A method, device, electronic device and storage medium for optimizing photoresist model consistency

Abstract

The invention relates to the field of model optimization, and discloses a method, device, electronic device, and storage medium for optimizing the consistency of a photoresist model. The method comprises step S11: after performing current generation search and simulation on simulation item parameters in the photoresist model, shifting a test pattern corresponding to the photoresist model; step S12: determining a photoresist parameter density difference corresponding to a key size at a preset sampling point after the test pattern is shifted; step S13: determining a function value of a cost function according to a relationship between the photoresist parameter density difference and a preset density difference threshold; step S14: performing next generation search and simulation on the simulation item parameters in the photoresist model, and entering step S11 until the number of generations searched and simulated for the simulation item parameters in the photoresist model is equal to the preset algebraic threshold; and step S15: determining a minimum value among all function values, and taking the minimum function value as a final result of photoresist optimization, so as to improve the consistency of the photoresist model.

Description

Technical Field

[0001] The present invention relates to the field of model optimization, and in particular to a method, device, electronic device, and computer-readable storage medium for optimizing the consistency of a photoresist model. Background Art

[0002] Photolithography is an important technology in semiconductor manufacturing process, including photoresist coating, exposure and development technology. Negative development technology is the key technology to solve the high-fidelity imaging of graphics with small feature size and large period. The photoresist used in negative development has good adhesion and blocking effect, fast photosensitivity, but it will deform and expand during development. The thermal shrinkage effect of negative development is stronger and the negative development effect is obvious. In order to ensure the accuracy of the model, more photoresist parameters are usually used for model calibration.

[0003] The consistency of the photoresist model is an important criterion for evaluating the quality of the photoresist model during the negative development process. The current control method for the consistency of the photoresist model performs multiple parameter searches and accesses on the photoresist model parameters, and outputs a characterization value after each parameter search and access, and then selects the smallest characterization value from all the characterization values ​​as the final optimization result. The current method only verifies after the model parameters are calibrated, and cannot curb the occurrence of consistency problems at the source. It has high uncertainty and the model optimization cycle may be prolonged due to this problem.

[0004] Therefore, how to solve the above technical problems should be the focus of technical personnel in this field. Summary of the invention

[0005] The purpose of the present invention is to provide a method, device, electronic device, and computer-readable storage medium for optimizing the consistency of a photoresist model, so as to improve the consistency of the photoresist model and shorten the optimization and verification cycle of the photoresist model.

[0006] In order to solve the above technical problems, the present invention provides a method for optimizing the consistency of a photoresist model, comprising:

[0007] Step S11: after performing current generation search and simulation on the simulation item parameters in the photoresist model, shifting the test pattern corresponding to the photoresist model;

[0008] Step S12: determining a photoresist parameter density difference corresponding to a critical dimension at a preset sampling point after the test pattern is shifted;

[0009] Step S13: determining a function value of a cost function according to a relationship between the photoresist parameter density difference and a preset density difference threshold;

[0010] Step S14: searching and simulating the next generation of the simulation item parameters in the photoresist model, and entering step S11, until the number of generations of searching and simulating the simulation item parameters in the photoresist model is equal to a preset algebraic threshold;

[0011] Step S15: determining the minimum value among all the function values, and taking the minimum function value as the final result of photoresist optimization.

[0012] Optionally, determining the function value of the cost function according to the relationship between the photoresist parameter density difference and a preset density difference threshold comprises:

[0013] Determining whether the photoresist parameter density difference is greater than the preset density difference threshold;

[0014] If the photoresist parameter density difference is not greater than the preset density difference threshold, determining the function value according to the critical dimension at the preset sampling point after the test pattern is shifted and the actually measured critical dimension;

[0015] If the photoresist parameter density difference is greater than the preset density difference threshold, the function value is determined according to the critical dimension at the preset sampling point after the test pattern is shifted, the actually measured critical dimension, and the penalty item parameter.

[0016] Optionally, determining the function value according to the key size at the preset sampling point after the test pattern is shifted and the actually measured key size includes:

[0017] The function value is determined according to a first preset formula, where the first preset formula is:

[0018] ;

[0019] In the formula, cost is the function value, CD s is the critical dimension of the test pattern at the preset sampling point after the shift, CD0 is the actually measured critical dimension of the test pattern at the preset sampling point after the shift, and i is the preset sampling point.

