Prediction method and device of three-dimensional mask pattern, equipment, medium and product

Abstract

The invention discloses a prediction method and device of a three-dimensional mask pattern, equipment, a medium and a product, and is applied to the technical field of photoetching. According to the method, when a three-dimensional mask pattern is calculated, a two-dimensional mask pattern is determined firstly, then target normal lines corresponding to target pixel points are formed based on projection components in a standard direction, and then a total diffraction field and a two-dimensional diffraction field after a target light source passes through a mask are determined. According to the scheme provided by the embodiment of the invention, the target normal in any direction from 0 degree to 360 degrees can be continuously represented by taking each standard direction as the projection component for combination, so that the effect of an actual curve is approximately obtained. Visibly, the curve mask is adopted, the curve mask can be better close to physical reality and has higher optimization freedom degree, so that the curve mask has a larger process window and smaller pattern distortion compared with a Manhattan mask, and the curve mask can also remarkably reduce the process change bandwidth in the mask manufacturing stage.

Description

Technical Field

[0001] The present application belongs to the field of photolithography technology, and in particular, relates to a method, device, equipment, medium and product for predicting three-dimensional mask graphics. Background Art

[0002] Optical Proximity Correction (OPC) is a photolithography resolution enhancement technology. OPC is mainly used in the production process of semiconductor devices. In the photolithography process, the pattern on the mask is projected onto the photoresist through the exposure system. Due to the imperfections of the optical system and the diffraction effect, the pattern on the photoresist is not completely consistent with the pattern on the mask. If these distortions are not corrected, they may greatly change the electrical performance of the produced circuit. OPC uses a calculation method to correct the pattern on the mask so that the pattern projected onto the photoresist meets the design requirements as much as possible. The mask model used in the current reverse lithography technology is mostly the Manhattan mask model, which predicts the actual pattern projected on the photoresist based on the original pattern on the mask, and then corrects the original pattern on the mask according to the required pattern.

[0003] However, the graphics predicted by the Manhattan mask model are composed of horizontal and vertical straight lines. When the mask is actually manufactured, the edges of the graphics formed by electron beam irradiation are not straight, but curved. This causes a deviation between the actual mask and the mask graphics in the OPC calculation. The graphics predicted by the model will be inaccurate, resulting in the obtained mask graphics being projected onto the photoresist, and the graphics on the photoresist not meeting actual requirements. Summary of the invention

[0004] The embodiments of the present application provide a method, device, equipment, medium and product for predicting three-dimensional mask graphics, which can improve the accuracy of graphic prediction so that the graphics on the photoresist can meet actual needs.

[0005] On the one hand, an embodiment of the present application provides a method for predicting a three-dimensional mask pattern, comprising:

[0006] Acquire a contour point set; each contour point in the contour point set is used to form the contour of the original graphic on the mask;

[0007] Based on the contour point set, determining a two-dimensional mask pattern after the target light source passes through the mask;

[0008] Acquire a plurality of target pixel points in a preset area around the original graphic;

[0009] Determining a target normal between the target pixel point and the original graphic;

[0010] Based on the projection components of the target normal line in each standard direction, determining the total diffraction field and the two-dimensional diffraction field of the target light source after passing through the mask; each of the standard directions can form the target normal line in any direction;

[0011] Determining a three-dimensional diffraction field according to a comparison result of the total diffraction field and the two-dimensional diffraction field;

[0012] The contour in the two-dimensional mask pattern is corrected based on the three-dimensional diffraction field to predict the three-dimensional mask pattern.

[0013] On the other hand, there are four standard directions, and two adjacent standard directions differ by 90°.

[0014] On the other hand, determining the two-dimensional mask pattern after the target light source passes through the mask based on the contour point set includes:

[0015] Based on each of the contour points in the contour point set, forming a curve mask binary graphic;

[0016] Based on the curved mask binary pattern, the two-dimensional mask pattern is determined.

[0017] On the other hand, forming a curve mask binary graphic based on each of the contour points in the contour point set includes:

[0018] Generate a quadratic B-spline curve using every three adjacent contour points;

[0019] Based on each of the quadratic B-spline curves, the curve mask binary graphic is formed.

