Construction method and construction device of SRAF rule, and readable storage medium

Abstract

The invention discloses an SRAF rule construction method and device and a computer readable storage medium. The construction method comprises the steps that a test pattern is designed; establishing a photoetching model; and according to the test pattern and the photoetching model, constructing an optimal SRAF rule through a simulated annealing algorithm. And according to the test pattern and the photoetching model, constructing an optimal SRAF rule through a simulated annealing algorithm. The simulated annealing algorithm can obtain a better effect in the solution of the multi-extremum problem, and the SRAF rule is constructed through the simulated annealing algorithm, so that a better result can be obtained in the multi-extremum solution problem of the shape of the auxiliary graph and the distance between the auxiliary graph and the main graph, and the obtained SRAF rule has a better improvement effect on the process window of the main graph. Moreover, the method is higher in calculation efficiency and shorter in calculation time.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor manufacturing, and in particular to a method and device for constructing a SRAF rule, and a readable storage medium. Background Art

[0002] As the critical dimension (CD) of integrated circuits decreases and the resolution limit of optical exposure tools (OET) is affected, when a high-density mask pattern is exposed for pattern transfer, an optical proximity effect (OPE) is easily generated, causing defects in mask pattern transfer.

[0003] To solve the problem of optical proximity effect, sub-resolution assistant feature (SRAF), namely scattering bar (sbar), is an indispensable part of resolution enhancement technology (RET). According to certain SRAF rules, auxiliary patterns are inserted around the main pattern, and the diffraction of the auxiliary pattern interacts with the diffraction of the main pattern to achieve the purpose of improving the optical proximity effect.

[0004] However, if the auxiliary graphics are placed in an inappropriate position or shape, it will not only increase the imaging analysis, but also greatly weaken the positive effect of the auxiliary graphics. The SRAF rule is used to specify the position and shape of the inserted auxiliary graphics. How to construct a reasonable SRAF rule has always been a major problem in SRAF technology. Summary of the invention

[0005] The problem solved by the present invention is how to construct SRAF rules to improve lithography effects and shorten operation time.

[0006] To solve the above problems, the present invention provides a method for constructing SRAF rules, comprising:

[0007] Design a test pattern; establish a photolithography model; and construct an optimal SRAF rule through a simulated annealing algorithm based on the test pattern and the photolithography model.

[0008] Optionally, the step of constructing the optimal SRAF rule through the simulated annealing algorithm includes: setting the initial annealing parameters; obtaining the current temperature and performing rule perturbation iteration to obtain the optimized rule group; judging whether to execute the step of obtaining the current temperature and performing rule perturbation iteration again according to the current temperature; when judging not to execute the step of obtaining the current temperature and performing rule perturbation iteration again, obtaining the optimal SRAF rule according to the optimized rule group obtained by the last rule perturbation iteration.

[0009] Optionally, the step of obtaining the current temperature and performing rule perturbation iteration to obtain the optimized rule group includes: obtaining the current temperature according to the annealing initial parameters; obtaining the rule group before perturbation, wherein the rule group before perturbation includes: multiple groups of SRAF rules before perturbation; performing lithography simulation according to the rule group before perturbation, in combination with the test pattern and the lithography model, to obtain the process window of the SRAF rules before perturbation; perturbing the SRAF rules before perturbation in the rule group before perturbation to obtain the rule group after perturbation, wherein the rule group after perturbation includes: multiple groups of SRAF rules after perturbation; The method comprises the following steps: optimizing the post-disturbance SRAF rule group, wherein the multiple groups of post-disturbance SRAF rules of the post-disturbance rule group correspond to the multiple groups of pre-disturbance SRAF rules of the pre-disturbance rule group in one-to-one correspondence; performing lithography simulation according to the post-disturbance rule group in combination with the test pattern and the lithography model to obtain a process window of the post-disturbance SRAF rule; comparing the process window of the post-disturbance SRAF rule with the process window of the corresponding pre-disturbance SRAF rule to determine whether to accept the post-disturbance SRAF rule; and obtaining the optimized rule group according to the result of determining whether to accept the post-disturbance SRAF rule.

[0010] Optionally, in the step of perturbing the pre-disturbance SRAF rules in the pre-disturbance rule group to obtain the post-disturbance rule group, the pre-disturbance SRAF rules in the pre-disturbance rule group are randomly perturbed to obtain the post-disturbance rule group.

[0011] Optionally, in the step of randomly perturbing the pre-disturbance SRAF rules in the pre-disturbance rule group to obtain the post-disturbance rule group, the pre-disturbance SRAF rules in the pre-disturbance rule group are randomly perturbed according to the current temperature to obtain the post-disturbance rule group.

[0012] Optionally, in the step of comparing the process window of the SRAF rule after the disturbance with the corresponding process window of the SRAF rule before the disturbance and determining whether to accept the SRAF rule after the disturbance, the relative sizes of the process window of the SRAF rule after the disturbance and the process window of the SRAF rule before the disturbance are compared. When the process window of the SRAF rule after the disturbance is larger, the SRAF rule after the disturbance is accepted.

[0013] Optionally, in the step of obtaining the optimized rule group according to the result of determining whether to accept the post-disturbance SRAF rules, the optimized rule group includes: the accepted SRAF rules of the post-disturbance rule group and the unaccepted SRAF rules of the pre-disturbance rule group.