[0020] Optionally, determining the function value according to the key size at a preset sampling point after the test pattern is shifted, the actually measured key size, and a penalty term parameter includes:

[0021] The function value is determined according to a second preset formula, where the second preset formula is:

[0022] ;

[0023] Among them, penalty=penalty_coeffient×(term_intensity_variation-term_intensity_variation_Spec;

[0024] In the formula, cost is the function value, CD s is the critical dimension of the test pattern at the preset sampling point after the shift, CD0 is the actually measured critical dimension of the test pattern at the preset sampling point after the shift, i is the preset sampling point, penalty is the penalty item parameter, term_intensity_variation is the photoresist parameter density difference, term_intensity_variation_Spec is a parameter related to consistency; penalty_coeffient is the degree of penalty.

[0025] Optionally, determining the photoresist parameter density difference corresponding to the critical dimension at the preset sampling point after the test pattern is shifted includes:

[0026] Determining the photoresist parameter density corresponding to the critical dimension at the preset sampling point at different shift positions of the test pattern;

[0027] determining a maximum photoresist parameter density and a minimum photoresist parameter density from all the photoresist parameter densities;

[0028] A difference between the maximum photoresist parameter density and the minimum photoresist parameter density is determined as the photoresist parameter density difference.

[0029] The present invention also provides a device for optimizing the consistency of a photoresist model, comprising:

[0030] A shift module, used to execute step S11: after performing a current generation search and simulation on the simulation item parameters in the photoresist model, shifting the test pattern corresponding to the photoresist model;

[0031] A first determination module is used to execute step S12: determining a photoresist parameter density difference corresponding to a critical dimension at a preset sampling point after the test pattern is shifted;

[0032] A second determination module is used for executing the module, and is used for executing step S13: determining a function value of a cost function according to a relationship between the photoresist parameter density difference and a preset density difference threshold;

[0033] An iteration module, for executing step S14: searching and simulating the next generation of the simulation item parameters in the photoresist model, and entering step S11, until the number of generations of searching and simulating the simulation item parameters in the photoresist model is equal to a preset algebraic threshold;

[0034] The third determination module is used to execute step S15: determine the minimum value among all the function values, and use the minimum function value as the final result of photoresist optimization.

[0035] Optionally, the second determining module includes:

[0036] A first judging unit, configured to judge whether the photoresist parameter density difference is greater than the preset density difference threshold;

[0037] A first determining unit, configured to determine the function value according to the critical dimension at the preset sampling point after the test pattern is shifted and the actually measured critical dimension if the photoresist parameter density difference is not greater than the preset density difference threshold;

[0038] The second determination unit is used to determine the function value according to the critical dimension at the preset sampling point after the test pattern is shifted, the actual measured critical dimension and the penalty item parameter if the photoresist parameter density difference is greater than the preset density difference threshold.

[0039] Optionally, the first determining module includes:

[0040] A third determination unit is used to determine the photoresist parameter density corresponding to the critical dimension at the preset sampling point at different shift positions of the test pattern;

[0041] a fourth determining unit, configured to determine a maximum photoresist parameter density and a minimum photoresist parameter density from all the photoresist parameter densities;

[0042] The fifth determining unit is used to determine a difference between the maximum photoresist parameter density and the minimum photoresist parameter density as the photoresist parameter density difference.

[0043] The present invention also provides an electronic device, comprising:

[0044] Memory for storing computer programs;

[0045] A processor is used to implement the steps of any of the above-mentioned methods for optimizing the consistency of the photoresist model when executing the computer program.

[0046] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods for optimizing the consistency of a photoresist model are implemented.

[0047] The method for optimizing the consistency of a photoresist model provided by the present invention comprises: step S11: after performing a current generation search and simulation on the simulation item parameters in the photoresist model, shifting the test pattern corresponding to the photoresist model; step S12: determining the photoresist parameter density difference corresponding to the key size at the preset sampling point after the test pattern is shifted; step S13: determining the function value of the cost function according to the relationship between the photoresist parameter density difference and the preset density difference threshold; step S14: performing a next generation search and simulation on the simulation item parameters in the photoresist model, and entering step S11 until the number of generations searched and simulated for the simulation item parameters in the photoresist model is equal to the preset algebraic threshold; step S15: determining the minimum value among all the function values, and taking the minimum function value as the final result of the photoresist optimization.