[0020] On the other hand, determining the two-dimensional mask pattern based on the curved mask binary pattern includes:

[0021] Transforming the curve mask binary image into a frequency domain space to obtain first frequency domain data;

[0022] Removing high-frequency information from the first frequency domain data to obtain second frequency domain data;

[0023] The second frequency domain data is transformed back to the real domain space to obtain the two-dimensional mask pattern.

[0024] On the other hand, determining the two-dimensional mask pattern after the target light source passes through the mask based on the contour point set includes:

[0025] Based on each of the contour points in the contour point set, forming a horizontal and a vertical target straight line;

[0026] The two-dimensional mask pattern is constructed based on each of the target straight lines.

[0027] On the other hand, after correcting the contour in the two-dimensional mask pattern based on the three-dimensional diffraction field and predicting the three-dimensional mask pattern, the method further includes:

[0028] Based on the three-dimensional mask pattern and the actually required target pattern, the number and / or position of each of the contour points in the contour point set are adjusted to make the three-dimensional mask pattern approach the target pattern.

[0029] In another aspect, an embodiment of the present application provides a three-dimensional mask pattern prediction device, comprising: a processor and a memory storing computer program instructions;

[0030] When the processor executes the computer program instructions, the three-dimensional mask pattern prediction method as described above is implemented.

[0031] On the other hand, an embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the three-dimensional mask graphic prediction method as described above is implemented.

[0032] On the other hand, an embodiment of the present application provides a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the three-dimensional mask graphic prediction method as described above.

[0033] The embodiment of the present application provides a method for predicting a three-dimensional mask pattern. When calculating the three-dimensional mask pattern, the two-dimensional mask pattern is first determined, and then the target normal corresponding to each target pixel point is composed based on the projection component of the standard direction, and then the total diffraction field and the two-dimensional diffraction field of the target light source after passing through the mask are determined. The scheme proposed in the embodiment of the present application can continuously characterize the target normal in any direction from 0° to 360° by combining each standard direction as a projection component, thereby approximating the effect of the actual curve. It can be seen that the present application adopts a curved mask, which can be closer to physical reality and has a higher degree of optimization freedom, so that it has a larger process window and smaller graphic distortion than the Manhattan mask. The curved mask can also significantly reduce the process variation bandwidth in the mask manufacturing stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0035] Figure 1 A schematic diagram showing a flow chart of a method for predicting a three-dimensional mask pattern provided by an embodiment of the present application;

[0036] Figure 2 A schematic diagram showing a standard direction provided by an embodiment of the present application;

[0037] Figure 3 A schematic diagram of a target normal provided by an embodiment of the present application is shown;

[0038] Figure 4 A schematic diagram showing correction values ​​of pixels near a curve boundary under a three-dimensional thick mask effect provided by an embodiment of the present application is shown;

[0039] Figure 5 A schematic diagram of a curved mask binary graphic provided by an embodiment of the present application is shown;

[0040] Figure 6 A schematic diagram of a quadratic B-spline curve provided by an embodiment of the present application is shown;

[0041] Figure 7 A schematic diagram of the structure of a three-dimensional mask pattern prediction device provided in an embodiment of the present application is shown;

[0042] Figure 8 A schematic diagram of the hardware structure of a three-dimensional mask pattern prediction device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0043] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0044] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "include..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0045] There are currently two technologies used for mask patterns: one is the Manhattan mask that uses jagged edges to simulate curved boundaries, and the other is the curved mask that uses real curves. Curved masks are more suitable for OPC and will provide better process windows, process variation bandwidth, and edge placement errors.

[0046] Since the graphics in the Manhattan mask model are composed of horizontal and vertical straight lines, they do not match the graphics of actual production. If a curved mask model is used in the OPC calculation, it can be closer to physical reality and have a higher degree of optimization freedom, so that it can have a larger process window and smaller graphic distortion than the Manhattan mask. The curved mask can also significantly reduce the process variation bandwidth in the mask manufacturing stage.

