Determining critical dimensional change in pattern

Abstract

A reference image of the pattern and a captured image of the pattern may be received. A contour of interest of the pattern may be identified. For each reference image and captured image, one or more measurements of the size of the pattern may be determined for the contour of interest of the pattern. Defects associated with the contour of interest may be classified based on one or more measurements of the pattern size determined for each reference image and captured image.

Description

[0001] This application is a divisional application of the invention patent application with application number 201880073469.4. Technical Field

[0002] The present disclosure relates to critical dimension, and more particularly, to determining critical dimension variation of a pattern. Background Art

[0003] The evolution of integrated circuit production or manufacturing on semiconductor wafers utilizes smaller and smaller design elements to implement the integrated circuits. For example, an integrated circuit may be implemented on a semiconductor wafer as a pattern of one or more design elements corresponding to different portions of the integrated circuit. The production or manufacturing process may cause one or more defects in the integrated circuit on the semiconductor wafer. Such defects may be identified and / or classified by measuring the size of the pattern of design elements corresponding to different portions of the integrated circuit. Summary of the invention

[0004] The following is a simplified summary of the disclosure to provide a basic understanding of some aspects of the disclosure. This summary is not an extensive overview of the disclosure. This summary is not intended to identify key or critical elements of the disclosure, nor is it intended to delimit any scope of a particular embodiment of the disclosure or any scope of the claims. The sole purpose of this summary is to present some concepts of the disclosure in a simplified form and to serve as a preface to the more detailed description presented below.

[0005] In an embodiment, a captured image of a pattern may be received. A reference image of the pattern may be received. A contour of interest of the pattern may be identified. One or more measurements of a size of the pattern may be determined for each reference image and the captured image for the contour of interest of the pattern. Defects associated with the contour of interest may be classified based on the one or more measurements of the size of the pattern determined for each reference image and the captured image.

[0006] In some implementations, the contour of interest corresponds to a location of a deformation of the pattern associated with a defect.

[0007] In some embodiments, determining one or more measurements of the size of the pattern further includes determining a first plurality of measurements of a plurality of points along a contour of interest of the pattern in the captured image at a particular point of the pattern, wherein each of the first plurality of measurements corresponds to a distance from the particular point to a respective point along the contour of interest in the captured image. In addition, determining a second plurality of measurements of a plurality of points along a contour of interest of the pattern in the reference image at the particular point of the pattern, wherein each of the second plurality of measurements corresponds to a distance from the particular point to a respective point along the contour of interest in the reference image.

[0008] In some embodiments, multiple points along a contour of interest are associated with different directions from a particular point of the pattern to the contour of interest.

[0009] In some embodiments, the method further comprises determining a critical dimension parameter value for each respective point along the contour of interest based on a pair of measurements from the first and second pluralities of measurements.

[0010] In some embodiments, defects are classified based on a CD parameter value having a maximum difference between a respective measurement from a first plurality of measurements associated with the captured image and another respective measurement from a second plurality of measurements associated with the reference image.

[0011] In some implementations, one or more measurements are determined based on a distance transfer function. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] A more complete understanding of the present disclosure will be obtained from the detailed description set forth below and the accompanying drawings of various embodiments of the disclosure.

[0013] Figure 1A A block diagram illustrates an example system for determining critical dimension (CD) variation of a pattern.

[0014] Figure 1B A flow chart illustrating an example method for determining critical dimension variation of a pattern according to some embodiments.

[0015] Figure 2A An example diagram of a semiconductor wafer according to some embodiments of the present disclosure is illustrated.

[0016] Figure 2B Another example diagram of a semiconductor wafer according to some embodiments of the present disclosure is illustrated.

[0017] Figure 2C Another example diagram of a semiconductor wafer according to some embodiments of the present disclosure is illustrated.

[0018] Figure 2D Another example diagram of a semiconductor wafer according to some embodiments of the present disclosure is illustrated.

[0019] Figure 2E Another example diagram of a semiconductor wafer according to some embodiments of the present disclosure is illustrated.

[0020] Figure 2F Another example diagram of a semiconductor wafer according to some embodiments of the present disclosure is illustrated.

[0021] Figure 3A An example pattern with defects associated with a critical dimension is illustrated in accordance with some embodiments.

[0022] Figure 3B Another example pattern with defects associated with a critical dimension is illustrated in accordance with some embodiments.

[0023] Figure 3C Another example pattern with defects associated with a critical dimension is illustrated in accordance with some embodiments.

[0024] Figure 4A Graph illustrating determination of critical dimension parameters of a pattern having an arbitrary shape according to some embodiments of the present disclosure.

[0025] Figure 4B Another example of determining critical dimension parameters of a line-based pattern according to some embodiments of the present disclosure is illustrated.

[0026] Figure 5A The diagram illustrates determining a critical dimension parameter based on distance measurement results according to some embodiments.

[0027] Figure 5B Another example of determining a CD parameter based on distance measurement results according to some embodiments is illustrated.

[0028] Figure 5C The diagram illustrates determining a critical dimension parameter based on a ratio associated with a reference image and an inspection image in accordance with some embodiments.

[0029] Figure 6 Schematic diagram illustrating measuring critical dimensions according to some embodiments.

[0030] Figure 7 Another example of measuring a critical dimension according to some embodiments is illustrated.

[0031] Figure 8 Another example of measuring a critical dimension according to some embodiments is illustrated.

[0032] Fig. 9 An exemplary method for determining critical dimension variation according to some embodiments of the present disclosure is illustrated.

[0033] Fig.10 An example method for determining variations in CD for a pattern having an arbitrary shape according to some embodiments is shown.

[0034] Fig.11 Another example method for determining variations in CD for a pattern having an arbitrary shape according to some embodiments.

[0035] Fig.12 An exemplary method for determining neighbor-wise critical dimension parameters of a pattern having an arbitrary shape and neighboring patterns according to some embodiments is provided.

[0036] Fig.13 is a block diagram of an example computer system in which embodiments of the present disclosure may operate. DETAILED DESCRIPTION

[0037] Aspects of the present disclosure relate to determining critical dimension variations of patterns. Generally, a semiconductor wafer that can be analyzed by a semiconductor wafer inspection system includes patterns corresponding to integrated circuits. For example, the inspection system can identify any defects at the patterns of the semiconductor wafer and can classify the identified defects.

[0038] A semiconductor wafer inspection system can identify patterns corresponding to defects at a semiconductor wafer. Patterns can correspond to various shapes and complex structures. During the period of manufacturing integrated circuits on a semiconductor wafer, the shape or structure of a particular pattern can change and be different from the shape or structure that a particular pattern sample should have. Such pattern changes can cause defects in the semiconductor wafer. An operator of a semiconductor wafer inspection can check each defect. For example, an operator can check the defects and can determine whether the changes in the pattern have an impact on the integrated circuits manufactured on the semiconductor wafer. However, the patterns manufactured on the semiconductor wafer can correspond to different shapes and / or structures. Therefore, an operator of a semiconductor wafer inspection system may need to spend a lot of time to view many different shapes and / or structures of different patterns.