[0048] It can be seen that the method of the present invention, after searching and simulating the parameters in the photoresist model, shifts the test pattern corresponding to the photoresist model, determines the photoresist parameter density difference corresponding to the key size at the preset sampling point on the shifted test pattern, and then determines the function value of the cost function according to the relationship between the obtained photoresist parameter density difference and the preset density difference threshold, and then repeats the damage process, performs the next search and simulation and obtains the function value corresponding to this search and simulation, after obtaining the function value of the preset algebraic threshold, selects the minimum function value as the final result of the optimization, can greatly improve the consistency of the photoresist model from the source, shorten the optimization and verification cycle of the photoresist model, and take into account the accuracy of the photoresist model. In addition, the method of the present invention realizes the control of consistency in the optimization process of the photoresist model, can affect the parameter optimization path, avoids the verification of consistency at the end of the photoresist model optimization, and the method of the present invention is more intuitive.

[0049] In addition, the present invention also provides a device, an electronic device and a computer-readable storage medium having the above advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 A flow chart of a method for optimizing photoresist model consistency provided by an embodiment of the present invention;

[0052] Figure 2 A schematic diagram of calculating the density difference of photoresist parameters provided by an embodiment of the present invention;

[0053] Figure 3 A structural block diagram of a device for optimizing photoresist model consistency provided by an embodiment of the present invention;

[0054] Figure 4 This is a structural block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0057] As described in the background technology section, currently when optimizing the consistency of the photoresist model, verification is performed after the model parameters are calibrated. This cannot prevent the occurrence of consistency problems at the source, has high uncertainty, and the model optimization cycle may be prolonged due to this problem.

[0058] In view of this, the present invention provides a method for optimizing the consistency of photoresist models, please refer to Figure 1 , the method may include:

[0059] Step S11: after performing current generation search and simulation on the simulation item parameters in the photoresist model, shifting the test pattern corresponding to the photoresist model.

[0060] It should be pointed out that the type of photoresist model is not specifically limited in this embodiment and can be determined according to the model actually used.

[0061] In this step, the current generation may be the first generation.

[0062] As an implementable method, before step S11, it may also include searching and simulating the current generation of simulation item parameters in the photoresist model.

[0063] It should be noted that the process of searching and simulating the photoresist model in this embodiment can refer to the prior art and will not be described in detail here.

[0064] Step S12: determining a photoresist parameter density difference corresponding to a critical dimension at a preset sampling point after the test pattern is shifted.

[0065] It should be pointed out that in this embodiment, the positions of the preset sampling points on the test pattern are not limited and can be set at will.

[0066] It should also be pointed out that the number of preset sampling points is not limited in this embodiment and can be set at will. The number of preset sampling points can be several, dozens, hundreds, etc.

[0067] In this embodiment, there is no limitation on the method for determining the photoresist parameter density difference, as long as the photoresist parameter density difference corresponding to the critical dimension at the preset sampling point can be determined.

[0068] As an implementation method, determining the photoresist parameter density difference corresponding to the critical dimension at the preset sampling point after the test pattern is shifted includes:

[0069] Step S121: determining the photoresist parameter density corresponding to the critical dimension at the preset sampling point at different shift positions of the test pattern.

[0070] Step S122: determining a maximum photoresist parameter density and a minimum photoresist parameter density from all the photoresist parameter densities.

[0071] It should be noted that in this embodiment, there is no limitation on the method for determining the maximum photoresist parameter density and the minimum photoresist parameter density.

