Optical proximity effect correction method and system, terminal and medium

Abstract

The invention provides an optical proximity effect correction method and system, a terminal and a medium, and the method comprises the steps: obtaining a plurality of initial parameter combinations of a photoetching mask design pattern, so as to form an initial population; wherein each initial individual in the initial population corresponds to a group of initial parameter combinations; determining a plurality of optimization indexes of the photoetching mask design graph, and determining an optical proximity effect correction function based on the plurality of optimization indexes and constraint conditions; and based on the optical proximity effect correction function, obtaining an optimal parameter combination of the photoetching mask design pattern in the iteration process of the initial population. According to the invention, the optimization algorithm is innovatively introduced into the field of optical proximity effect correction, comprehensive optical proximity effect correction is realized by introducing a plurality of optimization indexes, the manufacturing requirement of a high-precision silicon optical chip is effectively met, and the performance of the silicon optical chip is effectively improved.

Description

Technical Field

[0001] This application relates to the field of semiconductor manufacturing technology, and in particular to an optical proximity effect correction method, system, terminal, and medium. Background Technology

[0002] In the semiconductor manufacturing field, optical waveguides are core components for optical signal transmission, and their performance directly affects the overall functionality of the chip. Due to optical diffraction effects and process limitations during photolithography, optical proximity effects inevitably cause distortion in the waveguide pattern, potentially leading to deviations between the waveguide pattern and the design on the photomask. Optical Proximity Correction (OPC) algorithms pre-analyze the spacing, shape, and proximity effects of patterns on the pattern layout, making local adjustments to the mask layout (such as adding auxiliary patterns or adjusting edge positions) to compensate for distortions during photolithography, thereby improving pattern fidelity and resolution. Therefore, achieving accurate optical proximity correction is a crucial technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0003] This application provides an optical proximity effect correction method, system, terminal, and medium to solve the technical problem of how to accurately achieve optical proximity effect correction.

[0004] A first aspect of this application provides an optical proximity effect correction method, the method comprising:

[0005] Multiple sets of initial parameter combinations are obtained from the design pattern of the photolithographic mask to form an initial population; wherein each initial individual in the initial population corresponds to a set of initial parameter combinations;

[0006] Multiple optimization indices are determined for the design pattern of the photomask, and an optical proximity effect correction function is determined based on the multiple optimization indices and constraints.

[0007] The optimal parameter combination for the photomask design pattern is obtained during the iteration process of the initial population based on the optical proximity effect correction function.

[0008] In some implementations of the first aspect, obtaining multiple initial parameter combinations of the photomask design pattern includes:

[0009] The value range of each initial parameter of the photolithography mask design pattern is obtained based on the photolithography requirements;

[0010] Multiple sets of initial parameter combinations are randomly generated within the range of values ​​of each of the initial parameters; the initial parameters include the key dimensions, phase shift, and auxiliary pattern dimensions of the photomask design pattern.

[0011] In some implementations of the first aspect, determining several optimization metrics for the photomask design pattern includes:

[0012] Obtain key dimension optimization metrics; the key dimension optimization metrics include edge placement error metrics; and

[0013] Edge roughness optimization indices are obtained based on photolithography application factors, including root mean square roughness optimization indices and / or average roughness optimization indices.

[0014] Among some implementations of the first aspect, obtaining edge roughness optimization indices based on lithography application requirements includes:

[0015] When the lithography application factors include light scattering sensitive factors and / or dense line image feature factors, the edge roughness optimization index is obtained as the root mean square roughness optimization index, or the edge roughness optimization index is obtained as the root mean square roughness optimization index and the average roughness optimization index.

[0016] When the lithography application factors include optical waveguide stability factors and / or isolated line image feature factors, the edge roughness optimization index is obtained as the average roughness optimization index, or the edge roughness optimization index is obtained as the root mean square roughness optimization index and the average roughness optimization index.

[0017] In some implementations of the first aspect, determining the optical proximity effect correction function based on the plurality of optimization indices and constraints includes:

[0018] Based on the weight allocation strategy, the multiple optimization indicators are adaptively assigned corresponding indicator weights, and the sum of the multiple indicator weights is 1;

[0019] The multiple optimization indicators are weighted and calculated based on the indicator weights to obtain the basic correction function;

[0020] The optical proximity effect correction function is obtained based on the basic correction function and the constraints; the constraints include total variational constraints and / or mask rule constraints.

[0021] In some implementations of the first aspect, adaptively assigning corresponding indicator weights to the multiple optimization indicators based on a weight allocation strategy includes:

[0022] The first weight allocation strategy adaptively assigns corresponding weights to the key dimension optimization index and the edge roughness optimization index; wherein, when the edge roughness optimization index includes the root mean square roughness optimization index and the average roughness optimization index, the second weight allocation strategy assigns corresponding weights to the root mean square roughness optimization index and the average roughness optimization index.