[0039] Aspects of the present disclosure address the foregoing and other deficiencies by determining critical dimension variations of a pattern. Various critical dimensions of a pattern may be determined and used to determine whether variations in the critical dimensions may cause a degradation in the quality of the corresponding pattern (e.g., a defect). For example, a reference image (e.g., a desired pattern) and an inspection image (e.g., a pattern that has been manufactured and / or is associated with a defect) may be received. A critical dimension value or parameter of a particular pattern from the reference image and a critical dimension value or parameter of a corresponding pattern from the inspection image may be determined.

[0040] In some embodiments, a distance transform (DT) operation may be used to determine a critical dimension value. The critical dimension value may be determined by identifying a contour of interest (COI) of the pattern and measuring the distance based on the identified contour of interest. The contour of interest may be considered as a deformed portion (MP) of the pattern. For example, the deformed portion may correspond to a portion of the pattern of the inspection image that is different from a corresponding portion of the corresponding pattern of the reference image. A first critical dimension value may be determined for the contour of interest of the pattern of the reference image. In some embodiments, the first critical dimension value is the distance between the pattern position and the contour of interest on the reference image. The second critical dimension value may be the distance between the corresponding position of the pattern and the contour of interest on the inspection image. Thus, two dimension values ​​of the pattern may be determined. Subsequently, a parameter or change of the critical dimension may be determined based on a combination of the first critical dimension value and the second critical dimension value. In some embodiments, the change or parameter of the critical dimension value may be based on a ratio between the first and second critical dimension values. Thus, the critical dimension change or parameter may identify a change in the pattern as a result of the deformed portion of the pattern. Such a critical dimension change or parameter may then be used to determine whether the change or change in the pattern is significant. For example, if a critical dimension variation or parameter exceeds a threshold value associated with the pattern, the deformed portion may cause a defect to be addressed in a semiconductor process for the semiconductor wafer.

[0041] Advantages of the present disclosure include, but are not limited to, improved efficiency of semiconductor wafer inspection systems. For example, critical dimension variations or parameters may be determined for many different types of patterns without requiring input from an operator of the semiconductor wafer inspection system. Thus, inspection of semiconductor wafers may require less time. Furthermore, determination of critical dimension variations of patterns by a semiconductor wafer inspection system may improve the reliability of a semiconductor manufacturing process because some patterns having critical dimension variations indicative of defects may be processed during the manufacturing process, such that subsequent defects of subsequent semiconductor wafers are reduced.

[0042] Figure 1A A block diagram of an example system for determining critical dimension (CD) variation of a pattern is illustrated. The system may correspond to a semiconductor wafer inspection system.

[0043] like Figure 1AAs shown in the figure, the first system (system 1) may correspond to a computer system, which includes a processing device (e.g., a processor) that can implement a critical dimension component (SP). In some embodiments, the critical dimension component may correspond to a firmware unit, a software product, or a software application. The critical dimension component may implement a distance conversion (DT) function to determine a critical dimension value or measurement result of a pattern of a semiconductor wafer W. In some embodiments, the first system S1 may include a memory M, a computing device C, a display D, and an input unit (e.g., a keyboard) K. The computing device C may be operably coupled to an optical tool or a scanning electron microscope (SEM) via a communication link L. Although a SEM is illustrated, any other type of optical device may be used to obtain an image of the semiconductor wafer W.

[0044] In operation, the computing device C receives data of a semiconductor wafer W from a SEM. The SEM may generate one or more images of one or more features fabricated on the semiconductor wafer W. The computing device C may receive a reference image corresponding to the one or more features (e.g., from a memory M) to determine variations in critical dimensions of the one or more features.

[0045] The DT function can be used to determine the critical dimension of any arbitrary pattern relative to a particular location on the pattern when the pattern has been deformed or can be deformed. The CD variation can be determined relative to a reference image, which can be represented by a simulated computer-aided design (CAD) image of a design corresponding to the pattern and / or an image of a corresponding pattern that does not include a deformed portion (e.g., a pattern without defects).

[0046] In some embodiments, the DT function may allow CD to be accurately and precisely estimated when defects are present. The DT function may allow a semiconductor wafer inspection system to improve CD and critical dimension variation (CDV) monitoring for all types of patterns with different structures without the operator having to define the actual measurement results expected for each type of pattern.

[0047] Figure 1B A flowchart illustrating an example method 150 for determining pattern critical dimension variation. The method 150 may be performed by processing logic, which may include hardware (e.g., a processing device, a circuit system, dedicated logic, programmable logic, microcode, device hardware, integrated circuits, etc.), software (e.g., instructions running or executed on a processing device), or a combination thereof. In some embodiments, the method 150 may be performed by Figure 1A Method 150 is performed on a critical size feature (SP).

[0048] As shown, in box 151, processing logic may receive a reference image and a captured image (or inspection image). The reference image and the captured image may include a pattern of a semiconductor wafer. The pattern of the reference image may be considered as an ideal pattern without any defects or deformations, and the pattern of the captured image may include defects or deformations. Therefore, the reference image may include a pattern, and the captured image may include a pattern manufactured according to the reference image. In box 152, processing logic may identify a contour of interest of the pattern in the reference image and the captured image. For example, the contour of interest may be a boundary or portion of the pattern that includes a deformation in the captured image. The deformation may be a change in the pattern relative to the boundary or portion of the corresponding pattern in the reference image. In some embodiments, the deformation may be the result of a defect or a possible defect of the pattern. Therefore, a portion of the pattern may be identified as a contour of interest.

[0049] At block 153, processing logic may determine one or more measurements of the size of the pattern for each reference image and captured image for the contour of interest. For example, a location on the pattern may be identified. The location may be located at a normal location relative to the contour of interest of the pattern. Thus, the location may be spaced from the contour of interest. Measurements may be obtained for multiple points or locations along the contour of interest. In some embodiments, a first measurement may be obtained for the reference image and a second measurement may be obtained for the captured image. For example, a first measurement may be obtained to measure the distance of a location on the pattern relative to a point on the contour of interest on the captured image pattern. A second measurement may then be obtained for the captured image at the same location on the pattern in the reference image (i.e., at the same location as the captured image) to measure the distance between this location and a corresponding point on the pattern. In some embodiments, multiple pairs of measurements may be obtained relative to each different point on the contour of interest. Thus, for each point along the contour of interest, a distance value for the reference image and another distance value for the captured image may be obtained.

