Mask calibration method for mask defect detection

Abstract

Disclosed in the present invention is a mask calibration method for mask defect detection. Steps of the method comprise: S1: selecting a selected area, which has a pattern, from a mask design file, and outputting vector points of the selected area; S2: using an AOI detection device to collect from a corresponding physical mask an actual image corresponding to the selected area; and S3: setting a horizontal tolerance range in a horizontal direction of the selected area, and performing step S31: using different horizontal tolerance compensation values within the horizontal tolerance range to respectively fill and render the vector points, which are output in step S1, into first rendered images; respectively registering the first rendered images, which are rendered by means of the different horizontal tolerance compensation values, with the actual image, and using an assessment method to assess the influence of the different horizontal tolerance compensation values on the degree of fitting between each first rendered image and the actual image, and using the horizontal tolerance compensation value, which has the best assessment result, as a tolerance for horizontal direction calibration. By means of the method, the improvement in the precision and accuracy of defect detection is facilitated.

Description

A mask calibration method for mask defect detection Technical Field

[0001] This invention relates to a mask calibration method for mask defect detection. Background Technology

[0002] Currently, defect detection in semiconductor masks generally uses the DIE to DB (D2DB) method, which compares the final image of the physical mask with the mask design file to identify defects. However, during the photolithography process, due to differences in process technology, the dimensions of the manufactured mask patterns will have certain tolerances. These tolerances can lead to false defects in subsequent defect detection, sometimes making it even more difficult to determine whether they are genuine defects. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a mask calibration method for mask defect detection, which is beneficial to improving the accuracy and precision of defect detection.

[0004] To solve the above-mentioned technical problems, the technical solution of the present invention is: a mask calibration method for mask defect detection, the method comprising the following steps:

[0005] S1: Select a region containing graphics in the mask design file and output the vector points of the selected region;

[0006] S2: Use AOI detection equipment to acquire the actual image of the selected area on the corresponding physical mask;

[0007] S3: Set the horizontal tolerance range in the horizontal direction of the selected area, and perform step S31: Fill and render the vector points output in step S1 into the first rendered image using different horizontal tolerance compensation values ​​within the horizontal tolerance range; Register the first rendered images obtained with different horizontal tolerance compensation values ​​with the actual image, and use an evaluation method to evaluate the influence of different horizontal tolerance compensation values ​​on the fit between the first rendered image and the actual image, and take the horizontal tolerance compensation value with the best evaluation result as the tolerance calibrated in the horizontal direction;

[0008] S4: Set the vertical tolerance range in the selected area and perform step S41: Use different vertical tolerance compensation values ​​within the vertical tolerance range to fill and render the vector points output in step S1 into a second rendered image; register the second rendered images rendered with different vertical tolerance compensation values ​​with the actual image, and use an evaluation method to evaluate the influence of different vertical tolerance compensation values ​​on the fit between the second rendered image and the actual image, and take the vertical tolerance compensation value with the best evaluation result as the tolerance calibrated in the vertical direction.

[0009] Furthermore, in order to obtain a more accurate horizontal tolerance compensation value, in step S3, the horizontal tolerance compensation value with the best evaluation result is taken as the horizontal tolerance range of the secondary evaluation calibration, and step S31 is repeated to obtain the final calibration tolerance in the horizontal direction.

[0010] Furthermore, in order to obtain a more accurate vertical tolerance compensation value, in step S4, the vertical tolerance compensation value with the best evaluation result is used as the vertical tolerance range for secondary evaluation calibration, and step S41 is repeated to obtain the final calibrated tolerance in the vertical direction.

[0011] Furthermore, in step S3, the evaluation method is as follows:

[0012] Obtain the vertical edges of the first rendered image and the actual image;

[0013] Vertical edge regions are defined based on vertical edges.

[0014] Calculate the sum of the first difference between the first rendered image and the actual image in the vertical edge region, i.e., the first error sum;

[0015] The corresponding horizontal tolerance compensation value obtained by taking the first difference value and the minimum as the evaluation target is used as the tolerance for horizontal calibration.

[0016] The specific steps for obtaining the vertical edges of the first rendered image and the actual image are as follows: perform horizontal differentiation on the first rendered image or the actual image, set a horizontal differentiation threshold, and mark the region whose horizontal differentiation is greater than the horizontal differentiation threshold and whose vertical differentiation is close to 0 as the vertical edge.

[0017] Furthermore, defining the vertical edge region based on the vertical edge specifically involves setting a range of a certain number of pixels adjacent to the vertical edge as the vertical edge region.

[0018] Furthermore, in order to obtain a more accurate horizontal tolerance compensation value, a fitting method is used to obtain the minimum first difference value between the horizontal tolerance compensation value and the first difference value.

