Method to improve the efficiency of coordinated optimization of light source and mask

Abstract

The present invention discloses a method for improving the efficiency of collaborative optimization of light sources and mask plates, comprising the following steps: step S1, generating an OPC initial optical model and generating a simulation calculation graph; step S2, setting various parameters according to the SMO process, and setting n different normalized energy doses and m different focal length values; step S3, running n×m SMO optimization processes at the same time; obtaining process records of n×m SMO tasks and preliminary results of SMO; step S4, comparing the similarity of n×m SMO lighting source shapes, and comparing evaluation parameters and time; step S5, determining the most optimized energy dose and setting the focal length value, and performing SMO optimization. The present invention improves the SMO optimization efficiency, speeds up the SMO optimization speed, and also lays a solid foundation for SMO to output a suitable lighting source shape.

Description

Technical Field

[0001] The present invention relates to the field of semiconductors, and in particular to a method for improving the efficiency of coordinated optimization of a light source and a mask. Background Art

[0002] Source Mask Optimization (SMO) considers both the light source illumination mode and the mask pattern. Compared with the traditional resolution enhancement technology (OPC), SMO has greater freedom and is one of the key technologies to further improve the lithography resolution and process window. The basic principle of simulation calculation is similar to the model-based proximity effect correction. The edge of the mask pattern is moved and its deviation from the target pattern on the wafer is calculated, that is, the edge placement error. During the optimization, the model deliberately introduces perturbations of exposure dose, focus, and pattern size on the mask, and calculates the edge placement error of the image on the wafer caused by these perturbations. The evaluation function and optimization are both implemented based on the edge placement error. The results calculated by source mask collaborative optimization include not only a pixelated light source, but also the proximity effect correction made to the input design.

[0003] The basic principle of Source Mask Optimization (SMO) simulation calculation is similar to the model-based proximity effect correction. The edge of the mask pattern is moved and its deviation from the target pattern on the wafer is calculated, that is, the edge placement error. During the optimization, the model deliberately introduces perturbations of exposure dose, focus, and pattern size on the mask, and calculates the edge placement error of the image on the wafer caused by these perturbations. Both the evaluation function and the optimization are implemented based on the edge placement error. The result calculated by the Source Mask Optimization not only includes a pixelated light source, but also includes the proximity effect correction made to the input design. Since the illumination parameters and the pattern on the mask can change at the same time, the result of the optimization calculation may not be unique. Summary of the invention

[0004] The technical problem to be solved by the present invention is to improve the efficiency of coordinated optimization of light source and mask.

[0005] In order to solve the above technical problems, the present invention discloses a method for improving the efficiency of coordinated optimization of light source and mask, comprising the following steps:

[0006] Step S1, generating an OPC initial optical model and generating a simulation calculation graph;

[0007] Step S2, setting various parameters according to the SMO process, and setting n different normalized energy doses and m different focal length values;

[0008] Step S3, running n×m SMO optimization processes simultaneously; obtaining process records of n×m SMO tasks and preliminary results of SMO;

[0009] Step S4, comparing the similarities of the shapes of the n×m SMO lighting sources, and comparing the preset parameters and time;

[0010] Step S5, determine the optimal energy dose and set focal length value, and perform SMO optimization.

[0011] Preferably, step S1 also includes the following steps:

[0012] Step S11, collecting actual mask three-dimensional structural parameters and optical parameters of the thin film, the numerical aperture of the lithography machine prism system, and the optical parameters of the photoresist and thin film materials on the wafer, and generating a corresponding initial OPC optical model based on this information;

[0013] Step S12, according to the design rules of line width or hole level, organize the key dimension table of line width or hole, plan one-dimensional and two-dimensional simulation calculation graphics and special calculation graphics, and generate all simulation calculation graphics.

[0014] Preferably, n different normalized energy doses and m different focal length values ​​are set in the process window condition interface.

[0015] Preferably, the preliminary results in step S3 include: the shapes of n×m SMO lighting sources, the numerical results of evaluation parameters of all corresponding simulation calculation graphics, and the CPU resources and time used to complete the n×m SMO tasks.

[0016] Preferably, the evaluation parameters include CDOF, ILS, NILS or MEEF.

