Photomask correction method and device, equipment and medium

Abstract

The invention relates to the technical field of semiconductor manufacturing, in particular to a photomask correction method, device and equipment and a medium, and the photomask correction method comprises the steps: obtaining a test layout with known parameters; training the initial optical proximity correction model based on known parameters until the cost evaluation function and the root-mean-square value are minimum, and obtaining an optical correction model; obtaining photochemical reaction parameters of the test layout, training an optical correction model based on the photochemical reaction parameters until the cost evaluation function and the root mean square value are minimum, and obtaining a target layout correction model; and inputting the initial layout into a target layout correction model to obtain a corrected layout, so as to obtain a target photomask based on the corrected layout, and at least ensuring that the exposed photomask pattern is consistent with a target pattern actually expected by a user.

Description

Technical Field

[0001] The present application relates to the field of semiconductor manufacturing technology, and in particular to a mask correction method, device, equipment and medium. Background Art

[0002] The optical proximity effect (OPE) is a critical issue in semiconductor manufacturing, particularly in photolithography. As integrated circuit feature sizes continue to shrink (down to the nanometer scale), light diffraction and interference can cause the image of the pattern on the photoresist to deviate from the actual design. This phenomenon is known as the optical proximity effect.

[0003] In semiconductor manufacturing, optical proximity effect (OPE) can cause pattern distortion. For example, dense lines or contact holes may merge (bridge) or break due to light diffraction, resulting in device shorts or opens. OPE can also cause changes in critical dimensions of patterns after exposure, impacting device performance and yield. Summary of the Invention

[0004] Based on this, it is necessary to address the problems in the above-mentioned background technology and provide a method that can avoid the adverse effects of parameter errors such as optical errors and photochemical reactions on the mask pattern after exposure, and at least ensure that the mask pattern after exposure is consistent with the target pattern that the user actually wants to obtain.

[0005] To achieve the above objectives and other objectives, according to various embodiments of the present application, one aspect of the present application provides a mask correction method, comprising:

[0006] Obtain a test layout with known parameters, including pattern parameters, yellow light parameters, and film parameters. The film parameters include the number of layers, film material, film thickness, and photoresist parameters.

[0007] The initial optical proximity effect correction model is trained based on known parameters until the cost evaluation function and the root mean square value are minimized to obtain the optical correction model;

[0008] Obtain the photochemical reaction parameters of the test pattern, including the reaction rate and diffusion rate in the dense and sparse areas of the pattern, as well as the reaction rate and diffusion rate of the photoacid and photobase in the tangential and normal directions;

[0009] The optical correction model is trained based on the photochemical reaction parameters until the cost evaluation function and the root mean square value are minimized, thereby obtaining the target layout correction model;

[0010] The initial layout is input into the target layout correction model to obtain a corrected layout, so as to obtain a target mask based on the corrected layout.

[0011] In the mask correction method of the above-described embodiment, a test layout with known parameters is first obtained. The known parameters include pattern parameters, yellow light parameters, and film layer parameters. The film layer parameters include the number of layers, film material, film thickness, and photoresist parameters. An initial optical proximity effect correction model is then trained using the test layout and its known parameters until both the cost evaluation function and the root mean square value are minimized, resulting in an optical correction model. This optical correction model is capable of correcting for the optical proximity effect, preventing optical factors such as optical errors and the optical proximity effect from adversely affecting the pre-exposure pattern. Furthermore, optical factors can significantly contribute to errors in the final post-exposure pattern, sometimes exceeding 80%. Therefore, an optical correction model is first established that can at least avoid the optical proximity effect, thereby shortening model training time. The optical correction model is then trained using the photochemical reaction parameters of the test layout to obtain a target layout correction model. This target layout correction model is then capable of compensating for errors caused by the photochemical reaction parameters, thereby ensuring that the post-exposure mask pattern is consistent with the target pattern desired by the user.

[0012] In some embodiments, the test layout includes multiple periodically distributed sub-patterns; obtaining the optical correction model includes: using known parameters as input and target parameters of the multiple sub-patterns in the layout after exposure as output, training the initial optical proximity effect correction model until both the cost evaluation function and the root mean square value are minimized, thereby obtaining the optical correction model. This facilitates obtaining a test layout based on a block unit with known parameters by periodically repeating the block unit, thereby reducing the complexity and cost of obtaining the test layout with known parameters and ensuring the accuracy of the known parameters obtained for the test layout.

