function to facilitate the determination of costs associated with individual patterning process variables, and wherein such costs associated with individual patterning process variables are configured to facilitate the optimization of a patterning process. (61) 63. The media as described in any of the preceding clauses, wherein determining the manufacturing variation of the repeating features comprises: stacking the remaining contours in the detected contour set; and statistically analyzing the stacked remaining contours. (61) 64. The media as described in any of the preceding clauses, wherein the filtering based on individual points on the contours in the detection contour set includes determining the image contrast or noise level of the pixel position of a given contour in an image detected along a substrate pattern. (61) 65. The media as described in any of the preceding clauses, wherein the filtering based on the geometry of the contours in the detected contour set includes determining the smoothness of a geometry of a given contour. (61) 66. The media as described in any of the preceding clauses, wherein determining the contours in the substrate pattern detection images to form the detection contour set includes detecting repeating contours that span a unit cell or a 1x scale mask associated with a pattern; and wherein filtering the outlier contours from the detection contour set includes filtering each contour associated with an outlier unit cell or an outlier scale mask. (61) 67. The medium as described in any of the preceding clauses further comprises: determining a score for each contour in the set of detected contours; and filtering out the outlier contours from the set of detected contours based on the score. (61) 68. The media as described in any of the preceding clauses, wherein the score is determined by referring to the substrate pattern detection image based on individual points on the contours in the detection contour set or the geometry of the contours in the detection contour set. (67) 69. Media as described in any of the preceding clauses, wherein the score comprises an individual score for each of a plurality of points on a contour, and wherein the screening is based on such individual scores. (67) 70. Media as described in any of the preceding clauses, wherein the score comprises a total score associated with one geometry of a contour, and wherein the screening is based on the total score. (67) 71. Media as described in any of the preceding clauses, wherein the score indicates a level of trust or a degree of reliability. (67) 72. The media as described in any of the preceding clauses, wherein the substrate pattern inspection images are generated by an optical inspection system. (61) 73. The medium as described in any of the preceding clauses, wherein the optical detection system is a scatterometer. (72) 74. The media as described in any of the preceding clauses, wherein the substrate pattern detection images are generated by a charged particle detection system. (61) 75. The medium as described in any of the preceding clauses, wherein the charged particle detection system includes a scanning electron microscope. (74) 76. A system for electronically filtering out-of-line contours from a set of detected contours in substrate pattern inspection images, the system comprising one or more processors configured by machine-readable instructions to: receive the substrate pattern inspection images; determine contours based on the substrate pattern inspection images to form the set of detected contours, wherein the determined contours include edges of detected repeating features in the substrate pattern inspection images; filter the out-of-line contours from the set of detected contours, wherein the filtering is performed based on individual points on the contours in the set of detected contours or the geometry of the contours in the set of detected contours; and determine a manufacturing change of one of the repeating features based on the remaining contours in the set of detected contours after the filtering. 77. A system as described in any of the preceding clauses, wherein the manufacturing variation is configured to provide a cost function to facilitate the determination of costs associated with individual patterning process variables, and wherein such costs associated with individual patterning process variables are configured to facilitate the optimization of a patterning process. (76) 78. A system as described in any of the preceding clauses, wherein determining the manufacturing variation of the repeating features comprises: stacking the remaining contours in the detected contour set; and statistically analyzing the stacked remaining contours. (76) 79. A system as described in any of the preceding clauses, wherein the filtering based on individual points on the contours in the set of detected contours includes determining the image contrast or noise level of the pixel position of a given contour in an image detected along a substrate pattern. (76) 80. A system as described in any of the preceding clauses, wherein the filtering based on the geometry of the contours in the set of detected contours includes determining the smoothness of a geometry of a given contour. (76) 81. A system as described in any of the preceding clauses, wherein determining the contours in the substrate pattern detection images to form the detection contour set includes detecting repeating contours that span a unit cell or a 1x scale mask associated with a pattern; and wherein filtering the outlier contours from the detection contour set includes filtering each contour associated with an outlier unit cell or an outlier scale mask. (76) 82. A system as described in any of the preceding clauses, wherein the one or more processors are further configured to: determine a score for each profile in the set of detected profiles; and filter out the outlier profiles from the set of detected profiles based on the score. (76) 83. A system as described in any of the preceding clauses, wherein the score is determined by referring to the substrate pattern detection image based on individual points on the contours in the detection contour set or the geometry of the contours in the detection contour set. (82) 84. A system as described in any of the preceding clauses, wherein the score comprises an individual score for each of a plurality of points on a contour, and wherein the filtering is based on such individual scores. (82) 85. A system as described in any of the preceding clauses, wherein the score comprises a total score associated with one geometry of a contour, and wherein the filtering is based on the total score. (82) 86. A system as described in any of the preceding clauses, wherein the score indicates a level of trust or a degree of reliability. (82) 87. The system of any of the preceding clauses, wherein the substrate pattern inspection images are generated by an optical inspection system. (76) 88. A system as described in any of the preceding clauses, wherein the optical detection system is a scatterometer. (87) 89. A system as described in any of the preceding clauses, wherein the substrate pattern detection images are generated by a charged particle detection system. (76) 90. A system as described in any of the preceding clauses, wherein the charged particle detection system includes a scanning electron microscope. (89) 91. A non-transitory computer-readable medium having instructions thereon, which, when executed by a computer, cause the computer to electronically filter out outlier contours from a set of detected contours in a substrate pattern inspection image, the filtering being configured to enhance the determination of manufacturing variations in a patterning process and the optimization of the patterning process compared to a previous patterning process, the instructions causing operations including: receiving the substrate pattern inspection image, wherein the substrate pattern inspection image is generated by a charged particle detection system; determining contours based on the substrate pattern inspection image to form the set of detected contours, wherein determining the contours includes detecting the The process involves: identifying the edges of repetitive features in a substrate pattern detection image; filtering outlier contours from a set of detected contours, wherein the filtering is performed based on individual points on the contours in the set of detected contours or the geometry of the contours in the set of detected contours; and determining a manufacturing variation of one of the repetitive features based on the remaining contours in the set of detected contours after the filtering, wherein the manufacturing variation is configured to provide a cost function to facilitate the determination of costs associated with individual patterning process variables, and wherein the costs associated with individual patterning process variables are configured to facilitate the optimization of the patterning process. 