[0017] Preferably, the method for selecting the optimal Dose and Foucs values ​​is: using each of the evaluation parameters to evaluate the Dose and Foucs, and generating an evaluation result after one evaluation parameter evaluates one Dose and Foucs number; and selecting from all the best evaluation results the parameter settings of focal length and energy that are judged as optimal by the largest number of evaluation parameters.

[0018] Preferably, the time is the time required to complete the SMO task, and the shortest time is the best result.

[0019] Preferably, when the evaluation parameter is NILS, the one with the largest value is the best result.

[0020] Preferably, when the evaluation parameter is CDOF, the one with the largest value is the best result.

[0021] The invention is based on key figures obtained from design rules, and by adjusting different focal length and energy parameter settings, according to key parameter results such as CDOF (Common Depth of Focus), ILS (Image Log Slope), NILS (Normalized Image Log Slope), and MEEF (Mask Error Enhancement Factor), the best focal length and energy parameter settings are selected, thereby improving the SMO optimization efficiency, accelerating the SMO optimization speed, and also laying a solid foundation for the SMO to output a suitable lighting source shape. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The present invention is a flow chart of a method for improving the efficiency of coordinated optimization of light sources and masks.

[0023] Figure 2-Figure 4 A schematic diagram of SMO Source optimization results for different Dose / Focus combinations after using the method of the present invention to improve the collaborative optimization efficiency of light source and mask.

[0024] Figure 5 It is a schematic diagram of comparing the similarity of shapes of n×m SMO illumination light sources in step five of the present invention. DETAILED DESCRIPTION

[0025] The present invention is based on the key figures obtained by the design rules, and by adjusting different focal length and energy parameter settings, according to the key parameter results such as CDOF (Common Depth of Focus), ILS (Image Log Slope), NILS (Normalized Image Log Slope), MEEF (Mask Error Enhancement Factor), etc., the best focal length and energy parameter settings are selected, thereby improving the SMO optimization efficiency, accelerating the SMO optimization speed, and also laying a solid foundation for SMO to output a suitable lighting source shape. The specific method and process are as follows: Figure 1 As shown:

[0026] Step S1: Generate an OPC initial optical model and generate a simulation calculation graph; this step may include the following two sub-steps:

[0027] Step S11, according to the need of EDA software to generate OPC optical model, collect the actual mask three-dimensional structure parameters and optical parameters of the film, the numerical aperture of the lithography machine prism system, and the optical parameters of the photoresist and thin film materials on the wafer. Based on this information, use EDA software to generate the corresponding initial OPC optical model and import it into the input interface of SMO.

[0028] Step S12, then according to the design rules of the relevant line width or hole level, organize the key dimension tables of the line width or hole, plan the conventional one-dimensional and two-dimensional simulation calculation graphics, and other hot spot special calculation graphics, and use the relevant software to generate all the simulation calculation graphics; organize the coordinates and size information of all the simulation calculation graphics into a data file of a unified format, and import the calculation graphics and data files into the SMO input interface of the EDA software.

[0029] Step S2: In the SMO setting interface of the EDA software, various parameters are set according to the SMO process. In particular, in the PW condition (Process Window Condition) interface, n different normalized Dose (energy dose) and m different Focus (focal length) values ​​are set, such as Dose_1 / Focus_1, Dose_2 / Focus_1…Dose_n / Focus_m, so that n×m SMO tasks with the same settings except Dose and Focus can be generated.

[0030] Step S3: Based on step S2, run the n×m SMO tasks that have completed parameter settings, so as to obtain the process records of the n×m SMO tasks and the preliminary results of SMO: the shapes of the n×m SMO lighting sources, the numerical results of the evaluation parameters such as CDOF, ILS, NILS, MEEF of all corresponding simulation calculation graphics, and the CPU resources and time used to complete the n×m SMO tasks.

[0031] Step S4: Based on step S3, the similarity of the shapes of n×m SMO lighting sources is compared, such as Figure 5 shown.

[0032] Then, the evaluation parameters such as CDOF, ILS, NILS, MEEF, and the time required to complete the SMO task are comprehensively compared. Figure 2-Figure 4 The following are examples of SMO Source optimization results for different Dose / Focus combinations. Figure 2 Schematic diagram of different SMO Source optimization results produced by using different Dose / Focus combinations; Figure 3A schematic diagram of NILS evaluation parameters using the method of the present invention, wherein NILS and EL are correlated, and the largest value is the best result; Figure 5 Schematic diagram of CDOF evaluation parameters, where the largest value is the best result.

