Method for measuring line roughness based on scanning electron microscope images

Abstract

The application discloses a line roughness measurement method based on a scanning electron microscope image, and can improve stability and accuracy of the method. The method comprises the following steps: S1, acquiring a scanning electron microscope image, a measurement area and at least two sub-areas, and acquiring a preset smoothing parameter of a smoothing filter and a preset threshold condition; S2, respectively smoothing each sub-area under the current smoothing parameter of the smoothing filter, and determining positions of candidate edge points of each sub-area; S3, calculating a position variance of all candidate edge points of the measurement area, judging whether the position variance meets the threshold condition, adjusting the smoothing parameter of the smoothing filter when the position variance does not meet the threshold condition, and then repeatedly executing steps S2-S3 until the position variance meets the threshold condition, and obtaining all candidate edge points of all sub-areas of the measurement area as target edge points when the position variance meets the threshold condition; and S4, calculating line roughness according to positions of the target edge points of all sub-areas of the measurement area.

Description

Technical Field

[0001] This invention relates to the field of line roughness measurement technology, and in particular to a line roughness measurement method based on scanning electron microscope images. Background Technology

[0002] Critical Dimension-Scanning Electron Microscope (CD-SEM) is an important tool for measuring the dimensions of fine patterns, including lines, holes, pillars, and other intricate features. Among these, stable and accurate measurements of line roughness, such as line edge roughness (LER) and / or line width roughness (LWR), are particularly important because fluctuations in line width can significantly impact the performance of the corresponding devices.

[0003] In existing line roughness measurement methods, it is necessary to first use a CD-SEM device to acquire scanning electron microscope images of the object under test (such as a wafer or photomask), and then select a smoothing filter to smooth the measurement area in the scanning electron microscope image to reduce the noise in the measurement area. This smoothing filter has preset smoothing parameters. Then, pattern edge detection (i.e. line edge detection) is performed on the smoothed measurement area to detect the edge of the line. Finally, the line roughness (LER and / or LWR) is calculated based on the edge of the line and the line roughness measurement algorithm.

[0004] Existing methods for measuring line roughness have the following problems:

[0005] 1. Although a smoothing filter has been used to reduce noise in the measurement area, the edge detection of the line is still affected by the noise in the measurement area. Furthermore, the existing technology lacks analysis of the results of the line edge detection, resulting in low stability and accuracy of the line edge detection, which in turn leads to low accuracy and stability of the line roughness measurement method.

[0006] 2. The smoothing parameter has a global nature, that is, the entire measurement area is smoothed based on the smoothing parameter, and the smoothing parameter is a preset smoothing parameter and is not dynamically adjusted. This has the following disadvantages: during the smoothing process, the edge signals of some areas of the measurement area may be over-smoothed due to the smoothing parameter value being too large, or under-smoothed due to the smoothing parameter being too small, which will also lead to low stability and accuracy of line edge detection.

[0007] Problems 1 and 2 above both result in poor stability and accuracy of existing line roughness measurement methods based on scanning electron microscope images. Summary of the Invention

[0008] This invention provides a method for measuring line roughness based on scanning electron microscope (SEM) images. This method can solve the above-mentioned problem 1, thereby improving the stability and accuracy of the method for measuring line roughness based on SEM images.

[0009] In a first aspect, embodiments of the present invention provide a method for measuring line roughness based on scanning electron microscope images, the method comprising:

[0010] S1. Obtain the scanning electron microscope image to be measured, obtain the measurement region in the scanning electron microscope image and at least two sub-regions into which the measurement region is divided, obtain the smoothing parameters of the preset smoothing filter, and obtain the preset threshold conditions.

[0011] S2. Based on the smoothing filter, smooth each sub-region under the current smoothing parameters, and determine the position of the candidate edge point of each sub-region based on the edge detection algorithm;

[0012] S3. Calculate the variance of the positions of all candidate edge points in the measurement area to obtain the position variance. Determine whether the position variance meets the threshold condition. If the position variance does not meet the threshold condition, adjust the smoothing parameter of the smoothing filter. Then repeat steps S2-S3 until the position variance meets the threshold condition. When the position variance meets the threshold condition, obtain the candidate edge points of all sub-regions of the measurement area and use them as target edge points.

[0013] S4. Calculate the line roughness based on the positions of the target edge points in all sub-regions of the measurement area;

[0014] The measurement area includes the left edge region and / or right edge region of the line, and the line is on the left and right sides on both sides of its extension direction.

[0015] One advantage of the measurement method provided by the embodiments of the present invention is that: the inventors have found that noise can lead to high dispersion of edge points obtained by edge detection of lines. Based on this, the embodiments of the present invention calculate the position variance of candidate edge points obtained by edge detection of lines and determine the position variance and threshold conditions to adjust the smoothing parameters of the smoothing filter so that the position variance of the target edge points used to calculate line roughness meets the threshold conditions. This can reduce the dispersion of target edge points, reduce the impact of noise on the edge detection of lines, and improve the stability and accuracy of the measurement method. In other words, the embodiments of the present invention solve the above-mentioned problem 1.

