Masking process and optical proximity joint correction method, device and equipment

Abstract

The invention relates to the field of semiconductor processing, in particular to a mask process and optical proximity joint correction method, device and equipment. Inputting the target pattern into a pre-trained joint correction model, enabling the joint correction model to take the target pattern as an initial current mask pattern, and segmenting the edge of an optical proximity correction target pattern in the current mask pattern to obtain a plurality of edge sections, performing iterative adjustment on the position of the edge section according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image item and a second linear coefficient corresponding to the photoresist image item; the edge placement errors of all the edge sections of the current mask pattern are smaller than a preset qualified threshold value; and outputting the current mask pattern of which the edge placement error of the edge section is smaller than a preset qualified threshold value as a corrected mask pattern. According to the invention, the process period and cost are reduced.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor processing, and in particular to a mask process and an optical proximity combined correction method, device, equipment, computer-readable storage medium, and a training method and device for a combined correction model of a mask process and optical proximity. Background Art

[0002] In the manufacture of large-scale integrated circuits, the chip design pattern on a mask is transferred to a wafer through a lithography process. With the evolution of the integrated circuit manufacturing process node, the pattern linewidth size on the wafer gradually shrinks. When the pattern linewidth is significantly smaller than the wavelength of the light source used in the lithography process, the diffraction effect of lithographic imaging causes an obvious deviation between the pattern on the wafer after exposure and the pattern on the mask. Therefore, it is necessary to correct the mask pattern to offset this deviation so that the pattern on the wafer after lithography is consistent with the target pattern. This correction is called Optical Proximity Correction (OPC).

[0003] At the same time, the mask used in the lithography process is usually obtained by electron beam exposure and etching processes. Due to effects such as electron beam scattering and etching deviation, there are differences between the finally obtained mask pattern and the electron beam exposure pattern. To improve the mask manufacturing accuracy, Mask Process Correction (MPC) is introduced.

[0004] In the most advanced large-scale integrated circuit manufacturing processes (such as nodes of 28 nm and below), to meet the requirements of manufacturing accuracy and process window, both optical proximity correction and mask process correction are required in the process of generating mask data from the chip design layout. Whether it is an optical proximity correction program or a mask process correction program, their operations are very time-consuming and also bring huge computing power costs. In addition, it is necessary to collect wafer measurement data to establish an optical proximity correction model, and it is also necessary to collect mask measurement data to establish a mask process correction model, resulting in a large upfront investment and a long development cycle for the entire process.

[0005] Therefore, how to reduce the cost and time consumption of the simulation correction process of generating mask data from the chip design layout while ensuring accuracy has become an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0006] The object of the present invention is to provide a mask process and optical proximity combined correction method, device, equipment, computer-readable storage medium, and a training method and device for a combined correction model of a mask process and optical proximity, so as to solve the problem in the prior art that a high cost and a long cycle are required to accurately complete the process of generating mask data according to the chip design layout.

[0007] To solve the above technical problems, the present invention provides a mask process and an optical proximity correction method, including:

[0008] Receiving a target pattern, where the target pattern includes an optical proximity correction target pattern and a sub-resolution assist feature;

[0009] Inputting the target pattern into a pre-trained joint correction model, enabling the joint correction model to use the target pattern as an initial current mask pattern, and segmenting the edges of the optical proximity correction target pattern in the current mask pattern to obtain a plurality of edge segments, and then iteratively adjusting the positions of the edge segments according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image term, and a second linear coefficient corresponding to the photoresist image term until the edge placement error of all the edge segments of the current mask pattern is less than a preset qualified threshold; wherein, the joint correction model can obtain a corresponding production simulation pattern from the current mask pattern through the mask image density function and the first linear coefficient; and can determine a corresponding optical intensity distribution according to the production simulation pattern; and can obtain a corresponding photoresist image from the optical intensity distribution through the photoresist image term and the second linear coefficient;

[0010] Outputting the current mask pattern with the edge placement error of all the edge segments less than the preset qualified threshold as a corrected mask pattern.

[0011] Optionally, in the mask process and the optical proximity joint correction method, the joint correction model iteratively adjusts the edge segments of the current mask pattern through the following three formulas:

[0012] ;

[0013] ;

[0014] ;

[0015] wherein, M(x, y) is the current mask pattern, M'(x, y) is the production simulation pattern corresponding to the current mask pattern, D i is the mask image density function, b i is the first linear coefficient;

[0016] I(x, y) is the optical intensity distribution corresponding to the production simulation pattern, TCC i is a set of cross-transfer functions based on the Hopkins imaging principle, c i is TCC i corresponding third linear coefficient;

[0017] F i is the photoresist image item, d i is the second linear coefficient, T is a preset profile threshold, and R(x, y) is the photoresist image after linear superposition of the photoresist image items.

[0018] Optionally, in the mask process and optical proximity correction method described above, the position of the edge segment is iteratively adjusted according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image item, and a second linear coefficient corresponding to the photoresist image item until the edge placement error of all edge segments of the current mask pattern is less than a preset qualified threshold, including:

[0019] The position of the edge segment is iteratively adjusted according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image item, and a second linear coefficient corresponding to the photoresist image item until the number of iterations exceeds a preset iteration threshold;

[0020] Correspondingly, outputting the current mask pattern with the edge placement error of all edge segments less than a preset qualified threshold as the corrected mask pattern includes:

[0021] When the number of iterations exceeds a preset iteration threshold, output the current mask pattern of the last iteration as the corrected mask pattern.

[0022] A mask process and optical proximity correction device, comprising:

[0023] A first receiving module, configured to receive a target pattern, where the target pattern includes an optical proximity correction target pattern and a sub-resolution assist feature;

[0024] A joint correction model module, configured to input the target pattern into a pre-trained joint correction model, so that the joint correction model uses the target pattern as an initial current mask pattern, and cuts the edges of the optical proximity correction target pattern in the current mask pattern to obtain multiple edge segments, and then iteratively adjusts the positions of the edge segments according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image item, and a second linear coefficient corresponding to the photoresist image item until the edge placement error of all edge segments of the current mask pattern is less than a preset qualified threshold; wherein, the joint correction model can obtain a corresponding production simulation pattern from the current mask pattern through the mask image density function and the first linear coefficient; and can determine a corresponding optical intensity distribution according to the production simulation pattern; and can obtain a corresponding photoresist image from the optical intensity distribution through the photoresist image item and the second linear coefficient;

[0025] An output module, configured to output a current mask pattern in which the edge placement errors of all edge segments are less than a preset qualified threshold as a corrected mask pattern.