[0047] However, the current curved mask model usually uses high-order Bezier curves to construct graphics. The curve parameter equation is relatively complex, and the model takes a long time to calculate the mask graphics. It cannot be used for OPC calculation of the entire chip layout, and the first-order derivative discontinuity at the connection point between the curves also has the problem of sharp corners. For advanced processes, the impact of the thick mask effect is becoming more and more obvious, but the current curved mask model does not consider the thick mask effect compared to the Manhattan mask model. Therefore, the curved mask model without the thick mask effect is still difficult to use in advanced processes. In order for the curved mask model to be truly used in advanced processes, it is necessary to add the thick mask effect to the curved mask model, correct the deviation caused by the thick mask diffraction effect, and improve the process window.

[0048] In order to solve the problems of the traditional solution, the embodiment of the present application provides a method, device, equipment, medium and product for predicting a three-dimensional mask pattern. The following first introduces the method for predicting a three-dimensional mask pattern provided by the embodiment of the present application. Figure 1 FIG. 1 is a flow chart of a method for predicting a three-dimensional mask pattern provided by an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0049] S101: Obtain a set of contour points.

[0050] Each contour point in the contour point set here is used to form the contour of the original graphic on the mask. Specifically, the graphic to be calculated and the corresponding contour point set can be read from the Graphic Data System (GDS) file. i}.

[0051] After obtaining the set of contour points, a quadratic B-spline curve can be used to construct a curve mask model. Every three contour points constitute a curve, and the three contour points of adjacent curves will share two of the contour points. For example, the contour points of the first curve segment are P0P1P2, and the contour points of the adjacent second curve segment are P1P2P3, and P1P2 is a shared contour point.

[0052] S102: Based on the contour point set, determine a two-dimensional mask pattern after the target light source passes through the mask.

[0053] After obtaining the contour point set, it is necessary to determine the two-dimensional mask pattern after the target light source passes through the mask, so that after obtaining the three-dimensional diffraction field in the subsequent step, it is superimposed with the two-dimensional mask pattern to obtain a three-dimensional mask pattern with a thick mask effect.

[0054] S103: Acquire a plurality of target pixel points in a preset area around the original graphic.

[0055] The specific range of the preset area is not limited, and is generally an area near the original graphic, and the outline of the original graphic can be represented by the information of the corresponding pixel points.

[0056] S104: Determine a target normal line between the target pixel point and the original graphic.

[0057] In practical applications, assuming that a target pixel point is taken in a preset area around the original graphic, the normal between the target pixel point and the original graphic is the target normal. The target normal is then projected to each standard direction, and the target normal can be characterized according to the direction and length of the projection.

[0058] When setting the standard direction, you can first determine a polygon, and each side of the polygon is used as a standard boundary. The normal direction of each standard boundary is the standard direction. This method can ensure that the vectors in each standard direction can form a target normal in any direction. For example, the four sides of a rectangle can be used as standard boundaries, and the normal directions of these four sides are the standard directions; then take a length of 1 unit in these standard directions as the vectors in the corresponding directions, and then based on the mutual combination of these vectors, you can get a target normal in any direction.

[0059] S105: Based on the projection components of the target normal line in each standard direction, determine the total diffraction field and the two-dimensional diffraction field of the target light source after passing through the mask.

[0060] When determining the total diffraction field and the two-dimensional diffraction field, the outline of the original image is determined based on the projection of the target normal in each standard direction. The target normal is the normal between the target pixel point and the original image, and the target pixel point is the pixel point in a preset area around the original image.

[0061] In the above way, the target normals corresponding to the target pixels around the original figure are represented respectively, and then the outline of the original figure is depicted. And because each standard direction can form a target normal in any direction, if there is a curve in the original figure, the curve outline can be represented by the projection length of the target pixel points near the curve in each standard direction.

[0062] After obtaining the projection length of all target pixels in each standard direction, the outline of the original image is obtained, and then based on the attribute parameters of the mask, the corresponding total diffraction field and two-dimensional diffraction field can be determined. For a three-dimensional thick mask, thick mask modeling is required to obtain the material parameters of each layer, the mask etching thickness, and the etching edge inclination and other attribute parameters. The diffraction field of the target light source after passing through the thick mask is obtained by using the Finite-Difference Time-Domain (FDTD), Rigorous Coupled Wave Analysis (RCWA) or other electromagnetic field simulation methods to obtain a strict numerical solution, that is, the diffraction field of a specific target light source after passing through a thick mask with four standard directions is obtained.