[0072] For example, all photoresist parameter densities can be arranged in order from small to large or from large to small, and the first and last two photoresist parameter densities are taken out as the largest and smallest photoresist parameter densities. Alternatively, two photoresist parameter densities are randomly selected and compared, the larger photoresist parameter density is selected, and then one is randomly selected from the remaining photoresist parameter densities, and the larger photoresist parameter density is compared with the larger photoresist parameter density, and then the larger photoresist parameter density is selected, and then one is randomly selected from the remaining photoresist parameter densities, and the comparison process is repeated until all photoresist parameters are compared and the largest photoresist parameter density is selected. When the smallest photoresist parameter density is selected, any two photoresist parameter densities can also be selected for size comparison, and the smaller photoresist parameter density is selected. The subsequent process is similar to the aforementioned process of selecting the largest photoresist parameter density, except that the smaller value is selected from the two photoresist parameter densities each time, and the smallest photoresist parameter density is finally obtained.

[0073] Step S123: determining a difference between the maximum photoresist parameter density and the minimum photoresist parameter density as the photoresist parameter density difference.

[0074] Assume that the test pattern has 10 positions during the shift process, which are recorded as Location_1, Location_2, Location_3, ..., Location_10. Correspondingly, the photoresist parameter density at each position is recorded as term_shift1_intensity, term_shift2_intensity, term_shift3_intensity, ..., term_shift 10 _intensity.

[0075] Photoresist parameter density difference term_intensity_variation=Max(term_shift1_intensity,.., term_shift 10 _intensity)-Min(term_shift1_intensity,..,term_shift 10 _intensity).

[0076] Please refer to Figure 2 , taking the single parameter density function of the photoresist model as an example, the horizontal axis is the coordinate position of the test pattern, and the vertical axis is the photoresist parameter density. Figure 2 The key dimension of the test pattern between AB is selected, and then the density difference of the photoresist parameters is calculated.

[0077] Step S13: determining a function value of a cost function according to a relationship between the photoresist parameter density difference and a preset density difference threshold.

[0078] It should be noted that, in this embodiment, there is no limitation on the size of the preset density difference threshold, which can be set at will.

[0079] It should be noted that in this embodiment, the method for determining the function value of the cost function is not limited.

[0080] As an implementable method, determining the function value of the cost function according to the relationship between the photoresist parameter density difference and the preset density difference threshold includes:

[0081] Step S131: determining whether the photoresist parameter density difference is greater than the preset density difference threshold.

[0082] Step S132: if the photoresist parameter density difference is not greater than the preset density difference threshold, determining the function value according to the critical dimension at the preset sampling point after the test pattern is shifted and the actually measured critical dimension.

[0083] Step S133: if the photoresist parameter density difference is greater than the preset density difference threshold, determining the function value according to the critical dimension at the preset sampling point after the test pattern is shifted, the actually measured critical dimension and the penalty term parameter.

[0084] When the density difference of the photoresist parameters is greater than the preset density difference threshold, it indicates that the consistency of the photoresist model is relatively poor. At this time, a penalty term is added to optimize the photoresist model parameter optimization path.

[0085] Step S14: Search and simulate the next generation of the simulation item parameters in the photoresist model, and enter step S11 until the number of generations of the simulation item parameters in the photoresist model searched and simulated is equal to a preset algebraic threshold.

[0086] The process of searching and simulating the simulation item parameters in the photoresist model can refer to the prior art and will not be described in detail here.

[0087] It should be noted that the preset algebraic threshold is not limited in this embodiment and can be set at will. For example, the preset algebraic threshold can be 500, 1000, 2000, 5000, etc.

[0088] Step S15: determining the minimum value among all the function values, and taking the minimum function value as the final result of photoresist optimization.

[0089] The method in this embodiment, after searching and simulating the parameters in the photoresist model, shifts the test pattern corresponding to the photoresist model, determines the photoresist parameter density difference corresponding to the key size at the preset sampling point on the shifted test pattern, and then determines the function value of the cost function according to the relationship between the obtained photoresist parameter density difference and the preset density difference threshold, and then repeats the damage process, performs the next search and simulation and obtains the function value corresponding to this search and simulation, and after obtaining the function value of the preset algebraic threshold, selects the minimum function value as the final result of the optimization, which can greatly improve the consistency of the photoresist model from the source, shorten the optimization and verification cycle of the photoresist model, and take into account the accuracy of the photoresist model. In addition, the method of this embodiment realizes consistency control during the optimization process of the photoresist model, which can affect the parameter optimization path, avoid consistency verification at the end of the photoresist model optimization, and the method of this embodiment is more intuitive.