[0016] In one possible embodiment, in step S1, obtaining the preset threshold condition includes: obtaining the preset variance threshold condition, wherein the variance threshold condition includes an upper limit of the variance threshold; in step S3, determining whether the location variance meets the threshold condition, and when the location variance does not meet the threshold condition, adjusting the smoothing parameter of the smoothing filter includes: determining the size of the location variance and the upper limit of the variance threshold, and when the location variance is greater than the upper limit of the variance threshold, adjusting the smoothing parameter of the smoothing filter.

[0017] In another possible embodiment, in step S1, obtaining the preset threshold condition includes: obtaining a preset variance threshold condition, the variance threshold condition including an upper limit and a lower limit of the variance threshold, and obtaining a preset distance threshold condition; in step S3, determining whether the position variance meets the threshold condition, when the position variance does not meet the threshold condition, adjusting the smoothing parameter of the smoothing filter, and then repeating steps S2-S3, including: determining the magnitude between the position variance and the upper and lower limits of the variance threshold; when the position variance is greater than the upper limit of the variance threshold, adjusting the smoothing parameter of the smoothing filter, and then repeating steps S2-S3; when the position variance is greater than the lower limit of the variance threshold and less than or equal to the upper limit of the variance threshold, filtering the sub-regions in the measurement area that do not meet the distance threshold condition as sub-regions to be optimized, adjusting the smoothing parameter of the smoothing filter and smoothing the sub-regions to be optimized based on the smoothing filter under the adjusted smoothing parameter, and obtaining the positions of candidate edge points of the smoothed sub-regions to be optimized based on the edge detection algorithm.

[0018] Another beneficial effect of the measurement method provided by the embodiments of the present invention is that, unlike the prior art which smooths the entire measurement area based on smoothing parameters, in the embodiments of the present invention, the dispersion of candidate edge points is analyzed and different smoothing operations are performed, making the smoothing operation more targeted. Specifically, the magnitude between the upper and lower limits of the position variance and the variance threshold is determined, the smoothing parameters are adjusted according to the determination results, and the adjusted smoothing parameters are used to smooth the entire (i.e., all) measurement area or the sub-region to be optimized, thereby suppressing the adverse effects on the edge detection of the line due to the smoothing parameters being too large or too small, and improving the stability and accuracy of the measurement method. In other words, the embodiments of the present invention also solve the above-mentioned problem 2.

[0019] In other possible embodiments, in step S1, the distance threshold condition is the upper limit of the distance threshold. In step S3, filtering sub-regions in the measurement area that do not meet the distance threshold condition as sub-regions to be optimized includes: fitting all candidate edge points of the measurement area to obtain a fitted straight line, obtaining the distance from each candidate edge point to the fitted straight line, determining the magnitude of the distance and the upper limit of the distance threshold, and selecting the sub-regions where the candidate edge points with a distance greater than the upper limit of the distance threshold are located as sub-regions to be optimized that do not meet the threshold condition.

[0020] In another possible embodiment, obtaining the distance from each candidate edge point to the fitted line includes obtaining the vertical distance from each candidate edge point to the fitted line.

[0021] In one possible embodiment, fitting all candidate edge points of the measurement region to obtain a fitted straight line includes: fitting all candidate edge points of the measurement region into a straight line using the least squares method.

[0022] In other possible embodiments, in step S3, adjusting the smoothing parameters of the smoothing filter includes increasing the smoothing parameters of the smoothing filter by a preset step size.

[0023] In another possible embodiment, in step S1, obtaining the preset threshold condition includes: obtaining a preset smoothing parameter threshold and a preset variance threshold condition; in step S3, when the location variance does not meet the threshold condition, the smoothing parameter of the smoothing filter is adjusted, and then steps S2-S3 are repeated, including: when the location variance does not meet the variance threshold condition, the smoothing parameter of the smoothing filter is incremented by a preset step size based on the preset smoothing parameter, and it is determined whether the incremented smoothing parameter is greater than the preset smoothing parameter threshold. If not, steps S2-S3 are repeated; otherwise, the preset smoothing parameter threshold is used as the smoothing parameter of the smoothing filter, and then steps S2-S3 are repeated.