[0013] In some embodiments, the target parameters of the plurality of sub-patterns in the layout after exposure include coordinate information of the center point coordinate values of the plurality of sub-patterns in the layout after exposure.

[0014] In some embodiments, the yellow light parameters include lighting system parameters.

[0015] In some embodiments, the graphic parameters include at least one of graphic point coordinates, sub-graphic shape, spacing, and line width.

[0016] In some embodiments, the plurality of sub-patterns include gate patterns or conductive contact patterns.

[0017] In some embodiments, the photochemical reaction parameters further include a function of the effect of baking on the critical dimension of the layout after exposure.

[0018] In some embodiments, a function is described that describes the effect of development rate on the critical dimension of the layout after exposure.

[0019] One embodiment of the present disclosure further discloses a mask correction device, comprising a test layout acquisition module, an optical correction model acquisition module, a photochemical reaction parameter acquisition module, a target layout correction model acquisition module, and a target mask acquisition module. The test layout acquisition module is used to acquire a test layout with known parameters, wherein the known parameters include graphic parameters, yellow light parameters, and film layer parameters, wherein the film layer parameters include the number of stacked layers, film layer material, film layer thickness, and photoresist parameters. The optical correction model acquisition module is used to train an initial optical proximity effect correction model based on the known parameters until the cost evaluation function and the root mean square value are minimized, thereby obtaining to the optical correction model; the photochemical reaction parameter acquisition module is used to obtain the photochemical reaction parameters of the test layout, and the photochemical reaction parameters include: the reaction rate and diffusion rate in the dense and sparse areas of the graphics, as well as the reaction rate and diffusion rate of the photoacid and photobase in the tangential and normal directions; the target layout correction model acquisition module is used to train the optical correction model based on the photochemical reaction parameters until the cost evaluation function and the root mean square value are minimized, thereby obtaining the target layout correction model; the target mask acquisition module is used to input the initial layout into the target layout correction model to obtain the corrected layout, so as to obtain the target mask based on the corrected layout.

[0020] In some embodiments, the photochemical reaction parameters further include a function of the effect of baking on the critical dimension of the layout after exposure.

[0021] In some embodiments, a function is described that describes the effect of development rate on the critical dimension of the layout after exposure.

[0022] In some embodiments, a mask correction device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any mask correction method in the embodiments of the present application are implemented.

[0023] In some embodiments, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the mask correction methods in the embodiments of the present application are implemented.

[0024] In some embodiments, a computer program product is provided, comprising a computer program, which implements the steps of any one of the above-mentioned mask correction methods when executed by a processor.

[0025] The unexpected technical effects that can be produced by the embodiments of the present application include:

[0026] The initial optical proximity effect correction model is trained using the test layout and its known parameters until both the cost evaluation function and the root mean square value are minimized, resulting in an optical correction model. This optical correction model can correct for the optical proximity effect, preventing optical factors such as optical errors and the optical proximity effect from adversely affecting the pre-exposure pattern. Furthermore, the influence of optical factors on the final post-exposure pattern error is relatively high, even exceeding 80%. Therefore, an optical correction model that can at least avoid the optical proximity effect is first established, which can at least shorten the model training time. The optical correction model is then trained using the photochemical reaction parameters of the test layout to obtain a target layout correction model. This target layout correction model can compensate for the errors caused by the photochemical reaction parameters, ensuring that the post-exposure mask pattern is consistent with the target pattern actually desired by the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of 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.

[0028] Figure 1 A schematic flow chart of a mask correction method provided in one embodiment of the present application;

[0029] Figure 2 This is a schematic diagram of the structure of a test layout provided in one embodiment of the present application;

[0030] Figure 3 A schematic diagram of training an initial optical proximity effect correction model in one embodiment of the present application;

[0031] Figure 4 This is a schematic diagram of training an optical correction model in one embodiment of the present application;

[0032] Figure 5 Schematic diagram of a layout at different stages during the process of correcting a test layout including a grid pattern in one embodiment of the present application;

[0033] Figure 6 A schematic diagram showing a comparison of the correction time required for correcting a test layout including a grid pattern using a mask correction method according to an embodiment of the present application and the correction time required for correcting a test layout including a grid pattern using a conventional method;

[0034] Figure 7 Schematic diagram of a layout at different stages during the process of correcting a test layout including a target contact pattern in one embodiment of the present application;

[0035] Figure 8Schematic diagram showing a comparison of the correction time required for correcting a test layout including a target contact pattern using a mask correction method according to an embodiment of the present application and the correction time required for correcting a test layout including a target contact pattern using a conventional method;

[0036] Figure 9 This is a structural schematic diagram of a mask correction device provided in one embodiment of the present application.