[0014] Optionally, when the step of obtaining the current temperature and performing rule perturbation iteration to obtain the optimized rule group is executed for the first time, before obtaining the pre-disturbance rule group, an initial rule group is generated, and the initial rule group includes: multiple groups of initial SRAF rules; in the step of obtaining the pre-disturbance rule group, the pre-disturbance rule group is obtained according to the initial rule group.

[0015] Optionally, in the step of generating an initial rule group, the initial rule group is generated randomly.

[0016] Optionally, when determining to execute the step of obtaining the current temperature and performing rule perturbation iteration again, in the process of obtaining the current temperature and performing rule perturbation iteration again, in the step of obtaining the rule group before perturbation, the rule group before perturbation is obtained according to the optimized rule group obtained by the previous execution of rule perturbation iteration.

[0017] Optionally, in the step of obtaining the optimal SRAF rule based on the optimization rule group obtained in the last step of obtaining the current temperature and performing rule perturbation iteration, the SRAF rule with the largest corresponding process window in the optimization rule group obtained in the last step of performing rule perturbation iteration is used as the optimal SRAF rule.

[0018] Optionally, in the step of designing a test pattern, an isolated test pattern is designed.

[0019] Accordingly, the present invention also provides a device for constructing SRAF rules, comprising:

[0020] A design module, which is suitable for designing a test pattern; a model module, which is suitable for establishing a photolithography model; and an annealing module, which is suitable for constructing an optimal SRAF rule through a simulated annealing algorithm according to the test pattern and the photolithography model.

[0021] Optionally, the annealing module includes: an initialization unit, which is suitable for setting initial annealing parameters; an external loop unit, which is suitable for obtaining the current temperature and performing rule perturbation iteration to obtain an optimized rule group; an external control unit, which is suitable for controlling whether the external loop unit executes the steps of obtaining the current temperature and performing rule perturbation iteration again according to the current temperature; when the external control unit determines not to execute the steps of obtaining the current temperature and performing rule perturbation iteration again, the external loop unit obtains the optimal SRAF rule based on the optimized rule group obtained by the last rule perturbation iteration.

[0022] Optionally, the external circulation unit includes: a temperature controller, which is suitable for obtaining the current temperature according to the annealing initial parameters; an initializer, which is suitable for obtaining a pre-disturbance rule group, and the pre-disturbance rule group includes: multiple groups of pre-disturbance SRAF rules; a simulator, which is suitable for performing lithography simulation according to the pre-disturbance rule group, in combination with the test pattern and the lithography model, to obtain a process window of the pre-disturbance SRAF rule; a perturbator, which is suitable for perturbing the pre-disturbance SRAF rules in the pre-disturbance rule group to obtain a post-disturbance rule group, and the post-disturbance rule group includes: multiple groups of post-disturbance SRAF rules, and the post-disturbance rule The multiple groups of post-disturbance SRAF rules of the group correspond one to one with the multiple groups of pre-disturbance SRAF rules of the pre-disturbance rule group; the simulator is also suitable for performing lithography simulation according to the post-disturbance rule group in combination with the test pattern and the lithography model to obtain the process window of the post-disturbance SRAF rules; the comparator is suitable for comparing the process window of the post-disturbance SRAF rules obtained by the simulator with the corresponding process window of the pre-disturbance SRAF rules to determine whether to accept the post-disturbance SRAF rules; the resulter is suitable for determining whether to accept the post-disturbance SRAF rules according to the result of the comparator to obtain the optimized rule group.

[0023] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the method of the present invention.

[0024] Compared with the prior art, the technical solution of the present invention has the following advantages:

[0025] In the technical solution of the present invention, the optimal SRAF rule is constructed by a simulated annealing algorithm according to the test pattern and the lithography model. The simulated annealing algorithm can obtain better results in solving the multi-extreme value problem, and the SRAF rule constructed by the simulated annealing algorithm can obtain better results in solving the multi-extreme value problem of the auxiliary pattern shape and the distance between the auxiliary pattern and the main pattern. The obtained SRAF rule has a better effect on improving the main pattern process window, and this method has higher calculation efficiency and shorter calculation time. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flowchart of an embodiment of a method for constructing SRAF rules according to the present invention;

[0027] Figure 2 It is a test pattern designed in the first embodiment of the SRAF rule construction method of the present invention;

[0028] Figure 3 It is a flowchart of the steps of constructing the optimal SRAF rule by using a simulated annealing algorithm in an embodiment of the SRAF rule construction method of the present invention;

[0029] Figure 4 It is a flowchart of the steps of obtaining the current temperature and performing rule perturbation iteration to obtain an optimized rule group in an embodiment of the SRAF rule construction method of the present invention;

[0030] Figure 5 It is a layout constructed based on the first initial SRAF rule in the initial rule group generated in the first embodiment of the SRAF rule construction method of the present invention;

[0031] Figure 6 It is a layout constructed based on the second initial SRAF rule in the initial rule group generated in the first embodiment of the SRAF rule construction method of the present invention;

[0032] Figure 7 It is a layout constructed based on the third initial SRAF rule in the initial rule group generated in the first embodiment of the SRAF rule construction method of the present invention;

[0033] Figure 8 is a layout constructed based on the first post-disturbance SRAF rule in the post-disturbance rule group obtained in the first embodiment of the SRAF rule construction method of the present invention;

[0034] Fig. 9 is a layout constructed based on the second post-disturbance SRAF rule in the post-disturbance rule group obtained in the first embodiment of the SRAF rule construction method of the present invention;

[0035] Fig.10is a layout constructed based on the third post-disturbance SRAF rule in the post-disturbance rule group obtained in the first embodiment of the SRAF rule construction method of the present invention;

[0036] