A mask process and optical proximity joint correction method, device and equipment

Abstract

The present invention relates to the field of semiconductor processing, and in particular to a mask process and optical proximity joint correction method, device, and apparatus. The method comprises receiving a target pattern, inputting the target pattern into a pre-trained joint correction model, causing the joint correction model to use the target pattern as an initial current mask pattern, segmenting the edges of an 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 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; and outputting the current mask pattern with edge segments having edge placement errors less than the preset qualified threshold as a correction mask pattern. The present invention reduces process cycle time and costs.

Description

Technical Field

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

[0002] In the manufacture of large-scale integrated circuits, the chip design pattern on the mask is transferred to the wafer through the photolithography process. With the evolution of the integrated circuit manufacturing process node, the pattern line width on the wafer has gradually shrunk. When the pattern line width is significantly smaller than the wavelength of the light source used in the photolithography process, the diffraction effect of the photolithography imaging causes a significant deviation between the pattern on the wafer after exposure and the pattern on the mask. Therefore, the mask pattern needs to be corrected to offset this deviation so that the pattern on the wafer after photolithography is consistent with the target pattern. This correction is called optical proximity correction (OPC).

[0003] Meanwhile, masks used in photolithography are typically manufactured through electron beam exposure and etching processes. Due to effects such as electron beam scattering and etching deviation, there is a discrepancy between the final mask pattern and the electron beam exposure pattern. To improve mask manufacturing accuracy, Mask Process Correction (MPC) has been introduced.

[0004] In the most advanced large-scale integrated circuit manufacturing processes (such as the 28nm node and below), in order to meet the requirements of manufacturing accuracy and process window, the process of generating mask data from the chip design layout requires both optical proximity correction and mask process correction. Both the optical proximity correction program and the mask process correction program are extremely time-consuming to run and incur huge computing power costs. Furthermore, it is necessary to collect wafer measurement data to establish the optical proximity correction model, and also to collect mask measurement data to establish the mask process correction model, resulting in large initial 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 purpose of the present invention is to provide a method, device, equipment, computer-readable storage medium for joint correction of mask process and optical proximity, and a method and device for training a joint correction model of mask process and optical proximity, so as to solve the problem in the prior art that in order to complete the process of generating mask data according to the chip design layout with high precision, it requires high cost and a very long period.

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

[0008] receiving a target pattern, the target pattern comprising an optical proximity correction target pattern and a sub-resolution auxiliary pattern;

[0009] Inputting the target pattern into a pre-trained joint correction model, causing 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 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; 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 based on 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;

[0010] 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 corrected mask pattern.

[0011] Optionally, in the mask process and optical proximity joint correction method, the joint correction model iteratively adjusts the edge segment of the current mask pattern using 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, and 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 For TCC i The corresponding third linear coefficient;

[0017] F i is the photoresist image item, d i is the second linear coefficient, T is the preset contour threshold, and R(x, y) is the photoresist image after the photoresist image items are linearly superimposed.

[0018] Optionally, in the mask process and optical proximity joint correction method, 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 includes:

[0019] 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;

[0020] Accordingly, outputting the current mask pattern in which the edge placement errors of all edge segments are less than a preset qualified threshold as the correction mask pattern includes:

[0021] 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.

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

[0023] 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;

[0024] a joint correction model module, configured to input the target pattern into a pre-trained joint correction model, causing the joint correction model to use the target pattern as an initial current mask pattern, segment the edges of an optical proximity correction target pattern in the current mask pattern to obtain a plurality of edge segments, and then iteratively adjust 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 errors of all edge segments of the current mask pattern are 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; can also determine a corresponding optical intensity distribution based on 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] 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 the correction mask pattern.

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

[0027] 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 patterns include 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;

[0028] Measuring the photolithographic wafer pattern to obtain a measurement critical dimension of the wafer pattern corresponding to the modeling main pattern, and determining whether the modeling sub-resolution auxiliary pattern is printed or not;

[0029] According to the mask modeling pattern, the measured key dimension and the printing or not information, 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 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.