[0023] In some implementations of the first aspect, obtaining the optimal parameter combination of the photomask design pattern during the iteration process of the initial population based on the optical proximity correction function includes:

[0024] The correction value for each of the initial individuals is calculated based on the optical proximity effect correction function;

[0025] Parent individuals are selected based on the correction values ​​of each individual, and crossover and mutation operations are performed to obtain the next generation population.

[0026] The correction value of each individual in the next generation population is calculated based on the optical proximity effect correction function. This process is iterated until a preset termination condition is met, at which point the individual with the largest correction value is selected as the optimal individual.

[0027] The parameter combination corresponding to the optimal individual is taken as the optimal parameter combination of the photomask design pattern.

[0028] A second aspect of this application provides an optical proximity effect correction system, the system comprising:

[0029] The acquisition module is configured to acquire multiple sets of initial parameter combinations of the photomask design pattern to form an initial population; wherein each initial individual in the initial population corresponds to a set of initial parameter combinations;

[0030] The index module is configured to determine multiple optimization indices of the photomask design pattern, in order to determine an optical proximity correction function based on the multiple optimization indices and constraints.

[0031] The correction module is configured to obtain the optimal combination of parameters for the photomask design pattern during the iteration of the initial population based on the optical proximity effect correction function.

[0032] A third aspect of this application provides an optical proximity effect correction terminal, the terminal comprising: one or more processors; and one or more memories, wherein the memories store computer-readable code that, when executed by the one or more processors, implements the method as described in the first aspect.

[0033] A fourth aspect of this application provides a computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method described in the first aspect.

[0034] As described above, this application provides an optical proximity effect correction method, system, terminal, and medium, which have the following beneficial effects:

[0035] First, this application significantly reduces the edge roughness of the optical waveguide by selectively choosing an appropriate edge roughness optimization index in the optical proximity effect correction method and optimizing it synchronously with the critical dimension accuracy of the pattern. This effectively reduces light scattering loss and thus greatly improves the optical performance of the optical waveguide, fully meeting the stringent requirements for the optical performance of silicon photonic chips in fields such as high-speed optical communication and high-precision optical sensing.

[0036] Secondly, the algorithm of this application achieves comprehensive optimization of the mask pattern. While ensuring the accuracy of the key dimensions of the pattern, it also takes into account the optimization of edge roughness, making the optical waveguide structure after photolithography more in line with the design requirements, improving the overall accuracy and consistency of silicon photonic chip manufacturing, and effectively reducing the product defect rate caused by pattern distortion and edge roughness.

[0037] Finally, based on the different application scenarios and optical performance requirements of optical waveguides, this application can flexibly select roughness optimization indices and adjust the weights of each index according to application needs and scenarios, thus possessing stronger adaptability and meeting diverse silicon photonic chip manufacturing needs. Whether in light-scattering sensitive application scenarios or manufacturing scenarios with special requirements for the average deviation of edge contours, this application can achieve effective optimization, significantly enhancing the versatility of the algorithm. Attached Figure Description

[0038] Figure 1 The diagram shown is a flowchart of an optical proximity effect correction method in one embodiment of this application.

[0039] Figure 2 The diagram shows a flowchart of obtaining multiple sets of initial parameter combinations for a photomask design pattern in one embodiment of this application.

[0040] Figure 3 The diagram shows a flowchart of determining multiple optimization metrics for the photomask design pattern in one embodiment of this application.

[0041] Figure 4 The diagram shown illustrates the edge placement error in one embodiment of this application.

[0042] Figure 5 The diagram shown is a schematic representation of edge roughness in one embodiment of this application.

[0043] Figure 6 The diagram shown is a flowchart illustrating the process of determining the optical proximity effect correction function in one embodiment of this application.

[0044] Figure 7 The diagram shows a flowchart of obtaining the optimal parameter combination for the photomask design pattern in one embodiment of this application.

[0045] Figure 8 The diagram shown is a schematic representation of an optical proximity effect correction system according to an embodiment of this application.

[0046] Figure 9 The diagram shown is a structural schematic of an optical proximity effect correction terminal in one embodiment of this application. Detailed Implementation

[0047] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0048] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0049] Furthermore, the use of terms such as "first" and "second" in this application is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. If the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed in this application.