[0063] Here we explain the thick mask effect. The light shielding layer on the mask and the quartz glass substrate together form the three-dimensional structure of the mask. Under the two-dimensional Kirchhoff approximation, the intensity and phase of the diffraction order do not change with the period of the line, but after considering the three-dimensional effect of the mask, as the period of the line decreases, that is, the line width of the pattern is smaller and denser, the diffraction efficiency decreases and the phase deviation increases.

[0064] Due to the need for resolution, the size of the pattern on the phase-shift mask is small and the fluctuation is large, so the reduction of diffraction efficiency and phase deviation will be more obvious than that of the bipolar mask. This diffraction behavior changes with the size of the pattern, which will lead to a narrowing of the photolithography process window. The three-dimensional structure of the mask also includes non-vertical side walls produced by etching, deviations in line width between dense lines and independent lines, and shadow effects caused by oblique incident light on the mask.

[0065] The above parameters that produce the thick mask effect are the attribute parameters of the mask. The total diffraction field and the two-dimensional diffraction field are determined by the attribute parameters, which can add the thick mask effect to the curved mask model, correct the deviation caused by the thick mask diffraction effect, and improve the process window.

[0066] S106: Determine a three-dimensional diffraction field according to a comparison result of the total diffraction field and the two-dimensional diffraction field.

[0067] The total diffraction field contains both two-dimensional information and three-dimensional information, and the two-dimensional diffraction field only contains two-dimensional information. Therefore, by removing the two-dimensional diffraction field from the total diffraction field, a three-dimensional diffraction field (i.e., correction value) containing only three-dimensional information can be obtained. The three-dimensional diffraction field is the change in the pattern when the target light source passes through the mask, and the specific change is determined by the attribute parameters of the mask.

[0068] As mentioned above, the shape of the graphic is characterized by normal projections in four standard directions, and the final three-dimensional diffraction field is the correction value corresponding to the normal projections in the four standard directions under the thick mask condition.

[0069] S107: Correcting the contour of the two-dimensional mask pattern based on the three-dimensional diffraction field to predict the three-dimensional mask pattern.

[0070] Finally, based on the contour of the two-dimensional mask pattern obtained in the three-dimensional diffraction field correction, the final predicted three-dimensional mask pattern is obtained.

[0071] This embodiment proposes a method for calculating a curved mask model with a thick mask effect. The curved mask model can be constructed based on a quadratic B-spline curve. The curve parameter equation is simple and convenient for OPC calculation. At the same time, the first-order derivatives at the curve connection points are continuous. Compared with the Manhattan mask model, there will be no sharp corners. The mask contour is close to the actual mask contour, with a larger process window and smaller graphic distortion, which significantly reduces the process change bandwidth in the mask manufacturing stage. At the same time, the deviation caused by the thick mask diffraction effect is corrected to increase the process window.

[0072] The embodiment of the present application uses a rigorous simulation method to construct an approximate thick mask effect in the form of a curve, which can make up for the lack of the existing curve model in the thick mask effect and allow the curve mask model to be truly applied to more advanced nodes. By simulating the diffraction field in four standard directions and then approximating the diffraction field pointing to any boundary, the time used for rigorous simulation can be greatly reduced.

[0073] The embodiment of the present application provides a method for predicting a three-dimensional mask pattern. When calculating the three-dimensional mask pattern, the two-dimensional mask pattern is first determined, and then the target normal corresponding to each target pixel point is composed based on the projection component of the standard direction, and then the total diffraction field and the two-dimensional diffraction field of the target light source after passing through the mask are determined. The scheme proposed in the embodiment of the present application can continuously characterize the target normal in any direction from 0° to 360° by combining each standard direction as a projection component, thereby approximating the effect of the actual curve. It can be seen that the present application adopts a curved mask, which can be closer to physical reality and has a higher degree of optimization freedom, so that it has a larger process window and smaller graphic distortion than the Manhattan mask. The curved mask can also significantly reduce the process variation bandwidth in the mask manufacturing stage.

