OCD measurement method based on 3D imaging processing

Abstract

The invention provides an OCD measurement method based on 3D imaging processing. The method comprises the following steps: acquiring image data of a three-dimensional target when a microscope is at different heights; and calculating the critical dimension of the cross section according to the image data. The method has the key points that (1) a mode of constructing a 3D model by 2D imaging at different heights is adopted; (2) summarizing an interested key size by utilizing a relationship between the height and a plane result; and (3) carrying out preprocessing such as verification and abnormal and interference checking on the data, and calculating a measurement result by utilizing characteristics of a parabola. The method has the advantages that through verification of a large amount of data, the method is good in stability and accuracy and high in CDSEM matching degree.

Description

Technical Field

[0001] The present invention relates to the technical field of wafers and display panels, and in particular to an OCD measurement method based on 3D imaging processing. Background Art

[0002] In the complex process environment of multi-layer stacking, 3D shapes, and film thickness changes, the following problems arise when using plane measurement methods:

[0003] (1) Plane microscopes cannot stably focus on a certain layer or position;

[0004] (2) At sensitive locations, the focus height changes slightly, resulting in large differences in measured values;

[0005] (3) It is impossible to determine at which height the measurement is correct.

[0006] The above problems mean that the 2D measurement method has poor measurement stability in 3D environments, its accuracy cannot be guaranteed, and its credibility is not high.

[0007] With the development of the semiconductor and display panel industries, the manufacturing process of integrated circuits has been continuously improved and the process has become more and more complex. In order to ensure the quality of the process, precise monitoring is required during the manufacturing process to control the quality and adjust the process in time. OCD (optical critical dimension) measurement is a very important monitoring method. For processes with thinner or flat films, 2D measurement methods are generally used. For processes with thicker films or 3D shapes, 2D measurement methods cannot meet the needs and cannot be measured accurately and stably, so more accurate, stable and efficient 3D measurement methods are needed to deal with it.

[0008] Existing 3D measurement technologies generally include: (1) multi-dimensional acquisition, which requires the integration of multiple collectors, data acquisition at different angles and orientations, and complex fitting of the data. Only when a complete three-dimensional stereo image is constructed can OCD be measured. This method has a complex structure and is cumbersome. (2) SEM REVIEW machines can perform relatively accurate measurements, but generally only support the measurement requirements of one shot and one point, and cannot perform large-scale measurements. In addition, the measurement speed is relatively slow. This method needs to be improved before it can be applied, and it is also relatively expensive.

[0009] Existing 2D and 3D measurement methods have the following disadvantages when facing practical engineering problems:

[0010] 1. In the complex 3D process environment with multi-layer stacking, 3D shapes, and film thickness changes, the 2D measurement method cannot accurately measure OCD and has poor stability;

[0011] 2. The existing 3D measurement solutions have complex structures, complicated methods and high costs;

[0012] 3. Among the existing 3D measurement methods, the measurement speed is relatively slow, and the measurement precision, accuracy and stability need to be verified. Summary of the invention

[0013] The purpose of the present invention is to use 3D technology to simply, cost-effectively and efficiently solve the problem of unstable and unreliable measurement results faced by existing 2D measurement methods in a 3D measurement environment, while being able to accurately and stably measure the OCD of a 3D model.

[0014] The key points of the present invention are: (1) constructing a 3D model using 2D imaging at different heights;

[0015] (2) Use the relationship between height and plane results to summarize the key dimensions of interest; (3) Perform preprocessing such as verifying and troubleshooting the data, and use the characteristics of the parabola to obtain the measurement results.

[0016] Specifically, the present invention provides an OCD measurement method based on 3D imaging processing, comprising:

[0017] Collect image data of three-dimensional targets when the microscope is at different heights;

[0018] A cross-sectional critical dimension is calculated based on the image data.

