A mask calibration method for mask defect detection

Abstract

The present invention discloses a reticle calibration method for reticle defect detection. The method comprises the following steps: S1: selecting a selected area containing a graphic in a reticle design file and outputting vector points of the selected area; S2: using an AOI inspection device to capture an actual image corresponding to the selected area on a corresponding physical reticle; S3: setting a horizontal tolerance range in the horizontal direction of the selected area, and performing step S31: using different horizontal tolerance compensation values ​​within the horizontal tolerance range to fill and render the vector points output in step S1 into a first rendered image; registering the first rendered images rendered with different horizontal tolerance compensation values ​​with the actual image, evaluating the effects of different horizontal tolerance compensation values ​​on the fit between the first rendered image and the actual image using an evaluation method, and using the horizontal tolerance compensation value with the best evaluation result as the tolerance for horizontal calibration. This method is advantageous for improving the precision and accuracy of defect detection.

Description

Technical Field

[0001] The invention relates to a mask calibration method for mask defect detection. Background Art

[0002] Currently, semiconductor mask defect detection typically uses a DIE to DB (D2DB) model, comparing images of the final mask with the mask design files to identify defects. However, during the mask lithography process, due to varying process levels, the dimensions of the manufactured mask patterns can vary. These variations can lead to false defects during subsequent defect detection, making it difficult to determine whether they are true 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 conducive to improving the precision and accuracy of defect detection.

[0004] In order to solve the above 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 selected area with a pattern in the mask design file and output vector points of the selected area;

[0006] S2: Using AOI inspection equipment to collect the actual image corresponding to the selected area on the corresponding physical mask;

[0007] S3: setting a horizontal tolerance range in the horizontal direction of the selected area, and proceeding to step S31: using different horizontal tolerance compensation values ​​within the horizontal tolerance range to fill and render the vector points outputted in step S1 into first rendered images; registering the first rendered images obtained by rendering with different horizontal tolerance compensation values ​​with the actual image, and using an evaluation method to evaluate the effects of different horizontal tolerance compensation values ​​on the fit between the first rendered image and the actual image, and using the horizontal tolerance compensation value with the best evaluation result as the tolerance for horizontal calibration;

[0008] S4: Set a vertical tolerance range in the vertical direction of the selected area, and proceed to 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; align the second rendered images rendered with different vertical tolerance compensation values ​​with the actual image respectively, and use an evaluation method to evaluate the impact 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 for vertical calibration.

[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 used as the horizontal tolerance range for secondary evaluation and calibration, and step S31 is repeated to obtain the final calibrated 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 and 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 specifically as follows:

[0012] Obtaining vertical edges between the first rendered image and the actual image;

[0013] Demarcate vertical edge areas based on vertical edges;

[0014] Obtaining a first difference value sum between the first rendered image and the actual image in the vertical edge region, that is, a first error sum;

[0015] The first difference value and the minimum are used as evaluation targets to obtain a corresponding horizontal tolerance compensation value as the tolerance for horizontal direction calibration.

[0016] The vertical edges of the first rendered image and the actual image are obtained by performing horizontal differentiation on the first rendered image or the actual image, setting a horizontal differentiation threshold, and marking a region where the horizontal differentiation is greater than the horizontal differentiation threshold and the vertical differentiation is close to 0 as a vertical edge;

[0017] Furthermore, the vertical edge region is calibrated based on the vertical edge and specifically includes setting a set pixel range 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 between the horizontal tolerance compensation value and the first difference value sum to obtain the minimum first difference value sum.

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

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

[0021] Horizontal edge area is calibrated based on horizontal edge;

[0022] Obtaining a second difference value sum between the second rendered image and the actual image in the horizontal edge region, that is, a second error sum;

[0023] The corresponding vertical tolerance compensation value obtained by taking the second difference value and the minimum as evaluation targets is used as the tolerance for vertical direction calibration.

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

[0025] Furthermore, the horizontal edge region is calibrated based on the horizontal edge and specifically includes setting a set pixel range 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 steps of rendering in step S3 and step S4 include:

[0028] Scale the graphics corresponding to the vector points according to the tolerance compensation value;

[0029] Then fill the sub-pixel edges of the corrected vector points;

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

[0031] By employing the above technical solutions, the calibration method proposed in this invention enables high-precision inspection of reticles within acceptable tolerances without generating a large number of false defects. Furthermore, the separate calibration of the horizontal and vertical dimensions ensures the independence of the tolerances in these two dimensions, preventing them from interfering with each other and causing calibration errors. Furthermore, by processing only vertical and horizontal edge information, noise introduced by corners can be ignored, significantly improving the calibration tolerance and reliability. Furthermore, the present invention evaluates the quality of tolerance compensation values ​​through the sum of errors, reducing unnecessary noise caused by registration and improving calibration accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A vector point representation of a selected area in the mask design file; the intersection points are the locations of the points, and the dotted lines are polygons connecting the points.