[0074] The above embodiment does not limit the number and specific directions of the standard directions. If the number of standard directions is too small, the graphics cannot be well represented; if the number of standard directions is too large, the calculation efficiency is low. In addition, selecting the standard directions of appropriate directions can further improve the calculation efficiency.

[0075] Here, a specific implementation method is provided, in which there are four standard directions, and the difference between two adjacent standard directions is 90°. Through the projections in each standard direction of the embodiment of the present application, a target normal line in any direction can be formed.

[0076] Figure 2 A schematic diagram showing the standard direction provided by the embodiment of the present application is shown; Figure 2 As shown, when setting the standard direction, a rectangle is first determined, and each side of the rectangle is used as a standard boundary; they are the first standard boundary 201, the second standard boundary 202, the third standard boundary 203 and the fourth standard boundary 204; the normal direction corresponding to each standard boundary is the standard direction, namely the first standard direction 205, the second standard direction 206, the third standard direction 207 and the fourth standard direction 208. It can be seen intuitively from the figure that the difference between two adjacent standard directions is 90°.

[0077] A length of 1 unit can be taken in the above four standard directions as the vector in the corresponding direction. The first standard direction 205 corresponds to the first vector The second standard direction 206 corresponds to the second vector The third standard direction 207 corresponds to the third vector The fourth standard direction 208 corresponds to the fourth vector Based on the mutual combination of the vectors in these four directions, the target normal in any direction can be obtained.

[0078] Figure 3 Schematic diagram of target normal provided by an embodiment of the present application is shown; Figure 3 As shown, the original graphic 301, the target pixel point 302 in the preset area around the original graphic 301, and the target normal 303 (i.e., the vector ); Assume that Figure 2 The standard direction scheme shown represents the target normal 303 in this example, then Correction value f=f(a, b, c, d), the target normal line 303 can be obtained by taking corresponding values ​​of a, b, c, and d.

[0079] Assuming that the first standard direction 205 is 0°, the four standard directions are 0°, 90°, 180° and 270° respectively. Since the boundary normal direction of the curve figure can continuously transition from 0° to 360°, the minimum distance and intersection point from the target pixel point near the curve boundary to the curve are calculated. The line connecting the intersection point and the pixel point is the target normal line of the curve. The correction value of the target pixel point is calculated based on the projection of the target normal line to the four standard pointing directions and the corresponding three-dimensional diffraction field, and finally a three-dimensional mask image with a thick mask effect is obtained.

[0080] Figure 4 FIG. 1 shows a schematic diagram of correction values ​​of pixels near the boundary of a curve under a three-dimensional thick mask effect provided by an embodiment of the present application; Figure 4 As shown, the boundary distance is in the range of -200 to 200 nm, and the correction value fluctuates greatly.

[0081] Unlike the Manhattan mask, the curve mask cannot split the graphics into four directional edges, so the present application characterizes the graphics by normal projection in standard directions. However, considering the limitation of strict simulation solution speed, the present application embodiment only simulates the diffraction field results of four boundaries. The boundary of the curve mask is projected to the four standard directions of up, down, left and right, and the three-dimensional diffraction field of the pixel points near the curve boundary is calculated by comparison with the two-dimensional diffraction field, thereby obtaining a three-dimensional mask image with a thick mask effect.

[0082] The embodiment of the present application uses a strict simulation method to simulate the actual diffraction effect of the thick mask. Through the four standard directions with normal directions that differ by 90°, the diffraction field of the boundary in any direction can be approximated. And because only the diffraction field in four standard directions is simulated, a faster simulation speed can be obtained, so that both accuracy and speed can meet the requirements.

[0083] In practical applications, before calculating the final three-dimensional mask pattern, it is necessary to first calculate the two-dimensional mask pattern approximated by the two-dimensional thin mask. This application provides a specific implementation scheme. Figure 5 FIG. 1 is a schematic diagram of a curve mask binary pattern provided by an embodiment of the present application. Figure 5 As shown, in the process of determining the two-dimensional mask pattern after the target light source passes through the mask based on the contour point set, a curve contour is first generated based on each contour point in the contour point set to form a curve mask binary pattern. Then, the two-dimensional mask pattern is determined based on the curve mask binary pattern.