[0033] Select the best Dose and Focus values ​​Dose_i / Focus_j to improve the SMO optimization efficiency and speed up the SMO optimization.

[0034] The method for selecting the optimal Dose and Foucs values ​​is as follows: using each of the evaluation parameters to evaluate the Dose and Foucs, and generating an evaluation result after one evaluation parameter evaluates one Dose and Foucs number; and selecting from all the evaluation results the parameter settings of focal length and energy that are judged as the best by the largest number of evaluation parameters.

[0035] There are many adjustable input parameters for SMO technology, including: simulation calculation graphics, initial OPC (Optical Proximity Correction) model, light source shape symmetry, polarization direction, pupil filling degree and pupil filling area range, different focal length and energy conditions, etc. Since the calculation of SMO Cost Function is based on the determined focal length and energy, the parameter settings of focal length and energy have a crucial impact on the results of SMO.

[0036] The invention is based on key figures obtained from design rules, and by adjusting different focal length and energy parameter settings, according to key parameter results such as CDOF (Common Depth of Focus), ILS (Image Log Slope), NILS (Normalized Image Log Slope), and MEEF (Mask Error Enhancement Factor), the best focal length and energy parameter settings are selected, thereby improving the SMO optimization efficiency, accelerating the SMO optimization speed, and also laying a solid foundation for the SMO to output a suitable lighting source shape.

[0037] The present invention has been described in detail above through specific implementation modes and embodiments, but these do not constitute limitations of the present invention. Without departing from the principles of the present invention, those skilled in the art may also make many variations and improvements, which should also be regarded as the protection scope of the present invention.

Claims

1. A method for improving the efficiency of coordinated optimization of light source and mask, characterized in that: The following steps are involved: Step S1, generating an OPC initial optical model and generating a simulation calculation graph; Step S11, collecting actual mask three-dimensional structural parameters and optical parameters of the thin film, the numerical aperture of the lithography machine prism system, and the optical parameters of the photoresist and thin film materials on the wafer, and generating a corresponding initial OPC optical model based on this information; Step S12, according to the design rules of line width or hole level, organize the key dimension table of line width or hole, plan one-dimensional and two-dimensional simulation calculation graphics and special calculation graphics, and generate all simulation calculation graphics; Step S2, setting various parameters according to the SMO process, and setting n different normalized energy doses and m different focal length values; Step S3, running n×m SMO optimization processes simultaneously; Get the process records of n×m SMO tasks and the preliminary results of SMO; The preliminary results in step S3 include: the shapes of n×m SMO lighting sources, the numerical results of the evaluation parameters of all corresponding simulation calculation graphics, and the CPU resources and time used to complete the n×m SMO tasks; Step S4, comparing the similarity of the shapes of n×m SMO illumination sources, and comparing the evaluation parameters and time; the evaluation parameters include the common focus depth CDOF, the image intensity logarithmic slope ILS, the normalized image intensity logarithmic slope NILS or the mask error enhancement factor MEEF; the time is the time required to complete the SMO task; the shortest time is the best result; Step S5, determining the optimal energy dose and focal length setting value, and performing SMO optimization; the method of selecting the optimal Dose and Foucs values ​​is: using each evaluation parameter to evaluate the Dose and Foucs, and one evaluation parameter evaluates one Dose and Foucs value to generate an evaluation result; and selecting the parameter settings of focal length and energy judged as the best by the largest number of evaluation parameters from all the best judgment results.

2. The method for improving the efficiency of coordinated optimization of light source and mask according to claim 1, characterized in that: In the process window condition interface, set n different normalized energy doses and m different focal length values.

3. The method for improving the efficiency of coordinated optimization of light source and mask according to claim 1, characterized in that: When the evaluation parameter is NILS, the one with the largest value is the best result.

4. The method for improving the efficiency of coordinated optimization of light source and mask according to claim 1, characterized in that: When the evaluation parameter is CDOF, the one with the largest value is the best result.

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