[0026] A training method for a combined correction model of a mask process and optical proximity, wherein the combined correction model obtained by the training method for the combined correction model of the mask process and optical proximity is used for any one of the above-mentioned mask process and optical proximity combined correction methods, including:

[0027] Receiving a plurality of mask modeling patterns and lithography wafer patterns corresponding to the mask modeling patterns without mask process correction and optical proximity correction; the mask modeling patterns include a main modeling pattern and a sub-resolution assist feature; the main modeling pattern and the sub-resolution assist feature do not overlap;

[0028] Measuring the lithography wafer pattern to obtain a measured critical dimension of a wafer pattern corresponding to the main modeling pattern, and determining print-out or not information of the sub-resolution assist feature;

[0029] According to the mask modeling pattern, the measured critical dimension, and the print-out or not information, calibrating a mask image density function of a model to be trained, a first linear coefficient corresponding to the mask image density function, a photoresist image term, and a second linear coefficient corresponding to the photoresist image term; wherein, the model to be trained can obtain a corresponding training production simulation pattern from the mask modeling pattern through the mask image density function and the first linear coefficient; and can determine a corresponding training optical intensity distribution according to the training production simulation pattern; and can obtain a corresponding training photoresist image from the training optical intensity distribution through the photoresist image term and the second linear coefficient.

[0030] Optionally, in the training method for the combined correction model of the mask process and optical proximity, the model to be trained obtains a corresponding training photoresist image from the mask modeling pattern through the following three formulas, including:

[0031] ;

[0032] ;

[0033] ;

[0034] wherein, M t (x, y) is the mask modeling pattern, M t ’(x, y) is the training production simulation pattern corresponding to the mask modeling pattern, D i is the mask image density function, b i is the first linear coefficient;

[0035] I t (x, y) is the training optical intensity distribution corresponding to the training production simulation pattern, TCC i is a set of cross transfer functions based on the Hopkins imaging principle, c i is TCC i the corresponding third linear coefficient;

[0036] F i is the photoresist image term, d i is the second linear coefficient, T is a preset contour threshold, R t (x, y) is the training photoresist image after linear superposition of the photoresist image terms.

[0037] Optionally, in the training method of the combined correction model of the mask process and optical proximity, the method for calibrating the mask image density function, the first linear coefficient, the photoresist image term, and the second linear coefficient includes:

[0038] When the loss function value in the following formula is determined to be the smallest, the corresponding mask image density function, the first linear coefficient, the photoresist image term, and the second linear coefficient are calibrated:

[0039] ;

[0040] where cost is the loss function value, SCD j is the simulated critical dimension corresponding to the main modeled pattern in the training photoresist image of the mask modeled pattern, WCD j is the measured critical dimension of the mask modeled pattern, w j is the first weight coefficient corresponding to the mask modeled pattern; SP k is the simulated printed or not information corresponding to the modeled sub-resolution assist feature in the training photoresist image of the mask modeled pattern, WP k is the printed or not information of the mask modeled pattern, w k is the second weight coefficient corresponding to the modeled sub-resolution assist feature.

[0041] Optionally, in the training method of the combined correction model of the mask process and optical proximity, when the corresponding pattern is printed, the corresponding values of the printed or not information and the simulated printed or not information are 1;

[0042] When the corresponding pattern is not printed, the corresponding values of the printed or not information and the simulated printed or not information are 0.

[0043] A training device for a combined correction model of a mask process and optical proximity. The combined correction model obtained by the training device for the combined correction model of the mask process and optical proximity is used for any one of the above-mentioned mask process and optical proximity combined correction methods, and includes:

[0044] A second receiving module, configured to receive a plurality of mask modeling patterns and lithography wafer patterns corresponding to the mask modeling patterns without mask process correction and optical proximity correction; the mask modeling patterns include a modeling main pattern and a modeling sub-resolution assist feature; the modeling main pattern and the modeling sub-resolution assist feature do not overlap;

[0045] A measurement module, configured to measure the lithography wafer patterns, obtain the measured critical dimensions of the wafer patterns corresponding to the modeling main pattern, and determine the information on whether the modeling sub-resolution assist feature is printed out;

[0046] A calibration module, configured to calibrate the mask image density function of the model to be trained, the first linear coefficient corresponding to the mask image density function, the photoresist image term, and the second linear coefficient corresponding to the photoresist image term according to the mask modeling patterns, the measured critical dimensions, and the information on whether the modeling sub-resolution assist feature is printed out; wherein, the model to be trained can obtain a corresponding training production simulation pattern from the mask modeling patterns through the mask image density function and the first linear coefficient; and can determine a corresponding training optical intensity distribution according to the training production simulation pattern; and can obtain a corresponding training photoresist image from the training optical intensity distribution through the photoresist image term and the second linear coefficient.

[0047] A mask process and optical proximity combined correction device, including:

[0048] A memory, configured to store a computer program;

[0049] A processor, configured to implement the steps of any one of the above-mentioned mask process and optical proximity combined correction methods and / or the steps of any one of the above-mentioned training methods for the combined correction model of the mask process and optical proximity when executing the computer program.

[0050] The mask process and optical proximity combined correction method provided by the present invention receive a target pattern, where the target pattern includes an optical proximity correction target pattern and a sub-resolution assist feature; input the target pattern into a pre-trained combined correction model, so that the combined correction model uses the target pattern as the initial current mask pattern, and divides the edges of the optical proximity correction target pattern in the current mask pattern to obtain a plurality of edge segments, and then iteratively adjusts the positions of the edge segments according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image term, and a second linear coefficient corresponding to the photoresist image term until the edge placement error of all the edge segments of the current mask pattern is less than a preset qualified threshold; wherein, the combined correction model can obtain a corresponding production simulation pattern from the current mask pattern through the mask image density function and the first linear coefficient; and can determine a corresponding optical intensity distribution according to the production simulation pattern; and can obtain a corresponding photoresist image from the optical intensity distribution through the photoresist image term and the second linear coefficient; output the current mask pattern with the edge placement error of all the edge segments less than the preset qualified threshold as the corrected mask pattern. The combined correction model in the present invention takes into account both the deviations that may occur in the mask production process and the deviations that may occur during the imaging process of the mask lithography on the wafer. It can directly generate the corrected mask pattern after the grinding process from the target pattern required for optical proximity correction, greatly improving the operation speed while ensuring the accuracy. In addition, for the pre-training of the combined correction model, only the electron beam exposure pattern designed in the system (i.e., the mask modeling pattern) needs to be determined, and the measurement data of the corresponding obtained lithography wafer pattern is collected, which also greatly reduces the process cycle and the upfront investment cost. The present invention also provides a mask process and an optical proximity combined correction device, equipment, computer-readable storage medium with the above beneficial effects, and a training method and device for a combined correction model of the mask process and optical proximity. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0052] Figure 1 It is a schematic flowchart of a specific implementation manner of the mask process and optical proximity combined correction method provided by the present invention;

[0053] Figure 2Schematic diagram of another specific implementation of the mask process and optical proximity combined correction method provided by the present invention;

[0054] Figure 3 Process structure diagram of a specific implementation of the mask process and optical proximity combined correction device provided by the present invention;

[0055] Figure 4 Schematic diagram of a specific implementation of the training method of the combined correction model for the mask process and optical proximity provided by the present invention;

[0056] Figure 5 Schematic diagram of a specific implementation of the training device of the combined correction model for the mask process and optical proximity provided by the present invention.