[0019] Furthermore, in step S4, the evaluation method is as follows:

[0020] Obtain the horizontal edges of the second rendered image and the actual image;

[0021] The horizontal edge region is defined based on the horizontal edge.

[0022] Calculate the second difference between the second rendered image and the actual image in the horizontal edge region, i.e., the second error sum;

[0023] The vertical tolerance compensation value obtained by taking the second difference value and the minimum as the evaluation target is used as the tolerance calibrated in the vertical direction.

[0024] Furthermore, obtaining the horizontal edges of the second rendered image and the actual image specifically involves: performing vertical differentiation on the second rendered image or the actual image, setting a vertical differentiation threshold, and marking the region where the vertical differentiation is greater than the vertical differentiation threshold and the horizontal differentiation is close to 0 as the horizontal edge.

[0025] Furthermore, defining the horizontal edge region based on the horizontal edge involves setting a range of a certain number of pixels adjacent to the horizontal edge as the horizontal edge region.

[0026] Furthermore, in order to obtain a more accurate vertical tolerance compensation value, a fitting method is used between the vertical tolerance compensation value and the second difference value to obtain the minimum second difference value.

[0027] Furthermore, the specific rendering steps in steps S3 and S4 include:

[0028] The dimensions of the graphic corresponding to the vector point are scaled according to the tolerance compensation value;

[0029] Then, sub-pixel edge filling is performed on the corrected vector points;

[0030] The filled graphic is modified using the selected model to obtain the corresponding rendered image.

[0031] By adopting the above technical solution, the calibration method proposed in this invention can perform high-precision inspection of the mask within the tolerable tolerance range without causing a large number of false defects. Furthermore, the method of calibrating the horizontal and vertical directions separately ensures that the tolerances of the two dimensions are independent and will not affect each other, thus preventing errors in the calibration tolerances. Simultaneously, processing only the information of the vertical and horizontal edges can ignore noise from numerous corners, greatly improving the calibration tolerance and reliability. Secondly, this invention evaluates the quality of the tolerance compensation value through error summation, reducing unnecessary noise caused by registration and improving the accuracy of the calibration. Attached Figure Description

[0032] Figure 1 shows the vector point representation of a selected area in a mask design file; where the intersections are the positions of each point, and the dashed lines are the polygons formed by connecting the points.

[0033] Figure 2(a) shows the actual image on the mask corresponding to the selected area in Figure 1;

[0034] Figure 2(b) is a rendered image of the selected area in Figure 1 after low-pass filtering;

[0035] Figure 3(a) shows the difference between the actual image and the rendered image without tolerance correction;

[0036] Figure 3(b) shows the difference between the actual image and the rendered image after tolerance correction;

[0037] Figure 4 is a scatter plot of the evaluation error results obtained by different tolerance corrections in this invention. Detailed Implementation

[0038] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0039] As shown in Figures 1-4, a mask calibration method for mask defect detection includes the following steps:

[0040] S1: Select the area containing graphics in the mask design file using EDA software, and output the vector points of the selected area, as shown in Figure 1;

[0041] S2: After aligning the corresponding physical mask using an AOI inspection device, the inspection camera of the AOI inspection device is used to acquire the actual image of the selected area on the physical mask, as shown in Figure 2(a); The purpose of alignment is to map the platform coordinates of the AOI inspection device to the coordinates of the mask design file so as to acquire the actual image corresponding to the selected area marked in step S1.

[0042] S3: Set a horizontal tolerance range in the horizontal direction (i.e., the X direction) of the selected area, and perform step S31: Fill and render the vector points output in step S1 into a first rendered image using different horizontal tolerance compensation values ​​within the horizontal tolerance range; register the first rendered images obtained with different horizontal tolerance compensation values ​​with the actual image, and use an evaluation method to evaluate the influence of different horizontal tolerance compensation values ​​on the fit between the first rendered image and the actual image, and use the horizontal tolerance compensation value with the best evaluation result as the tolerance for horizontal calibration; wherein, using different horizontal tolerance compensation values ​​within the horizontal tolerance range specifically means: uniformly selecting several suitable numerical points within the horizontal tolerance range, and using these numerical points as different horizontal tolerance compensation values;

[0043] S4: Set a vertical tolerance range in the vertical direction (i.e., the Y direction) of the selected area, and perform step S41: Use different vertical tolerance compensation values ​​within the vertical tolerance range to fill and render the vector points output in step S1 into a second rendered image; register the second rendered images rendered with different vertical tolerance compensation values ​​with the actual image, and use an evaluation method to evaluate the influence of different vertical tolerance compensation values ​​on the fit between the second rendered image and the actual image, and use the vertical tolerance compensation value with the best evaluation result as the tolerance of the vertical direction calibration; wherein, using different vertical tolerance compensation values ​​within the vertical tolerance range specifically means: uniformly select several suitable numerical points within the vertical tolerance range, and use these numerical points as different vertical tolerance compensation values.

