A three-dimensional mask pattern prediction method, device, equipment, medium and product

Abstract

The present application discloses a method, device, equipment, medium and product for predicting three-dimensional mask graphics, which are applied to the field of photolithography technology. In this method, when calculating the three-dimensional mask graphics, the two-dimensional mask graphics are 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.

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 patterns. Background Art

[0002] Optical Proximity Correction (OPC) is a lithography resolution enhancement technology. OPC is primarily used in the production of semiconductor devices. In the lithography process, the pattern on the mask is projected onto the photoresist through the exposure system. Due to imperfections in the optical system and diffraction effects, the pattern on the photoresist and the pattern on the mask are not completely consistent. If these distortions are not corrected, they can significantly alter the electrical performance of the resulting circuit. OPC uses computational methods to correct the pattern on the mask so that the pattern projected onto the photoresist closely matches the design requirements. Current reverse lithography technology mostly uses the Manhattan mask model. This model predicts the actual pattern projected onto the photoresist based on the original pattern on the mask, and then corrects the original pattern on the mask based on the desired pattern.

[0003] However, the graphics predicted by the Manhattan mask model are composed of horizontal and vertical straight lines. However, when actually manufacturing the mask, 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 patterns, which can improve the accuracy of pattern prediction, so that the pattern on the photoresist can meet actual needs.

[0005] In one aspect, 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 multiple 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] Determining 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 in each standard direction; each of the standard directions can form the target normal 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 of 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 the difference between any two adjacent standard directions is 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 curved mask binary graphic;

[0016] The two-dimensional mask pattern is determined based on the curved mask binary pattern.

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

[0018] generating 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 graphic 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 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] forming horizontal and vertical target lines based on each of the contour points in the contour point set;

[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 of 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 an actually required target pattern, the number and / or position of each contour point 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. 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 pattern 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 solution 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 solutions 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 one embodiment of the present application is shown;

[0036] Figure 2 A schematic diagram showing the 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 one 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 structural diagram 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 merely 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 "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprise..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0045] There are currently two techniques used for mask patterning: the Manhattan mask, which uses jagged edges to simulate curved boundaries, and the curved mask, which uses true curves. Curved masks are more suitable for OPC, resulting in better process windows, process variation bandwidth, and edge placement errors.

[0046] Because the Manhattan mask model consists of horizontal and vertical lines, it doesn't match the actual manufacturing pattern. However, using a curved mask model in OPC calculations more closely matches physical reality and offers greater optimization freedom, resulting in a larger process window and less pattern distortion than the Manhattan mask. Curved masks can also significantly reduce the process variation bandwidth during mask manufacturing.

[0047] However, current curved mask models typically use high-order Bezier curves to construct graphics. The curve parameter equations are relatively complex, and the model takes a long time to calculate the mask graphics. It cannot be applied to the OPC calculation of the entire chip layout. In addition, the first-order derivative discontinuity at the connection points between curves also causes the problem of sharp corners. For advanced processes, the impact of the thick mask effect is becoming increasingly 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 to correct the deviation caused by the thick mask diffraction effect and improve the process window.

[0048] In order to solve the problems of traditional solutions, the embodiments of the present application provide 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 embodiments of the present application. Figure 1 FIG. 1 is a flow chart showing a method for predicting a three-dimensional mask pattern according to an embodiment of the present application. Figure 1 As shown, the method includes the following steps:

[0049] S101: Obtain a contour point set.

[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 segment, 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 can be superimposed with the two-dimensional mask pattern to obtain a three-dimensional mask pattern with a thick mask effect.

[0054] S103: Acquire multiple 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 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 standard directions, you can first define a polygon. Each side of the polygon serves as a standard boundary, and the normal direction of each standard boundary serves as the standard direction. This method ensures that 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 unit length in these standard directions as the vector in the corresponding direction. Based on the combination of these vectors, a target normal in any direction can be obtained.

