A method, device and computer equipment for optimizing and constructing an optical mask correction model

Abstract

The present invention relates to the field of computational lithography technology, and in particular to a method for optimizing and constructing an optical mask correction model, the method comprising the following steps: S0: acquiring multiple feature sizes of a mask layout and feature data of sub-resolution auxiliary graphics and preprocessing the feature data to obtain fitting data; S1: performing OPC modeling according to the feature sizes to obtain an initial OPC model; S2: optimizing the initial OPC model according to the fitting data to obtain an optimized OPC model. The present invention optimizes the traditional OPC model and introduces the feature data of sub-resolution auxiliary graphics, so that the optimized OPC model can more conveniently and accurately determine whether the sub-resolution auxiliary graphics are printed, so as to adjust the sub-resolution auxiliary graphics.

Description

Technical Field

[0001] The present invention relates to the field of computational lithography technology, and in particular to a method, device and computer equipment for optimizing and constructing an optical mask correction model. Background Art

[0002] At present, the conventional optical mask correction model cannot predict whether the sub-resolution auxiliary feature (SRAF) is printed, which makes it inconvenient to adjust the sub-resolution auxiliary feature later. Summary of the invention

[0003] In order to solve the problem that the conventional optical mask correction (OPC) model does not consider the printing technology of sub-resolution auxiliary graphics, the present invention provides an optical mask correction model optimization construction method, device and computer equipment.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for optimizing and constructing an optical mask correction model, the method comprising the following steps: S0: acquiring feature data of multiple feature sizes and sub-resolution auxiliary graphics of a mask layout and preprocessing the feature data to obtain fitting data; S1: performing OPC modeling according to the feature sizes to obtain an initial OPC model, the initial OPC model comprising an initial metrology plane; S2: optimizing the initial OPC model according to the fitting data, the optimization comprising a main optimization round, the main optimization round comprising the following steps:

[0005] S21: setting a plurality of simulation measurement planes based on the initial measurement plane;

[0006] S22: obtaining a main evaluation function based on the initial metrology plane, and obtaining additional evaluation functions corresponding to the multiple simulated metrology planes according to the fitting data;

[0007] S23: deriving a total evaluation function of the current main round according to the main evaluation function and the additional evaluation function;

[0008] When the preset number of main optimization rounds has not been completed and / or the optimization of the main evaluation function meets the preset standard, the next main optimization round is performed; otherwise, the optimized OPC model is obtained.

[0009] Preferably, in step S2, the OPC model is optimized based on the initial metrology plane.

[0010] Preferably, the characteristic data includes test pattern data containing a small amount of sub-resolution auxiliary patterns printed out and some data containing sub-resolution auxiliary patterns but not printed out selected from data used for ordinary OPC modeling.

[0011] Preferably, step S2 includes a sub-optimization round, the simulation metrology plane includes a corresponding threshold value, and the sub-optimization round includes the following steps: S220: selecting a certain simulation metrology plane; S221: calculating the printout probability of the sub-resolution auxiliary graphics under the current threshold value of the simulation metrology plane according to the threshold value of the current simulation metrology plane; S222: calculating the sub-evaluation function under the current threshold value of the simulation metrology plane according to the printout probability; S223: optimizing the threshold value according to the fitting data to obtain the optimized sub-evaluation function under the current simulation metrology plane; S224: calculating the optimized sub-evaluation functions of other simulation metrology planes, and selecting the smallest optimized sub-evaluation function as the additional evaluation function.

[0012] Preferably, step S223 comprises the following steps:

[0013] S2230: Optimizing the threshold according to the fitting data and obtaining an initial intermediate sub-evaluation function;

[0014] Inputting the initial intermediate sub-evaluation function into a preset optimizer and adjusting the threshold value accordingly for iterative optimization to obtain a smaller intermediate sub-evaluation function;

[0015] When the sub-evaluation function cannot be optimized to meet the preset standard after a preset number of iterative optimization times or when the value of the threshold is adjusted, the current intermediate sub-evaluation function is obtained as the optimized sub-evaluation function.