[0050] Reference Figure 1BAt block 154, processing logic may classify defects associated with the contour of interest based on the determined one or more measurement results. A defect may cause the contour of interest. In some embodiments, a defect may be classified as significant or non-significant based on the one or more measurement results. A defect may be significant if the defect causes an integrated circuit containing the pattern to fail. Defects may be classified based on a critical dimension parameter value, and the critical dimension parameter value may be selected based on one or more measurement results. For example, for each pair of measurement results, a critical dimension parameter may be determined relative to the same location on the pattern and the same point along the contour of interest based on a combination of a first measurement result from a reference image and a second measurement result from a captured image. The critical dimension parameter may be a ratio between the first measurement result and the second measurement result. Subsequently, the critical dimension parameter with the maximum value (indicating the largest change along the contour of interest between the reference image pattern and the captured image pattern) may be selected as the critical dimension parameter. The defect may then be classified based on the selected critical dimension parameter exceeding a threshold. For example, if the critical dimension parameter exceeds the threshold, the contour of interest may be considered a significant defect. Alternatively, if the critical dimension parameter does not exceed the threshold, the contour of interest may not be considered a significant defect. In some embodiments, the threshold value may vary based on the defect type (eg, shape) associated with the contour of interest.

[0051] The reference pattern (e.g., original, initial, desired, designed, planned pattern) mentioned herein may be compared only with a pattern (also referred to as arbitrary, captured, real, deformed, defective pattern, inspection, pattern affected by deformation). In some embodiments, the reference image of the pattern may be, for example, a CAD image, a simulated image of the desired reference pattern, a SEM-generated grayscale image of a reference pattern created during a manufacturing process, a reference pattern image created by an optical tool (e.g., in mask inspection), a die or a group of dielets (e.g., adjacent dielets), etc. The reference contour may be the contour of the pattern at the reference image. For a pattern that is a single feature, any contour of the reference image of such a pattern may be understood as a reference contour of such a pattern. For a pattern that contains an internal (or sub) contour, the external contour and the internal contour on the reference image of such a pattern may be used as a reference contour. In a group of patterns, the external contour of the pattern adjacent to the basic pattern may be regarded as the reference contour of the basic pattern.

[0052] The captured image of a pattern may involve information indicating the brightness produced on a photolithography mask or on a semiconductor wafer when irradiated with a primary electron beam. In this case, the captured image corresponds to a real / arbitrary / deformed pattern. Alternatively, the captured image may be a simulated image of a planned deformation of a reference pattern. The captured image may therefore be any image produced based on a captured signal (generated by scanning a mask or semiconductor wafer with a light beam or a primary electron beam), based on information of the captured signal (a file of digital measurements of the image brightness), functionality of the image, data obtained when processing the image, filtering or enhancement, or a simulated image. The reference image and the captured image may be in the form of a visible image (e.g. a grayscale image obtained by a SEM). The captured outline may involve the outline of the pattern contained in the captured image.

[0053] The deformed portion mentioned herein may refer to at least one item on the pattern. For example, the deformed portion may be (but not limited to) a change, a defect, an added feature, an area with characteristics different from other areas of the pattern. The deformed portion may have any shape / contour and may be located on any part of the pattern. The deformed portion may be a virtual deformed portion (such as an area on the pattern) where the mentioned change / defect / added feature / area is planned / expected. The contour of interest (COI) may refer to a set of points on the pattern and is affected (or expected to be affected) by the deformation of the initial (reference) pattern. For example, the COI may be at least a portion of the reference contour, which is adjacent to the (actual or virtual) deformed portion and is located where the deformed portion is "attached" (or should be attached) to the reference image / contour. For example, the contour of interest may be understood as a portion of the reference contour adjacent to the deformed portion, or may be understood as a boundary between the pattern and the deformed portion of the pattern. The contour of interest may be preselected in advance, or may be determined based on at least one pattern image (captured image or reference image) and based on information about the position and shape of the deformed portion (defect). The segment of the contour of the deformed portion (not being a portion of the reference contour) may be referred to as a deformed contour of interest (COI). The contour of interest may be defined for a pattern that does not have any visible deformed portion (defect). For example, a specific reference contour segment (where there may be potential deformation) may be considered as a contour of interest and referred to as an imaginary (or virtual) contour of interest. For a pattern where the imaginary contour of interest has a specific position on it, a CD parameter may be determined for such a pattern.

[0054] The distance transform (DT) function may be an operation performed on the derived digital image representation. The DT function may be defined as a map that labels each pixel of the image of interest by its distance to the nearest non-zero pixel. For example, non-zero obstacle pixels may be boundary pixels in a binary image.

[0055] Figure 2AThe icons illustrate example patterns of a semiconductor wafer. As shown, the basic pattern P1 may include its own outer contour labeled RO (reference self contour), and an inner / sub-contour labeled RCh (reference sub-contour). The adjacent pattern N1 is located near the pattern P1. The deformed portion MP1 may appear on one side of the pattern P1. MP1 may be a protrusion (illustrated by the protruding thick line labeled MP1a). In some embodiments, the deformed portion may be a recess, such as MP2 (e.g., a thick dashed line). Such a deformed portion is labeled MP1b. The contour of interest may be the boundary between MP1a or MP1b and RO (e.g., the portion of RO between the thick dashed lines).

[0056] As shown, two types of CD measurements can be determined. For example, the first type (Type I) can be a measurement of the CD of pattern P1 itself to estimate how the defect MP1 affects the quality of the pattern P1 itself. The second type (Type II) can be a measurement of the CD of the neighboring pattern P1 relative to pattern N1 to estimate how the defect MP1 affects the surrounding of pattern P1.

[0057] Figure 2B Another example pattern of a semiconductor wafer is illustrated. As shown, type III and type IV measurements can be determined. For type III measurement, pattern P2 has been reduced during the manufacturing process (the diameter / overall size of pattern P2 has been uniformly reduced). The thin contour on the outside is marked as the reference self-profile RO of P2, while the thick contour on the inside is the deformed new profile of P2. The difference is marked as the annular deformed portion MP2. The boundary between the deformed portion MP2 and the reference pattern is the reference profile RO of the pattern (considered as the profile of interest COI-2). In type IV measurement, the overall size / diameter of pattern P3 has been increased during manufacturing (the deformed new profile of P3 is illustrated by the thick line). The reference self-profile of pattern P3 is marked as RO. Because the overall RO has changed, RO also represents the boundary of the deformed portion MP3. In this case, the overall RO is the profile of interest COI-3. This defect type is type IV, which can be processed by the proposed critical dimension measurement technology to determine the self-CD of a pattern similar to P3.

[0058] Figure 2C Another example pattern of a semiconductor wafer is shown in the figure. For example, Figure 2C Can be considered as Figure 2AA variation of , where both pattern P4 and neighboring pattern N2 have defects (MP4, MP5) that together change the distance between adjacent patterns and thus change the proximity CD of any of such patterns. This defect type is type V, where the contour of interest for pattern P4 is COI-4, and CN (the captured contour of neighboring pattern N2) can be used as a reference contour (relative to which the impact of defects MP4 and MP5 is estimated). COI-5 is the contour of interest for pattern N2 if its own CD is estimated.

[0059] Figure 2D Another example pattern of a semiconductor wafer is illustrated. As shown, the pattern may be a circular pattern P6, which includes a contour of interest COI-6 on one of its arcs. Deformed portions may appear on the arc, as indicated by the contour of interest COI-6. The measurement of the CD parameters of the pattern P6 may be based on the position of COI-6.