[0057] Reference numerals:

[0058] 01 - Optical proximity correction target pattern; 02 - Sub - resolution assist pattern; 110 - First receiving module; 120 - Combined correction model module; 130 - Output module; 210 - Second receiving module; 220 - Measuring module; 230 - Calibration module. Specific implementation

[0059] In order to enable those skilled in the art to better understand the solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementations. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0060] The core of the present invention is to provide a mask process and optical proximity combined correction method. The schematic diagram of a specific implementation thereof is as shown in Figure 1 shown, which is called Specific Implementation 1 and includes:

[0061] S101: Receive a target pattern, where the target pattern includes an optical proximity correction target pattern and a sub - resolution assist pattern.

[0062] Of course, the sub - resolution assist pattern can be set inside the optical proximity correction target pattern or outside the optical proximity correction target pattern, and the present invention does not limit this here. In addition, the sub - resolution assist pattern added according to a preset rule can be a simple rectangle, such as Figure 2 shown in (a) of Figure 2 or a pattern pre - corrected according to a fixed rule, such as Figure 2The optical proximity correction target pattern is marked with 01, and the sub-resolution assist feature is marked with 02. The pre-corrected sub-resolution assist feature is closer to the target after mask manufacturing, which generally helps to increase the lithography process window. Since the subsequent mask process and optical proximity co-correction are based on the optical proximity correction target pattern, the sub-resolution assist feature cannot be iteratively corrected based on the model during this process. Based on this, in order to meet the requirements of the lithography process window, the pre-corrected sub-resolution assist feature can be directly added before co-correction.

[0063] S102: Input the target pattern into a pre-trained co-correction model, so that the co-correction model uses the target pattern as the initial current mask pattern, and divides the edges of the optical proximity correction target pattern in the current mask pattern to obtain a plurality of edge segments. Then, based on a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image term, and a second linear coefficient corresponding to the photoresist image term, iteratively adjust the positions of the edge segments until the edge placement errors of all the edge segments of the current mask pattern are less than a preset qualified threshold; wherein, the co-correction model can obtain a corresponding production simulation pattern from the current mask pattern through the mask image density function and the first linear coefficient; and can determine a corresponding optical intensity distribution according to the production simulation pattern; and can obtain a corresponding photoresist image from the optical intensity distribution through the photoresist image term and the second linear coefficient.

[0064] Specifically, the co-correction model iteratively adjusts the edge segments of the current mask pattern through the following formulas (1), (2), and (3):

[0065] ; (1)

[0066] ; (2)

[0067] ; (3)

[0068] Wherein, M(x, y) is the current mask pattern, M'(x, y) is the production simulation pattern corresponding to the current mask pattern, D i is the mask image density function, b i is the first linear coefficient;

[0069] I(x, y) is the optical intensity distribution corresponding to the production simulation pattern, TCC i is a set of cross transfer functions based on the Hopkins imaging principle, c i is the third linear coefficient corresponding to TCC i ;

[0070] F i is the photoresist image item, d i is the second linear coefficient, T is a preset profile threshold, and R(x, y) is the photoresist image after linear superposition of the photoresist image items.

[0071] Equation (1) corresponding to the joint correction model can obtain the corresponding production simulation pattern from the current mask pattern through the mask image density function and the first linear coefficient; Equation (2) corresponds to determining the corresponding optical intensity distribution according to the production simulation pattern; Equation (3) corresponds to obtaining the corresponding photoresist image from the optical intensity distribution through the photoresist image item and the second linear coefficient. Of course, the cross-transfer function and the third linear coefficient in Equation (2) are functions and coefficients determined according to process conditions, which can refer to the prior art and will not be elaborated herein. In addition, represents a convolution operation.

[0072] In Equation (3), R(x, y) on the left side is the photoresist image, which is essentially a monochromatic light and dark image, and T on the right side, that is, the profile threshold, is a specific light and dark value. Outlining a specific shape along the profile threshold on the photoresist image is the pattern formed on the wafer after lithography simulated by the system.

[0073] After edge segment segmentation of the optical proximity correction target pattern, control points need to be placed at the center points of each edge segment, and the segmentation of the edge segments is performed according to a preset rule.

[0074] Based on the current mask pattern, use the mask process and optical proximity joint correction model described in Equation (1), Equation (2), and Equation (3) to calculate the edge placement error (Edge Placement Error, i.e., EPE) of each edge segment on the optical proximity correction target pattern, that is, the distance from the predicted wafer profile after lithography (obtainable from the photoresist image) to the corresponding edge segment on the target pattern. It should be noted that although the initial current mask pattern is the target pattern, after each subsequent iteration, the current mask pattern changes as each edge segment on the optical proximity correction target pattern in the target pattern moves.

[0075] When moving the edge segment, the edge placement error of each edge segment obtained above is multiplied by a preset constant to obtain the distance that the edge segment needs to move. Move the corresponding edge segment along the normal direction according to the calculated moving distance.

[0076] S103: Output the current mask pattern with the edge placement errors of all edge segments less than a preset qualified threshold as the corrected mask pattern.

[0077] In the previous step, the movement of the edge segments and the calculation of the edge placement error after moving the edge segments are continuously repeated until the edge placement errors of all edge segments are less than a preset threshold, obtaining the final jointly corrected mask pattern, that is, the corrected mask pattern.

[0078] In actual use, it is necessary to magnify the corrected mask pattern according to the lithographic imaging reduction factor (usually 4), and then fragment it to obtain the electron beam exposure pattern, which is the final mask data.

[0079] As another specific implementation manner, the position of the edge segments is iteratively adjusted according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image item, and a second linear coefficient corresponding to the photoresist image item until the edge placement errors of all edge segments of the current mask pattern are less than a preset qualified threshold, including:

[0080] A1: The position of the edge segments is iteratively adjusted according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image item, and a second linear coefficient corresponding to the photoresist image item until the number of iterations exceeds a preset iteration threshold.