[0084] Figure 6 FIG. 1 is a schematic diagram of a quadratic B-spline curve provided by an embodiment of the present application. Figure 6 As shown, the contour point set {P iEach three adjacent contour points in} generate a quadratic B-spline curve, thereby forming a curve mask binary graph based on each quadratic B-spline curve. Each three contour points constitute a quadratic B-spline curve, and the three contour points of the adjacent quadratic B-spline curves share two of the contour points. For example, the contour points of the first curve 1 are P0P1P2, and the contour points of the adjacent second curve 2 are P1P2P3, and P1P2 is a shared contour point.

[0085] The embodiment of the present application uses a quadratic B-spline curve to construct a curve mask model, and its x, y component equation form is x(t)=a0+a1t+a2t 2 ,y(t)=b0+b1t+b2t 2 , where t∈[0,1], three contour points P0(x0,y0), P1(x1,y1), P2(x2,y2) form this curve, and the coefficients are defined as The two endpoints of the curve are the midpoints of P0P1 and P1P2 respectively. And the two endpoints are the tangent points of the two line segments P0P1 and P1P2 with the curve respectively, so the connection point of the two curve segments P0P1P2 and P1P2P3 is first-order differentiable. A closed figure is constructed by this quadratic B-spline curve.

[0087] When calculating the two-dimensional mask pattern, the embodiment of the present application specifically realizes the calculation by forming a curve mask binary pattern through contour points, which is closer to the mask contour manufactured in reality. Moreover, the quadratic B-spline curve has a first-order derivative that is continuous and smooth at the connection point between the curves, which is different from the Bezier curve that has sharp corners at the connection point (causing the same problem as the Manhattan mask model). As a result, the curve mask is closer to the mask contour manufactured in reality. Moreover, the form of the quadratic B-spline curve is relatively simple, and the curve mask model constructed by it will not cause a large increase in the amount of calculation in the optical proximity correction (OPC) calculation process, which is conducive to application in large-scale simulation calculations of the entire chip.

[0088] Since high-frequency information corresponds to an extremely small wavelength, and high-frequency electromagnetic waves that are much higher than the frequency of the light source correspond to high-order diffraction waves during diffraction, which are difficult to collect in the lens behind the mask, there is no need to take the high-frequency components into account.

[0089] Therefore, this embodiment provides a specific implementation scheme to determine a two-dimensional mask pattern based on a curve mask binary pattern, including: transforming the curve mask binary pattern to the frequency domain space to obtain first frequency domain data; removing high-frequency information in the first frequency domain data to obtain second frequency domain data; transforming the second frequency domain data back to the real domain space to obtain a two-dimensional mask pattern.

[0090] Under the Kirchhoff approximation, it is only necessary to transform the closed curve structure into the frequency domain space. Since high-frequency information corresponds to an extremely small wavelength, and high-frequency electromagnetic waves that are much higher than the frequency of the light source correspond to high-order diffraction waves during diffraction, which are difficult to collect in the lens behind the mask, there is no need to take the high-frequency components into account. Therefore, a truncation of the high-frequency information is made in the frequency domain space, and then transformed into the real domain space, and a two-dimensional mask pattern is obtained.

[0091] The solution proposed in the embodiment of the present application improves the calculation efficiency by discarding some useless information in the curve mask binary graphics.

[0092] The above embodiment mentioned that in the process of determining the two-dimensional mask pattern after the target light source passes through the mask based on the contour point set, the two-dimensional mask pattern is established by the curve mask. However, in actual applications, the Manhattan model can also be used to implement it, that is, based on each contour point in the contour point set, horizontal and vertical target straight lines are formed; and based on each target straight line, a two-dimensional mask pattern is formed.

[0093] In the process of establishing a two-dimensional mask pattern, the present application replaces the curve pattern with a Manhattan pattern, and then adds the thick mask diffraction effect. Because the two-dimensional mask pattern is different from the three-dimensional mask pattern, there is no need to consider the thick mask effect, so the two-dimensional mask pattern can be determined by the Manhattan mask model, thereby improving the calculation efficiency.