[0050] In the field of semiconductor silicon photonics chip manufacturing, optical waveguides are the core components for optical signal transmission, and their performance directly affects the realization of the overall chip functionality. During the photolithography process, the optical proximity effect inevitably causes distortion in the optical waveguide pattern, and optical proximity correction algorithms are used to compensate for this distortion and ensure the accuracy of the pattern. However, traditional OPC algorithms have limitations in practical applications. They mainly focus on the critical dimensional accuracy of the pattern, calculate edge placement errors based on the critical dimensional accuracy, and then correct the mask pattern (such as adding auxiliary features or adjusting edge positions), but neglect the edge roughness of the optical waveguide, a crucial factor that has a significant impact on optical performance.

[0051] With the widespread application of silicon photonics chips in cutting-edge fields such as high-speed optical communication, data center optical interconnects, and high-precision optical sensing, the requirements for the optical performance of optical waveguides are becoming increasingly stringent. Edge roughness of optical waveguides can induce light scattering, thereby increasing signal loss during transmission and severely affecting signal stability and quality. Taking long-distance optical communication systems as an example, even extremely small increases in light scattering due to edge roughness can cause significant signal attenuation, leading to a decline in communication quality. Therefore, fully considering and effectively optimizing the edge roughness of optical waveguides in the OPC algorithm has become a core key to improving the performance of silicon photonics chips. However, current technologies lack an efficient OPC algorithm that can simultaneously optimize edge roughness and critical pattern dimensional accuracy, making it difficult to meet the ever-growing demand for high-precision silicon photonics chip manufacturing.

[0052] To at least address the aforementioned technical problems, this application proposes an optical proximity effect correction method, system, terminal, and medium that can simultaneously optimize edge roughness and critical pattern dimension accuracy. Furthermore, it can select appropriate roughness optimization indices based on the characteristics of the optical waveguide edge and actual application requirements, thereby adapting to different application scenarios.

[0053] The optical proximity effect correction method provided in the embodiments of this application will be further described below. Figure 1 The diagram shown is a flowchart illustrating the optical proximity effect correction method provided in an embodiment of this application. Figure 1 As shown, the optical proximity effect correction method provided in this application includes steps S1 to S3.

[0054] Step S1: Obtain multiple sets of initial parameter combinations of the photolithographic mask design pattern to form an initial population; wherein, each initial individual in the initial population corresponds to a set of initial parameter combinations.

[0055] Figure 2 This diagram illustrates a process for obtaining multiple sets of initial parameter combinations for a photomask design pattern, as provided in an embodiment of this application. Figure 2As shown, the multiple sets of initial parameter combinations for obtaining the photomask design pattern provided in this application embodiment include steps S11 to S12.

[0056] Step S11: Obtain the value range of each initial parameter of the photolithography mask design pattern based on the photolithography requirements.

[0057] Step S12: Randomly generate multiple sets of initial parameter combinations within the value range of each of the initial parameters.

[0058] In some embodiments, the initial parameters include the critical dimension, phase shift, and auxiliary pattern dimensions of the photomask design. The critical dimension (CD) refers to the smallest feature size on the mask that needs precise control, typically corresponding to the minimum linewidth or spacing in chip manufacturing (such as transistor gate width). It directly affects chip performance, power consumption, and integration density, and is a core indicator for measuring photolithography resolution. Phase shift refers to improving resolution by altering the phase of light as it passes through a portion of the mask, utilizing interference effects to enhance the contrast of pattern edges. Furthermore, auxiliary patterns are sub-resolution structures added to the mask (not directly imaged) to improve exposure uniformity and process window during photolithography.

[0059] It should be further noted that this application does not impose any restrictions on the type of initial parameters for the photomask design pattern that needs to be optimized. Those skilled in the art can determine the initial parameters to be optimized and accurately determine the range of values ​​for the initial parameters based on the specific silicon photonics chip manufacturing requirements and the actual conditions of the photolithography process. For example, for critical dimensions, the variation range of the photomask design pattern size compensation amount can be reasonably set; and for phase shift, the selectable values ​​for the phase shift setting can be determined, etc.

[0060] Furthermore, after determining the initial parameters and corresponding value ranges of the photomask design pattern, each initial parameter can be assigned a value within its range and combined with other initial parameters to form multiple sets of initial parameter combinations. For example, a critical dimension can have N values ​​within its corresponding range; a phase shift can have M values ​​within its corresponding range; and auxiliary pattern dimensions can have Q values ​​within their corresponding range, resulting in a total of N*M*Q initial parameter combinations. Each initial parameter combination is then treated as an initial individual, thus forming an initial population.

[0061] Step S2: Determine multiple optimization indices of the photomask design pattern, and determine the optical proximity effect correction function based on the multiple optimization indices and constraints.