[0016] Preferably, step S24 includes the following steps:

[0017] When the preset number of main optimization rounds has not been completed and / or the optimization of the main evaluation function meets the preset standard, the next main optimization round is performed.

[0018] Preferably, in step S23, when calculating the total evaluation function, a balance coefficient γ is introduced to weigh the relationship between the OPC model for critical dimension prediction and sub-resolution auxiliary pattern printing prediction.

[0019] In order to solve the above technical problems, the present invention provides another technical solution as follows: a device for implementing the above method, the device comprising: an acquisition module: used to acquire the characteristic size of the mask pattern and the characteristic data of the sub-resolution auxiliary graphics; a preprocessing module: used to preprocess the characteristic data of the sub-resolution auxiliary graphics; a modeling module: used to perform OPC modeling according to the characteristic size; an optimization module: used to optimize the initial OPC model.

[0020] In order to solve the above technical problems, the present invention provides another technical solution as follows: a computer device, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the above method.

[0021] Compared with the prior art, the optical mask correction model optimization construction method, device and computer equipment provided by the present invention have the following beneficial effects:

[0022] 1. The first embodiment of the present invention provides a method for optimizing and constructing an optical mask correction model, and the method includes the following steps: S0: obtaining multiple feature sizes of the mask layout and feature data of sub-resolution auxiliary graphics and preprocessing the feature data to obtain fitting data; S1: performing OPC modeling according to the feature sizes to obtain an initial OPC model; S2: optimizing the initial OPC model according to the fitting data to obtain an optimized OPC model. It can be seen that compared with the traditional OPC model, introducing the feature data of the sub-resolution auxiliary graphics into the modeling process of the OPC model can make the optimized OPC model have the function of judging whether the sub-resolution auxiliary graphics are printed, and there is no need to calculate a large amount of data, the process is simple, and the practicality is stronger.

[0023] 2. In the method provided in the first embodiment of the present invention, the initial OPC model includes an initial metrology plane, and the OPC model is optimized based on the initial metrology plane in step S2. The OPC model is optimized based on the initial metrology plane, so that the model will be closer to the actual situation during the optimization process and have higher reliability.

[0024] 3. In the method provided by the first embodiment of the present invention, step S2 includes a main optimization round and a sub-optimization round, and the main optimization round includes the following steps: S21: based on the initial metrology plane, multiple simulated metrology planes are set; S22: based on the initial metrology plane, a main evaluation function is obtained, and additional evaluation functions corresponding to multiple simulated metrology planes are obtained according to the fitting data; S23: the total evaluation function of the current main round is obtained according to the main evaluation function and the additional evaluation function. Through the iterative optimization of the main optimization round, the optimization degree of the OPC model can be judged according to the change of the total evaluation function. In the process of repeated iterative optimization, the OPC model can be closer to the real situation.

[0025] 4. In the method provided by the first embodiment of the present invention, the simulation metrology plane includes a corresponding threshold value, and the sub-optimization round includes the following steps: S220: select a certain simulation metrology plane; S221: calculate the probability of printing out the sub-resolution auxiliary graphics under the current threshold value of the simulation metrology plane according to the threshold value of the current simulation metrology plane; S222: calculate the sub-evaluation function under the current threshold value of the simulation metrology plane according to the printing probability; S223: optimize the threshold value according to the fitting data to obtain the optimized sub-evaluation function under the current simulation metrology plane; S224: calculate the optimized sub-evaluation function of other simulation metrology planes, and select the smallest optimized sub-evaluation function as an additional evaluation function. In each sub-optimization round, the threshold value of the model is adjusted according to the change of the sub-evaluation function, so as to continuously optimize the model and improve the reliability of OPC.

[0026] 5. In the method provided by the first embodiment of the present invention, step S2 further includes the following steps: when the preset number of main optimization rounds has not been completed and / or the optimization of the main evaluation function meets the preset standard, the next main optimization round is performed. When the optimization benefit of the main optimization round to the model is small, the optimization round can be ended to save computing resources.