[0024] In other possible embodiments, in step S1, obtaining the preset threshold conditions includes: obtaining a preset iteration number threshold, a preset smoothing parameter threshold, and a preset variance threshold condition; in step S3, when the location variance does not meet the threshold conditions, the smoothing parameters of the smoothing filter are adjusted, and then steps S2-S3 are repeated, including:

[0025] When the location variance does not meet the variance threshold condition, it is determined whether the current iteration number is greater than the preset iteration number threshold. If yes, proceed to step S4. If no, the smoothing parameter of the preset smoothing filter is incremented by a preset step size, and it is determined whether the incremented smoothing parameter is greater than the preset smoothing parameter threshold. If no, steps S2-S3 are repeated. If yes, the preset smoothing parameter threshold is used as the smoothing parameter of the smoothing filter and the iteration number is counted, and then steps S2-S3 are repeated.

[0026] In one possible embodiment, the smoothing filter includes one of a bilateral filter, a mean filter, and a Gaussian filter; the smoothing parameter includes the size of the convolution kernel.

[0027] Secondly, embodiments of the present invention also provide a line roughness measurement device based on scanning electron microscope images, the device comprising modules / units for performing any of the possible design methods described in the first aspect. These modules / units can be implemented in hardware or by hardware executing corresponding software.

[0028] For details regarding the beneficial effects of the second aspect, please refer to the description in the first aspect above. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart illustrating the line roughness measurement method based on scanning electron microscope images provided in an embodiment of the present invention.

[0031] Figure 2 This is a schematic diagram of a measurement region in a scanning electron microscope image provided in an embodiment of the present invention;

[0032] Figure 3 for Figure 2 A schematic diagram of candidate edge points in the measurement area is shown.

[0033] Figure 4 To Figure 3 A schematic diagram of the fitted line formed by fitting the candidate edge points;

[0034] Figure 5 This is a schematic diagram of a target edge point in a measurement area provided by an embodiment of the present invention;

[0035] Figure 6A flowchart illustrating another method for measuring line roughness based on scanning electron microscope images provided in an embodiment of the present invention;

[0036] Figure 7 This is a schematic diagram of an electronic device configuration provided in an embodiment of the present invention. Detailed Implementation

[0037] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, and to make the above-mentioned objectives, features and advantages of the embodiments of the present invention more apparent and understandable, the prior art solutions and the technical solutions in the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0038] like Figure 1 As shown, this embodiment of the invention provides a method for measuring line roughness based on scanning electron microscope images, the method comprising the following steps:

[0039] S1, acquire the scanning electron microscope image to be measured, acquire the measurement region in the scanning electron microscope image and at least two sub-regions into which the measurement region is divided, acquire the smoothing parameters of the preset smoothing filter, and acquire the preset threshold conditions.

[0040] For example, such as Figure 2 As shown, the measurement region of the scanning electron microscope image 10 includes the left edge region 101 and / or the right edge region 102 of the line 30, with the line 30 having a left side and a right side on either side of its extension direction. Neither the left edge region 101 nor the right edge region 102 needs to encompass the entire line width in the line width direction, but this is not excluded if either or both include the entire line width. In one embodiment, the number of left edge regions 101 and right edge regions 102 is not limited. For example, the measurement region includes a left edge region 101 and a right edge region 102, and the number of each left edge region 101 and right edge region 102 is one, which can be referred to as the first and second measurement regions. Each measurement region is divided into at least two sub-regions 20, for example, each measurement region is equally divided into at least two sub-regions 20. It should be understood that... Figure 2 Regarding the division of subregion 20, some equal divisions are omitted and not illustrated.

[0041] For example, the preset threshold conditions may include preset variance threshold conditions, distance thresholds, smoothing parameter thresholds, and iteration number thresholds. In one embodiment, the preset variance threshold condition includes an upper limit of the variance threshold; in another embodiment, the preset variance threshold condition includes both an upper limit and a lower limit of the variance threshold.

[0042] For example, the smoothing filter includes one of a bilateral filter, a mean filter, and a Gaussian filter; the smoothing parameter includes the size of the convolution kernel. The size of the convolution kernel includes its length and width, which can be the same or different. When adjusting the size of the convolution kernel, the length and / or width are adjusted. In one embodiment, the length and width of the convolution kernel are adjusted with a preset stride.

[0043] S2, based on the smoothing filter, smooths each sub-region separately under the current smoothing parameters, and determines the position of the candidate edge point of each sub-region based on the edge detection algorithm (called the first edge detection algorithm).

[0044] It should be understood that a smoothing filter is a spatial domain filtering technique that adds low frequencies to an image for noise reduction. When a smoothing filter is used to smooth an image, the output of the smoothing filter is the average value of the pixels in the neighborhood of the filter kernel window. This average value is used to replace the value of each pixel in the image, and the smoothed image is the output image.

