A light source mask collaborative optimization method, device, equipment and storage medium

Abstract

The present application relates to the field of integrated circuit manufacturing, in particular to a light source mask collaborative optimization method, device, equipment and storage medium, by acquiring an etching target pattern; based on the etching target pattern, a preset cost function is used for light source mask collaborative optimization, and the light source and mask pattern when the function value of the cost function is the minimum are the optimized light source and optimized mask pattern; the cost function includes an added etching edge error term and a photolithography process deviation term; based on the optimized light source and the optimized mask pattern, under a plurality of preset process conditions, a photolithography profile corresponding to the process condition is obtained; according to the photolithography profile, a photolithography process window is determined; it is judged whether the photolithography process window exceeds a preset qualified threshold; when the photolithography process window exceeds the qualified threshold, the optimized light source and the optimized mask pattern are output. The present application takes into account the photolithography process window and the etching process window, and improves the yield.

Description

Technical Field

[0001] This invention relates to the field of integrated circuit manufacturing, and in particular to a method, apparatus, equipment, and storage medium for collaborative optimization of light source masks. Background Technology

[0002] Source mask optimization (SMO) is a technique that, given a set of lithography target patterns, collaboratively optimizes the illumination source and corresponding mask pattern used in the lithography process to obtain the optimal lithography process window. As an important technique for improving lithography resolution, source mask optimization is widely used in the lithography processes of advanced nodes in large-scale integrated circuits.

[0003] However, the co-optimization of light source and mask under related technologies is only based on the optimization of the photolithography target pattern to maximize the photolithography process window. Such a process has obvious drawbacks. That is, the etching process window is not considered at all when optimizing the light source and mask. As a result, in practical applications, the etching process window is often insufficient based on the optimized light source and mask pattern, leading to defects such as breakpoints and bridging.

[0004] Therefore, how to ensure sufficient lithography and etching process windows during the process of co-optimization of light source and mask, thereby improving the yield of finished products, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus, device, and storage medium for co-optimization of light source masks, in order to solve the problem that the etching process window in the co-optimization of light source masks has not been considered in the prior art.

[0006] To address the aforementioned technical problems, this invention provides a method for collaborative optimization of light source masks, comprising:

[0007] Obtain the etched target pattern;

[0008] Based on the etched target pattern, a pre-defined cost function is used to perform co-optimization of the light source and mask. The light source and mask pattern with the minimum value of the cost function are determined as the optimized light source and optimized mask pattern. The cost function includes an additive etch edge error term and a photolithography process deviation term.

[0009] Based on the optimized light source and the optimized mask pattern, a photolithography profile corresponding to the preset process conditions is obtained under various process conditions.

[0010] Based on the lithography profile, determine the lithography process window;

[0011] Determine whether the photolithography process window exceeds a preset acceptable threshold;

[0012] When the photolithography process window exceeds the qualified threshold, the optimized light source and the optimized mask pattern are output.

[0013] Optionally, in the aforementioned light source mask collaborative optimization method, the cost function is the sum of the product of the lithography process deviation term and the lithography weight coefficient and the etching edge error term;

[0014] After determining whether the photolithography process window exceeds a preset acceptable threshold, the method further includes:

[0015] When the lithography process window does not exceed the qualified threshold, the lithography weight coefficient is increased to obtain the updated cost function;

[0016] Based on the etched target pattern, a co-optimization of the light source and mask is performed using the updated cost function to obtain an updated optimized light source and an updated optimized mask pattern. The updated lithography process window is then calculated based on the updated optimized light source and the updated optimized mask pattern. It is then determined whether the updated lithography process window exceeds the qualified threshold. If the updated lithography process window does not exceed the qualified threshold, the lithography weight coefficient is increased until the updated lithography process window exceeds the qualified threshold.

[0017] Optionally, in the aforementioned light source and mask collaborative optimization method, based on the etched target pattern, a preset cost function is used to perform light source and mask collaborative optimization, and the light source and mask pattern with the minimum function value of the cost function are determined as the optimized light source and optimized mask pattern, including:

[0018] Based on the etched target pattern, the light source and mask are optimized together using the following formula, and the light source and mask pattern with the minimum function value in the following formula are determined as the optimized light source and optimized mask pattern:

[0019] ;

[0020] Where CF is the function value of the cost function; EPE(p,x) is the etching edge placement error at the corresponding position of evaluation point x under process condition p; w p The weighting coefficient corresponding to process condition p; w x β is the weighting coefficient corresponding to evaluation point x; β is the lithography weighting coefficient; PV(p,x') represents the lithography process deviation at the corresponding position of evaluation point x' under process condition p; w x’ The weighting coefficient is the value corresponding to the evaluation point x'. The evaluation point x' is the point on the photolithographic simulation contour corresponding to the etched target pattern under the nominal conditions that corresponds to the evaluation point x.

[0021] Optionally, in the aforementioned light source mask collaborative optimization method, the light source mask collaborative optimization based on the etched target pattern, and / or the simulation of the corresponding lithographic contour obtained based on the optimized light source and the optimized mask pattern, are performed through a pre-trained light source mask collaborative optimization model.

[0022] The training method for the light source mask collaborative optimization model includes:

[0023] An uncalibrated co-optimization model for the light source mask is established. The uncalibrated co-optimization model can determine the corresponding etching deviation related to geometric features based on the input lithography profile through geometric feature function terms and geometric feature linear coefficients, determine the corresponding etching deviation related to lithography imaging features through lithography imaging feature function terms and lithography imaging feature linear coefficients, and then determine the simulated etching deviation at any point on the lithography profile through the etching deviation related to geometric features, the etching deviation related to lithography imaging features, and the constant deviation.

[0024] Acquire modeling pattern sets and various lighting sources;

[0025] The modeling pattern set is subjected to optical proximity correction for various lighting sources to obtain mask patterns for each lighting source.

[0026] The mask pattern is subjected to photolithography under the corresponding illumination source to obtain the photolithographic wafer pattern corresponding to each illumination source.