92. The media as described in any of the preceding clauses, wherein: the filtering based on individual points on the contours in the detection contour set includes determining characteristics of the substrate pattern detection images, including determining the image contrast or noise level of a pixel position along a given contour in a substrate pattern detection image; and the filtering based on the geometry of the contours in the detection contour set includes determining the smoothness of a geometry of the given contour. (91) 93. As in any of the preceding clauses, such operations further include: determining a score for each contour in the detection contour set based on individual points on the contours in the detection contour set or the geometry of the contours in the detection contour set; and filtering outlier contours from the detection contour set based on the score. (91) 94. The media as described in any of the preceding clauses, wherein determining the manufacturing variation of the repeating features comprises: stacking the remaining contours in the detected contour set; and statistically analyzing the stacked remaining contours. (91) 95. The medium as described in any of the preceding clauses, wherein the charged particle detection system includes a scanning electron microscope. (91) 96. A method for electronically filtering outlier contours from a set of detected contours in substrate pattern inspection images, the filtering being configured to enhance the determination of a manufacturing variation in a patterning process and the optimization of the patterning process compared to a previous patterning process, the method comprising: receiving the substrate pattern inspection images, wherein the substrate pattern inspection images are generated by a charged particle detection system; determining contours based on the substrate pattern inspection images to form the set of detected contours, wherein the determined contours include detecting edges of repeating features in the substrate pattern inspection images; filtering the outlier contours from the set of detected contours, wherein the filtering is performed based on individual points on the contours in the set of detected contours or the geometry of the contours in the set of detected contours; and determining a manufacturing variation of repeating features based on the remaining contours in the set of detected contours after the filtering, wherein the manufacturing variation is configured to be provided to a cost function to facilitate the determination of costs associated with individual patterning process variables, and wherein the costs associated with individual patterning process variables are configured to facilitate the optimization of the patterning process. 97. The method of any of the preceding clauses, wherein: the filtering based on individual points on the contours in the set of detection contours includes determining characteristics of the substrate pattern detection images, including determining the image contrast or noise level of a pixel position along a given contour in a substrate pattern detection image; and the filtering based on the geometry of the contours in the set of detection contours includes determining the smoothness of a geometry of the given contour. (96) 98. The method of any of the preceding items, further comprising: determining a score for each contour in the detection contour set based on the individual points on the contours in the detection contour set or the geometry of the contours in the detection contour set; and filtering outlier contours from the detection contour set based on the score. (96) 99. The method of any of the preceding clauses, wherein determining the manufacturing variation of the repeating features comprises: stacking the remaining contours in the detected contour set; and statistically analyzing the stacked remaining contours. (96) 100. The method of any of the preceding clauses, wherein the charged particle detection system comprises a scanning electron microscope. (96) 101. A system for electronically filtering out-of-line profiles from a set of detected profiles in substrate pattern inspection images, the filtering being configured to enhance the determination of manufacturing variations in a patterning process and the optimization of the patterning process compared to a previous patterning process, the system comprising one or more processors configured by machine-readable instructions to: receive the substrate pattern inspection images, wherein the substrate pattern inspection images are generated by a charged particle detection system; determine profiles based on the substrate pattern inspection images to form the set of detected profiles, wherein determining the profiles includes detecting the substrate pattern... The image is measured for the edges of repeating features; outlier contours are filtered from the set of detected contours, wherein the filtering is performed based on individual points on the contours in the set of detected contours or the geometry of the contours in the set of detected contours; and a manufacturing variation of one of the repeating features is determined based on the remaining contours in the set of detected contours after the filtering, wherein the manufacturing variation is configured to provide a cost function to facilitate the determination of costs associated with individual patterning process variables, and wherein the costs associated with individual patterning process variables are configured to facilitate the optimization of the patterning process. 102. A system as described in any of the preceding clauses, wherein: the filtering based on individual points on the contours in the set of detection contours includes determining characteristics of the substrate pattern detection images, including determining the image contrast or noise level of a pixel position along a given contour in a substrate pattern detection image; and the filtering based on the geometry of the contours in the set of detection contours includes determining the smoothness of a geometry of the given contour. (101) 103. A system as described in any of the preceding clauses, wherein the one or more processors are further configured to: determine a score for each contour in the detection contour set based on individual points on the contours in the detection contour set or the geometry of the contours in the detection contour set; and filter outlier contours from the detection contour set based on the score. (101) 104. A system as described in any of the preceding clauses, wherein determining the manufacturing variation of the repeating features comprises: stacking the remaining contours in the detected contour set; and statistically analyzing the stacked remaining contours. (101) 105. A system as described in any of the preceding clauses, wherein the charged particle detection system includes a scanning electron microscope. (101) 106. A method for analyzing a manufacturing variation of one of the features, comprising: receiving a scanning electron microscope (SEM) image including the same features; determining a contour of each of the same features; filtering a first subset of the contours to leave a remaining second subset of the contours; and determining a manufacturing variation of the same feature based on the second subset of the contours. 107. The method of any of the preceding clauses further comprises: determining a score for each of the contours based on the characteristics of the contours, wherein the selection of the first subset is based on the scores. (106) 108. The method of any of the preceding clauses further comprises: determining a score for each of a plurality of points on each of the contours based on characteristics of the contours, wherein the filtering of the first subset is based on the scores. (106) 109. A non-transitory computer-readable medium having instructions thereon, the instructions causing operations when executed by a computer, the operations comprising: receiving scanning electron microscope (SEM) images including identical features; determining a contour of each of the identical features; filtering a first subset of the contours to leave a remaining second subset of the contours; and determining, based on the second subset of the contours, that a change is made to one of the identical features. 110. For any of the preceding clauses, the operation further includes: determining a score for each of the contours based on the characteristics of the contours, wherein the filtering of the first subset is based on the scores. (109) 111. For any of the preceding clauses, the operation further includes: determining a score for each of a plurality of points on each of the contours based on the characteristics of the contours, wherein the filtering of the first subset is based on the scores. (109) 112. A system for analyzing manufacturing variations of one of a feature, the system comprising one or more processors configured by machine-readable instructions to: receive scanning electron microscope (SEM) images including identical features; determine a profile of each of the identical features; filter a first subset of the profiles to leave a remaining second subset of the profiles; and determine a manufacturing variation of the identical feature based on the second subset of the profiles. 