[0081] Correspondingly, outputting the current mask pattern with the edge placement errors of all edge segments less than a preset qualified threshold as the corrected mask pattern includes:

[0082] A2: When the number of iterations exceeds a preset iteration threshold, output the current mask pattern of the last iteration as the corrected mask pattern.

[0083] In this specific implementation manner, an upper limit is set for the number of iterations, that is, the iteration threshold. When the edge placement error still cannot reach the qualified threshold after multiple iterations, the iteration can be directly ended, thus saving computing power.

[0084] The mask process and optical proximity combined correction method provided by the present invention receive a target pattern, where the target pattern includes an optical proximity correction target pattern and a sub-resolution assist feature; input the target pattern into a pre-trained combined correction model, so that the combined correction model uses the target pattern as the initial current mask pattern, and segments the edges of the optical proximity correction target pattern in the current mask pattern to obtain a plurality of edge segments, and then iteratively adjusts the positions of the edge segments according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image term, and a second linear coefficient corresponding to the photoresist image term until the edge placement error of all the edge segments of the current mask pattern is less than a preset qualified threshold; wherein, the combined correction model can obtain a corresponding production simulation pattern from the current mask pattern through the mask image density function and the first linear coefficient; and can determine a corresponding optical intensity distribution according to the production simulation pattern; and can obtain a corresponding photoresist image from the optical intensity distribution through the photoresist image term and the second linear coefficient; output the current mask pattern with the edge placement error of all the edge segments less than the preset qualified threshold as the corrected mask pattern. The combined correction model in the present invention takes into account the deviations that may occur in the mask production process and the deviations that may occur in the process of imaging on the wafer through mask lithography at the same time. It can directly generate the corrected mask pattern after the grinding process from the target pattern required for optical proximity correction, greatly improving the operation speed while ensuring the accuracy. In addition, for the pre-training of the combined correction model, only the electron beam exposure pattern designed in the system (i.e., the mask modeling pattern) needs to be determined, and the measurement data of the corresponding photolithographic wafer pattern is collected, which also greatly reduces the process cycle and the upfront investment cost.

[0085] The mask process and optical proximity combined correction device provided by the embodiments of the present invention will be introduced below. The mask process and optical proximity combined correction device described below can be correspondingly referred to the mask process and optical proximity combined correction method described above.

[0086] Figure 3 It is a structural block diagram of the mask process and optical proximity combined correction device provided by the embodiments of the present invention, which is called the second specific embodiment. Refer to Figure 3 The mask process and optical proximity combined correction device may include:

[0087] A first receiving module 110, configured to receive a target pattern, where the target pattern includes an optical proximity correction target pattern and a sub-resolution assist feature;

[0088] The joint calibration model module 120 is used to input the target pattern into a pre-trained joint calibration model, so that the joint calibration model takes the target pattern as the initial current mask pattern, and segments the edges of the optical proximity correction target graphics in the current mask pattern to obtain a plurality of edge segments. Then, according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image term, and a second linear coefficient corresponding to the photoresist image term, the positions of the edge segments are iteratively adjusted until the edge placement errors of all the edge segments of the current mask pattern are less than a preset qualified threshold. Wherein, the joint calibration model can obtain a corresponding production simulation pattern from the current mask pattern through the mask image density function and the first linear coefficient; and can determine a corresponding optical intensity distribution according to the production simulation pattern; and can obtain a corresponding photoresist image from the optical intensity distribution through the photoresist image term and the second linear coefficient.

[0089] The output module 130 is used to output the current mask pattern with the edge placement errors of all the edge segments less than a preset qualified threshold as the corrected mask pattern.

[0090] As a preferred embodiment, the joint calibration model module 120 includes:

[0091] The formula model unit is used to iteratively adjust the edge segments of the current mask pattern by the joint calibration model through the following formulas (1), (2), and (3):

[0092] ; (1)

[0093] ; (2)

[0094] ; (3)

[0095] Wherein, M(x, y) is the current mask pattern, M'(x, y) is the production simulation pattern corresponding to the current mask pattern, D i is the mask image density function, b i is the first linear coefficient;

[0096] I(x, y) is the optical intensity distribution corresponding to the production simulation pattern, TCC i is a set of cross transfer functions based on the Hopkins imaging principle, c i is the third linear coefficient corresponding to TCC i ;

[0097] F i is the photoresist image term, d iis the second linear coefficient, T is a preset profile threshold, and R(x, y) is the photoresist image after linear superposition of the photoresist image terms.

[0098] As a preferred embodiment, the joint correction model module 120 includes:

[0099] An iterative threshold unit, configured to iteratively adjust the position of the edge segment according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image term, and a second linear coefficient corresponding to the photoresist image term until the number of iterations exceeds a preset iteration threshold;

[0100] Correspondingly, the output module 130 includes:

[0101] A threshold output unit, configured to output the current mask pattern of the last iteration as a corrected mask pattern when the number of iterations exceeds the preset iteration threshold.

[0102] The mask process and optical proximity co - correction device provided by the present invention includes a first receiving module 110 for receiving a target pattern, where the target pattern includes an optical proximity correction target pattern and a sub - resolution assist feature; a co - correction model module 120 for inputting the target pattern into a pre - trained co - correction model, enabling the co - correction model to use the target pattern as an initial current mask pattern, and segmenting the edges of the optical proximity correction target pattern in the current mask pattern to obtain a plurality of edge segments, and then iteratively adjusting the positions of the edge segments according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image term, and a second linear coefficient corresponding to the photoresist image term until the edge placement error of all the edge segments of the current mask pattern is less than a preset qualified threshold; wherein, the co - correction model can obtain a corresponding production simulation pattern from the current mask pattern through the mask image density function and the first linear coefficient; and can determine a corresponding optical intensity distribution according to the production simulation pattern; and can obtain a corresponding photoresist image from the optical intensity distribution through the photoresist image term and the second linear coefficient; an output module 130 for outputting the current mask pattern with the edge placement error of all the edge segments less than the preset qualified threshold as a corrected mask pattern. The co - correction model in the present invention simultaneously considers the deviations that may occur in the mask production process and the deviations that may occur during the imaging process of the mask lithography on the wafer. It can directly generate a corrected mask pattern after grinding process correction from the target pattern required for optical proximity correction, greatly improving the operation speed while ensuring the accuracy. In addition, for the pre - training of the co - correction model, only the electron beam exposure pattern designed in the system (i.e., the mask modeling pattern) needs to be determined, and the measurement data of the corresponding lithography wafer pattern is collected, which also greatly reduces the process cycle and the upfront investment cost.