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

[0034] Figure 2(b) shows Figure 1 The rendered image is a low-pass filtered rendering of the selected area;

[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 tolerance-corrected rendered image;

[0037] Figure 4 The figure is a scatter plot of the evaluation error results obtained for different tolerance corrections in the present invention. DETAILED DESCRIPTION

[0038] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments in conjunction with the accompanying drawings.

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

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

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

[0042] S3: Setting a horizontal tolerance range in the horizontal direction (i.e., the X direction) of the selected area, and performing step S31: using different horizontal tolerance compensation values ​​within the horizontal tolerance range to fill and render the vector points outputted in step S1 into first rendered images; registering the first rendered images obtained by rendering with different horizontal tolerance compensation values ​​with the actual image, and using an evaluation method to evaluate the effects of different horizontal tolerance compensation values ​​on the fit between the first rendered image and the actual image, and using 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 comprises: evenly selecting a number of appropriate 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., 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; align the second rendered images rendered with different vertical tolerance compensation values ​​with the actual image respectively, and use an evaluation method to evaluate the impact 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 for vertical calibration; wherein, using different vertical tolerance compensation values ​​within the vertical tolerance range is specifically: evenly selecting a number of appropriate numerical points within the vertical tolerance range, and using these numerical points as different vertical tolerance compensation values.

[0044] In this embodiment, during steps S3 to S4 , a multi-threading approach may be used to increase the calculation speed.

[0045] In this embodiment, the registration method in step S3 and step S4 can use phase registration to achieve a sub-pixel level to improve the 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 used as the horizontal tolerance range for secondary evaluation calibration, and step S31 is repeated to obtain the final calibrated 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 used as the vertical tolerance range for secondary evaluation and 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] Obtaining vertical edges between the first rendered image and the actual image;

[0050] Demarcate vertical edge areas based on vertical edges;

[0051] Obtaining a first difference value sum between the first rendered image and the actual image in the vertical edge region, that is, a first error sum;

[0052] The first difference value and the minimum are used as evaluation targets to obtain a corresponding horizontal tolerance compensation value as the tolerance for horizontal direction calibration.

[0053] Obtaining vertical edges of the first rendered image and the actual image specifically includes: performing horizontal differentiation on the first rendered image or the actual image, setting a horizontal differentiation threshold, and marking as vertical edges areas where the horizontal differentiation is greater than the horizontal differentiation threshold and the vertical differentiation is close to 0 (set according to light uniformity, generally less than 5);

[0054] Specifically, the vertical edge area is calibrated based on the vertical edge as follows: a set pixel range adjacent to the vertical edge is set as the vertical edge area; in this embodiment, the set pixel range can be a 3-pixel range, and of course it can be 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 sum to obtain the minimum first difference value sum.

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

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

[0058] Horizontal edge area is calibrated based on horizontal edge;

[0059] Obtaining a second difference value sum between the second rendered image and the actual image in the horizontal edge region, that is, a second error sum;

[0060] The corresponding vertical tolerance compensation value obtained by taking the second difference value and the minimum as evaluation targets is used as the tolerance for vertical direction calibration.

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

[0062] Specifically, the horizontal edge region is calibrated based on the horizontal edge as follows: a set pixel range adjacent to the horizontal edge is set as the horizontal edge region. In this embodiment, the set pixel range can be a 3-pixel range, and of course, other numbers can also be used.

[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 is based on the point set of the mask design file and is rendered into a bitmap. During the rendering process, it is necessary to use corresponding models, such as low-pass filter models, scalar optical simulation models, and vector optical simulation models, to approximate the actual mask drawing. The rendering result is shown in Figure 2(b). The specific selection method can be selected based on the acceptable error. According to the set tolerance range, the tolerance range is divided into larger intervals, and a graphic rendering is performed for each tolerance compensation value. The specific steps of rendering in steps S3 and S4 include:

[0065] Scale the graphics corresponding to the vector points according to the tolerance compensation value;

[0066] Then fill the sub-pixel edges of the corrected vector points;