[0037] Description of reference numerals:

[0038] 1000. Mask correction device; 101. Test layout acquisition module; 102. Optical correction model acquisition module; 103. Photochemical reaction parameter acquisition module; 104. Target layout correction model acquisition module; 105. Target mask acquisition module; 100. Central sub-pattern; 200. Repeating sub-pattern; 20. Target grid pattern; 21. Post-exposure grid pattern; 30. Target contact pattern; 31. Post-exposure contact pattern. DETAILED DESCRIPTION

[0039] To facilitate understanding of the present application, a more comprehensive description of the present application will be provided below with reference to the accompanying drawings. The accompanying drawings illustrate preferred embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the disclosure of the present application.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application pertains. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0041] In the case of using “including,” “having,” and “comprising” described herein, another component may be added unless a clear limiting term such as “only,” “consisting of,” etc. is used. Unless mentioned otherwise, a term in the singular form may include a plural form and should not be understood as having one number.

[0042] In this application, unless otherwise expressly specified or limited, terms such as "mounted," "connected," "connect," and "fixed" should be understood in a broad sense. For example, they may refer to fixed or detachable connections, or integration; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on specific circumstances.

[0043] In addition, the terms "first", "second", etc. are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features.

[0044] During the semiconductor chip manufacturing process, a mask is used to create a pattern on the semiconductor using photolithography. To replicate this pattern on the wafer, an integrated circuit lithography machine photoetches the projected circuit. The production process generally includes exposure, development, photoresist stripping, and photolithography. The photolithography process generally involves first creating a specific pattern structure on a mask, then replicating the pattern onto a silicon wafer using a photolithography machine. However, the pattern generation process through photolithography introduces some degree of distortion, which becomes increasingly severe as line widths decrease. Typical examples include corner rounding and line end shortening. These phenomena are caused by the optical proximity effect (OPE), which is caused by nonlinear filtering in the optical imaging system. The industry uses Optical Proximity Correction (OPC) technology to solve the above problems. In Optical Proximity Correction (OPC) technology, the pattern on the integrated circuit mask is pre-corrected to compensate for the distortion caused by the photolithography process, so that the corrected pattern can obtain the pre-designed pattern structure after photolithography.

[0045] However, photochemical reactions play a central role in the photoresist exposure process, and improper control can lead to a variety of pattern defects. Even patterns corrected for optical proximity effects can be adversely affected by photochemical reaction parameters during exposure and development. For example, insufficient photoacid generation can lead to residual colloids or blurred pattern edges after development; lateral diffusion of photoacid can cause line edge roughness (LER) or poor linewidth uniformity; aromatic compounds in the photoresist oxidize under UV light to form carboxylic acids, altering the pH of the developer; and metallic impurities (such as Na and K) in the photoresist migrate to the silicon wafer surface after exposure. Therefore, purely optical correction models cannot account for the adverse effects of photochemical reaction parameters on the exposed pattern.

[0046] Therefore, the embodiments of the present application aim to provide a mask correction method, device, equipment and medium, which can at least avoid the adverse effects of parameter errors such as optical errors and photochemical reactions on the mask pattern after exposure, and ensure that the mask pattern after exposure is consistent with the target pattern that the user actually wants to obtain.

[0047] A mask correction method, device, equipment and medium provided in the embodiments of the present application can be applied to a lithography machine processor. The lithography machine processor communicates with a server through a network, and the server is communicatively connected to a server receiving end. The lithography machine processor can also be directly communicatively connected to a server receiving end. The communication connection method includes wired or wireless connection.