[0060] Figure 2E Another example pattern of a semiconductor wafer is illustrated. As shown, the pattern may correspond to a linear pattern P7 having a contour of interest COI-7, which may be imaginary (e.g., virtual or imaginary). The CD parameters of the pattern P7 may be determined based on the position of COI-7.

[0061] Figure 2F Another example pattern of a semiconductor wafer is illustrated. As shown, pattern P8 may include a complex shape. A critical dimension parameter of pattern P8 may be determined for contour of interest COI-8. In some embodiments, the critical dimension parameter may correspond to identifying which dimension of pattern P8 will be most affected when a deformed portion appears on COI-8.

[0062] Although FIG. 2A to FIG. 2F Various example patterns are described, but CD variations can be determined for any type or shape of pattern.

[0063] Figure 3A , Figure 3B and Figure 3C Illustration of how to determine the relative impact of pattern deformations (e.g., defects) on the pattern CD. Any pattern may contain deformed portions (e.g., defects) that change the outer contour of the pattern (and, accordingly, the CD of the pattern). Deformed portions on a pattern may affect the pattern CD relative to one or more neighboring patterns.

[0064] Figure 3AAn example pattern with defects associated with a critical dimension is illustrated. As shown, a pattern may include defects within the pattern (i.e., a clear target area). For example, pattern P10 may include a complex shape. The planned reference shape of the pattern is represented by contours 120 and 140. The actual pattern P10 includes defects or deformed portions MP (shaded area 130) that change the outer contour of the pattern. For example, the deformed pattern now ends at boundary / edge 160 (instead of 140). Edge 140 can be considered as the contour of interest (COI-10) of pattern P10.

[0065] In this case, the CD of pattern P10 is the dimension or measured distance that differs the most (or changes the most) relative to the reference pattern. For example, the most changed distance is the distance that is most affected by the deformed portion / defect 130. As can be seen in Figure 3A , the diameter Dr of the reference pattern P10 (measured close to, and preferably orthogonal to, the contour of interest 160) is significantly larger than the diameter Dd (captured) of the defect pattern measured in the same direction (measured close to, and preferably orthogonal to, the contour of interest 160). For example, the diameter Dd (i.e., the defect diameter) may be the distance between "o" at contour 120 and contour 160, and the diameter Dr (i.e., the reference diameter) may be the distance between "o" at contour 120 and "o" at contour 140. The ratio Dd / Dr may indicate the maximum effect of the defect, such that Dd (captured) may be considered the CD of the deformed pattern P10. Because Dr may be considered the CD of the reference pattern, the ratio may be specified as CD 捕获 / CD 参考 .

[0066] Given the scale and the specific direction of measurement, the critical dimension variation (CDV) of pattern P10 can be determined as follows:

[0067]

[0068] In some embodiments, the value Ratio is the ratio as previously described, the value C is the weight coefficient, and the values ​​α, β, γ are the angles formed by the measurement directions for CD selected for the reference contour RC, the contour of interest COI and the deformed contour of interest MCOI, respectively, as illustrated.

[0069] Figure 3B Another example pattern with defects associated with critical dimensions is illustrated. For example, Figure 3BA pair of patterns D11 and D12 are shown, where the patterns are straight lines. A defect 135 on pattern D12 changes the CD of pattern D12 itself and the neighboring CD (e.g., the critical dimension for the neighboring pattern D11). For the CD for the neighboring pattern D11, a reference distance Dr (measured between the unmodified / reference pattern D12 and the neighboring pattern D11) can be compared to a distance Dd (measured between the pattern D12 with the defect and the neighboring pattern D11). It is understood that this distance is most affected by the defect 135.

[0070] To estimate / measure the relative impact of a defect (or any deformed portion in a pattern) on the CD of this pattern, a Dd / Dr ratio may be used. The ratio may be based on a distance Dd measured on a captured image of a deformed pattern (e.g., a defect) and a distance Dr measured on a reference image of an undeformed pattern (e.g., without a defect), where both distances are measured close to the deformed portion. For example, the distance may be measured close to a contour of interest 137 (e.g., surrounded by a defect 135). The maximum ratio may be selected as the CD ratio.

[0071] Figure 3C Another example pattern with defects associated with a critical dimension is illustrated. For example, the effect of a deformed portion on a pattern and adjacent patterns can be determined. An example pattern surrounded by several adjacent patterns is used to illustrate the approach.

[0072] Figure 3C A group of patterns is shown. In the group of patterns, one of the patterns 122 is deformed by the protrusion 121, thereby affecting both the size of the pattern 122 and the distance between the pattern 122 and other adjacent patterns 124 and 126, which are closest to or approximate to the deformed portion 121. The impact of the protrusion 121 can be estimated or determined based on the maximum value of the relative CD changed by the protrusion (i.e., defect) 121.

[0073] In order to measure the effect of the protrusion 21 on the CD value or parameter of the pattern 122 , the Dd1 and Dr1 of the pattern 122 should be measured at the position / direction where the difference / change is the largest.

[0074] The relative CD change can be estimated as:

[0075] max|1-Dd (捕获) / Dref)|

[0076] As an example, the ratio Dd1 / Dr1 that can be measured in pattern 122 (relative to the profile of interest at the base of the protrusion) is equal to 1.31. In order to measure the relative impact of the defect or protrusion 121 on the CD parameters of pattern 122 with respect to pattern 126, Dd2 and Dr2 should be measured at the position / direction where the difference / change is the largest (or such direction / position should be found automatically). For example, the ratio Dd2 / Dr2 that can be measured for the closest neighboring pattern 126 (e.g., represented by the dashed line between pattern 122 and pattern 126) is equal to 0.71.

[0077] Figure 4A The diagram illustrates the determination of critical dimension parameters of a pattern with an arbitrary shape. As shown in the diagram, the CD parameters of an arbitrary pattern can be determined by using a distance conversion function (measuring distance in various directions).

[0078] As shown, pattern 131 may include a circular shape. The outer contour of pattern 131 is illustrated as the boundary of pattern 131. The outer contour may be considered as a reference contour. Arc 132 indicates a set of points S1 of the contour of interest, which may be deformed or impacted by a defect. A DT function may be applied in various directions near the contour of interest (S1 set), i.e., between points of the contour of pattern 131 and points of arc 132 including S1 set, to measure distances, and the DT values ​​of the plurality of distance measurements are plotted on a graph 134. The extreme values ​​of graph 134 correspond to the diameter 136 of the pattern, measured at the center of the arc (e.g., at the center of the set of points S1 of the contour of interest). The extreme values ​​may represent the maximum distance between any point at the contour of pattern 131 and any point at the arc 132. Such a measurement may be a CD parameter or value of the pattern for the contour of interest S1 (e.g., a local CD parameter of the pattern itself).