[0027] 6. In the method provided by the first embodiment of the present invention, step S223 includes the following steps: S2230: optimize the threshold value according to the fitting data to obtain an initial intermediate sub-evaluation function; input the initial intermediate sub-evaluation function into the preset optimizer to perform iterative optimization corresponding to the size of the adjusted threshold value to obtain a smaller intermediate sub-evaluation function; when the preset number of iterative optimizations or the size of the adjusted threshold value cannot optimize the sub-evaluation function to meet the preset standard, the current intermediate sub-evaluation function is obtained as the optimized sub-evaluation function. By repeatedly iteratively optimizing and adjusting the threshold value to make it closer to the actual situation, when the preset iterative rounds are completed or the optimization effect obtained by adjusting the threshold value is not obvious, the iterative loop is promptly jumped out, and computing resources are saved on the basis of ensuring the optimization effect.

[0028] 7. In the method provided by the first embodiment of the present invention, in step S23, when calculating the total evaluation function, a balance coefficient γ is introduced to weigh the relationship between the OPC model's prediction of key dimensions and the prediction of sub-resolution auxiliary graphics printing. The coefficient γ is set by the user before adjustment and optimization, and is used to weigh the relationship between the OPC model's prediction of key dimensions and the prediction of sub-resolution auxiliary graphics printing. The task of the OPC main model is to predict key dimensions, thereby driving specific mask optimization. After taking into account whether the sub-resolution auxiliary graphics are printed, the model is essentially more comprehensive and close to the real physical world. When evaluation functions of other dimensions are introduced, there will be some sacrifices in the numerical indicators of key dimension fitting. Therefore, the coefficient γ is needed to make a certain balance, so that the model can obtain the ability to predict the printing of sub-resolution auxiliary graphics on the basis of slightly sacrificing the numerical indicators of key dimension fitting.

[0029] 8. The second embodiment of the present invention also provides a device having the same beneficial effects as the above-mentioned method for optimizing and constructing an optical mask correction model, which will not be described in detail here.

[0030] 9. The third embodiment of the present invention also provides a computer device, which has the same beneficial effects as the above-mentioned method for optimizing and constructing an optical mask correction model, and will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flowchart of the method provided by the first embodiment of the present invention.

[0032] Figure 2 It is a flowchart of step S2 of the method provided in the first embodiment of the present invention.

[0033] Figure 3 It is a flowchart of step S22 of the method provided in the first embodiment of the present invention.

[0034] Figure 4 It is a schematic diagram of the prediction results of the OPC optimization model of the method provided in the first embodiment of the present invention.

[0035] Figure 5 It is a schematic diagram of the structure of the device provided by the second embodiment of the present invention.

[0036] Figure 6 It is a schematic diagram of the structure of a computer device provided in the third embodiment of the present invention.

[0037] Description of the accompanying drawings:

[0038] 1. Method for optimizing and constructing an optical mask correction model; 2. Device; 3. Computer equipment. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and implementation examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0040] See also Figure 1 The first embodiment of the present invention provides a method for optimizing and constructing an optical mask correction model, and the method includes the following steps: S0: obtaining feature data of multiple feature sizes and sub-resolution auxiliary graphics of the mask layout and preprocessing the feature data to obtain fitting data; S1: performing OPC modeling according to the feature size to obtain an initial OPC model; S2: optimizing the initial OPC model according to the fitting data to obtain an optimized OPC model. It can be seen that compared with the traditional OPC model, the modeling method of introducing the feature data of the sub-resolution auxiliary graphics into the OPC model can enable the optimized OPC model to have the function of judging whether the sub-resolution auxiliary graphics are printed, and there is no need to calculate a large amount of data, the process is simple, and the practicability is stronger.

[0041] In some embodiments, the initial OPC model includes an initial metrology plane, and the OPC model is optimized based on the initial metrology plane in step S2. The OPC model is optimized based on the initial metrology plane so that the model is closer to the actual situation during the optimization process and has higher reliability.