[0094] Finally, after the three-dimensional mask pattern is predicted by the prediction method of the embodiment of the present application, the technology compares the three-dimensional mask pattern with the actually required target pattern. When it is determined that the three-dimensional mask pattern does not conform to the actually required target pattern, the number and / or position of each contour point in the contour point set is adjusted based on the three-dimensional mask pattern and the actually required target pattern to make the three-dimensional mask pattern approach the target pattern.

[0095] Through the prediction scheme proposed in this application, a more accurate three-dimensional mask pattern can be predicted, and by comparing it with the actual target pattern, the number and position of contour points can be adjusted in reverse to obtain the actual required mask pattern.

[0096] In order to solve the above technical problems, the present application also provides a three-dimensional mask pattern prediction device. Figure 7 FIG. 1 is a schematic diagram showing the structure of a prediction device for a three-dimensional mask pattern provided in an embodiment of the present application. Figure 7 As shown, the device includes the following modules:

[0097] The first acquisition module 701 is used to acquire a contour point set; each contour point in the contour point set is used to form the contour of the original figure on the mask;

[0098] A first determination module 702 is used to determine a two-dimensional mask pattern after a target light source passes through the mask based on a set of contour points;

[0099] The second acquisition module 703 is used to acquire a plurality of target pixel points in a preset area around the original graphic;

[0100] The second determination module 704 is used to determine the target normal between the target pixel point and the original image;

[0101] The third determination module 705 is used to determine the total diffraction field and the two-dimensional diffraction field of the target light source after passing through the mask based on the projection components of the target normal line in each standard direction; each standard direction can form a target normal line in any direction;

[0102] A fourth determination module 706, configured to determine a three-dimensional diffraction field according to a comparison result of the total diffraction field and the two-dimensional diffraction field;

[0103] The prediction module 707 is used to correct the contour of the two-dimensional mask pattern based on the three-dimensional diffraction field and predict the three-dimensional mask pattern.

[0104] In some embodiments, there are four standard directions, and any two adjacent standard directions differ by 90°.

[0105] In some embodiments, the first determining module 702 is specifically configured to:

[0106] Based on each contour point in the contour point set, a curve mask binary figure is formed;

[0107] Based on the curve mask binary pattern, a two-dimensional mask pattern is determined.

[0108] In some embodiments, the first determination module 702 is specifically used to: generate a quadratic B-spline curve with every three adjacent contour points;

[0109] Based on each quadratic B-spline curve, a curve mask binary image is formed.

[0110] In some embodiments, the first determination module 702 is specifically used to: transform the curve mask binary graphic into the frequency domain space to obtain the first frequency domain data;

[0111] Removing high-frequency information from the first frequency domain data to obtain second frequency domain data;

[0112] The second frequency domain data is transformed back to the real domain space to obtain a two-dimensional mask pattern.

[0113] In some embodiments, the first determining module 702 is specifically configured to:

[0114] Based on each contour point in the contour point set, a horizontal and vertical target straight line is formed;

[0115] Based on each target straight line, a two-dimensional mask pattern is constructed.

[0116] In some embodiments, the prediction device of the three-dimensional mask pattern also includes: an adjustment module, which is used to correct the contour in the two-dimensional mask pattern based on the three-dimensional diffraction field, and after predicting the three-dimensional mask pattern, adjust the number and / or position of each contour point in the contour point set based on the three-dimensional mask pattern and the actually required target pattern to make the three-dimensional mask pattern approach the target pattern.

[0117] The device provided in the embodiment of the present application is the same as the method in the above embodiment, so both have the same embodiments and beneficial effects, which will not be repeated here.

[0118] Figure 8 FIG. 1 is a schematic diagram showing the hardware structure of a three-dimensional mask pattern prediction device provided in an embodiment of the present application. Figure 8 As shown, the device for predicting a three-dimensional mask pattern may include a processor 801 and a memory 802 storing computer program instructions.

[0119] Specifically, the processor 801 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0120] The memory 802 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 802 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 802 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 802 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 802 is a non-volatile solid-state memory.

[0121] The memory 802 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Therefore, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of the present disclosure.

[0122] The processor 801 reads and executes the computer program instructions stored in the memory 802 to implement any one of the three-dimensional mask pattern prediction methods in the above embodiments.