[0079] Figure 4B Another example pattern 140 having a line pattern is shown, the pattern including a contour of interest 142 having a set of points S2. The DT function applied to the pattern contour near the contour of interest 142 produces a graph 144 having three extrema. One of the extrema (e.g., the minimum of the graph) is associated with a point on the line pattern where the distance between the pattern contour and the contour 142 (e.g., the set of points S2) changes the most. The distance 146 (CD) measured at this point can be a CD parameter (e.g., local CD) of the deformed pattern. Furthermore, because the distance 146 has the smallest absolute value among the other distances that form the extrema 144 (the distance 146 is perpendicular to 142), the distance 146 can be selected as the local CD parameter or value of the pattern 140.

[0080] Figure 4A and Figure 4BIt is shown that the location of the defect / deformation on the pattern (represented by the pattern's contour of interest) affects the CD ratio of the pattern. The CD is called local CD and can be estimated by analyzing the changes in DT extrema and ratio. The estimation can be provided by considering the contour of the reference pattern and the contour of the deformed pattern (or the contour of one of them and the contour of interest of the virtual / planned or real deformation), the proximity of the measurement direction to the perpendicular direction of the contour of interest, and selecting the best candidate in the DT function measurement direction.

[0081] Figure 5A and Figure 5B The diagram illustrates the determination of critical dimension parameters based on distance measurement results. For example, Figure 5A and Figure 5B The diagram illustrates two examples where pattern P13 has a generally circular shape with differently positioned and shaped deformed portions (or contours of interest). In this example, the contour of interest indicates the boundary between the reference pattern and the deformed pattern. As shown, candidates for CD parameters or values ​​can be measured from various directions near the deformed portion / COI, and at least one candidate can be selected as a suitable local CD value or parameter. Directions are illustrated by dashed black lines (e.g., DT). In some embodiments, directions that are closer to perpendicular to the deformed pattern contour (and contour of interest COI) can be better candidates for determining the pattern CD parameter (relative change in maximum dimension, illustrated by measured Dr and Dd on the right).

[0082] Figure 5C The diagram illustrates determining a critical dimension parameter based on a ratio associated with a reference image and an inspection image. As shown, a reference image 150 of a pattern and a captured image 151 (e.g., a captured image of a defect) may be received. A contour of interest 153 may be obtained in a combined image 153, and a CD parameter or value may be estimated for the resulting combined image 153.

[0083] Figure 5C It is also verified that the maximum CD ratio (indicative of CD parameter or local CD) obtainable for the circular shape of pattern 150 (with defects present near the contour of interest 153) is determined to be 0.83. Measurements near the contour of interest 153 may be performed by applying a DT function.

[0084] Figure 6 , Figure 7 and Figure 8 The diagrams illustrate measuring critical dimensions. For example, each figure illustrates different patterns, selecting appropriate directions to measure CD parameters (e.g., DT operations) in different directions (illustrated by black dashed lines) near the contour of interest and the contour of the deformed portion (e.g., protrusion and / or recess). Corresponding DT value graphs are constructed for these different patterns by the DT function. Figure 6 , Figure 7 and Figure 8 The center graph is at least related to the corresponding example placed to the left of the graph (e.g., related to the left example and the right example because they have the same COI and symmetric deformation parts). Each DT graph illustrates the CD parameter candidates as the measurements that are most affected by the deformation part (i.e., the measurements that are close to the extreme value of the DT function). Then, the CD parameter is selected as the measurement with the largest CD ratio among the candidates. Exemplary values ​​of the maximum CD ratio are illustrated in the corresponding figures.

[0085] Fig. 9 An exemplary method for determining variations in critical dimensions. Fig. 9 The method may be performed by processing logic, which may include hardware (e.g., a processing device, a circuit system, dedicated logic, programmable logic, microcode, device hardware, integrated circuits, etc.), software (e.g., instructions running or executed on a processing device), or a combination of the above. Figure 1A A critical size part (SP) execution method is provided.

[0086] Generally speaking, the CD parameters of a deformed pattern can be determined to estimate the quality of the deformed pattern. The quality of the deformed pattern can correspond to a measurement result of the effect of the deformation or defect on the CD parameters of the deformed pattern. In addition, Fig. 9 Separate branches are included to determine CD parameters and estimate the impact of the deformed portion on the pattern based on the respective CD parameters. For example, a first branch may be related to determining CD parameters within the contained pattern (e.g., a left branch for determining its own CD parameters) and CD parameters for one or more neighboring patterns (e.g., a right branch for determining neighboring CD parameters).

[0087] In some embodiments, local CD is measured within and / or outside the pattern deformed by the change order or defect, and the degree to which the deformation changes the pattern is then determined. For example, the ratio between the deformed size and the original (reference) size can be calculated so that the output is the change caused. As shown, at box 10, the processing device can receive an input image. For example, the input image can include at least one image of the pattern and some specific location data, the data identifying whether the pattern has (or should have) a deformed portion MP and identifying where the pattern has (or should have) a deformed portion MP. In some embodiments, the image of the pattern can constitute a SEM image, which is a captured image of the pattern or a reference image of the pattern. The data can include information about a contour of interest (COI), which is the boundary between the pattern and the MP. If the MP is imaginary or planned, the COI can be considered virtual.

[0088] At block 12, the processing device may form a set of boundaries (contours) based on the input received from block 10. For example, the boundary set may include a contour PC of the pattern, a reference contour or a captured contour, a contour of a deformed portion (CMP) (if such a contour exists), a contour of interest COI is provided or created, etc. Other contours may be obtained based on the input data.

[0089] At block 14, the processing device measures its own CD parameters. For example, block 14 may correspond to a first branch for determining self CD measurements to estimate the impact of the MP on the containing pattern containing the contour of interest itself. Block 14 corresponds to measuring self CD near the COI and / or CMP. Block 16 indicates a second branch for determining proximity CD parameters or measurements to estimate the impact of the MP on one or more neighboring patterns (e.g., other adjacent patterns that do not contain the contour of interest). Block 16 corresponds to measuring proximity CD of the COI of the base pattern to the CMP.

[0090] The measurement of box 14 can be implemented by at least one of the sub-branches a) and b). As shown, in box 14, the processing device measures its own CD parameter in a solid containment pattern. In box 14b, the processing device measures its own CD parameter in a containment pattern with an inner contour (or sub-contour). Box 15 indicates that for each of options 14a and 14b, its corresponding own CD parameter can be selected as the measurement result that is most changed due to the presence of MP. However, among all the measurements performed in 14a and 14b, one own CD parameter that is most changed can be selected.

[0091] At block 15, the processing device may calculate the respective change ratios (and / or CDV) for the measurement results and may select the CD parameter characterized by the maximum of the ratio and CDV. Similarly or alternatively, block 16a may correspond to the processing device measuring the proximity CD parameter for a particular neighboring pattern. Block 16b may correspond to the processing device measuring the proximity CD for the deformed portion (MP') of the neighboring pattern, if also located between the pair of neighboring patterns.

[0092] At block 17, for each of options 16a and / or 16b, the processing means may determine the proximity CD parameter that is most changed due to the presence of one or more MPs. At block 18, the processing means may collect the determined CD parameters from the two branches of the flowchart into two combined groups with the proximity CD parameters, respectively, which are most changed due to the presence of the deformed portion MP at a particular location. Individual ratios and / or CDV values ​​may also be recorded.