[0059] S105: Based on the projection components of the target normal 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 the preset area around the original image.

[0061] Through this method, the target normals corresponding to each target pixel point around the original image are represented, thereby depicting the outline of the original image. Because each standard direction can form a target normal in any direction, if the original image contains a curve, 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 lengths of all target pixels in each standard direction, the outline of the original image is obtained. Based on the mask's attribute parameters, the corresponding total diffraction field and two-dimensional diffraction field can be determined. For three-dimensional thick masks, thick mask modeling is required to obtain attribute parameters such as the material parameters of each layer, the mask etching thickness, and the etching edge tilt angle. The diffraction field of the target light source after passing through the thick mask is rigorously numerically solved using Finite-Difference Time-Domain (FDTD), Rigorous Coupled Wave Analysis (RCWA), or other electromagnetic field simulation methods. This results in the diffraction field of a specific target light source after passing through the thick mask in four standard directions.

[0063] This section explains the effects of thick masks. The light-shielding layer on the mask, along with the quartz glass substrate, forms the three-dimensional structure of the reticle. Under the two-dimensional Kirchhoff approximation, the intensity and phase of the diffraction orders do not vary with the period of the lines. However, when the three-dimensional effects of the mask are taken into account, as the period of the lines decreases—that is, as the pattern width becomes smaller and denser—the diffraction efficiency decreases and the phase deviation increases.

[0064] Due to the required resolution, the patterns on phase-shift masks are small and have large fluctuations. This reduction in diffraction efficiency and phase deviation are more pronounced than with bipolar masks. This variation in diffraction behavior with pattern size narrows the lithography process window. The three-dimensional structure of the mask also affects the non-vertical sidewalls produced by etching, the deviation in line width between densely packed lines and isolated lines, and the shadowing effect 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. By determining the total diffraction field and the two-dimensional diffraction field through the attribute parameters, the thick mask effect can be added to the curved mask model, the deviation caused by the thick mask diffraction effect can be corrected, and the process window can be improved.

[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 and three-dimensional information, while the two-dimensional diffraction field contains only two-dimensional information. Therefore, by removing the two-dimensional diffraction field from the total diffraction field, we obtain a three-dimensional diffraction field containing only three-dimensional information (i.e., the corrected value). The three-dimensional diffraction field represents the changes in the pattern when the target light source passes through the mask, and the specific changes are determined by the properties of the mask.

[0068] As mentioned above, the shape of the graphic is represented 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 thick mask effects. This curved mask model can be constructed based on a quadratic B-spline curve. Its parameter equation is simple, facilitating OPC calculations. Furthermore, the first-order derivatives at the curve's intersections are continuous. Compared to the Manhattan mask model, this eliminates sharp corners and closely matches the contours of a manufactured mask, resulting in a larger process window and minimal image distortion. This significantly reduces the process variation bandwidth during mask manufacturing. It also corrects for deviations caused by the diffraction effect of thick masks, improving the process window.

[0072] This embodiment uses rigorous simulation to construct an approximate curved thick mask effect, addressing the shortcomings of existing curved mask models and enabling their application in more advanced nodes. Simulating diffraction fields in four standard directions and then approximating diffraction fields directed toward arbitrary boundaries significantly reduces the time required for rigorous simulation.

[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 solution 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 of standard directions or their specific directions. If the number of standard directions is too small, the image cannot be well represented; if the number of standard directions is too large, the computational efficiency will be low. In addition, selecting appropriate standard directions can further improve the computational 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 degrees. Through the projection of each standard direction in 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 standard directions, a rectangle is first determined, with each side of the rectangle serving as a standard boundary. These are first standard boundary 201, second standard boundary 202, third standard boundary 203, and fourth standard boundary 204. The normal direction corresponding to each standard boundary is the standard direction, namely first standard direction 205, second standard direction 206, third standard direction 207, and fourth standard direction 208. As can be seen from the figure, any two adjacent standard directions differ by 90°.