[0044] In this embodiment, multithreading can be used to improve the calculation speed during steps S3 to S4.

[0045] In this embodiment, the registration method in steps S3 and S4 can use phase registration to achieve sub-pixel level and improve registration accuracy.

[0046] Specifically, in order to obtain a more accurate horizontal tolerance compensation value, in step S3, the horizontal tolerance compensation value with the best evaluation result is taken as the horizontal tolerance range of the secondary evaluation calibration, and step S31 is repeated to obtain the final calibration tolerance in the horizontal direction.

[0047] Specifically, in order to obtain a more accurate vertical tolerance compensation value, in step S4, the vertical tolerance compensation value with the best evaluation result is taken as the vertical tolerance range for secondary evaluation calibration, and step S41 is repeated to obtain the final calibrated tolerance in the vertical direction.

[0048] Specifically, in step S3, the evaluation method is as follows:

[0049] Obtain the vertical edges of the first rendered image and the actual image;

[0050] Vertical edge regions are defined based on vertical edges.

[0051] Calculate the sum of the first difference between the first rendered image and the actual image in the vertical edge region, i.e., the first error sum;

[0052] The corresponding horizontal tolerance compensation value obtained by taking the first difference value and the minimum as the evaluation target is used as the tolerance for horizontal calibration.

[0053] The process of obtaining the vertical edges of the first rendered image and the actual image is as follows: perform horizontal differentiation on the first rendered image or the actual image, set a horizontal differentiation threshold, and mark the area where the horizontal differentiation is greater than the horizontal differentiation threshold and the vertical differentiation is close to 0 (the value is set according to the uniformity of light, generally less than 5) as the vertical edge.

[0054] Specifically, defining the vertical edge region based on the vertical edge means setting a range of a certain number of pixels adjacent to the vertical edge as the vertical edge region; in this embodiment, the set range of a certain number of pixels can be a range of 3 pixels, or of course, other numbers.

[0055] Specifically, in order to obtain a more accurate horizontal tolerance compensation value, a fitting method is used between the horizontal tolerance compensation value and the first difference value to obtain the minimum first difference value.

[0056] Specifically, in step S4, the evaluation method is as follows:

[0057] Obtain the horizontal edges of the second rendered image and the actual image;

[0058] The horizontal edge region is defined based on the horizontal edge.

[0059] Calculate the second difference between the second rendered image and the actual image in the horizontal edge region, i.e., the second error sum;

[0060] The vertical tolerance compensation value obtained by taking the second difference value and the minimum as the evaluation target is used as the tolerance calibrated in the vertical direction.

[0061] Furthermore, obtaining the horizontal edges of the second rendered image and the actual image specifically involves: performing vertical differentiation on the second rendered image or the actual image, setting a vertical differentiation threshold, and marking the area where the vertical differentiation is greater than the vertical differentiation threshold and the horizontal differentiation is close to 0 (set according to the uniformity of light, generally less than 5) as the horizontal edge.

[0062] Specifically, defining the horizontal edge region based on the horizontal edge involves setting a predetermined number of pixels adjacent to the horizontal edge as the horizontal edge region. In this embodiment, the predetermined number of pixels can be 3 pixels, but it can also be other numbers.

[0063] Specifically, in order to obtain a more accurate vertical tolerance compensation value, a fitting method is used between the vertical tolerance compensation value and the second difference value to obtain the minimum second difference value.

[0064] Specifically, the rendering in steps S3 and S4 involves rendering a bitmap based on the point set of the mask design file. During the rendering process, it is necessary to use appropriate models, such as low-pass filter models, scalar optical simulation models, and vector optical simulation models, to closely approximate the actual mask image. The rendering result is shown in Figure 2(b). The specific method chosen can be selected based on the acceptable error. Based on the set tolerance range, the tolerance range is divided using a larger interval, and a graphic rendering is performed for each tolerance compensation value. The specific steps of rendering in steps S3 and S4 include:

[0065] The dimensions of the graphic corresponding to the vector point are scaled according to the tolerance compensation value;

[0066] Then, sub-pixel edge filling is performed on the corrected vector points;

[0067] The filled graphic is modified using the selected model to obtain the corresponding rendered image.

[0068] In this embodiment, the difference method in steps S3 and S4, which involves directly summing the difference values, effectively neutralizes errors caused by inaccurate registration. Inaccurate registration often results in opposite signs of the difference values ​​at the symmetrical edges of the graphic. If the tolerance value is correct, the sum of these difference values ​​will be approximately 0. A larger value only occurs when there is a tolerance error, and the larger the tolerance deviation, the larger the sum of the differences. This is why this difference method can be used to determine the quality of tolerance compensation values ​​in the horizontal or vertical directions. Furthermore, because the difference method only calculates the error at the horizontal or vertical edges, errors at corners are not included, especially since corner errors are ignored. Corners often provide significant noise information due to the greater uncontrollability of the manufacturing process. Similarly, by calculating the differences separately for horizontal and vertical dimensions, the tolerances in the two dimensions are cleverly separated, thus providing higher accuracy. As shown in Figures 3(a) and 3(b), without tolerance compensation, there is a significant deviation between the rendered image and the actual image. After the tolerance compensation value determined by the minimum error calibration is selected for the mask design, the rendered image and the actual image become more consistent, with only a small deviation.