113. A system as described in any of the preceding clauses, wherein the one or more processors are further configured to: determine a score for each of the contours based on the characteristics of the contours, wherein the filtering of the first subset is based on the scores. (112) 114. A system as described in any of the preceding clauses, wherein the one or more processors are further configured to: determine a score for each of a plurality of points on each of the contours based on the characteristics of the contours, wherein the filtering of the first subset is based on the scores. (112) 115. A method for enhancing a patterning process, the method comprising: receiving substrate pattern detection images; determining contours in the substrate pattern detection images to form a set of detection contours, wherein determining the contours includes edges that detect features in the substrate pattern detection images; and filtering outlier contours from the set of detection contours. 116. The method of any of the preceding clauses, wherein: once a contour is determined to contain vertices; the edges of the detected features contain the vertices identified in the determined contour; and the filtering of the outlier contours is based on the vertices. (115) 117. The method of any of the preceding clauses further comprises: determining the angle formed at the vertices of the determined contours; and filtering the outlier contours from the set of detected contours based on the angles. (115) 118. The method of any of the preceding clauses, wherein the selection of outlier contours from the set of detected contours is based on a comparison of the determined angles with an angle threshold, and wherein a determined contour at a vertex with an angle less than the angle threshold is determined as an outlier contour and selected from the set of detected contours. (117) 119. The method as described in any of the preceding clauses, wherein the limiting angle is 120 degrees, 90 degrees, 60 degrees, or 45 degrees. (118) 120. The method of any of the preceding clauses further comprises: determining the distance between neighboring vertices of the determined contours; and filtering the outlier contours from the set of detected contours based on the distances. (115) 121. The method of any of the preceding clauses, wherein the selection of outlier contours from the set of detected contours is based on a comparison of the determined distances with a distance threshold, and a determined contour whose distance exceeds the distance threshold is determined as an outlier contour and selected from the set of detected contours. (120) 122. The method of any of the preceding clauses, wherein the distance threshold comprises: a distance that is a given multiple of an average distance between vertices or a distance that is greater than or less than a percentage of the average distance; or a distance corresponding to a profile edge roughness parameter. (121) 123. The method of any of the preceding clauses further comprises: determining the centroid of the determined contours; and filtering the outlier contours from the set of detected contours based on a relationship between a centroid and one or more vertices of a given contour. (115) 124. The method of any of the preceding clauses, wherein the filtering based on the relation includes filtering the given contour from the set of detected contours in response to one or more vertices of the given contour being at a distance from the centroid exceeding a centroid distance threshold. (123) 125. The method of any of the preceding clauses, wherein the centroid distance threshold is a distance that is a given multiple of or greater than or less than a percentage of an average distance between the vertex and the centroid. (124) 126. The method of any of the preceding clauses further comprises: determining the centroid of the determined contours; fitting a desired contour shape to the determined contours based on the centroid; and filtering the outlier contours from the set of detected contours based on a relationship between a fitted desired contour and the vertices of a given contour. (115) 127. The method of any of the preceding clauses further comprises: determining the centroid of the determined contours; fitting a circle or ellipse to the determined contours based on the centroid; and filtering the outlier contours from the set of detected contours based on a relationship between a fitted circle or ellipse and the vertices of a given contour. (115) 128. The method of any of the preceding clauses, wherein the filtering based on the relationship includes filtering the given contour from the set of detected contours in response to one or more vertices of the given contour being at a distance from the fitted circle or ellipse exceeding a fitted distance threshold. (127) 129. The method of any of the preceding clauses, wherein the fitted distance threshold is a distance that is a given multiple or a percentage greater than or less than one of the average distances between the vertex and the fitted circle or ellipse. (128) 130. The method of any of the preceding clauses, wherein the contour comprises a polygon. (115) 131. The method of any of the preceding clauses, wherein the substrate pattern detection images are generated by an optical detection system or a charged particle detection system. (115) 132. The method of any of the preceding clauses, wherein the substrate pattern detection images are generated by the charged particle detection system, and wherein the charged particle detection system includes a scanning electron microscope. (131) 133. The method of any of the preceding clauses further comprises determining, based on the remaining contours in the detected contour set after the filtering, that one of the features has been modified. (115) 134. The method of any of the preceding clauses, wherein the manufacturing variation is configured to provide a cost function to facilitate the determination of costs associated with individual patterning process variables, and wherein such costs associated with individual patterning process variables are configured to facilitate the optimization of a patterning process. (133) 135. The method of any of the preceding clauses, wherein determining the contours in the substrate pattern detection images to form the detection contour set includes detecting repeating contours that span a unit cell or a 1x scale mask associated with a pattern; and wherein filtering the outlier contours from the detection contour set includes filtering each contour or a portion of a contour that is associated with an outlier unit cell or an outlier scale mask. (115) 136. A non-transitory computer-readable medium having instructions thereon, the instructions, when executed by a computer, causing the computer to: receive substrate pattern detection images; determine contours based on the substrate pattern detection images to form a set of detection contours, wherein the determined contours include edges that detect features in the substrate pattern detection images; and filter outlier contours from the set of detection contours. 137. Media as described in any of the preceding clauses, wherein: once a contour is determined to contain vertices; the edges of the detected features contain the vertices that are identified in the determined contour; and the filtering of the outlier contours is based on the vertices. (136) 138. As in any of the preceding clauses, the instructions further cause the computer to: determine the angle formed at the vertices of the determined contours; and filter the outlier contours from the set of detected contours based on the angles. (136) 139. Media as described in any of the preceding clauses, wherein the selection of outlier contours from the set of detected contours is based on a comparison of the determined angles with an angle threshold, and wherein a determined contour at a vertex with an angle less than the angle threshold is determined as an outlier contour and selected from the set of detected contours. (138) 140. Media as described in any of the preceding clauses, wherein the limiting angle is 120 degrees, 90 degrees, 60 degrees, or 45 degrees. (139) 141. As in any of the preceding clauses, the instructions further cause the computer to: determine the distance between neighboring vertices formed in the determined contours; and filter the outlier contours from the set of detected contours based on the distances. (136) 142. The media as described in any of the preceding clauses, wherein the selection of outlier contours from the set of detected contours is based on a comparison of the determined distances with a distance threshold, and a determined contour whose distance exceeds the distance threshold is identified as an outlier contour and selected from the set of detected contours. (141) 143. The medium as described in any of the preceding clauses, wherein