[0093] Fig.10 An example method for determining variations in critical dimensions for patterns having arbitrary shapes is illustrated. Fig.10The method may be performed by processing logic, which may include hardware (e.g., a processing device, a circuit system, dedicated logic, programmable logic, microcode, device hardware, integrated circuits, etc.), software (e.g., instructions running or executed on a processing device), or a combination of the above. Figure 1A A critical size part (SP) execution method is provided.

[0094] In general, CD parameters may be determined based on a distance transform (DT) function. In some embodiments, the DT function may determine an optimal measurement direction (associated with an extreme value of the DT function) and determine a CD candidate in such a direction. As shown, at box 20, a processing device may receive an input. The input may include two grayscale scanning electron microscope (SEM) images of a pattern and a surrounding area (e.g., a reference image and a captured or inspected image), such that information about a deformed portion (MP) (e.g., a defect) may be obtained as a difference. For example, the difference may be between the pattern of the reference image and the inspected image. In the same or alternative embodiments, the input may include a reference image and information about a hypothetical contour of interest (COI). At box 22, a processing device may form a set of contours. The contour may include a reference contour (RC) of the pattern, and the reference contour (RC) may be selected from any or all of the following: a reference self contour RO; a reference sub-contour RCh; a reference neighboring contour RN; and a captured neighboring contour CN (which may serve as a reference contour when it also has its own deformed portion).

[0095] In some embodiments, the contour of the deformed portion (CMP) may be part of the self pattern or part of a neighboring pattern. The contour of interest (COI) may be the self (imaginary or real) or the COI of a neighboring pattern. If CD parameters are measured for a pattern including the COI (e.g., self CD parameter measurement), then at block 24 the processing device may apply a DT function to a reference contour RC near the COI / CMP to measure the self CD parameter value. The processing device may apply the DT function based on blocks 24a, 24b, and 24c.

[0096] In block 24a, the processing device applies the DT function by using the self-COI and the self-CMP, where RC is a reference self-contour (RO). In some embodiments, a captured self-contour CO near the COI may be used instead of the self-CMP. In block 24b, the processing device applies the DT function by using the self-COI and the self-CMP, where RC is a reference sub-contour (RCh). In some embodiments, a captured self-contour CO near the COI may be used instead of the self-CMP. In block 24c, the processing device may apply the DT function, where RC is a reference self-contour (RO), and the self-COI is used (e.g., there is no CMP). One or more extreme values ​​of the DT function may be obtained by applying the DT function to other points along the COI with reference to the RO.

[0097] Reference Fig.10 At block 25a, the processing device determines the CD parameter candidate as an extreme value of the DT function for each of blocks 24a, 24b, and 24c. At block 25b, the processing device may determine the ratio and / or CDV for each of the CD parameter candidates measured at block 25a. At block 25c, the processing device may determine the CD parameter candidate that produces the maximum CDV. In some embodiments, the CD parameter candidate that produces the minimum CDV may be determined.

[0098] like Fig.10 As shown, the proximity CD parameter measurement results can be determined for adjacent, adjacent, or immediately adjacent patterns. For example, at box 26, the processing device can apply a DT function to a reference contour (RC) near COI / CMP to measure the proximity CD parameter value. The DT function can be applied based on boxes 26a and / or 26b. For example, at box 26a, the processing device applies a DT function, where RC is a reference adjacent pattern contour (RN), and the DT function uses the self COI and the self CMP. In some embodiments, the captured self contour CO near COI can be used in contrast to the self CMP. At box 26b, the processing device applies a DT function to determine the proximity CD parameter between two deformed portions located at two different adjacent patterns. For example, two pattern contours RC can be considered (e.g., the first RC can be a reference adjacent contour (RN), and the second RC will be a reference self contour (RO)). Respectively, the deformed portion MP1 can be the MP of the self pattern, and the deformed portion MP2 can be the MP of the adjacent pattern. In the same or alternative embodiments, RC can be selected as the capture contour of the neighboring pattern (shown as CN in 26b), and DT can use its own COI and CMP. Thus, several CD parameter values ​​measuring the distance between two deformed parts can be detected.

[0099] At block 27a, the processing device determines the CD parameter candidate as the DT function extremum received at block 26a and / or 26b. At block 27b, the processing device determines the ratio and / or CDV for each of the CD parameter candidates found at block 27a (e.g., based on block 26a and / or 26b). At block 28, the processing device may collect the determined CD parameter and / or CDV values ​​from all branches and sub-branches of the method into two combined sets of (1) self and (2) neighboring CD / CDV parameters, respectively. The combined set may include the self CD / CDV of the pattern with the real deformation, and / or the self CD parameters determined for any pattern given an imaginary COI. If the pattern includes more than one deformation part MP, the self CD / CDV may be selected as the maximum CD / CDV from those defined for each MP. The combined set may further include proximity CD parameters for respective several neighboring patterns, wherein each of said CD parameters may be subjected to the largest change (e.g., having the largest ratio / CDV) due to the presence of a deformed portion MP on a base pattern (pattern of interest) at a specific location. The second set may include combined proximity CD parameters selected from the mentioned proximity parameters to determine the neighboring pattern that is most affected by the MP on the base pattern. In some embodiments, the second set may include dual combined proximity CD parameters selected for the pattern, having several MPs and several proximity parameters. The dual combined CD may indicate the largest size change (maximum CDV) resulting from the combination of a specific MP with a specific neighboring pattern.

[0100] The determination of the CD parameter candidate can be further based on calculating the ratio (e.g., Dd / Dr) and deriving the CDV as a selected ratio function according to formula [1]: CDV=f(Ratio). The determination can be further based on calculating the ratio and deriving the CDV in percentage form, for example, according to formula [2]:

[0101] CDV = max|(1-Ratio)|*100 (for any case, self-CD or neighboring CD)

[0102] In the same or alternative embodiments, the CDV is determined based on formula [3]:

[0103]

[0104] In some embodiments, the ratio is CD Capture / CD Reference, C is a weight factor, C is a weight factor, and α, β, γ are the measurement directions for CD selected for the reference profile RC, the profile of interest COI, and the deformed profile of interest MCOI, respectively (see, for example, Figure 3A ) is the angle formed by the

[0105] At block 28, the processing device may receive a combined set, the combined set comprising CD parameters and CDV values, which describe the impact of the deformed portion on its own deformed pattern and its neighboring regions. In some embodiments, a smoothing operation may be used to improve robustness and reduce the impact of noise (see, for example, Figure 5C ).

[0106] Fig.11 A flow chart of an example method for determining variations in critical dimensions for a pattern having an arbitrary shape is shown. Fig.11 The method may be performed by processing logic, which may include hardware (e.g., a processing device, a circuit system, dedicated logic, programmable logic, microcode, device hardware, integrated circuits, etc.), software (e.g., instructions running or executed on a processing device), or a combination of the above. Figure 1A A critical size part (SP) execution method is provided.