[0019] Furthermore, the collecting of image data of a three-dimensional target at different heights of the microscope includes:

[0020] Moving the microscope to a measurement point corresponding to the focus of the three-dimensional target;

[0021] Starting from the measurement point, the microscope is moved upward in the vertical direction by n steps in sequence, one step at a time, and the step length is m; wherein n is a positive integer greater than or equal to 1, and after each step length movement, the microscope collects a picture and calculates the critical dimension of the 2D process after the step length movement;

[0022] Starting from the measurement point, the microscope is moved downward in the vertical direction by n steps, one step at a time, and the step length is m; wherein n is a positive integer greater than or equal to 1, and after each step length movement, the microscope collects a picture and calculates the critical dimension of the 2D process after the step length movement;

[0023] The key size results are recorded into an array in order from top to bottom, and the pictures are stored in order as image data.

[0024] Further, the critical dimension is the distance between two parallel edge lines;

[0025] The calculation of the critical dimension of the 2D process after the step size is moved includes:

[0026] Two parallel rectangular frames are pre-set as the positions of interest, and the respective edges of interest are found in the frames according to the direction, and the distance between the two edges is used as the critical dimension of the 2D process after the step size is moved.

[0027] Further, the calculating the cross-sectional critical dimension according to the image data comprises:

[0028] Data preprocessing: Compare the measurement results of three adjacent heights, check according to the linear proportional relationship and size relationship, and exclude mutations and abnormal data;

[0029] According to the preprocessed image data, the vertex of the parabola is obtained, and the value of the vertex is output;

[0030] According to the values ​​of the vertices, a 3D measurement result is calculated.

[0031] Furthermore, the data preprocessing includes:

[0032] In the measurement result data of the three adjacent heights, if the size of the middle data is between the two end data, and the two differences between the middle data and the two end data are similar, it is judged as normal data, otherwise it is judged as an abnormal value;

[0033] The outliers are removed and replaced by linear interpolation.

[0034] Further, obtaining the vertex of the parabola and outputting the value of the vertex includes:

[0035] Extract the coordinates (Xmax, Ymax) corresponding to the maximum value Ymax, and the coordinates of the two data adjacent to its left and right in height (Xmax-1, Ymax-1) and (Xmax+1, Ymax+1), and use these three points to find the vertex of the parabola;

[0036] According to the general formula of parabola, find the vertex of the parabola;

[0037] According to the parabola formula y = ax 2 +bx+c, substitute the three data into the formula as the solution:

[0038]

[0039] Combine to obtain a, b, c;

[0040] According to the parabola vertex formula, the vertex value is obtained: Ymax = (4ac-b 2 ) / 4a.

[0041] Furthermore, the calculating and obtaining the 3D measurement result according to the vertex value includes:

[0042] Search the image data according to the serial number corresponding to the value of the vertex to obtain the image corresponding to the serial number;

[0043] The key dimension corresponding to the picture corresponding to the serial number is taken as the OCD measurement result.

[0044] The advantages of the present invention are that the present invention can use 3D technology to simply, cost-effectively and efficiently solve the problem of unstable and unreliable measurement results faced by existing 2D measurement methods in 3D measurement environments, and can accurately and stably measure the OCD of 3D models. Through a large amount of data verification, the method has good stability and accuracy, and has a high degree of matching with CDSEM. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0046] Figure 1 A schematic diagram of a hemispherical model according to an embodiment of the present invention is shown.

[0047] Figure 2 A schematic diagram of data collection scenarios at different heights according to an embodiment of the present invention is shown.

[0048] Figure 3 A flow chart of a method for collecting data at different heights according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0049] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0050] The process with thicker coating or higher height presents 3D shape. The width of the measured cross section is directly related to the focus position, and the width at different heights varies greatly. Figure 1 As shown, the hemisphere model and data relationship involved in the present invention, the measurement target is the bottom width of the hemisphere, the microscope field of view sees the cross-section of the hemisphere at different heights, and the plane measurement of the bottom width of the hemisphere is different at different heights, so it is impossible to obtain the accurate bottom width information of the hemisphere through a single focusing plane.