the distance threshold includes: a distance that is a given multiple of an average distance between vertices or a distance greater than or less than a percentage of that average distance; or a distance corresponding to a profile edge roughness parameter. (142) 144. As in any of the preceding clauses, the instructions further cause the computer to: determine the centroid of the determined contours; and filter the outlier contours from the set of detected contours based on a relationship between a centroid and one or more vertices of a given contour. (136) 145. Media as described in any of the preceding clauses, wherein the filtering based on the relation includes filtering the given contour from the detected contour set in response to one or more vertices of the given contour being at a distance from the centroid exceeding a centroid distance threshold. (144) 146. A medium as described in any of the preceding clauses, wherein the centroid distance threshold is a distance that is a given multiple of or greater than or less than a percentage of an average distance between the vertex and the centroid. (145) 147. As in any of the preceding clauses, the instructions further cause the computer to: determine the centroid of the determined contours; fit a desired contour shape to the determined contours based on the centroids; and filter out the outlier contours from the set of detected contours based on a relationship between the vertices of a fitted desired contour and a given contour. (136) 148. As in any of the preceding clauses, the instructions further cause the computer to: determine the centroid of the determined contours; fit a circle or ellipse to the determined contours based on the centroid; and filter out the outlier contours from the set of detected contours based on a relationship between a fitted circle or ellipse and the vertices of a given contour. (136) 149. Media as described in any of the preceding clauses, wherein the filtering based on the relationship includes filtering the given contour from the set of detected contours in response to one or more vertices of the given contour being at a distance from the fitted circle or ellipse exceeding a fitted distance threshold. (148) 150. The medium as described in any of the preceding clauses, wherein the fitted distance threshold is a distance that is a multiple of or greater than or less than a percentage of an average distance between the vertex and the fitted circle or ellipse. (149) 151. Media as described in any of the preceding clauses, wherein the outline comprises a polygon. (136) 152. The media as described in any of the preceding clauses, wherein the substrate pattern inspection images are generated by an optical inspection system or a charged particle detection system. (136) 153. The media as described in any of the preceding clauses, wherein the substrate pattern detection images are generated by the charged particle detection system, and wherein the charged particle detection system includes a scanning electron microscope. (152) 154. As in any of the preceding clauses, such instructions further cause the computer to determine, based on the remaining contours in the detected contour set after the filtering, that one of the features has been modified. (136) 155. The media as described in any of the preceding clauses, wherein the manufacturing variation is configured to provide a cost function to facilitate the determination of costs associated with individual patterning process variables, and wherein such costs associated with individual patterning process variables are configured to facilitate the optimization of a patterning process. (154) 156. The media as described in any of the preceding clauses, wherein determining the contours in the substrate pattern detection images to form the detection contour set includes detecting repeating contours that span a unit cell or a 1x scale mask associated with a pattern; and wherein filtering the outlier contours from the detection contour set includes filtering each contour or a portion of a contour that is associated with an outlier unit cell or an outlier scale mask. (136) 157. A system for enhancing a patterning process, the system comprising one or more processors configured by machine-readable instructions to: receive substrate pattern detection images; determine contours based on the substrate pattern detection images to form a set of detection contours, wherein determining the contours includes: detecting edges of features in the substrate pattern detection images; and filtering outlier contours from the set of detection contours. 158. The system of clause 43, wherein: once a contour is determined to contain vertices; the edges of the detected features contain the vertices identified in the determined contour; and the filtering of the outlier contours is based on the vertices. (157) 159. In a system as described in any of the preceding clauses, the one or more processors are further configured to: determine angles formed at the vertices of the determined contours; and filter outlier contours from the set of detected contours based on the angles. (157) 160. A system as described in any of the preceding clauses, wherein the selection of outlier contours from the set of detected contours is based on a comparison of the determined angles with an angle threshold, and wherein a determined contour at a vertex with an angle less than the angle threshold is identified as an outlier contour and selected from the set of detected contours. (159) 161. A system as described in any of the preceding clauses, wherein the limiting angle is 120 degrees, 90 degrees, 60 degrees, or 45 degrees. (160) 162. In a system as described in any of the preceding clauses, the one or more processors are further configured to: determine the distances between neighboring vertices formed in the determined contours; and filter the outlier contours from the set of detected contours based on the distances. (157) 163. A system as described in any of the preceding clauses, wherein the selection of outlier contours from the set of detected contours is based on a comparison of the determined distances with a distance threshold, and a determined contour whose distance exceeds the distance threshold is identified as an outlier contour and selected from the set of detected contours. (162) 164. A system as described in any of the preceding clauses, wherein the distance threshold comprises: a distance that is a given multiple of an average distance between vertices or a distance that is greater than or less than a percentage of that average distance; or a distance corresponding to a profile edge roughness parameter. (163) 165. In a system as described in any of the preceding clauses, the one or more processors are further configured to: determine the centroid of the determined contours; and filter the outlier contours from the set of detected contours based on a relationship between a centroid and one or more vertices of a given contour. (157) 166. A system as described in any of the preceding clauses, wherein the filtering based on the relation includes filtering the given contour from the set of detected contours in response to one or more vertices of the given contour being at a distance from the centroid exceeding a centroid distance threshold. (165) 167. A system as described in any of the preceding clauses, wherein the centroid distance threshold is a distance that is a given multiple of or a percentage greater than or less than one of the average distances between the vertex and the centroid. (166) 168. In a system as described in any of the preceding clauses, the one or more processors are further configured to: determine the centroids of the determined contours; fit a desired contour shape to the determined contours based on the centroids; and filter the outlier contours from the set of detected contours based on a relationship between a fitted desired contour and the vertices of a given contour. (157) 169. In a system as described in any of the preceding clauses, the one or more processors are further configured to: determine the centroid of the determined contours; fit a circle or ellipse to the determined contours based on the centroid; and filter the outlier contours from the set of detected contours based on a relationship between a fitted circle or ellipse and the vertices of a given contour. (157) 170. A system as described in any of the preceding clauses, wherein the filtering based on the relation comprises filtering the given contour from the set of detected contours in response to one or more vertices of the given contour being at a distance from the fitted circle or ellipse exceeding a fitted distance threshold. (169) 171. A system as described in any of the preceding clauses, wherein the fitted distance threshold is a distance that is a multiple of or greater than or less than a percentage of an average distance between the vertex and the fitted circle or ellipse. (170) 172. A system as described in any of the preceding clauses, wherein the outline comprises a polygon. (157) 173. The system as