[0077] The 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 is used to represent the target normal 303 in this example, then Correction value f=f(a, b, c, d), and 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 of 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 curve boundary under the 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 curved mask cannot be split into four directional edges. Therefore, this application uses the standard direction normal projection method to represent the image. However, considering the limitation of strict simulation solution speed, the embodiment of this application only simulates the diffraction field results of four boundaries. The boundary of the curved mask is projected into the four standard directions of up, down, left and right. By comparing with the two-dimensional diffraction field, the three-dimensional diffraction field of the pixel points near the curved boundary is calculated, thereby obtaining a three-dimensional mask image with a thick mask effect.

[0082] This embodiment of the present application uses a rigorous simulation method to simulate the actual diffraction effect of a thick mask. By using four standard directions, each with a 90° difference in normal orientation, it is possible to approximate the diffraction field of any boundary orientation. Furthermore, because only the diffraction field in these four standard directions is simulated, a relatively fast simulation speed can be achieved, ensuring both accuracy and speed 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 a two-dimensional thin mask. This application provides a specific implementation scheme. Figure 5 FIG. 1 shows 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 curved contour is first generated based on each contour point in the contour point set to form a curved mask binary pattern. Then, the two-dimensional mask pattern is determined based on the curved mask binary pattern.

[0084] Figure 6 FIG1 shows 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 the graph generate a quadratic B-spline curve, thereby forming a curve mask binary image based on each quadratic B-spline curve. Each three contour points form a quadratic B-spline curve, and the three contour points of adjacent quadratic B-spline curves share two of their contour points. For example, the contour points of the first segment, curve 1, are P0, P1, and P2, respectively, while the contour points of the second segment, curve 2, are P1, P2, and P1, P2 are shared contour points.

[0085] The embodiment of the present application uses a quadratic B-spline curve to construct a curve mask model, and its x, y component equations are in the form of 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

[0086] The two endpoints of the curve are the midpoints of P0P1 and P1P2, respectively. Furthermore, the two endpoints are the points of tangency between the two line segments P0P1 and P1P2. Therefore, the point where the curve P0P1P2 and the curve P1P2P3 meet is first-order differentiable. This quadratic B-spline curve constructs a closed graph.

[0087] When calculating the two-dimensional mask pattern, the embodiment of the present application specifically realizes the calculation by forming a curved mask binary pattern through contour points, which is closer to the mask contour manufactured in reality. In addition, the quadratic B-spline curve has a continuous and smooth first-order derivative 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 curved mask is closer to the mask contour manufactured in reality. In addition, the form of the quadratic B-spline curve is relatively simple, and the curved mask model constructed by it does not lead to 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 consideration.

[0089] Therefore, this embodiment provides a specific implementation scheme for determining a two-dimensional mask pattern based on a curve mask binary pattern, including: transforming the curve mask binary pattern into a 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; and transforming the second frequency domain data back into a real domain space to obtain a two-dimensional mask pattern.

[0090] Under the Kirchhoff approximation, it's sufficient to transform the closed curve into the frequency domain. Since high-frequency information corresponds to extremely small wavelengths, and high-frequency electromagnetic waves, which are much higher than the light source frequency, correspond to higher-order diffracted waves during diffraction, they are difficult to collect in the lens behind the mask. Therefore, there's no need to account for high-frequency components. Instead, the high-frequency information is truncated in the frequency domain and then transformed into the real domain, resulting in a two-dimensional mask pattern.

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

[0092] The above embodiment mentions that, based on a set of contour points, the two-dimensional mask pattern after the target light source passes through the mask is determined using a curved mask to create the two-dimensional mask pattern. However, in actual applications, the Manhattan model can also be used to form horizontal and vertical target lines based on each contour point in the set of contour points, and then construct a two-dimensional mask pattern based on each target line.