[0042] It should be understood that the feature data includes test pattern data containing a small amount of sub-resolution auxiliary graphics printed out and data for ordinary OPC modeling, and some data with sub-resolution auxiliary graphics but not printed out are selected, and the corresponding print-out positions and non-print-out positions are marked on the layout, and the above-mentioned print-out positions and non-print-out positions do not overlap. Exemplarily, in step S0, 1000 feature sizes of the mask layout are obtained, and 50 feature sizes with sub-resolution auxiliary graphics but not printed out are selected from the 1000 feature sizes, and the positions where these 50 feature sizes are located are marked as non-print-out positions; 50 test pattern data containing a small amount of sub-resolution auxiliary graphics printed out are obtained, and these 50 test patterns are marked as print-out positions. The real data of 100 positions (i.e., whether the sub-resolution auxiliary graphics are printed out or not) are preprocessed to obtain fitting data, and the OPC model is optimized through the fitting data of these 100 positions, so that the prediction result of whether the sub-resolution auxiliary graphics are printed out at each position of these 100 positions by the optimized OPC model is close to the actual situation.

[0043] See also Figure 2In some embodiments, step S2 includes a main optimization round and a sub-optimization round, and the main optimization round includes the following steps: S21: multiple simulated measurement planes are set based on the initial measurement plane; S22: a main evaluation function is obtained based on the initial measurement plane, and additional evaluation functions corresponding to multiple simulated measurement planes have been calculated based on the fitting data; S23: a total evaluation function of the current main round is obtained based on the main evaluation function and the additional evaluation function. Through the iterative optimization of the main optimization round, the optimization degree of the OPC model can be judged according to the change of the total evaluation function. In the process of repeated iterative optimization, the OPC model can be closer to the real situation.

[0044] It will be appreciated that in each primary optimization round, locations in the fitted data containing sub-resolution assist pattern non-printouts are always obtained from the feature dimensions.

[0045] Preferably, step S2 further includes the following steps: determining whether to proceed to the next main optimization round. When the optimization benefit of the main optimization round to the model is small, the optimization round can be ended to save computing resources.

[0046] In some embodiments, step S2 comprises the following steps:

[0047] Step S24: When the preset number of main optimization rounds has not been completed and / or the optimization of the main evaluation function meets the preset standard, the next main optimization round is performed. If the judgment result is that the next main optimization round is not performed, the current OPC model is obtained as the OPC optimization model.

[0048] See also Figure 3 In some embodiments, the simulation metrology plane includes a corresponding threshold value, and the sub-optimization round includes the following steps: S220: select a certain simulation metrology plane; S221: calculate the probability of printing out the sub-resolution auxiliary graphics under the current threshold value of the simulation metrology plane according to the threshold value of the current simulation metrology plane; S222: calculate the sub-evaluation function under the current threshold value of the simulation metrology plane according to the printing probability; S223: optimize the threshold value according to the fitting data to obtain the optimized sub-evaluation function under the current simulation metrology plane; S224: calculate the optimized sub-evaluation function of other simulation metrology planes, and select the smallest optimized sub-evaluation function as an additional evaluation function. In each sub-optimization round, the threshold value of the model is adjusted according to the change of the sub-evaluation function, so as to continuously optimize the model, make it closer to the actual situation, and improve the reliability of the model.

[0049] In some embodiments, in step S23, when calculating the total evaluation function, a balance coefficient γ is introduced to weigh the relationship between the OPC model's prediction of critical dimensions and the prediction of sub-resolution auxiliary graphics printing. Exemplarily, step S23 also includes the following steps: total evaluation function = main evaluation function + γ * additional evaluation function, and the balance coefficient γ is preset before optimization. The coefficient γ is set by the user before adjusting the optimization, and is used to weigh the relationship between the OPC model's prediction of critical dimensions and the prediction of sub-resolution auxiliary graphics printing. The task of the OPC main model is to predict the critical dimensions, thereby driving specific mask optimization. After taking into account whether the sub-resolution auxiliary graphics are printed, the model is essentially more comprehensive and close to the real physical world. When evaluation functions of other dimensions are introduced, there will be some sacrifices in the numerical indicators of critical dimension fitting. Therefore, the coefficient γ is needed to make a certain balance, so that the model can obtain the ability to predict the printing of sub-resolution auxiliary graphics on the basis of slightly sacrificing the numerical indicators of critical dimension fitting.

[0050] In some embodiments, step S221 includes the following steps:

[0051]

[0052] p(x i ) is the print-out probability;

[0053] ΔI=I sraf -Threshold

[0054] I sraf is the photoresist strength of the sub-resolution auxiliary pattern mark position (in the method provided in the embodiment of the present invention, I sraf is a constant value), Threshold is the threshold value.

[0055] In some embodiments, step S222 includes the following steps:

[0056]

[0057] printing_cost is a sub-evaluation function.

[0058] In some embodiments, step S223 includes the following steps: S2230: optimizing the threshold according to the fitting data to obtain an initial intermediate sub-evaluation function; transferring the initial intermediate sub-evaluation function to a preset optimizer, and the optimizer performs iterative optimization corresponding to the size of the adjusted threshold, and obtains a smaller intermediate sub-evaluation function; when the preset number of iterative optimizations or the size of the adjusted threshold cannot optimize the sub-evaluation function to meet the preset standard, the current intermediate sub-evaluation function is obtained as the optimized sub-evaluation function. By repeatedly iteratively optimizing and adjusting the threshold to make it closer to the actual situation, when the preset iteration rounds are completed or the optimization effect obtained by adjusting the threshold is not obvious, the iteration loop is promptly jumped out, and computing resources are saved on the basis of ensuring the optimization effect.

[0059] See also Figure 4 ,The RMS of the OPC optimization model based on 2000 critical dimension measurement data increased from 1.53 to 1.65. For 40 lead-out data, 38 were predicted accurately, and for 40 non-printing data, no error was predicted.

[0060] The mask pattern with sub-resolution auxiliary graphics is passed into the OPC optimization model. In addition to the functions of the traditional OPC model, the OPC optimization model also has the function of predicting whether the sub-resolution auxiliary graphics are printed, that is, it predicts whether the sub-resolution auxiliary graphics on the mask pattern are printed. If they are printed, the pattern is adjusted.

[0061] See also Figure 5 The second embodiment of the present invention further provides a device, the device comprising:

[0062] Acquisition module: used to obtain the characteristic size of the mask layout and the characteristic data of the sub-resolution auxiliary graphics;

[0063] Preprocessing module: used to preprocess the feature data of sub-resolution auxiliary graphics;

[0064] Modeling module: used for OPC modeling based on feature dimensions;

[0065] Optimization module: used to optimize the initial OPC model.

[0066] See also Figure 6 The third embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the above method.

[0067] In the embodiments provided by the present invention, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0068] It should be understood that the "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. Those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0069] In various embodiments of the present invention, it should be understood that the size of the serial numbers of the above-mentioned processes does not mean the necessary order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0070] The flow chart and block diagram in the accompanying drawings of the present invention illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which is determined based on the functions involved. It should be particularly noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs a specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0071] Compared with the prior art, the optical mask correction model optimization construction method, device and computer equipment provided by the present invention have the following beneficial effects:

[0072] 1. The first embodiment of the present invention provides a method for optimizing and constructing an optical mask correction model, and the method includes the following steps: S0: obtaining multiple feature sizes of the mask layout and feature data of sub-resolution auxiliary graphics and preprocessing the feature data to obtain fitting data; S1: performing OPC modeling according to the feature sizes to obtain an initial OPC model; S2: optimizing the initial OPC model according to the fitting data to obtain an optimized OPC model. It can be seen that compared with the traditional OPC model, introducing the feature data of the sub-resolution auxiliary graphics into the modeling process of the OPC model can make the optimized OPC model have the function of judging whether the sub-resolution auxiliary graphics are printed, and there is no need to calculate a large amount of data, the process is simple, and the practicality is stronger.