described in any of the preceding clauses, wherein the substrate pattern detection images are generated by an optical detection system or a charged particle detection system. (157) 174. The system of any of the preceding clauses, wherein the substrate pattern detection images are generated by the charged particle detection system, and wherein the charged particle detection system includes a scanning electron microscope. (173) 175. In a system as described in any of the preceding clauses, the one or more processors are further configured to determine a manufacturing change in one of the features based on the remaining contours in the set of detected contours after the filtering. (157) 176. A system as described in any of the preceding clauses, wherein the manufacturing variation is configured to provide a cost function to facilitate the determination of costs associated with individual patterning process variables, and wherein such costs associated with individual patterning process variables are configured to facilitate the optimization of a patterning process. (175) 177. A system as described in any of the preceding clauses, wherein determining contours in the substrate pattern detection images to form the detection contour set includes detecting repeating contours that span a unit cell or a 1x scale mask associated with a pattern; and wherein filtering the outlier contours from the detection contour set includes filtering each contour or a portion of a contour that is associated with an outlier unit cell or an outlier scale mask. (157) 178. A method for enhancing a patterning process, the method comprising: receiving substrate pattern detection images; determining contours in the substrate pattern detection images to form a set of detection contours, wherein determining the contours includes edges that detect features in the substrate pattern detection images; and filtering outlier contours from the set of detection contours. 179. The method of any of the preceding clauses, wherein: once a contour is determined to contain vertices; the edges of the detected features contain the vertices identified in the determined contour; and the filtering of the outlier contours is based on the vertices. (178) 180. The method of any of the preceding clauses further comprises: determining an angle formed at the vertices of the determined contours; and filtering the outlier contours from the set of detected contours based on the angle. (178) 181. The method of any of the preceding clauses, wherein the selection of outlier contours from the set of detected contours is based on a comparison of the determined angles with an angle threshold, and wherein a determined contour at a vertex with an angle less than the angle threshold is determined as an outlier contour and selected from the set of detected contours. (180) 182. The method of any of the preceding clauses further comprises: determining the distance between neighboring vertices of the determined contours; and filtering the outlier contours from the set of detected contours based on the distances. (178) 183. The method of any of the preceding clauses, wherein the selection of outlier contours from the set of detected contours is based on a comparison of the determined distances with a distance threshold, and a determined contour whose distance exceeds the distance threshold is determined as an outlier contour and selected from the set of detected contours. (182) 184. The method of any of the preceding clauses, wherein the distance threshold comprises: a distance that is a given multiple of an average distance between vertices or a distance that is greater than or less than a percentage of the average distance; or a distance corresponding to a profile edge roughness parameter. (183) 185. The method of any of the preceding clauses further comprises: determining the centroid of the determined contours; and filtering the outlier contours from the set of detected contours based on a relationship between a centroid and one or more vertices of a given contour. (178) 186. The method of any of the preceding clauses, wherein the filtering based on the relation includes filtering the given contour from the set of detected contours in response to one or more vertices of the given contour being at a distance from the centroid exceeding a centroid distance threshold. (185) 187. The method of any of the preceding clauses, wherein the centroid distance threshold is a distance that is a given multiple of or greater than or less than a percentage of an average distance between the vertex and the centroid. (186) 188. The method of any of the preceding clauses further comprises: determining the centroid of the determined contours; fitting a circle or ellipse to the determined contours based on the centroid; and filtering the outlier contours from the set of detected contours based on a relationship between a fitted circle or ellipse and the vertices of a given contour. (178) 189. The method of any of the preceding clauses, wherein the filtering based on the relationship includes filtering the given contour from the set of detected contours in response to one or more vertices of the given contour being at a distance from the fitted circle or ellipse exceeding a fitted distance threshold. (188) 190. The method of any of the preceding clauses, wherein the substrate pattern detection images are generated by the charged particle detection system, and wherein the charged particle detection system includes a scanning electron microscope. (178) 191. The method of any of the preceding clauses further comprises determining a manufacturing variation of one of the features based on the remaining contours in the set of detected contours after the screening, wherein the manufacturing variation is configured to provide to a cost function to facilitate the determination of costs associated with individual patterning process variables, and wherein the costs associated with individual patterning process variables are configured to facilitate the optimization of a patterning process. (178) 192. The method of any of the preceding clauses, wherein determining the contours in the substrate pattern detection images to form the detection contour set includes detecting repeating contours that span a unit cell or a scaled-down mask associated with a pattern; and wherein filtering the outlier contours from the detection contour set includes filtering each contour or a portion of a contour that is associated with an outlier unit cell or an outlier scaled-down mask. (178) 193. A non-transitory computer-readable medium having instructions thereon, which, when executed by a computer, cause the computer to electronically filter outoutline contours from a set of detected contours in a substrate pattern inspection image, the filtering being configured to enhance the determination of manufacturing variations in a patterning process and the optimization of the patterning process compared to a previous patterning process, the instructions causing operations including: receiving the substrate pattern inspection images, wherein the substrate pattern inspection images are generated by a charged particle detection system; and determining contours based on the substrate pattern inspection images to form the set of detected contours. The method comprises: determining the contours by including the edges of features detected in the substrate pattern detection images, the edges including the vertices of the contours; filtering the outlier contours from the set of detected contours, wherein the filtering is performed based on the vertices; and determining a manufacturing variation of one of the features based on the remaining contours in the set of detected contours after the filtering, wherein the manufacturing variation is configured to provide a cost function to facilitate the determination of costs associated with individual patterning process variables, and wherein the costs associated with individual patterning process variables are configured to facilitate the optimization of the patterning process. 194. As in any of the preceding clauses, such operations further include: determining the angle formed at the vertices of the determined contours; and filtering the outlier contours from the set of detected contours based on the angles. (193) 195. As in any of the preceding clauses, such operations further include: determining the distance between neighboring vertices of the determined contours; and filtering outlier contours from the set of detected contours based on the distances. (193) 196. The medium as described in any of the preceding clauses further comprises: determining the centroid of the determined contours; fitting a circle or ellipse to the determined contours based on the centroid; and filtering the outlier contours from the set of detected contours based on a relationship between a fitted circle or ellipse and the vertices of a given contour. (193) 197. The medium as described in any of the preceding clauses, wherein the charged particle detection system includes a scanning electron microscope. (193)