[0103] The mask process and optical proximity co - correction device of this embodiment is used to implement the foregoing mask process and optical proximity co - correction method. Therefore, the specific implementation manners in the mask process and optical proximity co - correction device can be seen in the embodiment part of the mask process and optical proximity co - correction method in the foregoing text. For example, the first receiving module 110, the co - correction model module 120, and the output module 130 are respectively used to implement steps S101, S102, and S103 in the foregoing mask process and optical proximity co - correction method. Therefore, its specific implementation manners can refer to the descriptions of the corresponding individual embodiment parts and will not be elaborated here.

[0104] The present invention also provides a training method for a co - correction model of a mask process and optical proximity. The schematic flow diagram of a specific implementation manner is as Figure 4As shown, which is called the third specific implementation manner, the joint correction model obtained by the training method of the mask process and the optical proximity joint correction model is used for any one of the above-mentioned mask process and optical proximity joint correction methods, including:

[0105] S201: Receive a plurality of mask modeling patterns and the lithography wafer patterns corresponding to the mask modeling patterns without mask process correction and optical proximity correction; the mask modeling patterns include modeling main patterns and modeling sub-resolution assist patterns; the modeling main patterns and the modeling sub-resolution assist patterns do not overlap.

[0106] A plurality of the mask modeling patterns can come from the typical patterns in the optical proximity correction model modeling and the typical patterns in the mask process correction model modeling, that is, one-dimensional and two-dimensional patterns with different geometric features, pattern densities, and lithography imaging features.

[0107] Of course, the lithography wafer pattern in this step is a pattern that has not undergone any correction at all, reflecting the appearance that the mask modeling pattern will finally form under various condition interferences during the production process. In addition, the modeling sub-resolution assist pattern can also be a simple rectangle or a pre-corrected pattern, which is not limited in the present invention.

[0108] S202: Measure the lithography wafer pattern, obtain the measured critical dimension of the wafer pattern corresponding to the modeling main pattern, and determine the information on whether the modeling sub-resolution assist pattern is printed out or not.

[0109] The acquisition of the measured critical dimension requires actual production operations. The mask modeling pattern is actually used to produce a wafer through the production process, and then the measurement is obtained on the wafer. Of course, it can also be directly seen from the produced wafer whether the modeling sub-resolution assist pattern is printed out.

[0110] Preferably, when the corresponding pattern is printed out, the value of the information on whether it is printed out or not and the information on whether it is printed out or not in simulation is 1;

[0111] When the corresponding pattern is not printed out, the value of the information on whether it is printed out or not and the information on whether it is printed out or not in simulation is 0.

[0112] In other words, the information on whether it is printed out or not only includes "yes" and "no", which can be represented by "1" and "0", and the calculation is convenient and fast. Of course, other ways can also be used to represent the different results of the information on whether it is printed out or not and the information on whether it is printed out or not in simulation, which will not be elaborated too much in the present invention.

[0113] S203: Calibrate the mask image density function of the model to be trained, the first linear coefficient corresponding to the mask image density function, the photoresist image term, and the second linear coefficient corresponding to the photoresist image term according to the mask modeling pattern, the measured critical dimension, and the printed or not information; wherein, the model to be trained can obtain the corresponding training production simulation pattern from the mask modeling pattern through the mask image density function and the first linear coefficient; and can determine the corresponding training optical intensity distribution according to the training production simulation pattern; and can obtain the corresponding training photoresist image from the training optical intensity distribution through the photoresist image term and the second linear coefficient.

[0114] Specifically, the model to be trained obtains the corresponding training photoresist image from the mask modeling pattern through the following formulas (4), (5), and (6), including:

[0115] ; (4)

[0116] ; (5)

[0117] ; (6)

[0118] wherein, M t (x, y) is the mask modeling pattern, M t ’(x, y) is the training production simulation pattern corresponding to the mask modeling pattern, D i is the mask image density function, b i is the first linear coefficient;

[0119] I t (x, y) is the training optical intensity distribution corresponding to the training production simulation pattern, TCC i is a set of cross transfer functions based on the Hopkins imaging principle, c i is the third linear coefficient corresponding to TCC i ;

[0120] F i is the photoresist image term, d i is the second linear coefficient, T is a preset contour threshold, and R t (x, y) is the training photoresist image after linear superposition of the photoresist image term.

[0121] The formulas (4), (5), and (6) in the model to be trained can be considered to correspond to the formulas (1), (2), and (3) of the mask process and optical proximity correction method in the previous text. For specific technical details, reference can also be made to the previous text, and the present invention will not elaborate here.

[0122] As a preferred embodiment, the method for calibrating the mask image density function, the first linear coefficient, the photoresist image term, and the second linear coefficient includes:

[0123] When the loss function value in the following formula (7) is determined to be the smallest, the corresponding mask image density function, the first linear coefficient, the photoresist image term, and the second linear coefficient are calibrated:

[0124] ; (7)

[0125] where cost is the loss function value, SCD j is the simulated critical dimension corresponding to the main modeled pattern in the training photoresist image of the mask modeled pattern, WCD j is the measured critical dimension of the main modeled pattern (on the mask modeled pattern), w j is the first weight coefficient corresponding to the mask modeled pattern; SP k is the simulated print-out or not information corresponding to the modeled sub-resolution assist feature in the training photoresist image of the mask modeled pattern, WP k is the print-out or not information of the mask modeled pattern, w k is the second weight coefficient corresponding to the modeled sub-resolution assist feature.

[0126] The first term of the loss function indicates that the critical dimension of the corresponding pattern (i.e., the simulated critical dimension) of the main modeled pattern after being simulated by the model to be trained in the training photoresist image should have as small a gap as possible from the measured critical dimension of the main modeled pattern obtained through actual production, so that the cost will decrease. The second term of the loss function is to check whether the modeled sub-resolution assist feature is printed. For example, in actual production, if the modeled sub-resolution assist feature is not printed (it can be set to 0 for not printed and 1 for printed in the system), then ideally, it should not be drawn in the training photoresist image obtained by model simulation either. In this way, when the two situations are the same, the difference between SP k and WP k is 0, otherwise it is not 0. Of course, when the difference is 0, the cost can be further reduced.