[0090] On the basis of the above embodiment, in one embodiment of the present invention, determining the function value according to the critical dimension at the preset sampling point after the test pattern is shifted and the actually measured critical dimension may include:

[0091] The function value is determined according to a first preset formula, where the first preset formula is:

[0092] ; (1)

[0093] In the formula, cost is the function value, CD s is the critical dimension of the test pattern at the preset sampling point after the shift, CD0 is the actually measured critical dimension of the test pattern at the preset sampling point after the shift, and i is the preset sampling point.

[0094] On the basis of any of the above embodiments, in one embodiment of the present invention, determining the function value according to the key size at the preset sampling point after the test pattern is shifted, the actually measured key size, and the penalty term parameter includes:

[0095] The function value is determined according to a second preset formula, where the second preset formula is:

[0096] ; (2)

[0097] in,

[0098] penalty=penalty_coeffient×(term_intensity_variation-term_intensity_variation_Spec; (3)

[0099] In the formula, cost is the function value, CD s is the critical dimension of the test pattern at the preset sampling point after the shift, CD0 is the actually measured critical dimension of the test pattern at the preset sampling point after the shift, i is the preset sampling point, penalty is the penalty item parameter, term_intensity_variation is the photoresist parameter density difference, term_intensity_variation_Spec is a parameter related to consistency; penalty_coeffient is the degree of penalty.

[0100] The term_intensity_variation_Spec can be set according to the user's consistency requirement. The higher the consistency requirement, the smaller the term_intensity_variation_Spec value is set.

[0101] Penalty_coeffient represents the degree of penalty for the photoresist parameter density difference exceeding the preset density difference threshold. The larger the number, the greater the degree of penalty.

[0102] The following is an introduction to a device for optimizing the consistency of a photoresist model provided by an embodiment of the present invention. The device for optimizing the consistency of a photoresist model described below and the method for optimizing the consistency of a photoresist model described above can be referred to each other.

[0103] Figure 3 The structural block diagram of the device for optimizing the consistency of the photoresist model provided by the embodiment of the present invention is shown in FIG. Figure 3 Means for optimizing photoresist pattern consistency may include:

[0104] The shift module 100 is used to execute step S11: after performing a current generation search and simulation on the simulation item parameters in the photoresist model, shifting the test pattern corresponding to the photoresist model;

[0105] The first determination module 200 is used to execute step S12: determine the photoresist parameter density difference corresponding to the critical dimension at the preset sampling point after the test pattern is shifted;

[0106] The second determination module 300 is used as an execution module, and is used to execute step S13: determining a function value of a cost function according to a relationship between the photoresist parameter density difference and a preset density difference threshold;

[0107] Iteration module 400 is used to execute step S14: search and simulate the next generation of simulation item parameters in the photoresist model, and enter step S11 until the number of generations of search and simulation of the simulation item parameters in the photoresist model is equal to a preset algebraic threshold;

[0108] The third determination module 500 is used to execute step S15: determine the minimum value among all the function values, and use the minimum function value as the final result of photoresist optimization.

[0109] The device for optimizing the consistency of the photoresist model of this embodiment is used to implement the aforementioned method for optimizing the consistency of the photoresist model. Therefore, the specific implementation method of the device for optimizing the consistency of the photoresist model can be seen in the embodiment part of the method for optimizing the consistency of the photoresist model in the previous text. For example, the shift module 100, the first determination module 200, the second determination module 300, the iteration module 400, and the third determination module 500 are respectively used to implement steps S101, S102, S103, S104 and S105 in the above-mentioned method for optimizing the consistency of the photoresist model. Therefore, its specific implementation method can refer to the description of the corresponding various partial embodiments and will not be repeated here.

[0110] As an implementable method, the second determining module 300 may include:

[0111] A first judging unit, configured to judge whether the photoresist parameter density difference is greater than the preset density difference threshold;

[0112] A first determining unit, configured to determine the function value according to the critical dimension at the preset sampling point after the test pattern is shifted and the actually measured critical dimension if the photoresist parameter density difference is not greater than the preset density difference threshold;

[0113] The second determination unit is used to determine the function value according to the critical dimension at the preset sampling point after the test pattern is shifted, the actual measured critical dimension and the penalty item parameter if the photoresist parameter density difference is greater than the preset density difference threshold.