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

[0031] ;

[0032] ;

[0033] ;

[0034] 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;

[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 For TCC i The corresponding third linear coefficient;

[0036] 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.

[0037] Optionally, in the training method of the joint correction model of mask process and optical proximity, the method of 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 minimum, the corresponding mask image density function, the first linear coefficient, the photoresist image term, and the second linear coefficient are calibrated:

[0039] ;

[0040] 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 graphics, WCD j The measured critical dimension of the mask model 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.

[0041] Optionally, in the training method of the joint correction model of the mask process and optical proximity, the printing information and the simulated printing information have a corresponding value of 1 when the corresponding graphics are printed;

[0042] The corresponding values ​​of the printing information and the simulation printing information are 0 when the corresponding graphics are not printed.

[0043] A training device for a joint correction model of mask process and optical proximity, wherein the joint correction model obtained by the training device for the joint correction model of mask process and optical proximity is used in any of the above-mentioned joint correction methods of mask process and optical proximity, comprising:

[0044] A second receiving module is configured to receive a plurality of mask modeling patterns and a lithography wafer pattern corresponding to the mask modeling patterns without undergoing mask process correction and optical proximity correction; the mask modeling patterns include 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;

[0045] a measurement module for measuring the photolithography wafer pattern, obtaining a measurement critical dimension of the wafer pattern corresponding to the modeling main pattern, and determining whether the modeling sub-resolution auxiliary pattern is printed or not;

[0046] 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 based on 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 based on 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.

[0047] A mask process and optical proximity joint correction device, comprising:

[0048] Memory for storing computer programs;

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

[0050] The mask process and optical proximity joint correction method provided by the present invention receive a target pattern, wherein the target pattern includes an optical proximity correction target pattern and a sub-resolution auxiliary pattern; input the target pattern into a pre-trained joint correction model, so that the joint 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 multiple edge segments; 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 errors of all edge segments of the current mask pattern are 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 based on 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; and outputs the current mask pattern with the edge placement errors of all edge segments less than the preset qualified threshold as the correction mask pattern. The joint correction model of the present invention simultaneously accounts for possible deviations in the mask production process and possible deviations in the process of imaging on the wafer through mask lithography. The target pattern required for optical proximity correction can be directly generated into a corrected mask pattern after the grinding process, significantly improving the operating speed while ensuring accuracy. Furthermore, the initial training of the joint correction model only requires determining the electron beam exposure pattern designed in the system (i.e., the mask modeling pattern) and collecting measurement data of the corresponding lithography wafer pattern, significantly reducing the process cycle and initial investment costs. The present invention also provides a mask process and optical proximity joint correction device, equipment, computer-readable storage medium, and a training method and device for the mask process and optical proximity joint correction model, all with the aforementioned beneficial effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 A schematic flow chart of a specific embodiment of the mask process and optical proximity joint correction method provided by the present invention;

[0053] Figure 2A schematic flow chart of another specific embodiment of the mask process and optical proximity joint correction method provided by the present invention;

[0054] Figure 3 A process structure diagram of a specific embodiment of the mask process and optical proximity joint correction device provided by the present invention;

[0055] Figure 4 A flowchart of a specific embodiment of the method for training a joint correction model of mask process and optical proximity provided by the present invention;

[0056] Figure 5 A schematic structural diagram of a specific embodiment of a training device for a joint correction model of mask process and optical proximity provided by the present invention.

[0057] Reference numerals:

[0058] 01-Optical proximity correction target pattern; 02-Sub-resolution auxiliary pattern; 110-First receiving module; 120-Joint correction model module; 130-Output module; 210-Second receiving module; 220-Measurement module; 230-Calibration module. DETAILED DESCRIPTION

[0059] In order to enable those skilled in the art to better understand the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0060] The core of the present invention is to provide a mask process and an optical proximity correction method, a flowchart of a specific embodiment of the invention is shown in FIG. Figure 1 As shown, it is called specific implementation method one, including:

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

[0062] Of course, the sub-resolution auxiliary pattern can be set inside the optical proximity correction target pattern or outside the optical proximity correction target pattern, and the present invention is not limited to this. In addition, the sub-resolution auxiliary pattern added according to the preset rule can be a simple rectangle, such as Figure 2 As shown in (a), it can also be a graph pre-corrected according to fixed rules, such as Figure 2 As shown in (b), Figure 2In the figure, the optical proximity correction target pattern is marked with 01, and the sub-resolution auxiliary pattern is marked with 02. Pre-corrected sub-resolution auxiliary patterns are closer to the target after mask fabrication, which generally helps increase the lithography process window. Since the subsequent mask processing and optical proximity correction are performed based on the optical proximity correction target pattern, model-based iterative correction of the sub-resolution auxiliary patterns is not possible during this process. Therefore, to meet the requirements of the lithography process window, pre-corrected sub-resolution auxiliary patterns can be added directly before the joint correction.

[0063] S102: Input the target pattern into a pre-trained joint correction model, so that the joint 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 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 errors of all edge segments of the current mask pattern are 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 based on 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.

[0064] Specifically, the joint correction model iteratively adjusts the edge segment of the current mask pattern through the following equations (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, and 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 For TCC i The corresponding third linear coefficient;

[0070] F i is the photoresist image item, d i is the second linear coefficient, T is the preset contour threshold, and R(x, y) is the photoresist image after the photoresist image items are linearly superimposed.

[0071] Formula (1) corresponds to the joint correction model that can obtain the corresponding production simulation pattern from the current mask pattern through the mask image density function and the first linear coefficient; Formula (2) corresponds to determining the corresponding optical intensity distribution based on the production simulation pattern; Formula (3) corresponds to obtaining the corresponding photoresist image from the optical intensity distribution through the photoresist image term and the second linear coefficient. Of course, the cross transfer function and the third linear coefficient in Formula (2) are functions and coefficients determined according to process conditions, and can refer to the existing technology. The present invention will not repeat them here. In addition, Formula (2) Represents a convolution operation.

[0072] In formula (3), R(x, y) on the left is the photoresist image, which is essentially a monochrome light and dark image. T on the right, that is, the contour threshold, is a specific light and dark value. A specific shape is outlined on the photoresist image along the contour threshold, which is the pattern formed on the wafer by photolithography simulated by the system.

[0073] After the optical proximity correction target pattern is segmented into edge segments, a control point needs to be placed at the center point of each edge segment. The segmentation of the edge segments is performed according to a preset rule.

[0074] Based on the current mask pattern, the mask process and optical proximity correction model described in equations (1), (2), and (3) are used to calculate the edge placement error (EPE) of each edge segment on the optical proximity correction target pattern, that is, the distance from the post-lithography wafer profile predicted by the model (which can be obtained 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 the edge segments on the optical proximity correction target pattern in the target pattern move.

[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 the edge segment needs to move. The corresponding edge segment is moved along the normal direction according to the calculated moving distance.

[0076] S103: Outputting the current mask pattern in which the edge placement errors of all edge segments are smaller than a preset qualified threshold as a correction mask pattern.

[0077] In the previous step, the movement of the edge segments and the calculation of the edge placement errors after the movement of the edge segments are repeated until the edge placement errors of all edge segments are less than a preset threshold, thereby obtaining the final joint corrected mask pattern, i.e., the corrected mask pattern.

[0078] In actual use, the correction mask pattern needs to be magnified according to the photolithography imaging reduction factor (generally 4), and then fragmented to obtain the electron beam exposure pattern, which is the final mask data.

[0079] As another specific embodiment, 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 includes:

[0080] A1: 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 item, and a second linear coefficient corresponding to the photoresist image item, until the number of iterations exceeds a preset iteration threshold.

[0081] Accordingly, outputting the current mask pattern in which the edge placement errors of all edge segments are less than a preset qualified threshold as the correction mask pattern includes:

[0082] A2: 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.

[0083] In this specific embodiment, an upper limit is set for the number of iterations, that is, the iteration threshold. When the edge placement error still does not meet the qualified threshold after multiple iterations, the iteration can be terminated directly, thereby saving computing power.