[0062] In some embodiments, this application determines multiple optimization indicators for the design pattern of the photomask by combining the application scenario of the optical waveguide and the specific requirements for optical performance. Figure 3 The diagram shown is a flowchart illustrating the process of determining multiple optimization metrics for the photomask design pattern, as provided in an embodiment of this application. Figure 3 As shown, determining multiple optimization indicators for the design pattern of the photomask includes steps S21 to S22.

[0063] Step S21: Obtain key dimension optimization indicators; the key dimension optimization indicators include edge placement error indicators.

[0064] In some embodiments, Figure 4 This diagram illustrates the edge placement error (EPE) provided in an embodiment of this application. Please refer to... Figure 4 As shown, edge placement error actually refers to the deviation between the actual edge position of the lithographic pattern and the design target position. By optimizing the edge placement error index, the key dimensions (such as line width and spacing) of the actual lithographic pattern can be made as close as possible to the design target values.

[0065] In some embodiments, the edge placement error index can be expressed by equation (1):

[0066]

[0067] Where, x i x represents the actual coordinates of the i-th sampling point of the pattern after photolithography. target This represents the target coordinates of the i-th sampling point in the design layout.

[0068] Step S22: Obtain edge roughness optimization index based on photolithography application factors. The edge roughness optimization index includes root mean square roughness optimization index and / or average roughness optimization index.

[0069] In some embodiments, line edge roughness (LER) refers to the non-ideal geometric undulations on the sidewall surface of an optical waveguide, manifesting as a random, irregular morphology at the nanometer scale, such as... Figure 5 As shown. This roughness is mainly caused by factors such as exposure scattering in the photolithography process, the discreteness of photoresist molecules, and the randomness in the etching process. It will cause scattering loss and mode coupling of the optical signal transmitted in the optical waveguide, reduce the transmission efficiency of the waveguide, and increase the insertion loss.

[0070] In some embodiments, the edge roughness optimization index includes the root mean square roughness optimization index and / or the average roughness optimization index.

[0071] Among them, the root mean square roughness optimization index characterizes the root mean square deviation of edge undulations, and it is more sensitive to high-frequency noise. It can be expressed by equation (2):

[0072]

[0073] Among them, the average roughness optimization index characterizes the absolute average value of the edge deviation from the average height, and is more sensitive to the gradual changes in the overall morphology. It can be expressed by equation (3):

[0074]

[0075] In equations (2) and (3), y i This represents the edge roughness of the i-th sampling point of the pattern after photolithography. This indicates the average edge position determined by measurement.

[0076] In practical applications, when optical waveguides are used in scenarios highly sensitive to light scattering, such as high-speed optical communication and high-precision optical sensing, it is necessary to suppress high-frequency noise. In such cases, the root mean square roughness optimization index is suitable. However, for optical waveguide applications that are more concerned with the average deviation of the edge profile, such as in the manufacture of certain special optical devices with high requirements for the stability of the optical waveguide transmission mode, it is even more necessary to maintain the stability of the optical waveguide transmission mode to ensure overall edge smoothness. In these cases, the average roughness optimization index is more suitable. Therefore, this application obtains corresponding edge roughness optimization indices based on different photolithography application factors, thereby providing a precise quantitative basis.

[0077] In some embodiments, when the lithography application factors include light scattering sensitive factors and / or dense line image feature factors, the edge roughness optimization index is obtained as the root mean square roughness optimization index, or the edge roughness optimization index is obtained as the root mean square roughness optimization index and the average roughness optimization index.

[0078] When light scattering-sensitive factors are included, such as random defects in light scattering-sensitive scenes, high-frequency noise can significantly affect imaging. Therefore, the root mean square roughness optimization index is prioritized to suppress high-frequency noise.

[0079] Dense line pattern features refer to a high-density arrangement of lines and spacing on a mask, with linewidths and spacings approaching the resolution limits of photolithography. In some embodiments, dense line pattern features include equally spaced lines / spacings, contact hole / via arrays, cut / blocking patterns, etc., such as the capacitor contact layer in a DRAM memory cell (e.g., 80nm spacing lines at a 28nm node). When dense line pattern features are included, prioritizing root mean square roughness optimization metrics can effectively reduce local variations.

[0080] In some embodiments, when the lithography application factors include optical waveguide stabilization factors and / or isolated line image feature factors, the edge roughness optimization index is obtained as the average roughness optimization index, or the edge roughness optimization index is obtained as the root mean square roughness optimization index and the average roughness optimization index.

[0081] When optical waveguide stability factors are included, such as when silicon photonic devices have optical waveguide transmission mode stability, the average roughness optimization index is preferred to ensure overall edge smoothness.

[0082] Isolated line image features refer to isolated line graphic features in a photolithographic mask, which are lines that are independently distributed on the mask and have no densely adjacent structures, such as gratings. When isolated line image features are included, the average roughness optimization index is prioritized to maintain long-range consistency.

[0083] It should be noted that when both the root mean square roughness optimization index and the average roughness optimization index are selected, different weights are assigned to the two optimization indices based on the lithography application factors. Please refer to the later part of the instruction manual for details.

[0084] Furthermore, once the optimization indices are determined, the optical proximity effect correction function is determined based on multiple optimization indices and constraints. Figure 6 The diagram shown is a flowchart illustrating the determination of the optical proximity effect correction function provided in an embodiment of this application. Figure 6 As shown, determining the optical proximity effect correction function based on multiple optimization indices and constraints includes steps S23 to S25.

[0085] Step S23: Based on the weight allocation strategy, adaptively assign corresponding indicator weights to the multiple optimization indicators, and the sum of the multiple indicator weights is 1.

[0086] After determining multiple optimization indices, corresponding weights are assigned to different indices to enable more accurate optical proximity effect correction. In some embodiments, adaptively assigning corresponding index weights to the multiple optimization indices based on a weight allocation strategy includes: adaptively assigning corresponding weights to the key size optimization index and the edge roughness optimization index based on a first weight allocation strategy; wherein, when the edge roughness optimization index includes the root mean square roughness optimization index and the average roughness optimization index, the root mean square roughness optimization index and the average roughness optimization index are assigned corresponding weights based on a second weight allocation strategy.

[0087] In some embodiments, the first weight allocation strategy actually refers to assigning corresponding weights to the critical dimension optimization index (edge ​​placement error index) and the edge roughness optimization index according to different correction stages. For example, in the initial correction stage, the critical dimension optimization index is given a high weight, meaning the critical dimension optimization index should be greater than the edge roughness optimization index, so that the mask pattern converges to the target pattern quickly. In the later correction stage, the edge roughness optimization index is given a high weight, meaning the edge roughness optimization index should be greater than the critical dimension optimization index, thereby refining the edge smoothness of the mask pattern, thus ensuring the accuracy of the critical dimensions of the pattern while also optimizing the edge roughness. This makes the post-lithographic waveguide structure more in line with design requirements, improves the overall accuracy and consistency of silicon photonics chip manufacturing, and effectively reduces the product defect rate caused by pattern distortion and edge roughness.

[0088] Furthermore, as mentioned above, when both the root mean square roughness optimization index and the average roughness optimization index are selected as the edge roughness optimization index, the root mean square roughness optimization index and the average roughness optimization index are assigned corresponding weights based on the second weight allocation strategy.

[0089] In some embodiments, the second weighting strategy assigns corresponding weights to the root mean square roughness optimization index and the average roughness optimization index based on lithography application factors. For example, when lithography application factors include light scattering sensitivity factors and / or dense line image feature factors, the weight of the root mean square roughness optimization index should be greater than that of the average roughness optimization index. Conversely, when lithography application factors include optical waveguide stabilization factors and / or isolated line image feature factors, the weight of the average roughness optimization index should be greater than that of the root mean square roughness optimization index.

[0090] In some other embodiments, the second weighting strategy can also assign corresponding weights to the root mean square roughness optimization index and the average roughness optimization index according to the process window requirements. For example, when the process window requirements include defocusing, the weights can be dynamically adjusted so that the weight of the average roughness optimization index is greater than that of the root mean square roughness optimization index.

[0091] Step S24: Perform a weighted calculation on the multiple optimization indicators based on the indicator weights to obtain the basic correction function.

[0092] In some embodiments, the basic correction function can be represented by equation (4):

[0093] cost = w1·EPE + w2·RMS + w3·Ra

[0094] Where EPE is the edge placement error index, and w1 is the weight of the edge placement error index; RMS is the root mean square roughness optimization index, and w2 is the weight of the root mean square roughness optimization index; Ra is the average roughness optimization index, and w3 is the weight of the average roughness optimization index. Where w1 + w2 + w3 = 1.

[0095] It should be noted that w2 or w3 can be 0, that is, when only one edge roughness optimization index is selected based on the lithography application factors, the weight of the other edge roughness optimization index is 0.

[0096] Taking photolithography applications involving light scattering sensitivity and / or dense line image features as an example, if only the root mean square roughness optimization index is selected, then w1 and w2 are determined solely based on the first weight allocation strategy. In this case, w3 = 0, meaning the average roughness optimization index is not selected.