[0127] The training method of the joint correction model for the mask process and optical proximity provided by the present invention receives a variety of mask modeling patterns and the lithography wafer patterns corresponding to the mask modeling patterns without mask process correction and optical proximity correction; the mask modeling patterns include modeling main patterns and modeling sub-resolution assist patterns; the modeling main patterns and the modeling sub-resolution assist patterns do not overlap; measure the lithography wafer patterns to obtain the measured critical dimensions of the wafer patterns corresponding to the modeling main patterns, and determine the print-out or not information of the modeling sub-resolution assist patterns; according to the mask modeling patterns, the measured critical dimensions and the print-out or not information, calibrate the mask image density function of the model to be trained, the first linear coefficient corresponding to the mask image density function, the photoresist image term, and the second linear coefficient corresponding to the photoresist image term; wherein, the model to be trained can obtain the corresponding training production simulation pattern from the mask modeling pattern through the mask image density function and the first linear coefficient; and can determine the corresponding training optical intensity distribution according to the training production simulation pattern; and can obtain the corresponding training photoresist image from the training optical intensity distribution through the photoresist image term and the second linear coefficient. The joint correction model in the present invention takes into account both the deviations that may occur in the mask production process and the deviations that may occur during the imaging process on the wafer through mask lithography. It can directly generate the corrected mask pattern after grinding process correction from the target pattern required for optical proximity correction, greatly improving the operation speed while ensuring the accuracy. In addition, for the preliminary training of the joint correction model, only the electron beam exposure pattern designed in the system (i.e., the mask modeling pattern) needs to be determined, and the measurement data of the corresponding lithography wafer patterns needs to be collected, which also greatly reduces the process cycle and the upfront investment cost.

[0128] The training device for the joint correction model of the mask process and optical proximity provided by the embodiments of the present invention will be introduced below. The training device for the joint correction model of the mask process and optical proximity described below can be correspondingly referred to the training method for the joint correction model of the mask process and optical proximity described above.

[0129] Figure 5 It is a structural block diagram of the training device for the joint correction model of the mask process and optical proximity provided by the embodiments of the present invention, which is called the specific implementation method four. Refer to Figure 5 The training device for the joint correction model of the mask process and optical proximity may include:

[0130] A second receiving module 210, configured to receive a variety of mask modeling patterns and the lithography wafer patterns corresponding to the mask modeling patterns without mask process correction and optical proximity correction; the mask modeling patterns include modeling main patterns and modeling sub-resolution assist patterns; the modeling main patterns and the modeling sub-resolution assist patterns do not overlap;

[0131] A measurement module 220, configured to measure the lithographic wafer pattern, obtain the measured critical dimension of the wafer pattern corresponding to the main modeled pattern, and determine the information on whether or not the modeled sub-resolution assist feature is printed out;

[0132] A calibration module 230, configured to calibrate the mask image density function of the model to be trained, the first linear coefficient corresponding to the mask image density function, the photoresist image term, and the second linear coefficient corresponding to the photoresist image term according to the mask modeled pattern, the measured critical dimension, and the information on whether or not the modeled sub-resolution assist feature is printed out; wherein, the model to be trained can obtain a corresponding training production simulation pattern from the mask modeled pattern through the mask image density function and the first linear coefficient; and can determine a corresponding training optical intensity distribution according to the training production simulation pattern; and can obtain a corresponding training photoresist image from the training optical intensity distribution through the photoresist image term and the second linear coefficient.

[0133] As a preferred embodiment, the calibration module 230 includes:

[0134] A formula calibration unit, configured to obtain a corresponding training photoresist image from the mask modeled pattern by the model to be trained through the following formulas (4), (5), and (6), including:

[0135] ; (4)

[0136] ; (5)

[0137] ; (6)

[0138] wherein, M t (x, y) is the mask modeled pattern, M t '(x, y) is the training production simulation pattern corresponding to the mask modeled pattern, D i is the mask image density function, b i is the first linear coefficient;

[0139] I t (x, y) is the training optical intensity distribution corresponding to the training production simulation pattern, TCC i is a set of cross transfer functions based on the Hopkins imaging principle, c i is the third linear coefficient corresponding to TCC i ;

[0140] F i is the photoresist image term, d i is the second linear coefficient, T is a preset contour threshold, Rt (x, y) is the training photoresist image after linear superposition of the photoresist image items.

[0141] As a preferred implementation, the calibration module 230 includes:

[0142] A loss function unit, configured to complete calibration of the mask image density function, the first linear coefficient, the photoresist image item, and the second linear coefficient when the loss function value in the following formula (7) is minimized:

[0143] ; (7)

[0144] where cost is the loss function value, SCD j is the simulated critical dimension corresponding to the main modeled pattern in the training photoresist image of the mask modeled pattern, WCD j is the measured critical dimension of the mask modeled pattern, w j is the first weight coefficient corresponding to the mask modeled pattern; SP k is the simulated printed or not information corresponding to the modeled sub-resolution assist feature in the training photoresist image of the mask modeled pattern, WP k is the printed or not information of the mask modeled pattern, w k is the second weight coefficient corresponding to the modeled sub-resolution assist feature.

[0145] The training device for the combined correction model of the mask process and optical proximity provided by the present invention includes a second receiving module 210, which is used to receive a variety of mask modeling patterns and the lithography wafer patterns corresponding to the mask modeling patterns without mask process correction and optical proximity correction; the mask modeling patterns include main modeling graphics and sub-resolution assist modeling graphics; the main modeling graphics and the sub-resolution assist modeling graphics do not overlap; a measurement module 220, which is used to measure the lithography wafer patterns, obtain the measured critical dimensions of the wafer graphics corresponding to the main modeling graphics, and determine the print-out or not information of the sub-resolution assist modeling graphics; a calibration module 230, which is used to calibrate the mask image density function of the model to be trained, the first linear coefficient corresponding to the mask image density function, the photoresist image term, and the second linear coefficient corresponding to the photoresist image term according to the mask modeling patterns, the measured critical dimensions, and the print-out or not information; wherein, the model to be trained can obtain the corresponding training production simulation pattern from the mask modeling patterns through the mask image density function and the first linear coefficient; and can determine the corresponding training optical intensity distribution according to the training production simulation pattern; and can obtain the corresponding training photoresist image from the training optical intensity distribution through the photoresist image term and the second linear coefficient. In the combined correction model of the present invention, the deviations that may occur in the mask production process and the deviations that may occur in the process of imaging on the wafer through mask lithography are considered simultaneously. The corrected mask pattern after the grinding process correction can be directly generated from the target pattern required for optical proximity correction, which greatly improves the operation rate while ensuring the accuracy. In addition, for the preliminary training of the combined correction model, only the electron beam exposure pattern designed in the system (i.e., the mask modeling pattern) needs to be determined, and the measurement data of the corresponding lithography wafer patterns needs to be collected, which also greatly reduces the process cycle and the upfront investment cost.