[0084] The mask process and optical proximity joint correction method provided by the present invention receive a target pattern, wherein the target pattern includes an optical proximity correction target pattern and a sub-resolution auxiliary pattern; input the target pattern into a pre-trained joint correction model, so that the joint 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 multiple edge segments; 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 errors of all edge segments of the current mask pattern are 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 based on 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; and outputs the current mask pattern with the edge placement errors of all edge segments less than the preset qualified threshold as the correction 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 in the process of imaging on the chip through mask lithography. The target pattern required for optical proximity correction can directly generate the correction mask pattern after the grinding process correction, which greatly improves the operating speed while ensuring accuracy. In addition, the preliminary training of the joint correction model only requires determining the electron beam exposure pattern designed in the system (that is, the mask modeling pattern) and collecting the measurement data of the corresponding lithography chip pattern, which also greatly reduces the process cycle and the initial investment cost.

[0085] The mask process and the optical proximity joint correction device provided by the embodiments of the present invention are introduced below. The mask process and the optical proximity joint correction device described below can correspond to the mask process and the optical proximity joint correction method described above.

[0086] Figure 3 The structural block diagram of the mask process and optical proximity joint correction device provided by the embodiment of the present invention is referred to as the specific embodiment 2, and the reference Figure 3 The mask process and optical proximity joint correction device may include:

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

[0088] The joint correction model module 120 is 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, 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 errors of all edge segments of the current mask pattern are 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; can also determine a corresponding optical intensity distribution based on 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 configured to output the current mask pattern in which the edge placement errors of all edge segments are smaller than a preset qualified threshold as a correction mask pattern.

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

[0091] The formula model unit is used to iteratively adjust the edge segment of the current mask pattern by using the joint correction 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, and 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 For TCC i The corresponding third linear coefficient;

[0097] F i is the photoresist image item, d iis the second linear coefficient, T is the preset contour threshold, and R(x, y) is the photoresist image after the photoresist image items are linearly superimposed.

[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 item, and a second linear coefficient corresponding to the photoresist image item, until the number of iterations exceeds a preset iteration threshold;

[0100] Accordingly, the output module 130 includes:

[0101] The threshold output unit is used to output the current mask pattern of the last iteration as the correction mask pattern when the number of iterations exceeds a preset iteration threshold.

[0102] The mask process and optical proximity joint correction device provided by the present invention include a first receiving module 110 for receiving 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 120 for inputting the target pattern into a pre-trained joint correction model, so that the joint correction model uses the target pattern as the 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 calculates the edge segments based on a preset mask image density function, a first linear coefficient corresponding to the mask image density function, a preset photoresist image item, and a photoresist image item corresponding to the photoresist image item. The second linear coefficient of the edge segment is used to iteratively adjust the position of the edge segment 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 based on 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; an output module 130 is used to output the current mask pattern with the edge placement error of all edge segments less than a 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 in the process of imaging on the chip through mask lithography. The target pattern required for optical proximity correction can directly generate the correction mask pattern after the grinding process correction, which greatly improves the operating speed while ensuring accuracy. In addition, the preliminary training of the joint correction model only requires determining the electron beam exposure pattern designed in the system (that is, the mask modeling pattern) and collecting the measurement data of the corresponding lithography chip pattern, which also greatly reduces the process cycle and the initial investment cost.

[0103] The mask process and optical proximity joint correction device of this embodiment is used to implement the aforementioned mask process and optical proximity joint correction method. Therefore, the specific implementation methods of the mask process and optical proximity joint correction device can be seen in the embodiment part of the mask process and optical proximity joint correction method in the previous text. For example, the first receiving module 110, the joint correction model module 120, and the output module 130 are respectively used to implement steps S101, S102 and S103 in the aforementioned mask process and optical proximity joint correction method. Therefore, its specific implementation methods can refer to the descriptions of the corresponding embodiments of each part and will not be repeated here.