[0045] like Figure 3 As shown, it should be noted that, in order to illustrate candidate edge point 40, in Figure 3 The various sub-regions are omitted in the text. Figures 4-5 Similarly, the various sub-regions are omitted. Figure 3 The diagram illustrates all candidate edge points 40 detected using an edge detection algorithm. These candidate edge points 40 are distributed across the left edge region 101 and the right edge region 102. The overall distribution of the candidate edge points 40 is relatively dispersed. The candidate edge points 40 include relatively concentrated actual edge points 401 and pseudo-edge points 402 that deviate from the actual edge points 401. Pseudo-edge points 402 are typically edge points detected by the first edge detection algorithm due to noise in the measurement area. Examples of pseudo-edge points 402 include... Figure 3 The highlight is shown in a small white box, which is only used to indicate the pseudo-edge point 402 enclosed by it, so as to distinguish it from the actual edge point 401.

[0046] S3, calculate the variance of the positions of all candidate edge points in the measurement area to obtain the position variance, determine whether the position variance meets the threshold condition, if the position variance does not meet the threshold condition, adjust the smoothing parameter of the smoothing filter, and then repeat steps S2~S3 until the position variance meets the threshold condition. When the position variance meets the threshold condition, obtain the candidate edge points of all sub-regions of the measurement area and use them as target edge points.

[0047] S4. Calculate the line roughness based on the positions of the target edge points of all sub-regions of the measurement area.

[0048] One advantage of the measurement method provided by the embodiments of the present invention is that: the inventors have found that noise can lead to high dispersion of edge points obtained by edge detection of lines. Based on this, the embodiments of the present invention calculate the position variance of candidate edge points obtained by edge detection of lines and determine the position variance and threshold conditions to adjust the smoothing parameters of the smoothing filter so that the position variance of the target edge points used to calculate line roughness meets the threshold conditions. This can reduce the dispersion of target edge points, reduce the impact of noise on the edge detection of lines, and improve the stability and accuracy of the measurement method. In other words, the embodiments of the present invention solve the above-mentioned problem 1.

[0049] In one embodiment, in step S3 above, it is determined whether the position variance meets a threshold condition. When the position variance does not meet the threshold condition, the smoothing parameters of the smoothing filter are adjusted, including: determining the position variance and the upper limit of the variance threshold. When the position variance is greater than the upper limit of the variance threshold, the smoothing parameters of the smoothing filter are adjusted. That is, when the position variance is greater than the upper limit of the preset threshold, it indicates that the overall distribution of the currently determined candidate edge points 40 is highly discrete, with many pseudo edge points 402. Therefore, the smoothing parameters of the smoothing filter are adjusted, and steps S2-S3 are repeated until the position variance of the candidate edge points is less than or equal to the upper limit of the preset threshold. At this time, similar to the prior art, the entire measurement area is smoothed based on the smoothing parameters. The smoothing parameters are global and can be called global smoothing parameters.

[0050] Alternatively, in one embodiment, in step S3 above, it is determined whether the location variance meets the threshold condition. When the location variance does not meet the threshold condition, the smoothing parameter of the smoothing filter is adjusted, and then steps S2-S3 are repeated. This includes: determining the size between the location variance and the upper and lower limits of the variance threshold; when the location variance is greater than the upper limit of the variance threshold, the smoothing parameter of the smoothing filter is adjusted, and then steps S2-S3 are repeated; when the location variance is greater than the lower limit of the variance threshold and less than or equal to the upper limit of the variance threshold, sub-regions in the measurement area that do not meet the distance threshold condition are selected as sub-regions to be optimized, the smoothing parameter of the smoothing filter is adjusted, and the sub-regions to be optimized are smoothed based on the smoothing filter under the adjusted (current) smoothing parameters. The positions of candidate edge points of the smoothed sub-regions to be optimized are obtained based on an edge detection algorithm (called the second edge detection algorithm, which can be the same as or different from the first edge detection algorithm). In other words, when the position variance of the candidate edge point 40 is greater than the lower limit of the preset threshold and less than or equal to the upper limit of the preset threshold, it indicates that only a small portion of the currently determined candidate edge points 40 are pseudo edge points 402. The candidate edge points of the unoptimized sub-regions, i.e. qualified sub-regions, in the measurement area are retained as a part of the target edge points. The smoothing parameter value of the smoothing filter used in the sub-region to be optimized is adjusted (at this time, the smoothing parameter has locality and can be called the local smoothing parameter) to obtain another part of the target edge points corresponding to the sub-region to be optimized. The two parts of target edge points together form the target edge points of all sub-regions in the measurement area.

[0051] Another beneficial effect of the measurement method provided by the embodiments of the present invention is that, unlike the prior art which smooths the entire measurement area based on smoothing parameters, in the embodiments of the present invention, the dispersion of candidate edge points is analyzed and different smoothing operations are performed, making the smoothing operation more targeted. Specifically, the magnitude between the upper and lower limits of the position variance and the variance threshold is determined, the smoothing parameters are adjusted according to the determination results, and the adjusted smoothing parameters are used to smooth the entire (i.e., all) measurement area or the sub-region to be optimized, thereby suppressing the adverse effects on the edge detection of the line due to the smoothing parameters being too large or too small, and improving the stability and accuracy of the measurement method. In other words, the embodiments of the present invention also solve the above-mentioned problem 2.