[0097] Taking lithography application factors including light scattering sensitivity factors and / or dense line image feature factors as an example, in this case, both root mean square roughness edge optimization index and average roughness optimization index are selected simultaneously. If we are currently in the initial calibration stage, we can determine w1>w2+w3 based on the first weight allocation strategy, and determine w2>w3 based on the second weight allocation strategy, and w1+w2+w3=1.

[0098] Step S25: Obtain the optical proximity effect correction function based on the basic correction function and the constraint conditions.

[0099] In some embodiments, the optical proximity effect correction function can be expressed by equation (5):

[0100] Cost=w1·EPE+w2·RMS+w3·Ra+Constraints

[0101] Here, "Constraints" refers to the constraints. In practice, constraints are used to ensure the manufacturability and physical plausibility of the mask pattern. In some embodiments, constraints include total variational constraints and / or mask rule constraints.

[0102] The total variation constraint limits the total variation of the mask pattern, avoiding overly complex designs (e.g., reducing unnecessary details). In some embodiments, the total variation constraint can be represented by equation (6):

[0103]

[0104] Where X is the mask pattern; ▽X is the gradient operator, which calculates the local changes of X; T max This is a preset threshold used to limit the maximum value of the total variation.

[0105] The mask rule constraints include limiting the minimum line width / spacing and the maximum slope. The minimum line width / spacing represents the minimum allowable size of the mask pattern and the minimum distance between adjacent patterns. The maximum slope refers to the maximum rate of change of the upper edge position of the mask pattern, used to quantify the steepness of the pattern edge. In some embodiments, the mask rule constraints can be represented by equation (7):

[0106] width(X)≥W min

[0107]

[0108] Where width(X) is the line width / spacing of the mask pattern. The slope of the mask pattern.

[0109] In other embodiments, the constraints may also include process window constraints, nonnegativity and binarization constraints, etc. This application does not limit these constraints, and those skilled in the art can set relevant constraints according to process requirements.

[0110] Step S3: Based on the optical proximity effect correction function, obtain the optimal parameter combination of the photomask design pattern during the iteration process of the initial population.

[0111] In some embodiments, after determining the optical proximity correction function, the edges of the photolithographic waveguide are precisely measured using a high-resolution scanning electron microscope (SEM) or atomic force microscope (AFM) to obtain microscopic morphology data of the edges. These data are then processed in depth using specialized image analysis software to calculate the specific values ​​of the selected roughness optimization indices (RMS and / or Ra). Simultaneously, the critical dimensions of the pattern are measured to obtain their deviation data from the design values. Finally, these data are used as the basis for algorithm optimization, and the optimal parameter combination of the photolithographic mask design pattern is obtained during the iterative process of the initial population based on the optical proximity correction function. Figure 7 This is a schematic diagram illustrating the process of obtaining the optimal parameter combination for the photomask design pattern provided in an embodiment of this application. Figure 7 As shown, obtaining the optimal parameter combination of the photomask design pattern based on the optical proximity effect correction function during the iteration process of the initial population includes steps S31 to S34.

[0112] Step S31: Calculate the correction value for each of the initial individuals based on the optical proximity effect correction function.

[0113] Step S32: Select parent individuals based on the correction value of each individual, and perform crossover and mutation operations to obtain the next generation population.

[0114] Step S33: Calculate the correction value of each individual in the next generation population based on the optical proximity effect correction function, and iterate in this way until a preset termination condition is met, and then select the individual with the largest correction value as the optimal individual.

[0115] Step S34: Use the parameter combination corresponding to the optimal individual as the optimal parameter combination of the photomask design pattern.

[0116] After obtaining the initial population, the correction value of each initial individual is calculated using the optical proximity effect correction function. Based on the correction value, superior individuals are selected as parent individuals (e.g., through roulette or tournament selection). Then, new solutions are generated through crossover (exchanging parts of chromosomes) and mutation (randomly changing certain genes). These new solutions, along with their parent individuals, form the next generation population. The correction value of each individual in the next generation population is calculated again, and this process is repeated until a termination condition is met, such as reaching the maximum number of iterations or the objective function value converging within a certain range. At this point, the individual with the highest correction value is taken as the optimal solution, and its corresponding mask pattern parameter combination is the optimized result.

[0117] Furthermore, the optimized mask pattern parameters are applied to the actual photolithography process to fabricate the optical waveguide structure. The edge roughness of the optical waveguide after photolithography is measured again using SEM or AFM, and the critical dimensions of the pattern are also measured. The measurement results are compared with the design requirements to comprehensively evaluate the optimization effect of the algorithm. If the results meet the requirements, the optimized scheme is applied to mass production; if they do not meet the requirements, the algorithm parameters or objective function weights are adjusted, and iterative optimization is performed again.