[0027] Measure the key dimensions corresponding to each of the photolithographically ...

[0028] A preset standard etching process is performed on the photolithographic wafer pattern to obtain the corresponding etched wafer pattern.

[0029] Measure the critical dimensions corresponding to each of the etched wafer patterns;

[0030] The etching deviation is determined based on the critical dimensions after photolithography and the critical dimensions after etching.

[0031] Using the measured etching deviation and the simulated etching deviation, the geometric feature function term, the geometric feature linear coefficient, the lithographic imaging feature function term, the lithographic imaging feature linear coefficient, and the constant deviation are calibrated so that the difference between the simulated etching deviation and the measured etching deviation is less than a preset calibration threshold.

[0032] Optionally, in the aforementioned light source mask collaborative optimization method, the light source mask collaborative optimization model calculates the simulated etching deviation at any point on the lithographic profile using the following formula:

[0033] ;

[0034] Among them, B S (x,y) represents the etching deviation at position (x,y); b0 represents the constant deviation; b g (x,y) represents the etching deviation related to the geometric features at position (x,y); b i (x,y) represents the etching deviation related to the lithographic imaging feature at position (x,y).

[0035] Optionally, in the aforementioned light source mask collaborative optimization method, the light source mask collaborative optimization model determines the etching deviation related to geometric features using the following formula:

[0036] ;

[0037] Among them, b g (x,y) represents the etching deviation related to the geometric features at position (x,y); R(x,y) is the photoresist image corresponding to the lithographic profile; j is the number of the related etching deviation term; G j c is the geometric characteristic function term; j These are the geometric feature linear coefficients corresponding to the geometric feature function terms;

[0038] And / or,

[0039] The light source mask collaborative optimization model determines the etching deviation related to the lithographic imaging features using the following formula:

[0040] ;

[0041] Among them, b i (x,y) represents the etching deviation related to the lithographic imaging feature at position (x,y); I(x,y) is the simulated optical image corresponding to the lithographic profile; j is the number of the related etching deviation term; H j d is the lithographic imaging feature function term; j , which are the linear coefficients of the lithography imaging feature function corresponding to the lithography imaging feature function term.

[0042] Optionally, in the aforementioned light source mask collaborative optimization method, the geometric feature function term, the geometric feature linear coefficient, the lithographic imaging feature function term, the lithographic imaging feature linear coefficient, and the constant deviation are calibrated using the measured etching deviation and the simulated etching deviation, including:

[0043] When the loss function in the following formula reaches its minimum value, the calibration of the geometric feature function term, the geometric feature linear coefficient, the lithographic imaging feature function term, the lithographic imaging feature linear coefficient, and the constant deviation is completed:

[0044] ;

[0045] Where Cost is the function value of the loss function, and B S K,L The simulated etching deviation of the evaluation points on the modeling pattern K in the modeling pattern set when exposed using light source L; B W K,L The measurement etching deviation of the evaluation point on the modeling pattern K in the modeling pattern set during exposure by the light source L; w K The weighting coefficients corresponding to the modeling pattern K; w L represents the weighting coefficient corresponding to the light source L.

[0046] A light source mask collaborative optimization device, comprising:

[0047] The acquisition module is used to acquire the etching target pattern;

[0048] The collaborative optimization module is used to perform collaborative optimization of the light source and mask based on the etched target pattern and using a preset cost function, and to determine the light source and mask pattern with the minimum function value of the cost function as the optimized light source and optimized mask pattern; the cost function includes an additive etch edge error term and a photolithography process deviation term;

[0049] The photolithography contour module is used to obtain a photolithography contour corresponding to the optimized light source and the optimized mask pattern under various preset process conditions.

[0050] A window module is used to determine the photolithography process window based on the photolithography profile;

[0051] The judgment module is used to determine whether the photolithography process window exceeds a preset qualified threshold.

[0052] The output module is used to output the optimized light source and the optimized mask pattern when the photolithography process window exceeds the qualified threshold.

[0053] A light source mask collaborative optimization device, comprising:

[0054] Memory, used to store computer programs;

[0055] A processor is configured to implement the steps of any of the above-described light source mask collaborative optimization methods when executing the computer program.

[0056] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the above-described light source mask collaborative optimization methods.

[0057] The light source and mask co-optimization method provided by this invention involves: acquiring an etching target pattern; performing light source and mask co-optimization based on the etching target pattern using a preset cost function, and determining the light source and mask pattern whose function value is minimized as the optimized light source and optimized mask pattern; the cost function includes an additive etching edge error term and a photolithography process deviation term; obtaining a photolithography profile corresponding to the preset process conditions based on the optimized light source and the optimized mask pattern; determining a photolithography process window based on the photolithography profile; determining whether the photolithography process window exceeds a preset acceptable threshold; and outputting the optimized light source and the optimized mask pattern when the photolithography process window exceeds the acceptable threshold.

[0058] This invention incorporates both etching edge placement errors and photolithography process deviations into the cost function of light source mask co-optimization. During the optimization process, both the photolithography and etching process windows are considered, significantly improving the accuracy and practicality of light source mask co-optimization and ensuring high yield of wafers obtained through optimized light sources and masks. This invention also provides a light source mask co-optimization device, equipment, and storage medium with the aforementioned beneficial effects. Attached Figure Description

[0059] To more clearly illustrate the technical solutions of the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 A flowchart illustrating a specific implementation of the light source mask collaborative optimization method provided by the present invention;

[0061] Figure 2 , Figure 3 , Figure 4 A process structure diagram of a specific embodiment of the light source mask collaborative optimization method provided by the present invention;

[0062] Figure 5 A flowchart illustrating a specific implementation of the light source mask collaborative optimization method provided by the present invention;

[0063] Figure 6 , Figure 7-1 , Figure 7-2 , Figure 8-1 , Figure 8-2 A process structure diagram of a specific embodiment of the light source mask collaborative optimization method provided by the present invention;

[0064] Figure 9This is a schematic diagram of a specific embodiment of the light source mask collaborative optimization device provided by the present invention.

[0065] Figure label:

[0066] 100 - Acquisition module, 200 - Collaborative optimization module, 300 - Photolithography contour module, 400 - Window module, 500 - Judgment module, 600 - Output module; 001 - Photoresist; 002 - Layer to be etched; 003 - Wafer pattern after etching. Detailed Implementation

[0067] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] The core of this invention is to provide a method for collaborative optimization of light source masks, and a flowchart of one specific implementation is shown below. Figure 1 As shown, this is referred to as Specific Implementation Method One, which includes:

[0069] S101: Obtain the etched target pattern.

[0070] The etched target pattern is obtained by rounding the corners of the chip design pattern according to certain rules, and can be considered as the ideal pattern that can be obtained after etching. Of course, each etched target pattern needs to have evaluation points placed on it for subsequent cost function evaluation; reference can be made to... Figure 2 , Figure 2 Points A, B, and C in the figure are the three evaluation points. The selection of the evaluation points can be adjusted according to actual needs, which will not be elaborated here.

[0071] S102: Based on the etched target pattern, a preset cost function is used to perform light source and mask co-optimization, and the light source and mask pattern with the minimum function value of the cost function are determined as the optimized light source and optimized mask pattern; the cost function includes an additive etch edge error term and a photolithography process deviation term.

[0072] In this step, a preset cost function is used to perform co-optimization of the light source mask. The cost function is the sum of the etching edge error term and the photolithography process deviation term.

[0073] S103: Based on the optimized light source and the optimized mask pattern, a photolithography profile corresponding to the preset process conditions is obtained under various process conditions.

[0074] The various process conditions in this step include nominal conditions used as a comparison standard and error conditions introduced by artificial manufacturing differences. These error conditions include process condition deviations such as defocus, off-dose, and mask size deviation, or combinations thereof. Through this step, the lithographic profile under the nominal conditions and the lithographic profiles under all the error conditions can be obtained.

[0075] S104: Determine the photolithography process window based on the photolithography profile.

[0076] It should be noted that in this step, all the lithographic contours need to be calculated in order to obtain the lithography process window. The specific calculation method for the lithography process window can be found in relevant technologies, which will not be elaborated here.

[0077] S105: Determine whether the photolithography process window exceeds a preset qualified threshold.

[0078] In this invention, the optimized light source and optimized mask pattern corresponding to the photolithography process window will simultaneously consider the etching edge error and photolithography process deviation during iteration. Therefore, the qualified threshold in this step is the maximum value of the photolithography process window considering the subsequent etching process window.

[0079] S106: When the photolithography process window exceeds the qualified threshold, output the optimized light source and the optimized mask pattern.

[0080] This step is a case after the judgment in step S105, that is, the processing solution when the photolithography process window is greater than or equal to the qualified threshold. At this time, the optimized light source and the optimized mask pattern are the light source and pattern that conform to the process window, and they are directly used as the final light source and pattern.

[0081] In a preferred embodiment, the cost function is the sum of the product of the photolithography process deviation term and the photolithography weighting coefficient and the etching edge error term;

[0082] After determining whether the photolithography process window exceeds a preset acceptable threshold, the method further includes:

[0083] A1: When the lithography process window does not exceed the qualified threshold, increase the lithography weight coefficient to obtain the updated cost function.

[0084] The larger the lithography weight coefficient, the greater the proportion of the lithography process deviation to the cost function value. Therefore, during the iteration process, it is more important to reduce the lithography process deviation, which is equivalent to increasing the process window for subsequent lithography.

[0085] A2: Based on the etched target pattern, the light source and mask are co-optimized using the updated cost function to obtain the updated optimized light source and the updated optimized mask pattern. The updated lithography process window is then calculated based on the updated optimized light source and the updated optimized mask pattern. It is then determined whether the updated lithography process window exceeds the qualified threshold. If the updated lithography process window still does not exceed the qualified threshold, the lithography weight coefficient is increased until the updated lithography process window exceeds the qualified threshold.

[0086] In this step, the updated cost function is first used to recalculate the optimized light source and the optimized mask pattern. After obtaining the updated optimized light source and the updated optimized mask pattern, steps S103 to S105 in the previous text are repeated until the ratio value exceeds the qualified upper limit value. At this time, the obtained light source and mask pattern maximize the etching process window while ensuring the preset photolithography process window.

[0087] Furthermore, in the initial light source mask co-optimization, the lithography weight coefficient is 0.

[0088] In other words, if the optimized light source and optimized mask pattern obtained can still meet the lithography process window without considering lithography, then the largest etching process window can be obtained. If the lithography process window does not exceed the qualified threshold, the lithography weight coefficient can be gradually increased.

[0089] Furthermore, based on the etched target pattern, a pre-defined cost function is used to perform collaborative optimization of the light source and mask. The light source and mask pattern with the minimum value of the cost function are determined as the optimized light source and optimized mask pattern, including:

[0090] Based on the etched target pattern, the light source and mask are optimized together using the following formula (1), and the light source and mask pattern with the minimum function value in the following formula are determined as the optimized light source and optimized mask pattern:

[0091] ; (1)

[0092] Wherein, CF is the function value of the cost function; EPE(p,x) is the etching edge placement error at the corresponding position of evaluation point x under process condition p; w p The weighting coefficient corresponding to process condition p; w x β is the weighting coefficient corresponding to evaluation point x; β is the lithography weighting coefficient; PV(p,x') represents the lithography process deviation at the corresponding position of evaluation point x' under process condition p; w x’ The weighting coefficient is the one corresponding to the evaluation point x'; the evaluation point x' is the point on the photolithographic simulation contour corresponding to the etched target pattern under the nominal conditions that corresponds to the evaluation point x.

[0093] The placement error of the etched edge at the corresponding position of the evaluation point x, that is, the deviation between the simulated etched contour obtained from the model simulation and the etched target pattern, such as... Figure 3 As shown, Figure 3 and Figure 2 Correspondingly, as mentioned above, the evaluation point is set on the etched target pattern. Therefore, the placement error of the etched edge at the corresponding position of evaluation point x is the distance from the intersection of the tangent normal direction of the etched target pattern at position x and the corresponding simulated etched contour to point x. Figure 3 EPE used in China A This indicates the placement error of the etched edge corresponding to evaluation point A.

[0094] The lithography process deviation at the corresponding position of evaluation point x' refers to the deviation between the simulated lithography profile under process condition p at the coordinate position of evaluation point x', and the simulated lithography profile under nominal process conditions. It is important to note that evaluation point x' is the intersection of the normal direction of the etched target pattern along which evaluation point x is located and the simulated lithography profile under nominal conditions. In other words, evaluation point x' is a point on the simulated lithography profile under nominal conditions, and it corresponds one-to-one with evaluation point x. (See reference...) Figure 4 , Figure 4 The lithography process deviation corresponding to the mid-evaluation point A' is represented by PV. A express.

[0095] Equation (1) provided in this specific embodiment sets weight coefficients for process condition p, evaluation point x, and corresponding evaluation point x'. This can give more computational freedom while taking into account low computational difficulty and high computational efficiency, thereby improving the versatility of this specific embodiment.

[0096] The light source and mask co-optimization method provided by this invention involves: acquiring an etching target pattern; performing light source and mask co-optimization based on the etching target pattern using a preset cost function; determining the light source and mask pattern whose cost function value is minimized as the optimized light source and optimized mask pattern; the cost function includes an additive etching edge error term and a photolithography process deviation term; obtaining a photolithography profile corresponding to the preset process conditions based on the optimized light source and the optimized mask pattern; determining the photolithography process window based on the photolithography profile; determining whether the photolithography process window exceeds a preset pass threshold; and outputting the optimized light source and the optimized mask pattern when the photolithography process window exceeds the pass threshold. This invention incorporates both etching edge placement error and photolithography process deviation into the cost function of light source and mask co-optimization, simultaneously considering both the photolithography process window and the etching process window during the optimization process, greatly improving the accuracy and practicality of light source and mask co-optimization, and ensuring a high yield of wafers obtained after optimizing the light source and optimized mask.

[0097] Based on the above specific embodiments, the light source mask collaborative optimization based on the etched target pattern and / or the simulation of obtaining the corresponding lithographic contour based on the optimized light source and the optimized mask pattern in this invention can both be performed using a pre-trained light source mask collaborative optimization model. Therefore, this invention also provides a training method for the light source mask collaborative optimization model, the corresponding flowchart of which is shown below. Figure 5 As shown, this is referred to as Specific Implementation Method Two, which includes:

[0098] S201: Establish an uncalibrated light source mask collaborative optimization model; the uncalibrated light source mask collaborative optimization model can determine the corresponding geometric feature-related etching deviation based on the input lithography profile through geometric feature function terms and geometric feature linear coefficients, determine the corresponding lithography imaging feature-related etching deviation through lithography imaging feature function terms and lithography imaging feature linear coefficients, and then determine the simulated etching deviation at any point on the lithography profile through the geometric feature-related etching deviation, the lithography imaging feature-related etching deviation, and the constant deviation.

[0099] You can refer to this. Figure 6 The etching deviation of a point (x,y) on the simulated lithographic profile is the distance from the simulated etching profile along the normal direction of the lithographic profile where that point is located.

[0100] S202: Obtain the modeling pattern set and various lighting sources.

[0101] The modeling pattern set is a set of modeling patterns designed for the co-optimization of the light source mask in etching models. This set includes patterns with different geometric features and different lithographic imaging features. The lithographic imaging features include the normalized image log slope (NILS), contrast, maximum light intensity value, minimum light intensity value, etc. For patterns with the same geometric features, mask patterns with different imaging features can be obtained by adding sub-resolution auxiliary patterns according to different rules.

[0102] The illumination source is a preset set of multiple light sources L1, L2, etc. These light sources cover different types, such as dipoles and annulars. This step ensures that when using different light sources for exposure, the same modeled pattern can be obtained as a photoresist pattern of similar size on the wafer after photolithography.

[0103] S203: Perform optical proximity correction on the modeling pattern set for various lighting sources to obtain mask patterns for each lighting source.

[0104] By running the optical proximity correction program corresponding to the illumination source in each of the aforementioned steps on the modeling pattern set, the corresponding mask pattern can be obtained, which is the mask required for actual photolithography.

[0105] S204: Perform photolithography on the mask pattern under the corresponding illumination source to obtain the photolithographic wafer pattern corresponding to each illumination source.

[0106] Using the various illumination sources defined in the previous step, a photolithography process is run on the corresponding masks to obtain photolithographically lithographically post-processed wafers under different illumination sources. These post-processed wafers contain the photolithographically post-processed wafer pattern. The photolithographically post-processed wafer pattern is the photolithographically post-processed pattern of the modeled pattern.

[0107] S205: Measure the critical dimensions corresponding to each of the photolithographically ...

[0108] In this step, the critical dimensions (CD) of the corresponding photolithographic pattern are collected.

[0109] S206: Perform a preset standard etching process on the photolithographic wafer pattern to obtain the corresponding etched wafer pattern.

[0110] In this step, all photolithographically patterned wafers undergo a uniform etching process, meaning the same etching technique is used. For example... Figure 7-1 and Figure 7-2As shown, patterned photoresist is applied to the layer to be etched (the photoresist is identified by 001 in the figure, and the layer to be etched by 002). For the same modeled pattern, although their critical dimensions after photolithography are similar, the different imaging characteristics of different illumination sources lead to different three-dimensional morphologies of the photoresist, which in turn results in different critical dimensions of the etched wafer pattern (identified by 003 in the figure). Figure 8-1 and Figure 8-2 As shown, where Figure 8-1 and Figure 7-1 correspond, Figure 8-2 and Figure 7-2 correspond.

[0111] S207: Measure the critical dimensions corresponding to each of the etched wafer patterns.