[0114] As an implementable method, the first determining module 200 may include:

[0115] A third determination unit is used to determine the photoresist parameter density corresponding to the critical dimension at the preset sampling point at different shift positions of the test pattern;

[0116] a fourth determining unit, configured to determine a maximum photoresist parameter density and a minimum photoresist parameter density from all the photoresist parameter densities;

[0117] The fifth determining unit is used to determine a difference between the maximum photoresist parameter density and the minimum photoresist parameter density as the photoresist parameter density difference.

[0118] As an implementable embodiment, the first determining unit may be specifically configured to determine the function value according to the key size at the preset sampling point after the test pattern is shifted and the actually measured key size, including:

[0119] The function value is determined according to a first preset formula, where the first preset formula is:

[0120] ; (1)

[0121] In the formula, cost is the function value, CD s is the critical dimension of the test pattern at the preset sampling point after the shift, CD0 is the actually measured critical dimension of the test pattern at the preset sampling point after the shift, and i is the preset sampling point.

[0122] As an implementable embodiment, the second determining unit may be specifically configured to determine the function value according to the key size at the preset sampling point after the test pattern is shifted, the actually measured key size, and the penalty term parameter, including:

[0123] The function value is determined according to a second preset formula, where the second preset formula is:

[0124] ; (2)

[0125] in,

[0126] penalty=penalty_coeffient×(term_intensity_variation-term_intensity_variation_Spec; (3)

[0127] In the formula, cost is the function value, CD s is the critical dimension of the test pattern at the preset sampling point after the shift, CD0 is the actually measured critical dimension of the test pattern at the preset sampling point after the shift, i is the preset sampling point, penalty is the penalty item parameter, term_intensity_variation is the photoresist parameter density difference, term_intensity_variation_Spec is a parameter related to consistency; penalty_coeffient is the degree of penalty.

[0128] The electronic device provided by the embodiment of the present invention is introduced below. The electronic device described below and the method for optimizing the consistency of the photoresist model described above can be referred to each other.

[0129] Please refer to Figure 4 The present invention also provides an electronic device, comprising:

[0130] A memory 11, used for storing computer programs;

[0131] The processor 12 is configured to implement the steps of the method for optimizing the consistency of the photoresist model described in any of the above embodiments when executing the computer program.

[0132] The computer-readable storage medium provided by an embodiment of the present invention is introduced below. The computer-readable storage medium described below and the method for optimizing the consistency of the photoresist model described above can be referenced to each other.

[0133] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for optimizing the consistency of a photoresist model described in any of the above embodiments are implemented.

[0134] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0135] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0136] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0137] The method, device, electronic device, and computer-readable storage medium for optimizing the consistency of photoresist models provided by the present invention are described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention, and the description of the above embodiments is only used to help understand the scheme of the present invention and its core idea. It should be pointed out that for those of ordinary skill in the art, without departing from the principles of the present invention, the present invention can also be improved and modified in a number of ways, and these improvements and modifications also fall within the scope of protection of the present invention.

Claims

1. A method for optimizing the consistency of a photoresist model, characterized in that: include: Step S11: after performing current generation search and simulation on the simulation item parameters in the photoresist model, shifting the test pattern corresponding to the photoresist model; Step S12: determining a photoresist parameter density difference corresponding to a critical dimension at a preset sampling point after the test pattern is shifted; Step S13: determining a function value of a cost function according to a relationship between the photoresist parameter density difference and a preset density difference threshold; Step S14: searching and simulating the next generation of the simulation item parameters in the photoresist model, and entering step S11, until the number of generations of searching and simulating the simulation item parameters in the photoresist model is equal to a preset algebraic threshold; Step S15: determining the minimum value among all the function values, and taking the minimum function value as the final result of photoresist optimization; Determining the function value of the cost function according to the relationship between the photoresist parameter density difference and the preset density difference threshold includes: Determining whether the photoresist parameter density difference is greater than the preset density difference threshold; If the photoresist parameter density difference is not greater than the preset density difference threshold, determining the function value according to the critical dimension at the preset sampling point after the test pattern is shifted and the actually measured critical dimension; If the photoresist parameter density difference is greater than the preset density difference threshold, the function value is determined according to the critical dimension at the preset sampling point after the test pattern is shifted, the actually measured critical dimension, and the penalty term parameter.