[0073] 2. In the method provided in the first embodiment of the present invention, the initial OPC model includes an initial metrology plane, and the OPC model is optimized based on the initial metrology plane in step S2. The OPC model is optimized based on the initial metrology plane, so that the model will be closer to the actual situation during the optimization process and have higher reliability.

[0074] 3. In the method provided by the first embodiment of the present invention, step S2 includes a main optimization round and a sub-optimization round, and the main optimization round includes the following steps: S21: based on the initial metrology plane, multiple simulated metrology planes are set; S22: based on the initial metrology plane, a main evaluation function is obtained, and additional evaluation functions corresponding to multiple simulated metrology planes are obtained according to the fitting data; S23: the total evaluation function of the current main round is obtained according to the main evaluation function and the additional evaluation function. Through the iterative optimization of the main optimization round, the optimization degree of the OPC model can be judged according to the change of the total evaluation function. In the process of repeated iterative optimization, the OPC model can be closer to the real situation.

[0075] 4. In the method provided by the first embodiment of the present invention, the simulation metrology plane includes a corresponding threshold value, and the sub-optimization round includes the following steps: S220: select a certain simulation metrology plane; S221: calculate the probability of printing out the sub-resolution auxiliary graphics under the current threshold value of the simulation metrology plane according to the threshold value of the current simulation metrology plane; S222: calculate the sub-evaluation function under the current threshold value of the simulation metrology plane according to the printing probability; S223: optimize the threshold value according to the fitting data to obtain the optimized sub-evaluation function under the current simulation metrology plane; S224: calculate the optimized sub-evaluation function of other simulation metrology planes, and select the smallest optimized sub-evaluation function as an additional evaluation function. In each sub-optimization round, the threshold value of the model is adjusted according to the change of the sub-evaluation function, so as to continuously optimize the model and improve the reliability of OPC.

[0076] 5. In the method provided by the first embodiment of the present invention, step S2 further includes the following steps: when the preset number of main optimization rounds has not been completed and / or the optimization of the main evaluation function meets the preset standard, the next main optimization round is performed. When the optimization benefit of the main optimization round to the model is small, the optimization round can be ended to save computing resources.

[0077] 6. In the method provided by the first embodiment of the present invention, step S223 includes the following steps: S2230: optimize the threshold value according to the fitting data to obtain an initial intermediate sub-evaluation function; input the initial intermediate sub-evaluation function into the preset optimizer to perform iterative optimization corresponding to the size of the adjusted threshold value to obtain a smaller intermediate sub-evaluation function; when the preset number of iterative optimizations or the size of the adjusted threshold value cannot optimize the sub-evaluation function to meet the preset standard, the current intermediate sub-evaluation function is obtained as the optimized sub-evaluation function. By repeatedly iteratively optimizing and adjusting the threshold value to make it closer to the actual situation, when the preset iterative rounds are completed or the optimization effect obtained by adjusting the threshold value is not obvious, the iterative loop is promptly jumped out, and computing resources are saved on the basis of ensuring the optimization effect.

[0078] 7. In the method provided by the first embodiment of the present invention, in step S23, when calculating the total evaluation function, a balance coefficient γ is introduced to weigh the relationship between the OPC model's prediction of key dimensions and the prediction of sub-resolution auxiliary graphics printing. The coefficient γ is set by the user before adjustment and optimization, and is used to weigh the relationship between the OPC model's prediction of key dimensions and the prediction of sub-resolution auxiliary graphics printing. The task of the OPC main model is to predict key dimensions, thereby driving specific mask optimization. After taking into account whether the sub-resolution auxiliary graphics are printed, the model is essentially more comprehensive and close to the real physical world. When evaluation functions of other dimensions are introduced, there will be some sacrifices in the numerical indicators of key dimension fitting. Therefore, the coefficient γ is needed to make a certain balance, so that the model can obtain the ability to predict the printing of sub-resolution auxiliary graphics on the basis of slightly sacrificing the numerical indicators of key dimension fitting.

[0079] 8. The second embodiment of the present invention also provides a device having the same beneficial effects as the above-mentioned method for optimizing and constructing an optical mask correction model, which will not be described in detail here.

[0080] 9. The third embodiment of the present invention also provides a computer device, which has the same beneficial effects as the above-mentioned method for optimizing and constructing an optical mask correction model, and will not be described in detail here.

[0081] The above is a detailed introduction to an optical mask correction model optimization construction method, device and computer equipment disclosed in an embodiment of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention. Any modifications, equivalent substitutions and improvements made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing and constructing an optical mask correction model, characterized in that: The method comprises the following steps: S0: Acquire the characteristic size of the mask layout and the characteristic data of the sub-resolution auxiliary pattern and pre-process the characteristic data to obtain fitting data; S1: performing OPC modeling according to the characteristic size to obtain an initial OPC model, wherein the initial OPC model includes an initial metrology plane; S2: Optimizing the initial OPC model according to the fitting data, wherein the optimization includes a main optimization round, and the main optimization round includes the following steps: S21: setting a plurality of simulation measurement planes based on the initial measurement plane; S22: obtaining a main evaluation function based on the initial metrology plane, and obtaining additional evaluation functions corresponding to the multiple simulated metrology planes according to the fitting data; S23: deriving a total evaluation function of the current main round according to the main evaluation function and the additional evaluation function; When the preset number of main optimization rounds has not been completed and / or the optimization of the main evaluation function meets the preset standard, the next main optimization round is performed; otherwise, the optimized OPC model is obtained.

2. The method according to claim 1, characterized in that: In step S2, the OPC model is optimized based on the initial measurement plane.

3. The method according to claim 1, characterized in that: The characteristic data includes test pattern data containing a small amount of sub-resolution auxiliary patterns printed out and some data containing sub-resolution auxiliary patterns but not printed out selected from data used for ordinary OPC modeling.

4. The method according to claim 1, characterized in that: Step S2 includes a sub-optimization round, the simulated metrology plane includes a corresponding threshold value, and the sub-optimization round includes the following steps: S220: Selecting one of the simulation measurement planes; S221: calculating the probability of printing out the sub-resolution auxiliary pattern under the current threshold value of the simulation measurement plane according to the current threshold value of the simulation measurement plane; S222: Calculating a sub-evaluation function under the current threshold value of the simulation measurement plane according to the print-out probability; S223: Optimizing the threshold according to the fitting data to obtain an optimized sub-evaluation function under the current simulation measurement plane; S224: Calculate the optimized sub-evaluation functions of the other simulated metrology planes, and select the smallest optimized sub-evaluation function as an additional evaluation function.

5. The method according to claim 4, characterized in that: Step S223 includes the following steps: S2230: Optimizing the threshold according to the fitting data to obtain an initial intermediate sub-evaluation function; Inputting the initial intermediate sub-evaluation function into a preset optimizer to perform iterative optimization according to the adjustment of the threshold value, and obtaining a smaller intermediate sub-evaluation function; When the sub-evaluation function cannot be optimized to meet the preset standard after a preset number of iterative optimization times or when the value of the threshold is adjusted, the current intermediate sub-evaluation function is obtained as the optimized sub-evaluation function.

6. The method according to claim 1, characterized in that: In step S23, when calculating the total evaluation function, a balance coefficient γ is introduced to weigh the relationship between the OPC model for key dimension prediction and sub-resolution auxiliary pattern printing prediction.

7. A device for implementing the method according to any one of claims 1 to 6, the device comprising: Acquisition module: used to obtain the characteristic size of the mask layout and the characteristic data of the sub-resolution auxiliary graphics; Preprocessing module: used for preprocessing the feature data of the sub-resolution auxiliary graphics; Modeling module: used for OPC modeling based on feature dimensions; Optimization module: used for optimizing the initial OPC model.

8. A computer device, characterized in that: The computer device comprises a memory, a processor and a computer program stored in the memory, and the processor executes the computer program to implement the method according to any one of claims 1 to 6.

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