[0146] The training device for the combined correction model of the mask process and optical proximity in this embodiment is used to implement the foregoing training method for the combined correction model of the mask process and optical proximity. Therefore, the specific implementation manners in the training device for the combined correction model of the mask process and optical proximity can be seen in the embodiment part of the training method for the combined correction model of the mask process and optical proximity in the foregoing text. For example, the second receiving module 210, the measurement module 220, and the calibration module 230 are respectively used to implement steps S201, S202, and S203 in the foregoing training method for the combined correction model of the mask process and optical proximity. Therefore, the specific implementation manners can refer to the descriptions of the corresponding individual embodiments and will not be elaborated here.

[0147] The present invention also provides a combined correction device for the mask process and optical proximity, including:

[0148] A memory for storing a computer program;

[0149] A processor, which is configured to implement the steps of any one of the above mask processes and optical proximity correction methods and / or the steps of the training method of the joint correction model of any one of the above mask processes and optical proximity when executing the computer program. The mask process and optical proximity joint correction method provided by the present invention receives a target pattern, where the target pattern includes an optical proximity correction target pattern and a sub-resolution assist feature; inputs the target pattern into a pre-trained joint correction model, so that the joint correction model uses the target pattern as an initial current mask pattern, and segments the edges of the optical proximity correction target pattern in the current mask pattern to obtain a plurality of edge segments, and then iteratively adjusts the positions of the edge segments according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image term, and a second linear coefficient corresponding to the photoresist image term until the edge placement error of all the edge segments of the current mask pattern is less than a preset qualified threshold; wherein, the joint correction model can obtain a corresponding production simulation pattern from the current mask pattern through the mask image density function and the first linear coefficient; and can determine a corresponding optical intensity distribution according to the production simulation pattern; and can obtain a corresponding photoresist image from the optical intensity distribution through the photoresist image term and the second linear coefficient; outputs the current mask pattern with the edge placement error of all the edge segments less than the preset qualified threshold as a corrected mask pattern. The joint correction model in the present invention takes into account both the deviations that may occur in the mask production process and the deviations that may occur during the imaging process on the wafer through mask lithography, and can directly generate a corrected mask pattern after grinding process correction from the target pattern required for optical proximity correction, greatly improving the operation speed while ensuring the accuracy. In addition, for the pre-training of the joint correction model, only the electron beam exposure pattern designed in the system (i.e., the mask modeling pattern) needs to be determined, and the measurement data of the corresponding lithography wafer pattern is collected, which also greatly reduces the process cycle and the upfront investment cost.

[0150] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the above-mentioned mask processes and optical proximity correction methods and / or the steps of the training method of the joint correction model of any one of the above-mentioned mask processes and optical proximity are implemented. The mask process and optical proximity joint correction method provided by the present invention receives a target pattern, where the target pattern includes an optical proximity correction target pattern and a sub-resolution assist feature; inputs the target pattern into a pre-trained joint correction model, so that the joint correction model uses the target pattern as an initial current mask pattern, and cuts the edges of the optical proximity correction target pattern in the current mask pattern to obtain a plurality of edge segments, and then iteratively adjusts the positions of the edge segments according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image term, and a second linear coefficient corresponding to the photoresist image term until the edge placement error of all the edge segments of the current mask pattern is less than a preset qualified threshold; wherein, the joint correction model can obtain a corresponding production simulation pattern from the current mask pattern through the mask image density function and the first linear coefficient; and can determine a corresponding optical intensity distribution according to the production simulation pattern; and can obtain a corresponding photoresist image from the optical intensity distribution through the photoresist image term and the second linear coefficient; outputs the current mask pattern with the edge placement error of all the edge segments less than the preset qualified threshold as a corrected mask pattern. The joint correction model in the present invention takes into account both the deviations that may occur in the mask production process and the deviations that may occur during the imaging process of the mask lithography on the wafer. It can directly generate a corrected mask pattern after grinding process correction from the target pattern required for optical proximity correction, greatly improving the operation speed while ensuring the accuracy. In addition, for the preliminary training of the joint correction model, only the electron beam exposure pattern designed in the system (i.e., the mask modeling pattern) needs to be determined, and the measurement data of the corresponding lithography wafer pattern is collected, which also greatly reduces the process cycle and the upfront investment cost.

[0151] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For the relevant parts, reference can be made to the description in the method part.

[0152] It should be noted that in this specification, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0153] Those skilled in the art can further realize that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled artisans can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0154] The steps of the methods or algorithms described in connection with the embodiments disclosed herein can be directly implemented by hardware, software modules executed by a processor, or a combination of the two. The software modules can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well known in the art.

[0155] The above has introduced in detail the mask process and the optical proximity correction method, device, equipment, computer-readable storage medium provided by the present invention, and a training method and device for a joint correction model of a mask process and optical proximity. Specific examples are used herein to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention. It should be noted that for those of ordinary skill in the art of this technology, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the protection scope of the present invention.

Claims

1. A mask process and optical proximity joint correction method, characterized in that: include: receiving a target pattern, wherein the target pattern includes an optical proximity correction target pattern and a sub-resolution auxiliary pattern; The target pattern is input into a pre-trained joint correction model, so that the joint correction model uses the target pattern as an initial current mask pattern, and segments the edge of the optical proximity correction target pattern in the current mask pattern to obtain a plurality of edge segments, and then iteratively adjusts the position of the edge segment according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image item, and a second linear coefficient corresponding to the photoresist image item, until the edge placement error of all edge segments of the current mask pattern is less than a preset qualified threshold value; wherein the joint correction model can obtain a corresponding production simulation pattern from the current mask pattern through the mask image density function and the first linear coefficient; and can determine a corresponding optical intensity distribution according to the production simulation pattern; and can obtain a corresponding photoresist image from the optical intensity distribution through the photoresist image item and the second linear coefficient; The current mask pattern in which the edge placement errors of all edge segments are smaller than a preset qualified threshold is output as the correction mask pattern.

2. The mask process and optical proximity joint correction method according to claim 1, characterized in that: The joint correction model iteratively adjusts the edge segment of the current mask pattern through the following three formulas: ； ； ； Wherein, M(x, y) is the current mask pattern, M'(x, y) is the production simulation pattern corresponding to the current mask pattern, and D i is the mask image density function, b i is the first linear coefficient; I(x, y) is the optical intensity distribution corresponding to the production simulation pattern, TCC i is a set of cross transfer functions based on the Hopkins imaging principle, c i For TCC i The corresponding third linear coefficient; F i is the photoresist image item, d i is the second linear coefficient, T is a preset contour threshold, and R(x, y) is the photoresist image after the photoresist image items are linearly superimposed.