[0123] In one example, the prediction device for three-dimensional mask patterns may further include a communication interface 803 and a bus 804. The processor 801, the memory 802, and the communication interface 803 are connected via the bus 804 and communicate with each other.

[0124] The communication interface 803 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0125] The bus 804 includes hardware, software, or both, coupling the components of the prediction device of the three-dimensional mask pattern to each other. By way of example and not limitation, the bus may include an Accelerate Graphical Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, the bus 804 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0126] In addition, in combination with the prediction method of the three-dimensional mask pattern in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the three-dimensional mask pattern prediction methods in the above embodiment is implemented.

[0127] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed, implements any one of the three-dimensional mask graphic prediction methods in the above embodiments.

[0128] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0129] The functional blocks shown in the block diagram above can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an ASIC, appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (Erasable ROM, EROM), floppy disks, compact disc read-only memory (Compact Disc Read-Only Memory, CD-ROM), optical discs, hard disks, optical fiber media, radio frequency (Radio Frequency, RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0130] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0131] The above reference is made to the flowchart and / or block diagram of a prediction method, device, equipment, medium and product of a three-dimensional mask pattern according to an embodiment of the present disclosure, and describes various aspects of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0132] The above contents are only specific implementation methods of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application.

Claims

1. A method for predicting a three-dimensional mask pattern, characterized in that: include: Acquire a contour point set; each contour point in the contour point set is used to form the contour of the original graphic on the mask; Based on the contour point set, determining a two-dimensional mask pattern after the target light source passes through the mask; Acquire a plurality of target pixel points in a preset area around the original graphic; Determining a target normal between the target pixel point and the original graphic; Based on the projection components of the target normal line in each standard direction, determining the total diffraction field and the two-dimensional diffraction field of the target light source after passing through the mask; each of the standard directions can form the target normal line in any direction; Determining a three-dimensional diffraction field according to a comparison result of the total diffraction field and the two-dimensional diffraction field; The contour in the two-dimensional mask pattern is corrected based on the three-dimensional diffraction field to predict the three-dimensional mask pattern.

2. The method for predicting a three-dimensional mask pattern according to claim 1, characterized in that: There are four standard directions, and any two adjacent standard directions differ by 90°.

3. The method for predicting a three-dimensional mask pattern according to claim 1, characterized in that: The step of determining the two-dimensional mask pattern after the target light source passes through the mask based on the contour point set includes: Based on each of the contour points in the contour point set, forming a curve mask binary graphic; Based on the curved mask binary pattern, the two-dimensional mask pattern is determined.

4. The method for predicting three-dimensional mask graphics according to claim 3, characterized in that: The forming of a curve mask binary graph based on each of the contour points in the contour point set comprises: Generate a quadratic B-spline curve using every three adjacent contour points; Based on each of the quadratic B-spline curves, the curve mask binary graphic is formed.

5. The method for predicting a three-dimensional mask pattern according to claim 3 or 4, characterized in that: The step of determining the two-dimensional mask pattern based on the curve mask binary pattern comprises: Transforming the curve mask binary image into a frequency domain space to obtain first frequency domain data; Removing high-frequency information from the first frequency domain data to obtain second frequency domain data; The second frequency domain data is transformed back to the real domain space to obtain the two-dimensional mask pattern.

6. The method for predicting three-dimensional mask graphics according to claim 1, characterized in that: The step of determining the two-dimensional mask pattern after the target light source passes through the mask based on the contour point set includes: Based on each of the contour points in the contour point set, forming a horizontal and a vertical target straight line; The two-dimensional mask pattern is constructed based on each of the target straight lines.

7. The method for predicting three-dimensional mask graphics according to claim 1, characterized in that: After correcting the contour in the two-dimensional mask pattern based on the three-dimensional diffraction field and predicting the three-dimensional mask pattern, the method further includes: Based on the three-dimensional mask pattern and the actually required target pattern, the number and / or position of each of the contour points in the contour point set are adjusted to make the three-dimensional mask pattern approach the target pattern.

8. A three-dimensional mask pattern prediction device, characterized in that: include: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the method for predicting a three-dimensional mask graphic according to any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method for predicting a three-dimensional mask graphic according to any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the three-dimensional mask graphic prediction method as described in any one of claims 1 to 7.

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