[0104] The present invention also provides a training method for a joint correction model of mask process and optical proximity, a flow chart of a specific embodiment of which is shown as follows: Figure 4As shown, it is referred to as specific embodiment three, the joint correction model obtained by the training method of the joint correction model of the mask process and optical proximity is used for any of the above-mentioned joint correction methods of the mask process and optical proximity, including:

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

[0106] The various mask modeling patterns may be from typical patterns in optical proximity correction modeling and typical patterns in mask process correction modeling, ie, one-dimensional and two-dimensional patterns with different geometric features, pattern densities and lithography imaging features.

[0107] Of course, the photolithographic wafer pattern in this step is completely uncorrected and reflects the final appearance of the mask modeling pattern due to various interference conditions during the production process. Furthermore, the modeled sub-resolution auxiliary pattern can also be a simple rectangle or a pre-corrected pattern, which is not limited in the present invention.

[0108] S202: measuring the photolithography wafer pattern to obtain a measurement critical dimension of the wafer pattern corresponding to the modeling main pattern, and determining whether the modeling sub-resolution auxiliary pattern is printed or not.

[0109] The acquisition of the critical dimensions requires actual production operations, where the mask modeling pattern is actually produced into a chip through a production process, and then measured on the chip. Of course, it can also be directly seen from the produced chip whether the modeling sub-resolution auxiliary pattern is printed.

[0110] Preferably, the printing information and the simulation printing information have corresponding values ​​of 1 when the corresponding graphics are printed;

[0111] The corresponding values ​​of the printing information and the simulation printing information are 0 when the corresponding graphics are not printed.

[0112] In other words, the printing or not information only includes "yes" and "no" and can be represented by "1" and "0", which is convenient and quick to calculate. Of course, other methods can also be used to represent the different results of the printing or not information and the simulated printing or not information. The present invention will not go into details here.

[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 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.

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

[0115] ; (4)

[0116] ; (5)

[0117] ; (6)

[0118] 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;

[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 For TCC i The corresponding third linear coefficient;

[0120] 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.

[0121] Formulas (4), (5) and (6) in the model to be trained can be considered to correspond to Formulas (1), (2) and (3) in the mask process and optical proximity correction method mentioned above. The specific technical details can also be referred to above, and the present invention will not repeat them 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 minimum, the corresponding mask image density function, the first linear coefficient, the photoresist image term, and the second linear coefficient are calibrated:

[0124] ; (7)

[0125] 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 graphics, WCD j The measured critical dimension of the modeling pattern (modeling main pattern on the mask), 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.

[0126] The first term of the loss function indicates that the key dimension of the modeled main figure in the training photoresist image (that is, the simulated key dimension) should be as small as possible from the measured key dimension of the modeled main figure obtained through actual production, so that the cost will be reduced. The second term of the loss function is a test of whether the modeled sub-resolution auxiliary figure is printed. If the modeled sub-resolution auxiliary figure is not printed in actual production (the system can set not printed to 0 and printed to 1), then the ideal situation is that it is not printed in the training photoresist image obtained by model simulation. In this way, when the two situations are the same, SP k With WP k The difference is 0, otherwise it is not 0. Of course, when the difference is 0, the cost can be further reduced.

[0127] The present invention provides a training method for a joint correction model of mask process and optical proximity, comprising receiving a plurality of mask modeling patterns and corresponding photolithography wafer patterns obtained without mask process correction and optical proximity correction; the mask modeling patterns include 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 a measurement critical dimension of a wafer figure corresponding to the modeling main figure, and determining printout information of the modeling sub-resolution auxiliary figure; calibrating a mask image density function of a to-be-trained model, a first linear coefficient corresponding to the mask image density function, a photoresist image item, and a second linear coefficient corresponding to the photoresist image item based on the mask modeling pattern, the measurement critical dimension, and the printout information; wherein the to-be-trained model can obtain a corresponding training production simulation pattern from the mask modeling pattern using the mask image density function and the first linear coefficient; can also determine a corresponding training optical intensity distribution based on the training production simulation pattern; and can obtain a corresponding training photoresist image from the training optical intensity distribution using the photoresist image item 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 in the process of imaging on the chip through mask lithography. The target pattern required for optical proximity correction can directly generate the correction mask pattern after the grinding process correction, which greatly improves the operating speed while ensuring accuracy. In addition, the preliminary training of the joint correction model only requires determining the electron beam exposure pattern designed in the system (that is, the mask modeling pattern) and collecting the measurement data of the corresponding lithography chip pattern, which also greatly reduces the process cycle and the initial investment cost.