[0112] Of course, the measurement of the critical dimensions after photolithography and the critical dimensions after etching can also be performed uniformly after the standard etching process is completed. That is, steps S204 and S206 are completed first, followed by steps S205 and S207. The present invention does not limit the order of the above steps.

[0113] S208: Determine the etching deviation based on the critical dimension after photolithography and the critical dimension after etching.

[0114] S209: Using the measured etching deviation and the simulated etching deviation, calibrate the geometric feature function term, the geometric feature linear coefficient, the lithographic imaging feature function term, the lithographic imaging feature linear coefficient, and the constant deviation, so that the difference between the simulated etching deviation and the measured etching deviation is less than a preset calibration threshold.

[0115] Of course, the light source mask collaborative optimization model obtained by the training method of the light source mask collaborative optimization model in this specific embodiment is used in the light source mask collaborative optimization method mentioned above. For specific technical details, please refer to the previous text. This specific embodiment will not repeat them here.

[0116] Furthermore, the light source mask collaborative optimization model calculates the simulated etching deviation at any point on the lithographic profile using the following equation (2):

[0117] ; (2)

[0118] Among them, B S (x,y) represents the etching deviation at position (x,y); b0 represents the constant deviation; b g (x,y) represents the etching deviation related to the geometric features at position (x,y); b i (x,y) represents the etching deviation related to the lithographic imaging feature at position (x,y).

[0119] In this specific embodiment, the simulated etching deviation is the sum of three deviations. The constant deviation, the etching deviation related to geometric features, and the etching deviation related to photolithography imaging features are calculated from three aspects to account for the influence of the three factors on the etching deviation. Under the premise of ensuring simple calculation and low computing power consumption, the simulated etching deviation can be accurately calculated.

[0120] As a specific implementation, the light source mask collaborative optimization model determines the etching deviation related to geometric features through the following equation (3):

[0121] ; (3)

[0122] Among them, b g (x,y) represents the etching deviation related to the geometric features at position (x,y); R(x,y) is the photoresist image corresponding to the lithographic profile; j is the number of the related etching deviation term; G j c is the geometric characteristic function term; j These are the geometric characteristic linear coefficients corresponding to the geometric characteristic function terms.

[0123] The photoresist image is obtained by simulating the photoresist outline using a corresponding optical proximity correction model. The geometric feature function term may include pattern density function, etc., and can be referred to in related technologies. This invention does not limit it here.

[0124] The light source mask co-optimization model determines the etching deviation related to the lithographic imaging features through the following equation (4):

[0125] ; (4)

[0126] Among them, b i (x,y) represents the etching deviation related to the lithographic imaging feature at position (x,y); I(x,y) is the simulated optical image corresponding to the lithographic profile; j is the number of the related etching deviation term; H j d is the lithographic imaging feature function term; j , which are the linear coefficients of the lithography imaging feature function corresponding to the lithography imaging feature function term.

[0127] The simulated optical image can also be obtained by simulating the photoresist contour using a corresponding optical proximity correction model, and related technologies can be referenced; this invention is not limited thereto. For the same modeling pattern, different light sources correspond to b i The different (x,y) values ​​indicate that the etching deviation related to the lithographic imaging features reflects the influence of different light sources on the etching profile.

[0128] Furthermore, using the measured etching deviation and the simulated etching deviation, the geometric characteristic function term, the geometric characteristic linear coefficient, the lithographic imaging characteristic function term, the lithographic imaging characteristic linear coefficient, and the constant deviation are calibrated, including:

[0129] When the loss function in the following formula (5) is minimized, the calibration of the geometric feature function term, the geometric feature linear coefficient, the lithography imaging feature function term, the lithography imaging feature linear coefficient, and the constant deviation is completed:

[0130] ; (5)

[0131] Where Cost is the value of the loss function, and B S K,L The simulated etching deviation of the evaluation points on the modeling pattern K in the modeling pattern set when exposed using light source L; B W K,L The measurement etching deviation of the evaluation point on the modeling pattern K in the modeling pattern set during exposure by the light source L; w K The weighting coefficients corresponding to the modeling pattern K; w L represents the weighting coefficient corresponding to the light source L.

[0132] The difference between the simulated etching deviation and the measured etching deviation is obtained in the above formula (5). This difference should naturally be as small as possible, as a smaller difference indicates that the simulation result is closer to the real value. Based on this, in this specific embodiment, the corresponding weight coefficients are given according to the light source L and the modeling pattern K to further increase the calculation flexibility and facilitate adaptation to various usage scenarios.

[0133] Furthermore, the measured etching deviation is determined by the following formula (6):

[0134] ; (6)

[0135] Among them, B W K,L The measurement etching deviation of the evaluation point on the modeling pattern K in the modeling pattern set during exposure by the light source L; DCD K,L FCD is used to determine the critical dimensions of the pattern after photolithography when exposed using light source L. K,L This represents the critical dimension of the pattern after etching when exposed using light source L.

[0136] Using formula (6) can significantly reduce computing power consumption and improve computing efficiency.

[0137] The light source mask collaborative optimization device provided in the embodiments of the present invention will be described below. The light source mask collaborative optimization device described below can be referred to in correspondence with the light source mask collaborative optimization method described above.

[0138] Figure 9 This is a structural block diagram of the light source mask collaborative optimization device provided in an embodiment of the present invention, with reference to... Figure 9 The light source mask collaborative optimization device may include:

[0139] Acquisition module 100 is used to acquire the etching target pattern;

[0140] The collaborative optimization module 200 is used to perform collaborative optimization of the light source and mask based on the etched target pattern and using a preset cost function, and to determine the light source and mask pattern with the minimum function value of the cost function as the optimized light source and optimized mask pattern; the cost function includes an additive etch edge error term and a photolithography process deviation term;

[0141] The photolithography contour module 300 is used to obtain a photolithography contour corresponding to the optimized process conditions based on the optimized light source and the optimized mask pattern under various preset process conditions.