2. The method for optimizing the consistency of a photoresist pattern as claimed in claim 1, characterized in that: Determining the function value according to the key size at the preset sampling point after the test pattern is shifted and the actually measured key size includes: The function value is determined according to a first preset formula, where the first preset formula is: ； In the formula, cost is the function value, CD s is the critical dimension of the test pattern at the preset sampling point after the shift, CD0 is the actually measured critical dimension of the test pattern at the preset sampling point after the shift, and i is the preset sampling point.

3. The method for optimizing the consistency of a photoresist model as claimed in claim 1, characterized in that: Determining the function value according to the key size at the preset sampling point after the test pattern is shifted, the actually measured key size, and the penalty term parameter includes: The function value is determined according to a second preset formula, where the second preset formula is: ； in, penalty=penalty_coeffient ×( term_intensity_variation - term_intensity_variation_Spec ）； In the formula, cost is the function value, CD s is the critical dimension of the test pattern at the preset sampling point after the shift, CD0 is the actual measured critical dimension of the test pattern at the preset sampling point after the shift, i is the preset sampling point, penalty is the penalty parameter, term_intensity_variation is the photoresist parameter density difference, term_intensity_variation_ Spec are parameters related to consistency; penalty_coeffient The degree of punishment.

4. The method for optimizing the consistency of a photoresist pattern according to any one of claims 1 to 3, characterized in that: Determining the photoresist parameter density difference corresponding to the critical dimension at the preset sampling point after the test pattern is shifted includes: Determining the photoresist parameter density corresponding to the critical dimension at the preset sampling point at different shift positions of the test pattern; determining a maximum photoresist parameter density and a minimum photoresist parameter density from all the photoresist parameter densities; A difference between the maximum photoresist parameter density and the minimum photoresist parameter density is determined as the photoresist parameter density difference.

5. A device for optimizing the consistency of a photoresist pattern, characterized in that: include: A shift module, used to execute step S11: after performing a current generation search and simulation on the simulation item parameters in the photoresist model, shifting the test pattern corresponding to the photoresist model; A first determination module is used to execute step S12: determining a photoresist parameter density difference corresponding to a critical dimension at a preset sampling point after the test pattern is shifted; A second determination module is used for executing the module, and is used for executing step S13: determining a function value of a cost function according to a relationship between the photoresist parameter density difference and a preset density difference threshold; An iteration module, for executing step S14: searching and simulating the next generation of the simulation item parameters in the photoresist model, and entering step S11, until the number of generations of searching and simulating the simulation item parameters in the photoresist model is equal to a preset algebraic threshold; A third determination module is used to execute step S15: determine the minimum value among all the function values, and use the minimum function value as the final result of photoresist optimization; The second determining module comprises: A first judging unit, configured to judge whether the photoresist parameter density difference is greater than the preset density difference threshold; A first determining unit, configured to determine the function value according to the critical dimension at the preset sampling point after the test pattern is shifted and the actually measured critical dimension if the photoresist parameter density difference is not greater than the preset density difference threshold; The second determination unit is used to determine the function value according to the critical dimension at the preset sampling point after the test pattern is shifted, the actually measured critical dimension and the penalty item parameter if the photoresist parameter density difference is greater than the preset density difference threshold.

6. The device for optimizing the consistency of a photoresist pattern as claimed in claim 5, characterized in that: The first determining module comprises: A third determination unit is used to determine the photoresist parameter density corresponding to the critical dimension at the preset sampling point at different shift positions of the test pattern; a fourth determining unit, configured to determine a maximum photoresist parameter density and a minimum photoresist parameter density from all the photoresist parameter densities; The fifth determining unit is used to determine a difference between the maximum photoresist parameter density and the minimum photoresist parameter density as the photoresist parameter density difference.

7. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the method for optimizing the consistency of a photoresist model as claimed in any one of claims 1 to 4 when executing the computer program.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for optimizing the consistency of a photoresist model as claimed in any one of claims 1 to 4 are implemented.

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