[0128] The following is an introduction to the training device for the joint correction model of the mask process and optical proximity provided in an embodiment of the present invention. The training device for the joint correction model of the mask process and optical proximity described below and the training method for the joint correction model of the mask process and optical proximity described above can be referenced to each other.

[0129] Figure 5 The structural block diagram of the training device for the joint correction model of the mask process and optical proximity provided by the embodiment of the present invention is referred to as the specific embodiment 4, and Figure 5 The training apparatus for the joint correction model of mask process and optical proximity may include:

[0130] The second receiving module 210 is configured to receive 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 patterns include 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;

[0131] The measurement module 220 is used to measure the photolithography wafer pattern, obtain the measurement critical dimensions of the wafer pattern corresponding to the modeling main pattern, and determine whether the modeling sub-resolution auxiliary pattern is printed or not;

[0132] The calibration module 230 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.

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

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

[0135] ; (4)

[0136] ; (5)

[0137] ; (6)

[0138] 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;

[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 For TCC i The corresponding third linear coefficient;

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

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

[0142] The loss function unit is used to determine that when the loss function value in the following formula (7) is minimized, the corresponding mask image density function, the first linear coefficient, the photoresist image term, and the second linear coefficient are calibrated:

[0143] ; (7)

[0144] 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 graphics, WCD j The measured critical dimension of the mask model 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.

[0145] The training device for the joint correction model of mask process and optical proximity provided by the present invention includes a second receiving module 210 for receiving a plurality of mask modeling patterns and a photolithography wafer pattern corresponding to 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 measuring module 220 for measuring the photolithography wafer pattern, obtaining a measurement critical dimension of a wafer figure corresponding to the modeling main figure, and determining whether the modeling sub-resolution auxiliary figure is printed out; a calibration module 230 for According to the mask modeling pattern, the measured key dimension and the printing or not information, 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 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. 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 in the process of imaging on the chip through mask lithography. The target pattern required for optical proximity correction can directly generate the correction mask pattern after the grinding process correction, which greatly improves the operating speed while ensuring accuracy. In addition, the preliminary training of the joint correction model only requires determining the electron beam exposure pattern designed in the system (that is, the mask modeling pattern) and collecting the measurement data of the corresponding lithography chip pattern, which also greatly reduces the process cycle and the initial investment cost.

[0146] The training device for the joint correction model of the mask process and optical proximity of this embodiment is used to implement the aforementioned training method for the joint correction model of the mask process and optical proximity. Therefore, the specific implementation method of the training device for the joint correction model of the mask process and optical proximity can be seen in the embodiment part of the training method for the joint correction model of the mask process and optical proximity in the previous 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 training method for the joint correction model of the mask process and optical proximity. Therefore, its specific implementation method can refer to the description of the corresponding embodiments of each part and will not be repeated here.

[0147] The present invention also provides a mask process and an optical proximity joint correction device, comprising:

[0148] Memory for storing computer programs;