[0142] Window module 400 is used to determine the photolithography process window based on the photolithography profile;

[0143] The judgment module 500 is used to determine whether the photolithography process window exceeds a preset qualified threshold.

[0144] The output module 600 is used to output the optimized light source and the optimized mask pattern when the photolithography process window exceeds the qualified threshold.

[0145] In one specific implementation, the cost function is the sum of the product of the photolithography process deviation term and the photolithography weighting coefficient and the etching edge error term;

[0146] The judgment module 500 further includes:

[0147] The weighting unit is used to increase the lithography weight coefficient when the lithography process window does not exceed the qualified threshold, so as to obtain the updated cost function;

[0148] The loop judgment unit is used to perform light source and mask co-optimization based on the etched target pattern using the updated cost function to obtain the updated optimized light source and the updated optimized mask pattern, calculate the updated lithography process window based on the updated optimized light source and the updated optimized mask pattern, and then judge whether the updated lithography process window exceeds the qualified threshold. When the updated lithography process window does not exceed the qualified threshold, the lithography weight coefficient is increased until the updated lithography process window exceeds the qualified threshold.

[0149] As one specific implementation, the collaborative optimization module 200 includes:

[0150] The computational coordination unit is used to perform light source and mask coordination optimization based on the etched target pattern using the following formula, and to determine the light source and mask pattern with the minimum function value in the following formula as the optimized light source and optimized mask pattern:

[0151] ;

[0152] Wherein, CF is the function value of the cost function; EPE(p,x) is the etching edge placement error at the corresponding position of evaluation point x under process condition p; w p The weighting coefficient corresponding to process condition p; w x β is the weighting coefficient corresponding to evaluation point x; β is the lithography weighting coefficient; PV(p,x') represents the lithography process deviation at the corresponding position of evaluation point x' under process condition p; w x’ The weighting coefficient is the one corresponding to the evaluation point x'; the evaluation point x' is the point on the photolithographic simulation contour corresponding to the etched target pattern under the nominal conditions that corresponds to the evaluation point x.

[0153] As one specific implementation, the light source mask collaborative optimization based on the etched target pattern, and / or the simulation of the corresponding lithographic contour obtained based on the optimized light source and the optimized mask pattern, are performed through a pre-trained light source mask collaborative optimization model;

[0154] The training device for the light source mask collaborative optimization model includes:

[0155] The model building module is used to build an uncalibrated light source mask co-optimization model. The uncalibrated light source mask co-optimization model can determine the corresponding geometric feature-related etching deviation based on the input lithography profile through geometric feature function terms and geometric feature linear coefficients, determine the corresponding lithography imaging feature-related etching deviation through lithography imaging feature function terms and lithography imaging feature linear coefficients, and then determine the simulated etching deviation at any point on the lithography profile through the geometric feature-related etching deviation, the lithography imaging feature-related etching deviation, and the constant deviation.

[0156] The sample acquisition module is used to acquire modeling pattern sets and various lighting sources;

[0157] The OPC module is used to perform optical proximity correction on the modeling pattern set for various lighting sources to obtain mask patterns for each lighting source.

[0158] The photolithography module is used to perform photolithography on the mask pattern under the corresponding illumination source to obtain the photolithographic wafer pattern corresponding to each illumination source.

[0159] The post-lithography measurement module is used to measure the key dimensions corresponding to each post-lithography wafer pattern;

[0160] The etching module is used to perform a preset standard etching process on the photolithographic wafer pattern to obtain the corresponding etched wafer pattern.

[0161] The post-etching measurement module is used to measure the key dimensions corresponding to each of the post-etched wafer patterns.

[0162] The deviation calculation module is used to determine the measurement etching deviation based on the critical dimension after photolithography and the critical dimension after etching.

[0163] The model calibration module is used to calibrate the geometric feature function term, the geometric feature linear coefficient, the lithography imaging feature function term, the lithography imaging feature linear coefficient, and the constant deviation by using the measured etching deviation and the simulated etching deviation, so that the difference between the simulated etching deviation and the measured etching deviation is less than a preset calibration threshold.

[0164] As one specific implementation, the model building module includes:

[0165] The simulation calculation unit is used to enable the light source mask co-optimization model to calculate the simulated etching deviation at any point on the lithographic profile using the following formula:

[0166] ;

[0167] Among them, B S (x,y) represents the etching deviation at position (x,y); b0 represents the constant deviation; b g (x,y) represents the etching deviation related to the geometric features at position (x,y); b i (x,y) represents the etching deviation related to the lithographic imaging feature at position (x,y).

[0168] As one specific implementation, the model building module includes:

[0169] The geometric calculation unit is used to enable the light source mask co-optimization model to determine the etching deviation related to geometric features using the following formula:

[0170] ;

[0171] Among them, b g (x,y) represents the etching deviation related to the geometric features at position (x,y); R(x,y) is the photoresist image corresponding to the lithographic profile; j is the number of the related etching deviation term; G j c is the geometric characteristic function term; jThese are the geometric feature linear coefficients corresponding to the geometric feature function terms;

[0172] And / or,

[0173] The imaging calculation unit is used to enable the light source mask co-optimization model to determine the etching deviation related to the lithographic imaging features using the following formula:

[0174] ;

[0175] Among them, b i (x,y) represents the etching deviation related to the lithographic imaging feature at position (x,y); I(x,y) is the simulated optical image corresponding to the lithographic profile; j is the number of the related etching deviation term; H j d is the lithographic imaging feature function term; j , which are the linear coefficients of the lithography imaging feature function corresponding to the lithography imaging feature function term.

[0176] As one specific implementation, the model calibration module includes:

[0177] The function calibration unit is used to calibrate the geometric feature function term, the geometric feature linear coefficient, the lithographic imaging feature function term, the lithographic imaging feature linear coefficient, and the constant deviation when the function value of the loss function in the following formula is minimized:

[0178] ;

[0179] Where Cost is the value of the loss function, and B S K,L The simulated etching deviation of the evaluation points on the modeling pattern K in the modeling pattern set when exposed using light source L; B W K,L The measurement etching deviation of the evaluation point on the modeling pattern K in the modeling pattern set during exposure by the light source L; w K The weighting coefficients corresponding to the modeling pattern K; w L represents the weighting coefficient corresponding to the light source L.