[0048] For example, the mask correction method, device, equipment and medium are applied to the lithography machine processor. The lithography machine processor can obtain a test layout with known parameters from the receiving end of the server. The known parameters include graphic parameters, yellow light parameters and film parameters. The film parameters include the number of layers, film material, film thickness and photoresist parameters; the lithography machine processor trains an initial optical proximity effect correction model based on the known parameters until the cost evaluation function and the root mean square value are minimized to obtain an optical correction model; the lithography machine processor can obtain the photochemical reaction parameters of the test layout from the receiving end of the server. The photochemical reaction parameters include: reaction rate and diffusion rate in graphic dense areas and sparse areas, as well as reaction rate and diffusion rate of photoacid and photobase in tangential and normal directions; the lithography machine processor trains an optical correction model based on the photochemical reaction parameters until the cost evaluation function and the root mean square value are minimized to obtain a target layout correction model; the lithography machine processor inputs the initial layout into the target layout correction model to obtain a corrected layout, thereby facilitating the subsequent acquisition of the target mask based on the corrected layout.

[0049] For another example, the mask correction method, device, equipment and medium are applied to a server, and the server obtains a test layout with known parameters from a receiving end of the server. The known parameters include graphic parameters, yellow light parameters and film parameters. The film parameters include the number of layers, film material, film thickness and photoresist parameters; the server trains an initial optical proximity effect correction model based on the known parameters until the cost evaluation function and the root mean square value are minimized to obtain an optical correction model; the server can obtain the photochemical reaction parameters of the test layout from the receiving end of the server, and the photochemical reaction parameters include: the reaction rate and diffusion rate in the graphic dense area and sparse area, and the reaction rate and diffusion rate of the photoacid and photobase in the tangential and normal directions; the server trains the optical correction model based on the photochemical reaction parameters until the cost evaluation function and the root mean square value are minimized to obtain a target layout correction model; the server inputs the initial layout into the target layout correction model to obtain a corrected layout, thereby facilitating the subsequent acquisition of the target mask based on the corrected layout.

[0050] like Figure 1 As shown, in some embodiments, a mask correction method is provided, comprising the following steps:

[0051] Step S12: obtaining a test layout with known parameters, wherein the known parameters include pattern parameters, yellow light parameters, and film parameters, wherein the film parameters include the number of layers, film material, film thickness, and photoresist parameters;

[0052] Step S14: training the initial optical proximity effect correction model based on known parameters until the cost evaluation function and the root mean square value are minimized, thereby obtaining the optical correction model;

[0053] Step S15: Acquire the photochemical reaction parameters of the test pattern, including the reaction rate and diffusion rate in the dense and sparse areas of the pattern, as well as the reaction rate and diffusion rate of the photoacid and photobase in the tangential and normal directions;

[0054] Step S16: training the optical correction model based on the photochemical reaction parameters until the cost evaluation function and the root mean square value are minimized, thereby obtaining a target layout correction model;

[0055] Step S18: inputting the initial layout into the target layout correction model to obtain a corrected layout, and obtaining a target mask based on the corrected layout.

[0056] Specifically, a test layout with known parameters is first obtained. The known parameters include pattern parameters, yellow light parameters, and film layer parameters. The film layer parameters include the number of layers, film material, film thickness, and photoresist parameters. The test layout and its known parameters are then used to train an initial optical proximity effect correction model until both the cost evaluation function and the root mean square value are minimized, thereby obtaining an optical correction model. This optical correction model can correct for the optical proximity effect and avoid the adverse effects of optical factors such as optical errors and optical proximity effects on the pre-exposure pattern. In addition, the influence of optical factors on the final post-exposure pattern error is relatively high, even reaching over 80%. Therefore, an optical correction model that can at least avoid the optical proximity effect is first established, which can at least shorten the model training time. The photochemical reaction parameters of the test layout are then used to train the optical correction model to obtain a target layout correction model. This target layout correction model can compensate for the errors caused by the photochemical reaction parameters and at least ensure that the post-exposure mask pattern is consistent with the target pattern actually desired by the user.

[0057] like Figure 2 As shown, in some embodiments, a central sub-pattern 100 including known parameters can be used as the center of a test layout, and then a repeated sub-pattern 200 is determined to be mirror-symmetrically distributed with the central sub-pattern 100 as the center. The graphic parameters, yellow light parameters, and film layer parameters of the repeated sub-pattern 200 are known. Thus, through simple mirroring processing, a test layout with precisely known parameters can be obtained. The known parameters include graphic parameters, yellow light parameters, and film layer parameters. The film layer parameters include the number of stacked layers, film layer material, film layer thickness, and photoresist parameters.

[0058] In some embodiments, based on block monomers with known parameters, a test layout is obtained by periodically repeating the block monomers, thereby reducing the complexity and cost of obtaining the test layout with known parameters and ensuring the accuracy of the known parameters of the test layout.