[0052] like Figure 3 As shown, Figure 3 Among the candidate edge points 40, fewer false edge points 402 are included. Figure 3 In the candidate edge point 40, the position variance is greater than the lower limit of the variance threshold and less than or equal to the upper limit of the variance threshold.

[0053] In one embodiment, in step S1, the distance threshold condition is the upper limit of the distance threshold; in step S3, filtering sub-regions in the measurement area that do not meet the distance threshold condition as sub-regions to be optimized includes:

[0054] like Figure 4 As shown, a fitting line 50 is formed by fitting all candidate edge points of the current measurement area. The distance from each candidate edge point to the fitting line 50 is obtained. The distance is compared with the upper limit of the distance threshold. The sub-regions where the candidate edge points with a distance greater than the upper limit of the distance threshold are located are taken as sub-regions to be optimized that do not meet the threshold condition.

[0055] For example, fitting a straight line to all candidate edge points of the measurement region includes: fitting all candidate edge points of the measurement region into a straight line using the least squares method; obtaining the distance from each candidate edge point to the fitted straight line includes: obtaining the perpendicular distance from each candidate edge point to the fitted straight line.

[0056] In some embodiments, the vertical distance from the candidate edge points of each sub-region to the fitted line 50 is calculated. It is then determined whether the vertical distance of the candidate edge points in each sub-region is greater than the upper limit of the distance threshold. If the vertical distances of all candidate edge points in a sub-region are less than or equal to the upper limit of the distance threshold, the candidate edge points of that sub-region are taken as target edge points. If the vertical distances of the candidate edge points in a sub-region are greater than the upper limit of the distance threshold, the sub-region containing the candidate edge points whose distances are greater than the upper limit of the distance threshold is taken as a sub-region to be optimized that does not meet the threshold condition. The smoothing parameter value of the smoothing filter is adjusted, and then the smoothing filter and the second edge detection algorithm are called to recalculate and obtain the candidate edge points of the sub-region to be optimized. Then, the vertical distance from the candidate edge points of each sub-region to be optimized to the fitted line is calculated again until the vertical distance of the candidate edge points of the sub-region to be optimized is less than or equal to the upper limit of the distance threshold. At this point, the candidate edge points of the sub-region to be optimized are another part of the target edge points. Adding this to the target edge points of the qualified sub-regions mentioned above yields the target edge points of all sub-regions in the measurement area. The final target edge points 60 of all sub-regions are as follows: Figure 5 As shown.

[0057] In one embodiment, in step S3, adjusting the smoothing parameters of the smoothing filter includes increasing the smoothing parameters of the smoothing filter by a preset step size.

[0058] Generally, a larger kernel window results in better smoothing and noise reduction. However, if the kernel window is too large, the edge information in the output image will be severely distorted after smoothing by the smoothing filter, making the output image excessively blurry. Therefore, it is necessary to reasonably select the kernel window size, that is, to reasonably select the smoothing parameters. Based on this, in one embodiment, obtaining the preset threshold conditions in step S1 includes obtaining a preset smoothing parameter threshold and a preset variance threshold condition; in step S3, when the position variance does not meet the threshold conditions, the smoothing parameters of the smoothing filter are adjusted, and then steps S2-S3 are repeated, including: when the position variance does not meet the variance threshold condition, the smoothing parameters of the preset smoothing filter are increased by a preset step size, and it is determined whether the increased smoothing parameter threshold is greater than the preset smoothing parameter threshold. If not, steps S2-S3 are repeated; otherwise, the preset smoothing parameter threshold is used as the smoothing parameter of the smoothing filter, and then steps S2-S3 are repeated.

[0059] In another possible embodiment, in step S1, obtaining the preset threshold conditions includes: obtaining a preset iteration number threshold, a preset smoothing parameter threshold, and a preset variance threshold condition; in step S3, when the location variance does not meet the variance threshold condition, the smoothing parameter of the smoothing filter is adjusted, and then steps S2-S3 are repeated, including: when the location variance does not meet the variance threshold condition, determining whether the current iteration number is greater than the preset iteration number threshold; if yes, proceeding to step S4; if no, increasing the smoothing parameter of the preset smoothing filter by a preset step size, and determining whether the increased smoothing parameter is greater than the preset smoothing parameter threshold; if no, repeating steps S2-S3; if yes, using the preset smoothing parameter threshold as the smoothing parameter of the smoothing filter and counting the iteration number, and then repeating steps S2-S3.