[0163] The concepts revealed in this paper can be used with any imaging, etching, polishing, inspection, and other systems for sub-wavelength characterization, and are useful for emerging imaging technologies capable of generating increasingly shorter wavelengths. Emerging technologies already in use include extreme ultraviolet (EUV) 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 nm to 50 nm range by using synchrotrons or by bombarding materials (solids or plasmas) with high-energy electrons, thereby generating photons within this range.

[0164] While the concepts disclosed herein can be used in conjunction with the manufacture of substrates such as silicon wafers, it should be understood that the disclosed concepts can be used in conjunction with any type of manufacturing system (e.g., a manufacturing system for manufacturing on substrates other than silicon wafers).

[0165] Furthermore, combinations and sub-combinations of the disclosed elements may comprise individual embodiments. For example, the point-level and feature-level filtering described herein may be included in separate embodiments, or may be included together in the same embodiment.

[0166] The above description is intended to be illustrative, not restrictive. 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 patent application as set forth below.

[0167] As used herein, unless otherwise specifically stated, the term "or" covers all possible combinations, unless impractical. For example, if a statement indicates that components may include A or B, then unless otherwise specifically stated or impractical, the components may include A, or B, or A and B. As a second example, if a statement indicates that components may include A, B, or C, then unless otherwise specifically stated or impractical, the components may include A, or B, or C, or A and B, or A and C, or B and C, or A, B, and C.