[0059] In some embodiments, the test pattern includes multiple test points with known coordinate values, so as to determine the offset, expansion, shrinkage and other change parameters of the pattern after exposure based on the coordinate information of the multiple test points in the layout after exposure.

[0060] For example, please continue to refer to Figure 2 Based on the coordinate values (x1, y1) of the first test point and the coordinate values (x2, y2) of the second test point in the central sub-pattern 100, the line width and center coordinate value of the central sub-pattern 100 can be calculated. After obtaining the target parameters of the multiple sub-patterns in the exposed layout, the target parameters include the coordinate information of the center point coordinate values of the multiple sub-patterns in the exposed layout. Based on the coordinate values in the test layout and the coordinate information in the exposed layout, the offset, expansion, contraction and other change parameters of the exposed pattern can be determined to assist in determining whether the exposed mask pattern is consistent with the target pattern actually desired by the user.

[0061] In some embodiments, the test layout includes multiple sub-patterns distributed periodically; obtaining the optical correction model includes: taking known parameters as input, taking target parameters of the multiple sub-patterns in the layout after exposure as output, training the initial optical proximity effect correction model until the cost evaluation function and the root mean square value are minimized, and obtaining the optical correction model.

[0062] In some embodiments, yellow light parameters include lighting system parameters. For example, yellow safety lights typically use sodium lamps or LEDs with a wavelength between 550nm and 600nm (avoiding photoresist-sensitive wavelengths, such as the 365nm i-line, 248nm KrF, and 193nm ArF). Light intensity must be controlled to less than 1 lux to avoid accidental exposure of the photoresist. Light source parameters for photolithography exposure include light source type, numerical aperture (NA), coherence factor (σ), and dose.

[0063] In some embodiments, photoresist parameters include sensitive wavelength and contrast. Yellow light should be avoided (for example, positive / negative photoresists are sensitive to UV or EUV but not to light above 550nm). Contrast can reflect the photoresist's exposure response characteristics.

[0064] In some embodiments, the shape of the sub-graphics in the test layout includes at least one of a circle, a polygon, a crescent, a sector, and a ring.

[0065] In some embodiments, the sub-pattern in the test layout includes a pad oxide layer, a silicon nitride layer, a silicon oxide layer, an α-carbon layer, an anti-reflective layer, and a photoresist layer stacked sequentially in a direction away from the substrate. The pad oxide layer has a thickness of 20 angstroms to 25 angstroms, for example, 20 angstroms, 22 angstroms, or 25 angstroms. The pad oxide layer has an extinction coefficient of 1.5711, and a refractive index of approximately 0. The silicon nitride layer has a thickness of 300 angstroms to 400 angstroms, for example, 300 angstroms, 350 angstroms, 380 angstroms, or 400 angstroms. The silicon nitride layer has an extinction coefficient of 2.62165, and a refractive index of approximately 0.38843. The silicon oxide layer has a thickness of 220 angstroms to 230 angstroms, for example, 220 angstroms, 225 angstroms, or 230 angstroms. The silicon oxide layer has an extinction coefficient of 1.563, and a refractive index of approximately 0. The thickness of the α-carbon layer is 1900 angstroms to 2000 angstroms, for example, the thickness of the α-carbon layer can be 1900 angstroms, 1950 angstroms, or 2000 angstroms; the extinction coefficient of the α-carbon layer can be 1.5165, and the refractive index is approximately 0.6935. The thickness of the anti-reflection layer is 320 angstroms to 350 angstroms, for example, the thickness of the anti-reflection layer can be 320 angstroms, 330 angstroms, or 350 angstroms; the extinction coefficient of the anti-reflection layer can be 1.88, and the refractive index is approximately 0.46. The thickness of the photoresist layer is 980 angstroms to 1020 angstroms, for example, the thickness of the photoresist layer can be 980 angstroms, 1000 angstroms, or 1020 angstroms; the extinction coefficient of the photoresist layer can be 1.7, and the refractive index is approximately 0.049.

[0066] In some embodiments, the graphic parameters include at least one of graphic point coordinates, sub-graphic shape, spacing, and line width.

[0067] Please continue to refer to Figure 2 In some embodiments, the line width of the central sub-pattern 100 may be calculated based on the coordinate value (x1, y1) of the first test point in the central sub-pattern 100 and the coordinate value (x2, y2) of the second test point.