[0060] In one embodiment, the following is combined with Figure 6 The following example further illustrates a method for measuring line roughness based on scanning electron microscope images. This method specifically includes the following steps:

[0061] Step b1: Provide the scanning electron microscope image to be measured and parameter information, including the location of the measurement area and the location of the sub-region, the initial value of the smoothing parameter and various preset thresholds. The preset thresholds include: the upper and lower limits of the variance threshold, the global iteration number threshold and the global smoothing parameter threshold of the global iteration process, the local iteration number threshold and the local smoothing parameter threshold of the local iteration process, and the upper limit of the distance threshold.

[0062] Step b2: Obtain the value of the smoothing parameter. If it is the initialization phase (in the initialization phase, the smoothing parameter has not yet been assigned an initial value), then use the initial value of the smoothing parameter to initialize the value of the smoothing parameter. Otherwise, as in step b7, based on the initial value of the smoothing parameter, increment it by the first step length (for example, increment the length and width of the convolution kernel by a stride of 2) to reset the value of the smoothing parameter. The smoothing parameter during global iteration is called the global smoothing parameter.

[0063] Step b3: Based on the information provided in steps b1 and b2, use a smoothing filter such as a bilateral filter to smooth each sub-region of the measurement area, and then call the edge detection algorithm to calculate and obtain the edge points, i.e., candidate edge points, of each sub-region.

[0064] In one embodiment, the above operations are performed in parallel (i.e. simultaneously) for each sub-region of the measurement region to obtain candidate edge points for each sub-region.

[0065] Step b4: Calculate the positional variance of the candidate edge points in the measurement area based on the information provided in step b3.

[0066] In one embodiment, the positional variance of candidate edge points in each measurement region (such as the first and second measurement regions, i.e., the left edge region 101 and the right edge region 102, as described above) is calculated in parallel (i.e. simultaneously).

[0067] Step b5: Based on the information provided in step b4, determine the magnitude of the position variance of the candidate edge points in the entire measurement area and the upper limit of the variance threshold. If it is greater than the upper limit of the variance threshold, then jump to step b6; otherwise, jump to step b9.

[0068] Step b6: Based on the information provided in steps b1 and b5, determine the size of the global iteration count and the global iteration count threshold. If it is less than or equal to the global iteration count threshold, then jump to step b7; otherwise, jump to step b19.

[0069] Step b7: Based on the information provided in steps b2 and b6, add a preset step size to the value of the smoothing parameter used in the previous iteration. For example, the preset step size is 2, i.e., Sw = Sw + 2. Sw represents the smoothing parameter, which can be called the global smoothing parameter. Sw represents both the length and width of the convolution kernel, i.e., the length and width of the convolution kernel are adjusted simultaneously.

[0070] Step b8: Based on the information provided in step b7, determine the size of the increased global smoothing parameter and the preset global smoothing parameter threshold. If it is less than the global smoothing parameter threshold, jump to step b2. If it is greater than the global smoothing parameter threshold, then assign the global smoothing parameter threshold to the global smoothing parameter, that is, set the global smoothing parameter to the global smoothing parameter threshold, and increase the global iteration count, i.e., Nw = Nw + 1, where Nw represents the global iteration count, and its initial value is 0. Then jump to step b2.

[0071] Step b9: Based on the information provided in steps b1 and b5, determine the magnitude of the positional variance of the candidate edge points in the measurement area compared to the lower limit of the preset variance threshold. If it is greater than the lower limit of the preset variance threshold, then jump to step b10; otherwise, jump to step b19.

[0072] Step b10: Based on the information provided in step b3, fit all candidate edge points of the measurement area into a straight line using the least squares method to obtain the fitted straight line.

[0073] Step b11: Based on the information provided in steps b3 and b10, calculate the vertical distance from the candidate edge point of each sub-region of the measurement area to the fitted line.

[0074] Step b12: Based on the information provided in step b11, determine the vertical distance from the candidate edge points of all sub-regions of the measurement area to the fitted line and the distance threshold. If the vertical distance from the candidate edge points of the sub-region to the fitted line is greater than the distance threshold, then the sub-region is the sub-region to be optimized. At this time, jump to step b13; otherwise, jump to step b17.

[0075] Step b13: Based on the information provided in steps b2 and b12, determine whether it is the initialization stage (of the local iteration process). This can be determined by whether the local iteration count is 0 or whether the local smoothing parameter has been assigned a value. If it is the initialization stage, then initialize the local smoothing parameter with the initial value of the smoothing parameter. Otherwise, add a preset second step size to the local smoothing parameter used in the previous local iteration. For example, the preset second step size is 2, Sl = Sl + 2, where Sl represents the local smoothing parameter. The smoothing parameter used during local iteration is the local smoothing parameter. In this embodiment, both the global smoothing parameter and the local smoothing parameter are initialized with the initial value of the smoothing parameter, but this is not limited to this. Different initial values ​​can be used to initialize both. For example, Sl represents both the length and width of the convolution kernel, that is, the length and width of the convolution kernel are adjusted simultaneously.