[0149] A processor is configured to implement the steps of any one of the above-mentioned methods for joint correction of mask process and optical proximity and / or the steps of any one of the above-mentioned methods for training a joint correction model of mask process and optical proximity when executing the computer program. The mask process and optical proximity joint correction method provided by the present invention receive a target pattern, wherein the target pattern includes an optical proximity correction target pattern and a sub-resolution auxiliary pattern; input the target pattern into a pre-trained joint correction model, so that the joint 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 multiple edge segments; 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 errors of all edge segments of the current mask pattern are 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 based on 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; and outputs the current mask pattern with the edge placement errors of all edge segments less than the preset qualified threshold as the correction 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 in the process of imaging on the chip through mask lithography. The target pattern required for optical proximity correction can directly generate the correction mask pattern after the grinding process correction, which greatly improves the operating speed while ensuring accuracy. In addition, the preliminary training of the joint correction model only requires determining the electron beam exposure pattern designed in the system (that is, the mask modeling pattern) and collecting the measurement data of the corresponding lithography chip pattern, which also greatly reduces the process cycle and the initial 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 computer program implements the steps of any of the above-mentioned methods for joint correction of mask process and optical proximity and / or the steps of any of the above-mentioned methods for training a joint correction model of mask process and optical proximity. The mask process and optical proximity joint correction method provided by the present invention receive a target pattern, wherein the target pattern includes an optical proximity correction target pattern and a sub-resolution auxiliary pattern; input the target pattern into a pre-trained joint correction model, so that the joint 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 multiple edge segments; 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 errors of all edge segments of the current mask pattern are 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 based on 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; and outputs the current mask pattern with the edge placement errors of all edge segments less than the preset qualified threshold as the correction 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 in the process of imaging on the chip through mask lithography. The target pattern required for optical proximity correction can directly generate the correction mask pattern after the grinding process correction, which greatly improves the operating speed while ensuring accuracy. In addition, the preliminary training of the joint correction model only requires determining the electron beam exposure pattern designed in the system (that is, the mask modeling pattern) and collecting the measurement data of the corresponding lithography chip pattern, which also greatly reduces the process cycle and the initial investment cost.

[0151] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Reference can be made to the descriptions of the identical or similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and the relevant parts can be referred to the descriptions of the methods.

[0152] It should be noted that, in this specification, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element.

[0153] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

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

[0155] The above is a detailed introduction to the mask process and optical proximity joint correction method, device, equipment, computer-readable storage medium and a training method and device for a mask process and optical proximity joint correction model provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, the present invention can also be improved and modified in several ways, and these improvements and modifications also fall within the scope of protection of the present invention.

Claims

1. A mask process and optical proximity correction method, characterized in that: include: receiving a target pattern, the target pattern comprising an optical proximity correction target pattern and a sub-resolution auxiliary pattern; Inputting the target pattern into a pre-trained joint correction model, causing 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 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; 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 based on 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 corrected mask pattern.

2. The mask process and optical proximity correction method according to claim 1, wherein: The joint correction model iteratively adjusts the edge segment of the current mask pattern using the following three equations: ； ； ； 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 the 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 correction method according to claim 1, wherein: 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 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 in which the edge placement errors of all edge segments are 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, configured to input the target pattern into a pre-trained joint correction model, causing the joint correction model to use the target pattern as an initial current mask pattern, segment the edges of an optical proximity correction target pattern in the current mask pattern to obtain a plurality of edge segments, and then iteratively adjust 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 errors of all edge segments of the current mask pattern are 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; can also determine a corresponding optical intensity distribution based on 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 the 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 according to 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 patterns include 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 photolithographic wafer pattern to obtain a measurement critical dimension of the wafer pattern corresponding to the modeling main pattern, and determining whether the modeling sub-resolution auxiliary pattern is printed or not; According to the mask modeling pattern, the measured key dimension and the printing or not information, 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 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 method for training a joint correction model of mask process and optical proximity according to claim 5, wherein: 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 method for training a joint correction model of mask process and optical proximity according to claim 5, wherein: 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 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 graphics, WCD j The measured critical dimension of the mask model 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 method for training a joint correction model of mask process and optical proximity according to claim 7, wherein: The printing information and the simulation printing information have corresponding values ​​of 1 when the corresponding graphics are printed; The corresponding values ​​of the printing information and the simulation printing information are 0 when the corresponding graphics are not printed.

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 according to any one of claims 1 to 3, comprising: A second receiving module is configured to receive a plurality of mask modeling patterns and a lithography wafer pattern corresponding to the mask modeling patterns without undergoing mask process correction and optical proximity correction; the mask modeling patterns include 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 for measuring the photolithography wafer pattern, obtaining a measurement critical dimension of the wafer pattern corresponding to the modeling main pattern, and determining whether the modeling sub-resolution auxiliary pattern is printed or not; 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 based on 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 based on 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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