[0068] Please refer to Figure 3 In some embodiments, in step S12, after obtaining a test layout with known parameters, the known parameters include pattern parameters, light parameters, and film parameters, including the number of layers, film material, film thickness, and photoresist parameters. In step S14, the test layout and its known parameters are used to train an initial optical proximity effect correction model until both the cost evaluation function and the root mean square value are minimized, thereby obtaining an optical correction model. Because pure optical model training converges quickly and pattern parameters, light parameters, and film parameters can effectively correct for optical proximity effect, first establishing an optical correction model that can at least avoid the optical proximity effect can at least shorten model training time.

[0069] Please refer to Figure 4 In some embodiments, in step S15, after obtaining the photochemical reaction parameters of the test layout, the photochemical reaction parameters include: reaction rate and diffusion rate in the dense and sparse areas of the graphics, and reaction rate and diffusion rate of the photoacid and photobase in the tangential and normal directions; in step S16, the optical correction model is trained based on the photochemical reaction parameters until the cost evaluation function and the root mean square value are minimized, thereby obtaining the target layout correction model.

[0070] Please refer to Figure 5 , in some embodiments, Figure 5 Figure (1) shows a test board including multiple target grating patterns 20. The pattern parameters, yellow light parameters and film parameters of the multiple target grating patterns 20 are known. The film parameters include the number of stacking layers, film material, film thickness and photoresist parameters. Figure 5 Figure (2) in the middle shows the purely optically corrected figure obtained by inputting the figure in Figure (1) and its known parameters into the optical correction model for correction. Figure 5 Figure (3) in the middle shows the corrected layout obtained after the graphics in Figure (2) and its known parameters are input into the target layout correction model for correction. Figure 5 Figure (4) shows the target grating pattern 21 after exposure on the mask based on the corrected layout in Figure (3). By comparing the target grating pattern 20 in Figure (4) with the grating pattern 21 after exposure, it is found that pattern defects such as broken bars or bridging are effectively avoided. The key dimensions of the target grating pattern 20 and the grating pattern 21 after exposure are basically consistent, meeting the expected requirements.

[0071] Please refer to Figure 6 , Figure 6 The middle green line shows the distribution of correction time for the first, second, third, fourth, and fifth test layouts DB1, DB2, DB3, DB4, and DB5, each including a grid pattern (1G), using the target layout correction model of the present application to correct the outline error to less than or equal to 0.1 nm. The first, second, third, fourth, and fifth test layouts DB1, DB2, DB3, DB4, and DB5 have different known parameters. Figure 6 The red line in the middle shows the distribution of correction time for the first, second, third, fourth, and fifth test layouts DB1, DB2, DB3, DB4, and DB5, each containing a grid pattern (1G), using a conventional mask pattern correction method to achieve a profile error of less than or equal to 0.1 nm. Comparing the green and red lines, it can be seen that the mask correction method in this embodiment reduces the layout correction time by 17% while maintaining layout correction accuracy.

[0072] Please refer to Figure 7 , in some embodiments, Figure 7 Figure (1) shows a test board including multiple target contact patterns 30. The pattern parameters, yellow light parameters and film parameters of the multiple target contact patterns 30 are known. The film parameters include the number of stacking layers, film material, film thickness and photoresist parameters. Figure 7 Figure (2) shows the Figure 7 The graph in (1) and its known parameters are input into the optical correction model for correction, resulting in a purely optically corrected graph. Figure 7 Figure (3) shows the Figure 7 The graphics and their known parameters in (2) are input into the target layout correction model for correction, and the corrected layout is obtained. Figure 7 Figure (4) shows the Figure 7 The corrected layout in Figure (3) obtains the contact pattern 31 on the target mask after exposure. Figure 7 In the figure (4), the target contact pattern 30 and the contact pattern 31 after exposure are found to have substantially the same key dimensions as those of the contact pattern 31 after exposure, which meets the expected requirements.

[0073] Please refer to Figure 8 , Figure 8 The middle green line shows the distribution of correction time for correcting the first, second, third, fourth, and fifth test layouts DB1, DB2, DB3, DB4, and DB5, each including a contact pattern (1C), using the target layout correction model of the present invention to reduce the profile error to less than or equal to 0.1 nm. The first, second, third, fourth, and fifth test layouts DB1, DB2, DB3, DB4, and DB5 have different known parameters. Figure 8 The red line in the middle shows the distribution of correction time for the first, second, third, fourth, and fifth test layouts DB1, DB2, DB3, DB4, and DB5, each containing a grid pattern (1G), using a conventional mask pattern correction method to achieve a profile error of less than or equal to 0.01 nm. Comparing the green and red lines, it can be seen that the mask correction method in this embodiment reduces the layout correction time by 20% while maintaining layout correction accuracy.