[0076] Step b14: Based on the information provided in step b13, determine the size of the increased local smoothing parameter and the preset local smoothing parameter threshold. If it is less than the local smoothing parameter threshold, proceed to step b15. If it is greater than the local smoothing parameter threshold, then assign the local smoothing parameter threshold to the local smoothing parameter, that is, set the local smoothing parameter to the local smoothing parameter threshold. Otherwise, proceed to step b15.

[0077] Step b15: Based on the information provided in steps b1 and b14, use a bilateral filter to smooth the sub-region to be optimized in the measurement area, and then call the edge detection algorithm to obtain candidate edge points of the sub-region to be optimized.

[0078] Step b16: Based on the information provided in step b15, calculate the vertical distance from the candidate edge points of all the sub-regions to be optimized to the fitted line after the update, and increase the number of local iterations, i.e., Nl=Nl+1, where Nl represents the number of local iterations, and its initial value is 0. Then jump to step b12.

[0079] Step b17: Based on the information provided in step b12, if the vertical distance from the candidate edge point of the sub-region to the fitted line is not greater than, i.e., less than or equal to, the distance threshold, then the candidate edge point of the sub-region is obtained as the target edge point of the sub-region.

[0080] Step b18: Based on the information provided in step b17, determine the size of the local iteration count and the local iteration count threshold. If it is less than or equal to the local iteration count threshold, then jump to step b4; otherwise, jump to step b19.

[0081] Step b19: Obtain the target edge points of all sub-regions of the measurement area;

[0082] Step b20: Measure the line roughness based on the target edge point and line roughness measurement algorithm to calculate LER and / or LWR.

[0083] In one embodiment, the formula for calculating LER satisfies the following formula: .

[0084] In one embodiment, This represents the position of the target edge point in each sub-region of the measurement area, and the average position of the target edge point is... The number of target edge points is N+1, where N is a positive integer. The positional error between the target edge point position and the average position in each sub-region is... , That is, LER.

[0085] In one embodiment, the formula for calculating LWR satisfies the following formula: .

[0086] In one embodiment, and Let represent the positions of the i-th target edge points in the left and right measurement regions (left edge region 101 and right edge region 102), respectively (assuming the number of target edge points in both measurement regions is the same, N+1, where N is a positive integer). Then, two target edge points along the line width direction in each of the left and right sub-regions form a pair of target edge points. Based on these pairs of target edge points, the line width can be calculated. The line width is... - The average line width is The error between the line width corresponding to the i-th target edge point and the average line width is , That is, LWR.

[0087] In some embodiments of the present invention, an electronic device is disclosed, such as... Figure 7 As shown, the electronic device 700 may include: one or more processors 701; a memory 702; a display 703; one or more application programs (not shown); and one or more computer programs 704. These devices can be connected via one or more communication buses 705. The one or more computer programs 704 are stored in the memory 702 and configured to be executed by the one or more processors 701. The one or more computer programs 704 include instructions that can be used to perform actions such as... Figure 1 or Figure 6 And the various steps in the corresponding embodiments.

[0088] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer, implements the method described in the above-described method embodiments. Specific effects can be found in the above-described embodiments.

[0089] This invention also provides a computer program product that, when executed by a computer, implements the method described in the above-described method embodiments. Specific effects can be found in the above embodiments.

[0090] Through the above description of the embodiments, those skilled in the art will clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal configuration of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0091] In the various embodiments of the present invention, the functional units can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0092] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as flash memory, portable hard disk, read-only memory, random access memory, magnetic disk, or optical disk.

[0093] The above description is merely a specific implementation of the embodiments of the present invention, but the protection scope of the embodiments of the present invention is not limited thereto. Any changes or substitutions within the technical scope disclosed in the embodiments of the present invention should be covered within the protection scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention should be determined by the protection scope of the claims.

Claims

1. A method for measuring line roughness based on scanning electron microscope images, characterized in that, include: S1. Obtain the scanning electron microscope image to be measured, obtain the measurement region in the scanning electron microscope image and at least two sub-regions into which the measurement region is divided, obtain the smoothing parameters of the preset smoothing filter, and obtain the preset threshold conditions, wherein the preset threshold conditions include the upper limit of the variance threshold. S2. Based on the smoothing filter, smooth each sub-region under the current smoothing parameters, and determine the position of the candidate edge point of each sub-region based on the edge detection algorithm; S3. Calculate the variance of the positions of all candidate edge points in the measurement area to obtain the position variance. Determine whether the position variance meets the threshold condition. If the position variance does not meet the threshold condition, adjust the smoothing parameter of the smoothing filter, and then repeat steps S2-S3 until the position variance meets the threshold condition. When the position variance meets the threshold condition, obtain the candidate edge points of all sub-regions of the measurement area and use them as target edge points. Wherein, if the position variance is greater than the upper limit of the variance threshold, the position variance does not meet the threshold condition. S4. Calculate the line roughness based on the positions of the target edge points in all sub-regions of the measurement area; The measurement area includes the left edge region and / or right edge region of the line, and the line is on the left and right sides on both sides of its extension direction.