[0107] In general, an input may be received, where the input may include two binary images of the pattern (a reference self image and a captured or inspected self image), and the position information of the deformed portion may be obtained as the difference between the two images. The contour group may be similar to Fig.10 In some embodiments, blocks 34-38 may correspond to Fig.10 Blocks 24-28 of FIG. 24-28. For example, the contour group may include RO, RCh and MPC. COI may be obtained as the boundary between MP (MPC) and RO. In some embodiments, COI may be the boundary between MP and RCh. For each deformed portion MP detected on the pattern, some actions as described below may be performed.

[0108] At block 34, the processing device may apply the DT function to the COI for the RC (either RO or RCh) to determine the function extrema. The RC may be selected as the reference profile excluding the COI and may be adjacent to (closest to) the deformed portion (e.g., blocks 34.1, 34.2, and 34.3). At block 35, the processing device may use the extrema found on the RC to determine the measurement directions by creating a set of rays between the RC and the COI points and performing a pair of measurements of the reference distance Dr and the capture / deformation / defect distance Dd in each of those directions (e.g., blocks 35.1 and 35.2). These measurement pairs will then generate CD parameter candidates. While Dr may be measured between the RC and the COI, Dd may be measured in the same direction as the ray between the RC and the deformed portion profile MPC. Based on the measurements of Dr and Dd, the ratio and / or CDV may be estimated for each pair of such measurements (e.g., for each direction / ray).

[0109] At block 38, the processing device may determine or measure the native CD parameters and the pattern. For example, at block 38.1, the processing device selects the CD candidate with the largest scale and / or CDV. At block 38.2, the processing device may find a CD candidate for each deformed pattern if more than one deformed portion is present in the pattern. All CD candidates may then be compared to select the most critical native CD parameter of the pattern as the native CD measurement (with the largest scale and CDV among all deformed portions).

[0110] At block 38.2, the processing means may measure the selected own CD parameter. The selected own CD parameter may also be transmitted (preferably with the ratio and CDV of the selected own CD parameter) to the combined CD set for this pattern (see blocks 18 and 28). It should be noted that for each MP (e.g. if there is more than one MP on the pattern of interest), a CD and CDV set corresponding to this MP will be generated, and then the maximum own CD (and / or CDV) will be selected among all MPs. hereinafter referred to as Fig.12 The proximity CD binding group (generated by blocks 38.3 and 38.4) is illustrated.

[0111] Fig.12 An exemplary method for determining the proximity critical dimension of a pattern and adjacent patterns having arbitrary shapes is illustrated. Thus, measurement of proximity CD parameters can be performed. Fig.12 The method may be performed by processing logic, which may include hardware (e.g., a processing device, a circuit system, dedicated logic, programmable logic, microcode, device hardware, integrated circuits, etc.), software (e.g., instructions running or executed on a processing device), or a combination of the above. Figure 1A A critical size part (SP) execution method is provided.

[0112] In some embodiments, an input may be received, wherein the input may include a binary image of the pattern and its surroundings (reference self-image, reference neighboring image, captured neighboring image), and the position information of the deformed portion may include MP on the self-image and MP' on the neighboring image. The contour group may be similar to Fig.10 The contour group illustrated in the block 22 of FIG. For example, the contour group may include RO, RN, CN and MPC. CN (the contour of the captured neighboring region) may be used as RC if the neighboring pattern also has a deformed portion on the basic pattern side. COI may be obtained as the boundary between MP (MPC) and RO. The method may be performed for each deformed portion (MP) detected on the basic self pattern.

[0113] At block 36, the processing device may apply the DT function to the COI for the RC (either RN or CN) to find the function extrema. For example, the processing device may use the function extrema (minimum, maximum) and endpoints found on the RC to determine the measurement directions by creating a set of rays between the RC (either RN or CN) and the COI point, and performing a pair of measurements for Dr and Dd in each of those directions (e.g., blocks 36.1, 36.2, and 36.3). Each such measurement pair will then generate a proximity CD candidate.

[0114] At blocks 37 (e.g., 37.1 and 372), the processing device may measure Dr between RC and COI, and may measure Dd in the same direction as the ray between RC and the deformed portion contour MPC. Based on the measurements of Dr and Dd, a proximity ratio and / or CDV may be estimated for each such direction / ray.

[0115] At block 38 (e.g., block 38.3), the processing means may select the proximity CD candidates by the maximum ratio / CDV for a particular MP / defect on the base pattern. At block 38.4, the processing means may determine the proximity CD candidates for each base pattern if more than one deformed portion is present in the base pattern. All CD candidates may then be compared to select the most critical (combined) proximity CD parameters for the pattern as the CD measurement with the largest ratio and CDV in the deformed portion. For each MP on the base pattern, the proximity CD of the pattern may be selected as the CD that gives the maximum CDV value among all neighboring patterns. If all options are completed together, the combined CD / CDV may be converted to a dual combined CD / CDV.

[0116] At block 38.4, the processing means selects a proximity CD parameter for the pattern. The selected proximity CD is transferred to the combined CD set for this pattern (see blocks 18 and 28).

[0117] In some embodiments, the proximity CD may be measured for each deformed portion MP (e.g., defect) on the base pattern (with or without applying the DT function). For example, a CD may be defined between a defect and a neighboring pattern, as the distance between the defect contour and the neighboring pattern, to determine the maximum CDV. If a defect is also present on the neighboring pattern, the CD change or ratio due to the defect or due to two defects may be determined. A criterion for the change (CDV) may be calculated, and the best segment associated with the maximum CDV value (and the maximum proximity ratio) may be selected. This process may then be repeated for all neighboring patterns for each MP on the pattern of interest, and the neighboring pattern CD whose combined proximity CD gives the maximum CD change is selected for this MP. In some embodiments, the neighboring pattern closest to the MP gives the largest CD change. This process may be repeated for all defects (MP) of the base pattern to select the maximum proximity ratio / CDV value among them, as well as the appropriate segment for measuring the dual combined proximity CD for the base pattern.

[0118] Fig.13 The diagram illustrates an example machine of a computer system 1300 on which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein. In alternative embodiments, the machine may be connected (e.g., via a network) to other machines in a LAN, an intranet, an extranet, and / or the Internet. The machine may operate in the capacity of a server or a client machine in a client-server network environment, as a peer machine in a peer-to-peer (or distributed) network environment, or as a server or a client machine in a cloud computing infrastructure or environment.

[0119] The machine may be a personal computer (PC), a tablet computer, a set-top box (STB), a personal digital assistant (PDA), a cellular phone, a web device, a server, a network router, a switch or a bridge, or any machine capable of executing (sequentially or otherwise) a set of instructions that specify actions to be taken by the machine. In addition, while a single machine is shown, the term "machine" shall also be construed to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0120] The exemplary computer system 1300 includes a processing device 1302, a main memory 1304 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM)), etc.), a static memory 1306 (e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device 1318, which communicate with each other via a bus 1330.