[0180] The light source and mask collaborative optimization device provided by the present invention includes an acquisition module 100 for acquiring an etching target pattern; a collaborative optimization module 200 for performing light source and mask collaborative optimization based on the etching target pattern using a preset cost function, and determining the light source and mask pattern with the minimum function value of the cost function as the optimized light source and optimized mask pattern; the cost function includes an additive etching edge error term and a photolithography process deviation term; a photolithography contour module 300 for obtaining a photolithography contour corresponding to the preset process conditions based on the optimized light source and the optimized mask pattern; a window module 400 for determining a photolithography process window based on the photolithography contour; a judgment module 500 for judging whether the photolithography process window exceeds a preset qualified threshold; and an output module 600 for outputting the optimized light source and the optimized mask pattern when the photolithography process window exceeds the qualified threshold. This invention incorporates both etching edge placement error and photolithography process deviation into the cost function of light source mask co-optimization. During the optimization process, both the photolithography process window and the etching process window are taken into account, which greatly improves the accuracy and practicality of light source mask co-optimization and ensures the high yield of wafers obtained by optimizing the light source and mask.

[0181] The light source mask collaborative optimization device of this embodiment is used to implement the aforementioned light source mask collaborative optimization method. Therefore, the specific implementation of the light source mask collaborative optimization device can be found in the embodiment section of the light source mask collaborative optimization method above. For example, the acquisition module 100, collaborative optimization module 200, lithography contour module 300, window module 400, judgment module 500, and output module 600 are respectively used to implement steps S101, S102, S103, S104, S105, and S106 in the above-mentioned light source mask collaborative optimization method. Therefore, its specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0182] The present invention also provides a light source mask collaborative optimization device, comprising:

[0183] Memory, used to store computer programs;

[0184] A processor is configured to implement the steps of any of the above-described light source mask co-optimization methods when executing the computer program. The light source mask co-optimization method provided by this invention involves: acquiring an etching target pattern; performing light source mask co-optimization based on the etching target pattern using a preset cost function; determining the light source and mask pattern whose cost function value is minimized as the optimized light source and optimized mask pattern; the cost function includes an additive etching edge error term and a photolithography process deviation term; obtaining a photolithography profile corresponding to the preset process conditions based on the optimized light source and the optimized mask pattern; determining a photolithography process window based on the photolithography profile; determining whether the photolithography process window exceeds a preset acceptable threshold; and outputting the optimized light source and the optimized mask pattern when the photolithography process window exceeds the acceptable threshold. This invention incorporates both etching edge placement error and photolithography process deviation into the cost function of light source mask co-optimization, simultaneously considering both the photolithography process window and the etching process window during the optimization process, greatly improving the accuracy and practicality of light source mask co-optimization, and ensuring a high yield of wafers obtained after optimizing the light source and optimized mask.

[0185] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described light source mask collaborative optimization methods. The light source mask collaborative optimization method provided by this invention involves: acquiring an etching target pattern; performing light source mask collaborative optimization based on the etching target pattern using a preset cost function, determining the light source and mask pattern whose cost function value is minimized as the optimized light source and optimized mask pattern; the cost function includes an additive etching edge error term and a photolithography process deviation term; obtaining a photolithography profile corresponding to the preset process conditions based on the optimized light source and the optimized mask pattern; determining a photolithography process window based on the photolithography profile; determining whether the photolithography process window exceeds a preset acceptable threshold; and outputting the optimized light source and the optimized mask pattern when the photolithography process window exceeds the acceptable threshold. This invention incorporates both etching edge placement error and photolithography process deviation into the cost function of light source mask co-optimization. During the optimization process, both the photolithography process window and the etching process window are taken into account, which greatly improves the accuracy and practicality of light source mask co-optimization and ensures the high yield of wafers obtained by optimizing the light source and mask.

[0186] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0187] It should be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0188] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0189] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main 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 known in the art.

[0190] The above provides a detailed description of the light source mask collaborative optimization method, apparatus, device, and storage medium provided by this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and core ideas of this invention. It should be noted that those skilled in the art can make various improvements and modifications to this invention without departing from its principles, and these improvements and modifications also fall within the protection scope of this invention.

Claims

1. A method for collaborative optimization of light source masks, characterized in that, include: Obtain the etched target pattern; Based on the etched target pattern, a preset cost function is used to perform light source and mask co-optimization. The light source and mask pattern with the minimum function value of the cost function are determined as the optimized light source and optimized mask pattern. The cost function includes an additive etch edge error term and a photolithography process deviation term; Based on the optimized light source and the optimized mask pattern, a photolithography profile corresponding to the preset process conditions is obtained under various process conditions. Based on the lithography profile, determine the lithography process window; Determine whether the photolithography process window exceeds a preset acceptable threshold; When the photolithography process window exceeds the qualified threshold, the optimized light source and the optimized mask pattern are output.

2. The light source mask collaborative optimization method as described in claim 1, characterized in that, The cost function is the sum of the product of the photolithography process deviation term and the photolithography weighting coefficient and the etching edge error term; After determining whether the photolithography process window exceeds a preset acceptable threshold, the method further includes: When the lithography process window does not exceed the qualified threshold, the lithography weight coefficient is increased to obtain the updated cost function; Based on the etched target pattern, a co-optimization of the light source and mask is performed using the updated cost function to obtain an updated optimized light source and an updated optimized mask pattern. The updated lithography process window is then calculated based on the updated optimized light source and the updated optimized mask pattern. It is then determined whether the updated lithography process window exceeds the qualified threshold. If the updated lithography process window does not exceed the qualified threshold, the lithography weight coefficient is increased until the updated lithography process window exceeds the qualified threshold.