[0093] This application implements the process of creating a 2D mask pattern by replacing the curved pattern with a Manhattan pattern and then adding the thick mask diffraction effect. Because 2D mask patterns differ from 3D mask patterns, there is no need to consider the thick mask effect. Therefore, the 2D mask pattern can be determined using the Manhattan mask model, thereby improving computational 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 the 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 invention also provides a three-dimensional mask pattern prediction device. Figure 7 FIG. 1 shows a schematic diagram of 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 image on the mask;

[0098] A first determining module 702 is configured 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 image;

[0100] The second determining module 704 is used to determine a 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 in each standard direction; each standard direction can form a target normal in any direction;

[0102] A fourth determining module 706 is configured to determine a three-dimensional diffraction field based on a comparison result of the total diffraction field and the two-dimensional diffraction field;

[0103] The prediction module 707 is configured 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 the difference between any two adjacent standard directions is 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 curved mask binary graphic is formed;

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

[0108] In some embodiments, the first determining module 702 is specifically configured to: generate a quadratic B-spline curve using 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 determining module 702 is specifically configured to: transform the curve mask binary graphic into a frequency domain space to obtain 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 line is formed;

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

[0116] In some embodiments, the prediction device for the three-dimensional mask pattern further includes: an adjustment module for correcting the contour in the two-dimensional mask pattern based on the three-dimensional diffraction field, and after predicting the three-dimensional mask pattern, adjusting 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, so as 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 the two have the same embodiments and beneficial effects, which will not be repeated here.

[0118] Figure 8 FIG. 1 shows a schematic diagram of the hardware structure of a three-dimensional mask pattern prediction device provided by 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. Where appropriate, the memory 802 may include removable or non-removable (or fixed) media. Where appropriate, 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. Thus, 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 an aspect of the present disclosure.

[0122] The processor 801 reads and executes 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 three-dimensional mask pattern prediction device 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 that couples the components of the three-dimensional mask pattern prediction device to each other. By way of example and not limitation, the bus may include an Accelerated Graphical Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (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 herein describe and illustrate a particular bus, this application contemplates any suitable bus or interconnect.

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

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

[0128] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of 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, they can be, for example, electronic circuits, ASICs, appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of the present application are programs or code segments that are 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 communication link via 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, ROMs, flash memories, erasable ROMs (Erasable ROM, EROMs), floppy disks, compact disc read-only memory (Compact Disc Read-Only Memory, CD-ROMs), optical discs, hard disks, optical fiber media, radio frequency (RF) links, etc. The 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. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0131] The above describes various aspects of the present disclosure with reference to the flowcharts and / or block diagrams of a method, apparatus, device, medium, and product for predicting a three-dimensional mask pattern according to an embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram, as well as the combination of boxes 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 functions / actions specified in one or more boxes in 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 is also understood that each box in the block diagram and / or flowchart, as well as the combination of boxes in the block diagram and / or flowchart, can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0132] The above content is only a specific implementation method of the present application. Those skilled in the art can clearly understand that for the convenience and brevity 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 scope of protection of the present application is not limited to this. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the scope of protection 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 multiple target pixel points in a preset area around the original graphic; Determining a target normal between the target pixel point and the original graphic; Determining 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 in each standard direction; each of the standard directions can form the target normal 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 of 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, wherein: 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, wherein: The step of determining a 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 curved mask binary graphic; The two-dimensional mask pattern is determined based on the curved mask binary pattern.

4. The method for predicting a three-dimensional mask pattern according to claim 3, wherein: The forming of a curve mask binary graphic based on each contour point in the contour point set includes: generating 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, wherein: The determining of the two-dimensional mask pattern based on the curved mask binary pattern includes: Transforming the curve mask binary graphic 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 real domain space to obtain the two-dimensional mask pattern.

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

7. The method for predicting a three-dimensional mask pattern according to claim 1, wherein: After correcting the contour of 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 an actually required target pattern, the number and / or position of each contour point 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 pattern 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 pattern 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 pattern prediction method according to any one of claims 1 to 7.

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