[0074] In some embodiments, the photochemical reaction parameters also include a function of the effect of baking on the post-exposure layout critical dimensions (CDs). Post-exposure baking (PEB) is a critical step in the photolithography process, particularly in chemically amplified resist (CAR) systems, where it significantly impacts the critical dimensions (CDs) of the exposed pattern. After exposure, the photoacid generator in the photoresist produces acid. During baking, the acid diffuses within the photoresist, catalyzing the deprotection reaction. The diffusion length is related to the baking temperature and time. Increasing the temperature accelerates acid diffusion and the reaction rate, resulting in smaller (positive resist) or larger (negative resist) CDs. However, excessively high temperatures can lead to excessive diffusion, resulting in rough edges. Extending the baking time has a similar effect to increasing the temperature, but with a saturation effect (continuing baking after the reaction is complete has no significant effect). The exposure dose can act synergistically with the baking. At high exposure doses, the bake's sensitivity to CDs decreases (due to saturation of the acid concentration).

[0075] In some embodiments, a function is described that describes the effect of development rate on the critical dimensions (CDs) of the exposed layout. Development rate is one of the core parameters in the photolithography process that determines the post-exposure pattern morphology and critical dimensions (CDs). Development rate is closely related to the solubility characteristics of the photoresist, exposure dose, and baking conditions, and its functional relationship directly affects pattern resolution, sidewall morphology, and CD uniformity. The development rate saturates with increasing dose (a nonlinear relationship). High-dose areas develop faster, while low-dose areas develop slower. Dense patterns may have a lower development rate than isolated patterns due to limited developer penetration, resulting in CD variations.

[0076] The effect of development rate on critical dimensions is a dynamic balance process. In the embodiments of the present application, the target layout correction model is used to precisely control development parameters, photoresist properties, and cooperate with other process steps (exposure, baking) to achieve high-fidelity transfer of graphics.

[0077] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1At least part of the steps in the above process may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but may be performed at different times. The order of performing these steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0078] like Figure 9 As shown, in some embodiments, a mask correction device 1000 is provided, including a test layout acquisition module 101, an optical correction model acquisition module 102, a photochemical reaction parameter acquisition module 103, a target layout correction model acquisition module 104, and a target mask acquisition module 105. The test layout acquisition module 101 is used to acquire a test layout with known parameters, the known parameters including graphic parameters, yellow light parameters and film parameters, the film parameters including the number of stacking layers, film material, film thickness and photoresist parameters; the optical correction model acquisition module 102 is used to train an initial optical proximity effect correction model based on the known parameters, to obtain a cost evaluation function and The root mean square values are minimized to obtain an optical correction model; the photochemical reaction parameter acquisition module 103 is used to obtain the photochemical reaction parameters of the test layout, and the photochemical reaction parameters include: the reaction rate and diffusion rate in the dense and sparse areas of the graphics, as well as the reaction rate and diffusion rate of the photoacid and photobase in the tangential and normal directions; the target layout correction model acquisition module 104 is used to train the optical correction model based on the photochemical reaction parameters until the cost evaluation function and the root mean square value are minimized to obtain the target layout correction model; the target mask acquisition module 105 is used to input the initial layout into the target layout correction model to obtain a corrected layout, so as to obtain the target mask based on the corrected layout.

[0079] In some embodiments, the photochemical reaction parameters further include a function of the effect of baking on the critical dimension of the layout after exposure.

[0080] In some embodiments, a function is described that describes the effect of development rate on the critical dimension of the layout after exposure.

[0081] In some embodiments, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the mask correction methods in the embodiments of the present application are implemented.

[0082] In some embodiments, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the mask correction methods in the embodiments of the present application are implemented.

[0083] In some embodiments, a computer program product is provided, comprising a computer program, which implements the steps of any one of the above-mentioned mask correction methods when executed by a processor.