[0118] This application provides an optical proximity effect correction method, system, terminal, and medium. Firstly, by selectively choosing appropriate edge roughness optimization indices in the optical proximity effect correction method and simultaneously optimizing them with the critical dimension accuracy of the pattern, this application significantly reduces the edge roughness of the optical waveguide, effectively reducing light scattering loss and thus greatly improving the optical performance of the optical waveguide. This fully meets the stringent requirements for the optical performance of silicon photonic chips in fields such as high-speed optical communication and high-precision optical sensing. Secondly, this application achieves comprehensive optimization of the mask pattern. While ensuring the critical dimension accuracy of the pattern, it also considers edge roughness optimization, making the post-lithographic optical waveguide structure more consistent with design requirements. This improves the overall accuracy and consistency of silicon photonic chip manufacturing and effectively reduces the product defect rate caused by pattern distortion and edge roughness. Finally, based on different application scenarios and optical performance requirements of the optical waveguide, this application can flexibly select roughness optimization indices and adjust the weights of each index according to application needs and scenarios, exhibiting greater adaptability and meeting diverse silicon photonic chip manufacturing needs. Whether in applications sensitive to light scattering or in manufacturing scenarios with specific requirements for the average deviation of edge contours, this application can achieve effective optimization and significantly enhance the versatility of the algorithm.

[0119] The scope of protection of the optical proximity effect correction method described in this application is not limited to the order of steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this application is included within the scope of protection of this application.

[0120] This application also provides an optical proximity effect correction system, which can implement the optical proximity effect correction method described in this application. However, the implementation device of the optical proximity effect correction method described in this application includes, but is not limited to, the structure of the optical proximity effect correction system listed in this embodiment. All structural modifications and substitutions of the prior art made in accordance with the principles of this application are included within the protection scope of this application.

[0121] Figure 8 The diagram shown is a structural schematic of the optical proximity effect correction system provided in an embodiment of this application. Figure 8 As shown, the optical proximity effect correction system 40 includes an acquisition module 401, an index module 402, and a correction module 403.

[0122] The acquisition module 401 is configured to acquire multiple sets of initial parameter combinations of the photomask design pattern to form an initial population; wherein each initial individual in the initial population corresponds to a set of initial parameter combinations.

[0123] The index module 402 is configured to determine multiple optimization indices of the photomask design pattern in order to determine an optical proximity effect correction function based on the multiple optimization indices and constraints.

[0124] The correction module 403 is configured to obtain the optimal combination of parameters for the photomask design pattern during the iteration of the initial population based on the optical proximity effect correction function.

[0125] It should be noted that the principles of the acquisition module 401, index module 402 and correction module 403 provided in this application embodiment correspond one-to-one with the steps in the optical proximity effect correction method described above, so they will not be repeated here.

[0126] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0127] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this application, depending on actual needs. For example, the functional modules / units in the various embodiments of this application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0128] 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 implementation should not be considered beyond the scope of this application.

[0129] The optical proximity effect correction method provided in this application can be implemented on the terminal side or the server side. For the hardware structure of the optical proximity effect correction terminal, please refer to [link to relevant documentation]. Figure 9 This is a schematic diagram of an optional hardware structure of an optical proximity correction terminal 700 provided in an embodiment of this application. The optical proximity correction terminal 700 includes one or more processors 701, one or more memories 702, a network interface 704, and a user interface 706. Furthermore, the various components in the optical proximity correction terminal 700 are coupled together via a bus system. It is understood that the bus system is used to realize communication between these components. In addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus. The user interface 706 may include a display or a touchscreen, etc.

[0130] It is understood that memory 702 can be volatile memory or non-volatile memory, or both. This application does not specifically limit this. In the embodiments of this application, memory 702 is used to store various types of data to support the operation of the optical proximity correction terminal 700. Examples of this data include: any executable program for operation on the optical proximity correction terminal 700, such as operating system 7021 and application program 7022; operating system 7021 includes various system programs, such as framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. Application program 7022 can include various applications, such as media player, browser, etc. The implementation of the optical proximity correction method provided in the embodiments of this application can be included in application program 7022.

[0131] The optical proximity effect correction method disclosed in the above embodiments of this application can be applied to, or implemented by, processor 701. Processor 701 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the hardware of processor 701 or by instructions in software form. Processor 701 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 701 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor 701 may be a microprocessor or any conventional processor, etc.

[0132] In an exemplary embodiment, the optical proximity correction terminal 700 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to perform the aforementioned optical proximity correction method.

[0133] This application also provides a computer-readable storage medium. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state drive (SSD)).