3. The mask process and optical proximity joint correction method according to claim 1, characterized in that: Iteratively adjusting the position of the edge segment according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image item, and a second linear coefficient corresponding to the photoresist image item until the edge placement error of all edge segments of the current mask pattern is less than a preset qualified threshold value includes: Iteratively adjusting the position of the edge segment according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image item, and a second linear coefficient corresponding to the photoresist image item until the number of iterations exceeds a preset iteration threshold; Accordingly, outputting the current mask pattern with edge placement errors of all edge segments less than a preset qualified threshold as the correction mask pattern includes: When the number of iterations exceeds a preset iteration threshold, the current mask pattern of the last iteration is output as the correction mask pattern.

4. A mask process and optical proximity joint correction device, characterized in that: include: A first receiving module, configured to receive a target pattern, wherein the target pattern includes an optical proximity correction target pattern and a sub-resolution auxiliary pattern; A joint correction model module, used for inputting the target pattern into a pre-trained joint correction model, so that the joint correction model uses the target pattern as an initial current mask pattern, and segments the edge of the optical proximity correction target pattern in the current mask pattern to obtain a plurality of edge segments, and then iteratively adjusts the position of the edge segment according to a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image item, and a second linear coefficient corresponding to the photoresist image item, until the edge placement error of all edge segments of the current mask pattern is less than a preset qualified threshold value; wherein the joint correction model can obtain a corresponding production simulation pattern from the current mask pattern through the mask image density function and the first linear coefficient; and can determine a corresponding optical intensity distribution according to the production simulation pattern; and can obtain a corresponding photoresist image from the optical intensity distribution through the photoresist image item and the second linear coefficient; The output module is used to output the current mask pattern with edge placement errors of all edge segments being less than a preset qualified threshold as a correction mask pattern.

5. A method for training a joint correction model of mask process and optical proximity, characterized in that: The joint correction model obtained by the training method of the joint correction model of the mask process and optical proximity is used in the joint correction method of the mask process and optical proximity as claimed in any one of claims 1 to 3, comprising: Receiving a plurality of mask modeling patterns and a lithography wafer pattern corresponding to the mask modeling patterns without mask process correction and optical proximity correction; the mask modeling pattern comprises a modeling main figure and a modeling sub-resolution auxiliary figure; the modeling main figure and the modeling sub-resolution auxiliary figure do not overlap; Measuring the photolithography wafer pattern to obtain the measured critical dimensions of the wafer pattern corresponding to the modeling main pattern, and determining whether the modeling sub-resolution auxiliary pattern is printed out; According to the mask modeling pattern, the measured key dimensions and the printing or not information, the mask image density function, the first linear coefficient corresponding to the mask image density function, the photoresist image item and the second linear coefficient corresponding to the photoresist image item of the model to be trained are calibrated; wherein, the model to be trained can obtain the corresponding training production simulation pattern from the mask modeling pattern through the mask image density function and the first linear coefficient; and can determine the corresponding training optical intensity distribution according to the training production simulation pattern; and can obtain the corresponding training photoresist image from the training optical intensity distribution through the photoresist image item and the second linear coefficient.

6. The training method of the joint correction model of mask process and optical proximity as claimed in claim 5, characterized in that: The model to be trained obtains the corresponding training photoresist image from the mask modeling pattern through the following three formulas, including: ； ； ； Among them, M t (x, y) is the mask modeling pattern, M t '(x, y) is the training production simulation pattern corresponding to the mask modeling pattern, D i is the mask image density function, b i is the first linear coefficient; I t (x, y) is the training optical intensity distribution corresponding to the training production simulation pattern, TCC i is a set of cross transfer functions based on the Hopkins imaging principle, c i For TCC i The corresponding third linear coefficient; F i is the photoresist image item, d i is the second linear coefficient, T is the preset contour threshold, R t (x, y) is the training photoresist image after linear superposition of the photoresist image items.

7. The training method of the joint correction model of mask process and optical proximity as claimed in claim 5, characterized in that: The method of calibrating the mask image density function, the first linear coefficient, the photoresist image term and the second linear coefficient comprises: When the loss function value in the following formula is determined to be the minimum, the corresponding mask image density function, the first linear coefficient, the photoresist image term and the second linear coefficient are calibrated: ； Among them, cost is the loss function value, SCD j In the training photoresist image of the mask modeling pattern, the simulation critical dimension corresponding to the modeling main figure, WCD j The measured critical dimension of the mask modeling pattern, w j A first weight coefficient corresponding to the mask modeling pattern; SP k The information of whether the simulation is printed out or not corresponding to the modeled sub-resolution auxiliary pattern in the training photoresist image of the mask modeling pattern, WP k The information of whether the mask modeling pattern is printed or not, w k The second weight coefficient corresponding to the modeling sub-resolution auxiliary graphic.

8. The training method of the joint correction model of mask process and optical proximity according to claim 7, characterized in that: The corresponding values ​​of the printing information and the simulation printing information are 1 when the corresponding graphics are printed out; When the corresponding graphics are not printed out, the corresponding values ​​of the printing information and the simulation printing information are 0.

9. A training device for a joint correction model of mask process and optical proximity, characterized in that: The joint correction model obtained by the training device of the joint correction model of the mask process and optical proximity is used in the joint correction method of the mask process and optical proximity as claimed in any one of claims 1 to 3, comprising: The second receiving module is used to receive a plurality of mask modeling patterns and a photolithography wafer pattern obtained by the mask modeling patterns without mask process correction and optical proximity correction; the mask modeling pattern includes a modeling main figure and a modeling sub-resolution auxiliary figure; the modeling main figure and the modeling sub-resolution auxiliary figure do not overlap; A measurement module, used to measure the photolithography wafer pattern, obtain the measurement critical dimension of the wafer pattern corresponding to the modeling main pattern, and determine whether the modeling sub-resolution auxiliary pattern is printed out; A calibration module is used to calibrate the mask image density function of the model to be trained, the first linear coefficient corresponding to the mask image density function, the photoresist image item and the second linear coefficient corresponding to the photoresist image item according to the mask modeling pattern, the measured key dimension and the printing or not information; wherein the model to be trained can obtain the corresponding training production simulation pattern from the mask modeling pattern through the mask image density function and the first linear coefficient; and can determine the corresponding training optical intensity distribution according to the training production simulation pattern; and can obtain the corresponding training photoresist image from the training optical intensity distribution through the photoresist image item and the second linear coefficient.

10. A mask process and optical proximity joint correction device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the method for joint correction of mask process and optical proximity as described in any one of claims 1 to 3 and / or the steps of the method for training a joint correction model of mask process and optical proximity as described in any one of claims 5 to 8 when executing the computer program.

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