[0084] The unexpected technical effect of the present application is: using the test layout and its known parameters to train the initial optical proximity effect correction model until the cost evaluation function and the root mean square value are minimized, thereby obtaining an optical correction model. This optical correction model can correct for the optical proximity effect and avoid the adverse effects of optical factors such as optical errors and optical proximity effects on the pre-exposure pattern. In addition, the influence of optical factors on the final post-exposure pattern error is relatively high, even up to 80% or more. Therefore, first establish an optical correction model that can at least avoid the optical proximity effect, which can at least shorten the model training time. Then, use the photochemical reaction parameters of the test layout to train the optical correction model to obtain a target layout correction model, so that the target layout correction model can compensate for the errors caused by the photochemical reaction parameters, at least ensuring that the post-exposure mask pattern is consistent with the target pattern actually desired by the user.

[0085] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include non-volatile, volatile memory, or a combination thereof. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), or graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided in this application may include a relational database, a non-relational database, or a combination thereof. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided in this application may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, or a data processing logic unit based on quantum computing.

[0086] Please note that the above embodiments are for illustrative purposes only and are not intended to limit the present application.

[0087] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0088] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0089] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the spirit of the present application, and such modifications and improvements are all within the scope of protection of the present application.

Claims

1. A mask correction method, characterized in that: include: Obtaining a test layout with known parameters, wherein the known parameters include pattern parameters, yellow light parameters, and film parameters, wherein the film parameters include the number of layers, film material, film thickness, and photoresist parameters; Training the initial optical proximity effect correction model based on the known parameters until the cost evaluation function and the root mean square value are minimized, thereby obtaining an optical correction model; Obtaining photochemical reaction parameters of the test pattern, the photochemical reaction parameters including reaction rate and diffusion rate in dense and sparse areas of the pattern, and reaction rate and diffusion rate of the photoacid and photobase in the tangential and normal directions; Training the optical correction model based on the photochemical reaction parameters until the cost evaluation function and the root mean square value are minimized, thereby obtaining a target layout correction model; The initial layout is input into the target layout correction model to obtain a corrected layout, and a target mask is obtained based on the corrected layout.

2. The mask correction method according to claim 1, wherein: The test layout includes a plurality of periodically distributed sub-patterns; Acquiring the optical correction model includes: The known parameters are used as input and the target parameters of the multiple sub-patterns in the layout after exposure are used as output, and an initial optical proximity effect correction model is trained until the cost evaluation function and the root mean square value are minimized to obtain the optical correction model.

3. The mask correction method according to claim 2, wherein: The target parameters of the plurality of sub-patterns in the layout after exposure include: coordinate information of the coordinate values of the center points of the plurality of sub-patterns in the layout after exposure.

4. The mask correction method according to any one of claims 1 to 3, characterized in that: The yellow light parameters include lighting system parameters; and / or The graphic parameters include at least one of graphic point coordinates, sub-graphic shape, spacing, and line width.

5. The mask correction method according to claim 2 or 3, characterized in that: The plurality of sub-patterns include gate patterns or conductive contact patterns.

6. The mask correction method according to any one of claims 1 to 3, characterized in that: The photochemical reaction parameters further include: a function describing the effect of baking on the critical dimensions of the layout after exposure, and / or a function describing the effect of development rate on the critical dimensions of the layout after exposure.

7. A mask correction device, characterized in that: include: A test layout acquisition module is used to acquire a test layout with known parameters, wherein the known parameters include pattern parameters, yellow light parameters, and film parameters, wherein the film parameters include the number of layers, film material, film thickness, and photoresist parameters; an optical correction model acquisition module, configured to train an initial optical proximity effect correction model based on the known parameters until both the cost evaluation function and the root mean square value are minimized, thereby obtaining the optical correction model; A photochemical reaction parameter acquisition module is used to obtain the photochemical reaction parameters of the test pattern, including the reaction rate and diffusion rate in the dense and sparse areas of the pattern, as well as the reaction rate and diffusion rate of the photoacid and photobase in the tangential and normal directions; a target layout correction model acquisition module, configured to train the optical correction model based on the photochemical reaction parameters until the cost evaluation function and the root mean square value are minimized, thereby obtaining a target layout correction model; The target mask acquisition module is used to input the initial layout into the target layout correction model to obtain a corrected layout, so as to obtain the target mask based on the corrected layout.

8. The mask correction device according to claim 7, wherein: The photochemical reaction parameters further include: a function describing the effect of baking on the critical dimensions of the layout after exposure, and / or a function describing the effect of development rate on the critical dimensions of the layout after exposure.

9. A mask correction device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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