[0069] The specific embodiments described above further illustrate the technical problems, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A mask calibration method for mask defect detection, characterized in that, The method includes the following steps: S1: Select a region containing graphics in the mask design file and output the vector points of the selected region; S2: Use AOI detection equipment to acquire the actual image of the selected area on the corresponding physical mask; S3: Set the horizontal tolerance range in the horizontal direction of the selected area, and perform step S31: Fill and render the vector points output in step S1 into the first rendered image using different horizontal tolerance compensation values ​​within the horizontal tolerance range; Register the first rendered images obtained with different horizontal tolerance compensation values ​​with the actual image, and use an evaluation method to evaluate the influence of different horizontal tolerance compensation values ​​on the fit between the first rendered image and the actual image, and take the horizontal tolerance compensation value with the best evaluation result as the tolerance calibrated in the horizontal direction; S4: Set the vertical tolerance range in the selected area and perform step S41: Use different vertical tolerance compensation values ​​within the vertical tolerance range to fill and render the vector points output in step S1 into a second rendered image; register the second rendered images rendered with different vertical tolerance compensation values ​​with the actual image, and use an evaluation method to evaluate the influence of different vertical tolerance compensation values ​​on the fit between the second rendered image and the actual image, and take the vertical tolerance compensation value with the best evaluation result as the tolerance calibrated in the vertical direction.

2. The method according to claim 1, characterized in that, In step S3, the horizontal tolerance compensation value with the best evaluation result is taken as the horizontal tolerance range of the secondary evaluation calibration, and step S31 is repeated to obtain the final calibration tolerance in the horizontal direction.

3. The method according to claim 1, characterized in that, In step S4, the vertical tolerance compensation value with the best evaluation result is taken as the vertical tolerance range for secondary evaluation calibration, and step S41 is repeated to obtain the final calibrated tolerance in the vertical direction.

4. The method according to claim 1, characterized in that, In step S3, the evaluation method is as follows: Obtain the vertical edges of the first rendered image and the actual image; Vertical edge regions are defined based on vertical edges. Calculate the sum of the first difference between the first rendered image and the actual image in the vertical edge region, i.e., the first error sum; The corresponding horizontal tolerance compensation value obtained by taking the first difference value and the minimum as the evaluation target is used as the tolerance for horizontal calibration.

5. The method according to claim 4, characterized in that, The specific steps for obtaining the vertical edges of the first rendered image and the actual image are as follows: perform horizontal differentiation on the first rendered image or the actual image, set a horizontal differentiation threshold, and mark the region whose horizontal differentiation is greater than the horizontal differentiation threshold and whose vertical differentiation is close to 0 as the vertical edge. Specifically, defining the vertical edge region based on the vertical edge involves setting a range of pixels adjacent to the vertical edge as the vertical edge region.

6. The method according to claim 4, characterized in that, The minimum first difference value is obtained by fitting the horizontal tolerance compensation value and the first difference value.

7. The method according to claim 1, characterized in that, In step S4, the evaluation method is as follows: Obtain the horizontal edges of the second rendered image and the actual image; The horizontal edge region is defined based on the horizontal edge. Calculate the second difference between the second rendered image and the actual image in the horizontal edge region, i.e., the second error sum; The vertical tolerance compensation value obtained by taking the second difference value and the minimum as the evaluation target is used as the tolerance calibrated in the vertical direction.

8. The method according to claim 6, characterized in that, The specific steps for obtaining the horizontal edges of the second rendered image and the actual image are as follows: perform vertical differentiation on the second rendered image or the actual image, set a vertical differentiation threshold, and mark the region where the vertical differentiation is greater than the vertical differentiation threshold and the horizontal differentiation is close to 0 as the horizontal edge. Specifically, defining the horizontal edge region based on the horizontal edge involves setting a range of a certain number of pixels adjacent to the horizontal edge as the horizontal edge region.

9. The method according to claim 7, characterized in that, The minimum second difference value is obtained by fitting the vertical tolerance compensation value and the second difference value.

10. The method according to claim 1, characterized in that, The specific rendering steps in steps S3 and S4 include: The dimensions of the graphic corresponding to the vector point are scaled according to the tolerance compensation value; Then, sub-pixel edge filling is performed on the corrected vector points; The filled graphic is modified using the selected model to obtain the corresponding rendered image.

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