[0168] 21: Radiation beam 22: Faceted Field Mirror Device 24: Faceted pupil mirror device 26: Patterned beam 28: Reflective element 30: Reflective element 50: Charged particle (electron beam) detection equipment 52: Primary Electron Beam 54: Electronic source 56: Condenser Lens 58: Beam deflector 60:E×B deflector 62: Objective lens 70:Substrate 72: Secondary electronic detector 74: Analog / Digital (A / D) Converter 76: Image Processing System 78: Memory 80: Processing Unit 81: Charged Particle Beam Generator 82: Storage Media 83: Probe forming objective lens module 84: Display device 85: Secondary charged particle detector module 86: Image Forming Module 87: Monitoring Module 88: Charged Particle Beam Deflection Module 89: Sample stage 90: Sample 91: Primary charged particle beam 92: Scanning charged particle beam probe 93: Secondary charged particles 94: Secondary charged particle detection signal 99: Condenser Lens Module 210: EUV radiation-emitting plasma 211: Source Chamber / Collector Chamber 212: Collector Chamber 220: Enclosure Structure 221: Opening 230: Contaminant Retention Device 240: Grating Spectral Filter 251: Upstream radiation collector side 252: Downstream radiation collector side 253: Grazing incidence reflector 254: Grazing incidence reflector 255: Grazing incidence reflector 600: Method 602: Operation 604: Operation 606: Operation 608: Operation 610: Operation 700: points 702: Outline 704: Repeatability Feature 706: Substrate Pattern Inspection Image 708: Heat Map 710: Low Trust Level 712: High Trust Level 800: Geometry 802: Geometry 804: Outline 806: Outline 807: Shape deviation 808: Repeatability Feature 900: Filter 902: Angle 904: Vertex 906: Vertex 908: Vertex 910: Outline 912: Screening 914: Distance 916: Distance 918: Vertex 920: Vertex 922: Vertex 924: Outline 930: Screening 932: Distance 934: Center of Gravity 936: Outline 938: Vertex 940: Filtering 942: Expected outline shape 1000: Angle 1002: Angle 1004: Angle 1006: Angle 1008: Angle 1010: Angle 1012: Angle 1014: Angle 1016: Angle 1020: Vertex 1022: Vertex 1024: Vertex 1026: Vertex 1028: Vertex 1030: Vertex 1032: Vertex 1034: Vertex 1036: Vertex 1038: Vertex 1040: Vertex 1042: Vertex 1044: Vertex 1046: Vertex 1048: Vertex 1050: Vertex 1052: Vertex 1054: Vertex 1056: Vertex 1060: Determined Profile / Outlier Profile 1062: Determined Profile / Outlier Profile 1064: Outline determined 1100: Distance 1102: Distance 1104: Distance 1106: Distance 1108: Distance 1110: Distance 1112: Distance 1114: Distance 1120: Vertex 1122: Vertex 1124: Vertex 1126: Vertex 1128: Vertex 1132: Vertex 1136: Vertex 1138: Vertex 1140: Vertex 1142: Vertex 1144: Vertex 1146: Vertex 1150: Vertex 1160: Determined Profile / Outlier Profile 1162: Determined Profile / Outlier Profile 1164: Outline determined 1200: Center of gravity 1202: Center of gravity 1204: Center of gravity 1206: Determined Profile / Outlier Profile 1208: Determined Profile / Outlier Profile 1210: Outline determined 1220: Vertex 1222: Vertex 1224: Vertex 1226: Vertex 1228: Vertex 1230: Vertex 1232: Vertex 1234: Vertex 1240: Expected outline shape 1242: Expected outline shape 1244: Expected outline shape 1300: Outline 1302: Substrate pattern 1304: Outline 1306: Outline 1308: Outline 1310: Outline 1400: Program 1402: Stacking 1406: Filtering 1408: Outline 1410: Repeating feature 1411:Substrate 1412: Percentage Outline 1414: Percentage Outline AD: Adjustment components B: Radiation beam BD: Beam Delivery System BK: Baking Plate BS: Busbar CC: Vernier Control CH: Cooling plate CI: Communication Interface CL: Computer System CO: Concentrator CS: Computer System DS: Display HC: Mainframe PC ID: Input device IF: Interferometric measurement component / intermediate focus / virtual source IN: Optical Accumulator INT: Internet IL: Lighting System / Light Fixture I / O1: Input / Output Port I / O2: Input / Output Port LA: Microfilm Equipment LB: Loading box LACU: Microfilm Control Unit LAN: Local Area Network LC: Microfilm Cell LPA: Lithography Equipment M1: Patterning device alignment mark M2: Patterned device alignment mark MA: Patterning device MM: Main Memory MT: First worktable / measuring equipment / measuring tools NDL: Network Connection O: Optical axis / line P1: Substrate alignment mark P2: Substrate alignment mark PM: First Positioner PRO: Processor PS: Projection system / item PS1: Position Sensor PS2: Position Sensor PW: Second Positioner RO: Substrate processor or robot ROM: Read-only memory SC1: First Scale SC2: Second Scale SC3: Third Scale SCS: Supervisory Control System SD: Storage device SO: Radiation Source / Source Collector Module ST: Substrate Stage T: First Object Platform TCU: Coating and Developing System Control Unit W: substrate WT: Second Table