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

[0068] In this embodiment, the difference method in steps S3 and S4, namely, by directly summing the difference values, effectively neutralizes errors caused by inaccurate registration. This is because inaccurate registration often results in the difference values ​​of the edges of a symmetrical figure having opposite signs. If the tolerance values ​​are correct, the sum of these difference values ​​will be approximately equal to 0. A larger value will only be achieved if there is a tolerance error, and the greater the tolerance deviation, the larger the difference sum. 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 errors at horizontal or vertical edges, errors at corners are not included, and in particular, corner errors are ignored. Corners often provide a lot of noise information due to the greater uncontrollable process level. Similarly, by calculating the differences separately for horizontal and vertical dimensions, the tolerances in the horizontal and vertical dimensions are cleverly separated, thus providing higher accuracy. As shown in Figures 3(a) and 3(b), if tolerance compensation is not performed, there is a significant deviation between the rendering and the actual image. After the mask design is corrected using the tolerance compensation value calibrated with the minimum error, the rendering 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 solved by the present invention. It should be understood that the above are only 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 in the scope of protection of the present invention.

Claims

1. A mask calibration method for mask defect detection, characterized in that: The steps of the method include: S1: Select a selected area with a pattern in the mask design file and output vector points of the selected area; S2: Using AOI inspection equipment to collect the actual image corresponding to the selected area on the corresponding physical mask; S3: setting a horizontal tolerance range in the horizontal direction of the selected area, and proceeding to step S31: using different horizontal tolerance compensation values ​​within the horizontal tolerance range to fill and render the vector points outputted in step S1 into first rendered images; registering the first rendered images obtained by rendering with different horizontal tolerance compensation values ​​with the actual image, and using an evaluation method to evaluate the effects of different horizontal tolerance compensation values ​​on the fit between the first rendered image and the actual image, and using the horizontal tolerance compensation value with the best evaluation result as the tolerance for horizontal calibration; S4: Set a vertical tolerance range in the vertical direction of the selected area, and proceed to 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; align the second rendered images rendered with different vertical tolerance compensation values ​​with the actual image respectively, and use an evaluation method to evaluate the impact 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 for vertical calibration.

2. The method according to claim 1, characterized in that In step S3, the horizontal tolerance compensation value with the best evaluation result is used as the horizontal tolerance range for secondary evaluation and calibration, and step S31 is repeated to obtain the final calibrated 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 used as the vertical tolerance range for secondary evaluation and calibration, and step S41 is repeated to obtain the final calibrated tolerance in the vertical direction.

4. The method according to claim 1, wherein In step S3, the evaluation method is specifically as follows: Obtaining vertical edges between the first rendered image and the actual image; Demarcate vertical edge areas based on vertical edges; Obtaining a first difference value sum between the first rendered image and the actual image in the vertical edge region, that is, a first error sum; The first difference value and the minimum are used as evaluation targets to obtain a corresponding horizontal tolerance compensation value as the tolerance for horizontal direction calibration.

5. The method according to claim 4, characterized in that The vertical edges of the first rendered image and the actual image are obtained by performing horizontal differentiation on the first rendered image or the actual image, setting a horizontal differentiation threshold, and marking a region where the horizontal differentiation is greater than the horizontal differentiation threshold and the vertical differentiation is close to 0 as a vertical edge; The vertical edge region is calibrated based on the vertical edge and specifically includes setting a set pixel range adjacent to the vertical edge as the vertical edge region.

6. The method according to claim 4, characterized in that A fitting method is used between the horizontal tolerance compensation value and the first difference value sum to obtain the minimum first difference value sum.

7. The method according to claim 1, characterized in that In step S4, the evaluation method is specifically as follows: Obtaining horizontal edges of the second rendered image and the actual image; Horizontal edge area is calibrated based on horizontal edge; Obtaining a second difference value sum between the second rendered image and the actual image in the horizontal edge region, that is, a second error sum; The corresponding vertical tolerance compensation value obtained by taking the second difference value and the minimum as evaluation targets is used as the tolerance for vertical direction calibration.

8. The method according to claim 6, characterized in that The horizontal edges of the second rendered image and the actual image are obtained by vertically differentiating the second rendered image or the actual image, setting a vertical differential threshold, and marking a region where the vertical differential is greater than the vertical differential threshold and the horizontal differential is close to 0 as a horizontal edge. The horizontal edge region is calibrated based on the horizontal edge and specifically includes setting a set pixel range adjacent to the horizontal edge as the horizontal edge region.

9. The method according to claim 7, characterized in that A fitting method is used between the vertical tolerance compensation value and the second differential value to obtain the minimum second differential value.

10. The method according to claim 1, characterized in that The specific steps of rendering in step S3 and step S4 include: Scale the graphics corresponding to the vector points according to the tolerance compensation value; Then fill the sub-pixel edges of the corrected vector points; The filled graphics are modified using the selected model to obtain the corresponding rendered image.

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