[0051] This method proposes a data processing method based on 3D imaging to solve this problem. By collecting images at different heights and calculating the key dimensions of the cross section, the focus position is O, and the Z axis moves in the offset range of ±n. The relationship between imaging, plane measurement results and height exists:

[0052] 1) When the Z axis is near -n\+n, the image is in a fuzzy or slightly fuzzy state, and the plane measurement result increases rapidly (large change)\the plane measurement result increases slowly (small change);

[0053] 2) When the Z axis is near O, the image is clear and relatively clear, and the plane measurement result is at or near the maximum value (changing slowly);

[0054] 3) When the Z axis is near +n\-n, the image is slightly blurred or fuzzy, and the plane measurement result decreases slowly (small change)\the plane measurement result decreases quickly (large change).

[0055] Through the above data collection and analysis of different focal planes, it is concluded that the maximum value when the Z axis is near O approaches the accurate width. The top of the data conforms to the parabola law. According to the characteristics of the parabola, the maximum value of the vertex is obtained, which is the bottom width of the hemisphere. The specific process is as follows:

[0056] 1. Data collection at different heights:

[0057] The wafer is placed on the stage, and the microscope moves to the measurement point corresponding to the shot. The model is as follows Figure 2 As shown; starting from the focus position or near O, move up\down the Z axis n steps, the step length is m; move the z axis from top to bottom from -n to +n\from bottom to top from +n to -n, take a picture and measure the plane for each step, the step length is m, move 2n steps in total, and obtain 2n images and data; as shown Figure 3 As shown in the figure, the specific steps of microscope acquisition and data collection at different focal planes are as follows:

[0058] (1) The microscope initially focuses on the object to be measured;

[0059] (2) The microscope Z axis moves Offset = n to the position n\-n;

[0060] (3) Collect one image using a microscope;

[0061] (4) calculating the critical dimensions of the 2D process under the focal plane;

[0062] (5) Record the key size results in an array in order and store the graph in order;

[0063] (6) Move the Z axis 1 step, with a step length of m;

[0064] (7) Repeat (3) to (6) until the cumulative number of steps is 2n;

[0065] (8) End the process.

[0066] In 2D key dimension measurement, CD is the distance between two parallel edge lines. Two parallel rectangular frames (positions of interest) are pre-set, and the respective edges of interest are found in the frames according to the direction. The distance between the two edges is the plane line width CD. 2D key dimension measurement also includes other methods. This 3D imaging processing method is used on the basis of plane key dimension measurement, and its usage is the same as line width CD.

[0067] Through the above imaging process, a key size data array of length 2n is obtained. The data is processed and the results are calculated as follows.

[0068] 2. Data processing and calculation results:

[0069] (1) Data preprocessing

[0070] Compare the measurement results of three adjacent heights. If Yn-1, Yn, and Yn+1 differ greatly, check them according to the linear proportional relationship and size relationship to prevent sudden changes and abnormal interference.

[0071] 1) The intermediate data Yn should conform to a similar linear comparison relationship and its size should be between the two end data Yn-1 and Yn+1;

[0072] 2) The terminal Yn-1 (Yn+1) data should conform to the linear proportional relationship, and the size should continue the size relationship of the previous two data Yn and Yn+1 (Yn-1 and Yn);

[0073] 3) Remove outliers and use linear interpolation to replace them.

[0074] (2) Find the vertex of the parabola and output the result

[0075] 1) Extract the coordinates (Xmax, Ymax) corresponding to the maximum value Ymax, and the coordinates of the two data adjacent to its left and right in height (Xmax-1, Ymax-1) and (Xmax+1, Ymax+1). At this time, the maximum value is not necessarily the vertex of the parabola. Use these three points to find the vertex of the parabola.