Claims

1. A non-transitory computer-readable medium for enhancing a patterning program, having instructions thereon, which, when executed by a computer, cause the computer to: receive substrate pattern detection images; determine contours in the substrate pattern detection images to form a set of detection contours, wherein the determined contours include edges that detect features in the substrate pattern detection images; and filter outlier contours from the set of detection contours.

2. As in request item 1, a non-transitory computer-readable medium, wherein: Once a contour is determined to contain vertices; the edges of such features are detected to contain and identify such vertices in the determined contours; and the filtering of such outlier contours is based on such vertices.

3. As in the non-transitory computer-readable medium of claim 1, the instructions further cause the computer to: determine the angle formed at the vertices of the determined contours, and filter the outlier contours from the set of detected contours based on the angles, wherein the filtering of the outlier contours from the set of detected contours is based on a comparison of the determined angles with an angle threshold, and wherein a determined contour at a vertex with an angle less than the angle threshold is determined as an outlier contour and filtered from the set of detected contours.

4. As in the non-transitory computer-readable medium of claim 1, the instructions further cause the computer to: determine the distance between neighboring vertices formed in the determined contours, and filter the outlier contours from the set of detected contours based on the distances, wherein the filtering of the outlier contours from the set of detected contours is based on a comparison of the determined distances with a distance threshold, and wherein a determined contour whose distance exceeds the distance threshold is determined as an outlier contour and filtered from the set of detected contours.

5. As in the non-transitory computer-readable medium of request item 1, the instructions further cause the computer to: determine the centroid of the determined contours, and filter the outlier contours from the set of detected contours based on a relationship between a centroid and one or more vertices of a given contour, wherein the filtering based on the relationship includes filtering the given contours from the set of detected contours in response to one or more vertices of the given contour being at a distance from the centroid exceeding a centroid distance threshold.

6. As in the non-transitory computer-readable medium of request item 1, the instructions further cause the computer to: determine the centroid of the determined contours, fit a desired contour shape to the determined contours based on the centroid, and filter the outlier contours from the set of detected contours based on a relationship between a fitted desired contour and the vertices of a given contour.

7. As in the non-transitory computer-readable medium of claim 1, the instructions further cause the computer to: determine the centroid of the determined contours, fit a circle or ellipse to the determined contours based on the centroid, and filter the outlier contours from the set of detected contours based on a relationship between a fitted circle or ellipse and a vertex of a given contour, wherein the filtering based on the relationship includes filtering the given contour from the set of detected contours in response to one or more vertices of the given contour being at a distance from the fitted circle or ellipse exceeding a fitting distance threshold.

8. The non-transitory computer-readable medium as claimed in claim 1, wherein the substrate pattern detection images are generated by an optical detection system or a charged particle detection system.

9. For any non-transitory computer-readable medium as requested in any of items 1 to 8, the instructions further cause the computer to: determine a manufacturing change in one of the features based on the remaining contours in the set of detected contours after the filtering, wherein the manufacturing change is associated with a change in the semiconductor manufacturing process.

10. A system for enhancing a patterning process, the system comprising one or more processors configured by machine-readable instructions to: receive substrate pattern detection images; determine contours in the substrate pattern detection images to form a set of detection contours, wherein the determined contours include edges that detect features in the substrate pattern detection images; and filter outlier contours from the set of detection contours.

Images