2. The method according to claim 1, characterized in that, In step S1, obtaining the preset threshold condition includes: obtaining the preset variance threshold condition, wherein the variance threshold condition includes an upper limit of the variance threshold; in step S3, determining whether the location variance meets the threshold condition, and when the location variance does not meet the threshold condition, adjusting the smoothing parameters of the smoothing filter, including: Determine the magnitude of the position variance and the upper limit of the variance threshold. When the position variance is greater than the upper limit of the variance threshold, adjust the smoothing parameters of the smoothing filter.

3. The method according to claim 1, characterized in that, In step S1, obtaining the preset threshold conditions includes: obtaining preset variance threshold conditions, wherein the variance threshold conditions include an upper limit and a lower limit of the variance threshold, and obtaining preset distance threshold conditions; in step S3, determining whether the location variance meets the threshold conditions, and when the location variance does not meet the threshold conditions, adjusting the smoothing parameters of the smoothing filter, and then repeating steps S2-S3, including: Determine the relationship between the location variance and the upper and lower limits of the variance threshold; When the location variance is greater than the upper limit of the variance threshold, the smoothing parameter of the smoothing filter is adjusted, and then steps S2-S3 are repeated. When the location variance is greater than the lower limit of the variance threshold and less than or equal to the upper limit of the variance threshold, the sub-regions in the measurement area that do not meet the distance threshold condition are selected as sub-regions to be optimized. The smoothing parameter of the smoothing filter is adjusted, and the sub-regions to be optimized are smoothed based on the smoothing filter under the adjusted smoothing parameter. The positions of the candidate edge points of the smoothed sub-regions to be optimized are obtained based on the edge detection algorithm.

4. The method according to claim 3, characterized in that, In step S1, the distance threshold condition is the upper limit of the distance threshold. In step S3, filtering out sub-regions in the measurement area that do not meet the distance threshold condition includes: Fit a straight line by fitting all candidate edge points of the measurement area, obtain the distance from each candidate edge point to the fitted straight line, determine the magnitude of the distance and the upper limit of the distance threshold, and take the sub-region where the candidate edge point with the distance greater than the upper limit of the distance threshold is located as the sub-region to be optimized that does not meet the threshold condition.

5. The method according to claim 4, characterized in that, Obtaining the distance from each candidate edge point to the fitted line includes: obtaining the vertical distance from each candidate edge point to the fitted line.

6. The method according to claim 4, characterized in that, The process of fitting a straight line to all candidate edge points of the measurement region includes: fitting a straight line to all candidate edge points of the measurement region using the least squares method.

7. The method according to claim 2 or 3, characterized in that, In step S3, adjusting the smoothing parameters of the smoothing filter includes increasing the smoothing parameters of the smoothing filter by a preset step size.

8. The method according to claim 1, characterized in that, In step S1, obtaining the preset threshold conditions includes: obtaining a preset smoothing parameter threshold and a preset variance threshold condition; in step S3, when the location variance does not meet the threshold conditions, the smoothing parameters of the smoothing filter are adjusted, and then steps S2-S3 are repeated, including: When the location variance does not meet the variance threshold condition, the smoothing parameter of the preset smoothing filter is increased by a preset step size, and it is determined whether the increased smoothing parameter is greater than the preset smoothing parameter threshold. If not, steps S2-S3 are repeated. Otherwise, the preset smoothing parameter threshold is used as the smoothing parameter of the smoothing filter, and steps S2-S3 are repeated.

9. The method according to claim 1, characterized in that, In step S1, obtaining the preset threshold conditions includes: obtaining a preset iteration number threshold, a preset smoothing parameter threshold, and a preset variance threshold condition; in step S3, when the location variance does not meet the threshold conditions, the smoothing parameters of the smoothing filter are adjusted, and then steps S2-S3 are repeated, including: When the location variance does not meet the variance threshold condition, it is determined whether the current iteration number is greater than the preset iteration number threshold. If yes, proceed to step S4. If no, the smoothing parameter of the preset smoothing filter is incremented by a preset step size, and it is determined whether the incremented smoothing parameter is greater than the preset smoothing parameter threshold. If no, steps S2-S3 are repeated. If yes, the preset smoothing parameter threshold is used as the smoothing parameter of the smoothing filter and the iteration number is counted, and then steps S2-S3 are repeated.

10. The method according to any one of claims 1 to 9, characterized in that, The smoothing filter includes one of a bilateral filter, a mean filter, and a Gaussian filter; the smoothing parameter includes the size of the convolution kernel.

Images