[0134] This application embodiment may also provide a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this application embodiment are generated. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0135] When the computer program product is executed by a computer, the computer performs the method described in the foregoing method embodiments. The computer program product can be a software installation package; when the foregoing method is required, the computer program product can be downloaded and executed on the computer.

[0136] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0137] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for correcting the optical proximity effect, characterized in that, The method includes: Multiple sets of initial parameter combinations are obtained from the design pattern of the photolithographic mask to form an initial population; wherein each initial individual in the initial population corresponds to a set of initial parameter combinations; Multiple optimization indices are determined for the design pattern of the photomask, and an optical proximity effect correction function is determined based on the multiple optimization indices and constraints. The optimal parameter combination for the photomask design pattern is obtained during the iteration process of the initial population based on the optical proximity effect correction function.

2. The optical proximity effect correction method according to claim 1, characterized in that, Obtaining multiple initial parameter combinations for the photomask design pattern includes: The value range of each initial parameter of the photolithography mask design pattern is obtained based on the photolithography requirements; Multiple sets of initial parameter combinations are randomly generated within the range of values ​​of each of the initial parameters; the initial parameters include the key dimensions, phase shift, and auxiliary pattern dimensions of the photomask design pattern.

3. The optical proximity effect correction method according to claim 1, characterized in that, Several optimization metrics for determining the design pattern of the photomask include: Obtain key dimension optimization metrics; the key dimension optimization metrics include edge placement error metrics; and Edge roughness optimization indices are obtained based on photolithography application factors, including root mean square roughness optimization indices and / or average roughness optimization indices.

4. The optical proximity effect correction method according to claim 3, characterized in that, The edge roughness optimization indicators obtained based on the requirements of photolithography applications include: When the lithography application factors include light scattering sensitive factors and / or dense line image feature factors, the edge roughness optimization index is obtained as the root mean square roughness optimization index, or the edge roughness optimization index is obtained as the root mean square roughness optimization index and the average roughness optimization index. When the lithography application factors include optical waveguide stability factors and / or isolated line image feature factors, the edge roughness optimization index is obtained as the average roughness optimization index, or the edge roughness optimization index is obtained as the root mean square roughness optimization index and the average roughness optimization index.

5. The optical proximity effect correction method according to claim 1, characterized in that, The optical proximity effect correction function is determined based on the aforementioned multiple optimization indices and constraints, including: Based on the weight allocation strategy, the multiple optimization indicators are adaptively assigned corresponding indicator weights, and the sum of the multiple indicator weights is 1; The multiple optimization indicators are weighted and calculated based on the indicator weights to obtain the basic correction function; The optical proximity effect correction function is obtained based on the basic correction function and the constraints; the constraints include total variational constraints and / or mask rule constraints.

6. The optical proximity effect correction method according to claim 5, characterized in that, Based on a weight allocation strategy, adaptive weights are assigned to the multiple optimization indicators, including: Based on the first weight allocation strategy, corresponding weights are adaptively assigned to the key size optimization index and the edge roughness optimization index; among which... When the edge roughness optimization index includes the root mean square roughness optimization index and the average roughness optimization index, the root mean square roughness optimization index and the average roughness optimization index are assigned corresponding weights based on the second weight allocation strategy.

7. The optical proximity effect correction method according to claim 1, characterized in that, The optimal parameter combination for obtaining the photomask design pattern during the iteration process of the initial population, based on the optical proximity correction function, includes: The correction value for each of the initial individuals is calculated based on the optical proximity effect correction function; Parent individuals are selected based on the correction values ​​of each individual, and crossover and mutation operations are performed to obtain the next generation population. The correction value of each individual in the next generation population is calculated based on the optical proximity effect correction function. This process is iterated until a preset termination condition is met, at which point the individual with the largest correction value is selected as the optimal individual. The parameter combination corresponding to the optimal individual is taken as the optimal parameter combination of the photomask design pattern.

8. An optical proximity effect correction system, characterized in that, The system includes: The acquisition module is configured to acquire multiple sets of initial parameter combinations of the photomask design pattern to form an initial population; Each initial individual in the initial population corresponds to a set of initial parameter combinations; The index module is configured to determine multiple optimization indices of the photomask design pattern, in order to determine an optical proximity correction function based on the multiple optimization indices and constraints. The correction module is configured to obtain the optimal combination of parameters for the photomask design pattern during the iteration of the initial population based on the optical proximity effect correction function.

9. An optical proximity effect correction terminal, characterized in that, The terminal includes: One or more processors; and One or more memories, wherein computer-readable code is stored in the memories, the computer-readable code, when executed by the one or more processors, implements the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by a processor, cause the processor to perform the method as described in any one of claims 1-7.

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