[0076] 2) According to the general formula of parabola, find the vertex of the parabola;

[0077] According to the parabola formula y = ax 2 +bx+c, substitute the above three solutions into the formula:

[0078]

[0079] Combine and find a, b, and c.

[0080] According to the parabola vertex formula, the vertex is obtained: Ymax = (4ac-b 2 ) / 4a.

[0081] (3) 3D measurement result output

[0082] The vertex value Ymax is calculated from the above, that is, the measurement result of the model: Ymax.

[0083] From the above calculation, the Ymax sequence number is the max group of data, that is, the output image is the max-th image, and the key size corresponding to the image is the key size of the measurement target.

[0084] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. An OCD measurement method based on 3D imaging processing, characterized in that: include: Collect image data of three-dimensional targets when the microscope is at different heights; A cross-sectional critical dimension of the three-dimensional object is calculated based on the image data.

2. The OCD measurement method based on 3D imaging processing according to claim 1, characterized in that: The collecting of image data of a three-dimensional target under a microscope at different heights includes: Moving the microscope to a measurement point corresponding to the focus of the three-dimensional target; Starting from the measurement point, the microscope is moved upward in the vertical direction by n steps in sequence, one step at a time; wherein n is a positive integer greater than or equal to 1, and after each step, the microscope collects a picture and calculates the critical dimension of the 2D process after the step; Starting from the measurement point, the microscope is moved downward in the vertical direction by n steps in sequence, one step at a time; wherein n is a positive integer greater than or equal to 1, and after each step, the microscope collects a picture and calculates the critical dimension of the 2D process after the step; The key size results are recorded into an array in order from top to bottom in the vertical direction, and the pictures are stored in order as image data.

3. The OCD measurement method based on 3D imaging processing according to claim 2, characterized in that: The critical dimension is the distance between two parallel edge lines of the three-dimensional object; The calculation of the critical dimension of the 2D process after the step size is moved includes: Two parallel rectangular frames are pre-set as positions of interest, and the respective edges of interest are found in the frames according to the direction, and the distance between the two parallel edges is used as the critical dimension of the 2D process after the step size is moved.

4. The OCD measurement method based on 3D imaging processing according to claim 2 or 3, characterized in that: The step of calculating the critical cross-sectional dimensions of the three-dimensional object according to the image data comprises: Data preprocessing: compare the measurement results of three adjacent heights, check according to the linear proportional relationship and size relationship, and exclude mutations and abnormal data; According to the preprocessed image data, the vertex of the parabola is obtained, and the value of the vertex is output; According to the values ​​of the vertices, a 3D measurement result is calculated.

5. The OCD measurement method based on 3D imaging processing according to claim 4, characterized in that: The data preprocessing includes: In the measurement result data of the three adjacent heights, if the size of the middle data is between the two end data, and the two differences between the middle data and the two end data are similar, it is judged as normal data, otherwise it is judged as an abnormal value; The outliers are removed and replaced by linear interpolation.

6. The OCD measurement method based on 3D imaging processing according to claim 4, characterized in that: The step of obtaining the vertex of the parabola and outputting the value of the vertex includes: Extract the coordinates (Xmax, Ymax) corresponding to the maximum value Ymax of the key size, and the coordinates of the two data adjacent to its left and right heights (Xmax-1, Ymax-1) and (Xmax+1, Ymax+1); According to the parabola formula y = ax 2 +bx+c, substitute the three data into the formula as the solution: Combine to obtain a, b, c; According to the parabola vertex formula, the vertex value is obtained: Ymax = (4ac-b 2 ) / 4a.

7. The OCD measurement method based on 3D imaging processing according to claim 5 or 6, characterized in that: The step of calculating the 3D measurement result according to the vertex value includes: Search the image data according to the serial number corresponding to the value of the vertex to obtain the image corresponding to the serial number; The key dimension corresponding to the picture corresponding to the serial number is taken as the OCD measurement result.

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