[0121] Processing device 1302 represents one or more general-purpose processing devices, such as a microprocessor, a central processing unit, and the like. More specifically, the processing device may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or a processor that implements other instruction sets, or a processor that implements a combination of instruction sets. Processing device 1302 may also be one or more special-purpose processing devices, such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, and the like. Processing device 1302 is configured to execute instructions 1326 to perform the operations and steps discussed herein.

[0122] The computer system 1300 may further include a network interface device 1308 to communicate over a network 1320. The computer system 1300 may also include a video display unit 1310 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 1312 (e.g., a keyboard), a cursor control device 1314 (e.g., a mouse), a graphics processing unit 1322, a signal generating device 1316 (e.g., a speaker), a graphics processing unit 1322, a video processing unit 1328, and an audio processing unit 1332.

[0123] The data storage device 1318 may include a machine-readable storage medium 1324 (also referred to as a computer-readable medium) having stored thereon one or more sets of instructions or software 1326 that embody any one or more of the methodologies or functions described herein. The instructions 1326 may also reside, completely or at least partially, within the main memory 1304 and / or within the processing device 1302 during execution by the computer system 1300, the main memory 1304 and the processing device 1302 also constituting machine-readable storage media.

[0124] In one embodiment, instructions 1326 include instructions to implement a method corresponding to a critical dimension feature (e.g., Figure 1A The term "machine-readable storage medium" should be construed as including any medium capable of storing or encoding a set of instructions to be executed by a machine and causing the machine to perform any one or more of the methods of the present disclosure. The term "machine-readable storage medium" should accordingly be construed as including, but not limited to, solid-state memory, optical media, and magnetic media.

[0125] Some portions of the foregoing embodiments have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is considered here (and generally) to be a self-consistent sequence of operations that produces a desired result. The operations require physical manipulation of physical quantities. Typically (but not necessarily) these quantities are in the form of electrical or magnetic signals that can be stored, combined, compared, and otherwise manipulated. At times, primarily for reasons of common usage, it has proven convenient to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

[0126] It should be remembered, however, that all of these and similar terms should be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless expressly stated otherwise, it will be apparent from the above discussion that throughout the specification, terms such as "identify" or "determine" or "execute" or "run" or "collect" or "create" or "transmit" and the like are used to refer to operations and procedures of a computer system or similar electronic computing device that manipulate and transform data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system's memories or registers or other such information storage devices.

[0127] The present disclosure also relates to an apparatus for performing the operations described herein. This apparatus may be specially constructed for the intended purpose, or it may comprise a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as (but not limited to) any type of disk, including floppy disks, optical disks, CD-ROMs, and magneto-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of medium suitable for storing electronic instructions, each coupled to a computer system bus.

[0128] The algorithms and displays presented herein are not inherently related to any particular computer or other device. Various general purpose systems may be used together with the programs taught herein, or it may prove convenient to build more specialized equipment to perform the method. The structures of various of these systems will be presented below. In addition, the disclosure is not described with reference to any particular programming language. It should be understood that the teachings of the disclosure illustrated herein may be implemented using various programming languages.

[0129] The present disclosure may be provided as a computer program product or software, which may include a machine-readable medium having instructions stored thereon, which may be used to program a computer system (or other electronic device) to perform a process according to the present disclosure. A machine-readable medium includes any mechanism for storing information in a form that can be read by a machine (e.g., a computer). For example, a machine-readable (e.g., computer-readable) medium includes a machine (e.g., computer) readable storage medium, such as a read-only memory ("ROM"), a random access memory ("RAM"), a disk storage medium, an optical storage medium, a flash memory device, and the like.

[0130] In the foregoing description, embodiments of the disclosure have been described with reference to specific exemplary embodiments of the disclosure. It will be apparent that various modifications may be made without departing from the broad spirit and scope of the disclosure as set forth in the appended claims. The specification and drawings are therefore to be regarded as illustrative rather than restrictive.

Claims

1. A method comprising: receiving input data including an image of a pattern and position data identifying a changed portion of the pattern; determining, by a processing device, a first parameter of a first dimension within the pattern by measuring a critical dimension parameter value with respect to at least one of an outer contour of the altered portion or an inner contour of the altered portion; determining, by the processing device, a second parameter of a second dimension outside the pattern; generating a combination set based on the first parameter and the second parameter; and Defects associated with the changed portion are classified based on the combination set.

2. The method according to claim 1, further comprising: A boundary set is determined based on the input data, wherein the boundary set includes at least one of a reference contour, a capture contour, a contour of the changed portion, or a contour of interest. 3 . The method of claim 1 , wherein determining the second parameter comprises measuring a critical dimension parameter value with respect to adjacent patterns. 4 . The method of claim 1 , wherein determining the second parameter comprises measuring a critical dimension parameter value with respect to another altered portion of an adjacent pattern.

5. The method of claim 1, wherein the defects are classified based on a critical dimension parameter value having a maximum difference between a first value from the combined set and a second value associated with a reference image.

6. A system comprising: Memory; and a processing device operatively coupled to the memory for: receiving input data including an image of a pattern and position data identifying a changed portion of the pattern; determining a first parameter of a first dimension within the pattern by measuring a critical dimension parameter value with respect to at least one of an outer contour of the altered portion or an inner contour of the altered portion; determining a second parameter of a second dimension outside the pattern; generating a combination set based on the first parameter and the second parameter; and Defects associated with the changed portion are classified based on the combination set.

7. The system according to claim 6, wherein the processing device is further configured to: A boundary set is determined based on the input data, wherein the boundary set includes at least one of a reference contour, a capture contour, a contour of the changed portion, or a contour of interest.

8. The system of claim 6, wherein the processing device is further configured to measure critical dimension parameter values ​​of adjacent patterns to determine the second parameter.

9. The system of claim 6, wherein the processing device is further configured to measure a critical dimension parameter value of another changed portion of an adjacent pattern to determine the second parameter.

10. The system of claim 6, wherein the defects are classified based on a critical dimension parameter value having a maximum difference between a first value from the combined set and a second value associated with a reference image.

11. A non-transitory computer-readable medium comprising instructions that, when executed by a processing device, cause the processing device to perform operations comprising: receiving input data including an image of a pattern and position data identifying a changed portion of the pattern; determining a first parameter of a first dimension within the pattern by measuring a critical dimension parameter value with respect to at least one of an outer contour of the altered portion or an inner contour of the altered portion; determining a second parameter of a second dimension outside the pattern; generating a combination set based on the first parameter and the second parameter; and Defects associated with the changed portion are classified based on the combination set.

12. The non-transitory computer readable medium of claim 11, wherein the operations further comprise: A boundary set is determined based on the input data, wherein the boundary set includes at least one of a reference contour, a capture contour, a contour of the changed portion, or a contour of interest.

13. The non-transitory computer readable medium of claim 11, wherein the processing device further performs operations including measuring critical dimension parameter values ​​with respect to adjacent patterns to determine the second parameter.

14. The non-transitory computer readable medium of claim 11, wherein the processing device further performs an operation including measuring a critical dimension parameter value with respect to another changed portion of an adjacent pattern to determine the second parameter.

Images