3. The light source mask collaborative optimization method as described in claim 2, characterized in that, Based on the etched target pattern, a pre-defined cost function is used to perform collaborative optimization of the light source and mask. The light source and mask pattern with the minimum value of the cost function are determined as the optimized light source and optimized mask pattern, including: Based on the etched target pattern, the light source and mask are optimized together using the following formula, and the light source and mask pattern with the minimum function value in the following formula are determined as the optimized light source and optimized mask pattern: ； Wherein, CF is the function value of the cost function; EPE(p,x) is the etching edge placement error at the corresponding position of evaluation point x under process condition p; w p The weighting coefficient corresponding to process condition p; w x β is the weighting coefficient corresponding to evaluation point x; β is the lithography weighting coefficient; PV(p,x') represents the lithography process deviation at the corresponding position of evaluation point x' under process condition p; w x’ The weighting coefficient is the one corresponding to the evaluation point x'; the evaluation point x' is the point on the photolithographic simulation contour corresponding to the etched target pattern under the nominal conditions that corresponds to the evaluation point x.

4. The light source mask collaborative optimization method as described in claim 1, characterized in that, The light source mask collaborative optimization based on the etched target pattern, and / or the simulation of the corresponding lithographic contour obtained based on the optimized light source and the optimized mask pattern, are performed through a pre-trained light source mask collaborative optimization model. The training method for the light source mask collaborative optimization model includes: An uncalibrated co-optimization model for the light source mask is established. The uncalibrated co-optimization model can determine the corresponding etching deviation related to geometric features based on the input lithography profile through geometric feature function terms and geometric feature linear coefficients, determine the corresponding etching deviation related to lithography imaging features through lithography imaging feature function terms and lithography imaging feature linear coefficients, and then determine the simulated etching deviation at any point on the lithography profile through the etching deviation related to geometric features, the etching deviation related to lithography imaging features, and the constant deviation. Acquire modeling pattern sets and various lighting sources; The modeling pattern set is subjected to optical proximity correction for various lighting sources to obtain mask patterns for each lighting source. The mask pattern is subjected to photolithography under the corresponding illumination source to obtain the photolithographic wafer pattern corresponding to each illumination source. Measure the key dimensions corresponding to each of the photolithographically ... A preset standard etching process is performed on the photolithographic wafer pattern to obtain the corresponding etched wafer pattern. Measure the critical dimensions corresponding to each of the etched wafer patterns; The etching deviation is determined based on the critical dimensions after photolithography and the critical dimensions after etching. Using the measured etching deviation and the simulated etching deviation, the geometric feature function term, the geometric feature linear coefficient, the lithographic imaging feature function term, the lithographic imaging feature linear coefficient, and the constant deviation are calibrated so that the difference between the simulated etching deviation and the measured etching deviation is less than a preset calibration threshold.

5. The light source mask collaborative optimization method as described in claim 4, characterized in that, The light source mask collaborative optimization model calculates the simulated etching deviation at any point on the lithographic profile using the following formula: ； Among them, B S (x,y) represents the etching deviation at position (x,y); b0 represents the constant deviation; b g (x,y) represents the etching deviation related to the geometric features at position (x,y); b i (x,y) represents the etching deviation related to the lithographic imaging feature at position (x,y).

6. The light source mask collaborative optimization method as described in claim 4, characterized in that, The light source mask collaborative optimization model determines the etching deviation related to geometric features using the following formula: ； Among them, b g (x,y) represents the etching deviation related to the geometric features at position (x,y); R(x,y) is the photoresist image corresponding to the lithographic profile; j is the number of the related etching deviation term; G j c is the geometric characteristic function term; j These are the geometric feature linear coefficients corresponding to the geometric feature function terms; And / or, The light source mask collaborative optimization model determines the etching deviation related to the lithographic imaging features using the following formula: ； Among them, b i (x,y) represents the etching deviation related to the lithographic imaging feature at position (x,y); I(x,y) is the simulated optical image corresponding to the lithographic profile; j is the number of the related etching deviation term; H j d is the lithographic imaging feature function term; j , which are the linear coefficients of the lithography imaging feature function corresponding to the lithography imaging feature function term.

7. The light source mask collaborative optimization method as described in claim 4, characterized in that, Using the measured etching deviation and the simulated etching deviation, the geometric characteristic function term, the geometric characteristic linear coefficient, the lithographic imaging characteristic function term, the lithographic imaging characteristic linear coefficient, and the constant deviation are calibrated, including: When the loss function in the following formula reaches its minimum value, the calibration of the geometric feature function term, the geometric feature linear coefficient, the lithographic imaging feature function term, the lithographic imaging feature linear coefficient, and the constant deviation is completed: ； Where Cost is the value of the loss function, and B S K,L The simulated etching deviation of the evaluation points on the modeling pattern K in the modeling pattern set when exposed using light source L; B W K,L The measurement etching deviation of the evaluation point on the modeling pattern K in the modeling pattern set during exposure by the light source L; w K The weighting coefficients corresponding to the modeling pattern K; w L represents the weighting coefficient corresponding to the light source L.

8. A light source mask collaborative optimization device, characterized in that, include: The acquisition module is used to acquire the etching target pattern; The collaborative optimization module is used to perform collaborative optimization of the light source and mask based on the etched target pattern and using a preset cost function, and to determine the light source and mask pattern with the minimum function value of the cost function as the optimized light source and optimized mask pattern. The cost function includes an additive etch edge error term and a photolithography process deviation term; The photolithography contour module is used to obtain a photolithography contour corresponding to the optimized light source and the optimized mask pattern under various preset process conditions. A window module is used to determine the photolithography process window based on the photolithography profile; The judgment module is used to determine whether the photolithography process window exceeds a preset qualified threshold. The output module is used to output the optimized light source and the optimized mask pattern when the photolithography process window exceeds the qualified threshold.

9. A light source mask collaborative optimization device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the light source mask collaborative optimization method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the light source mask collaborative optimization method as described in any one of claims 1 to 7.

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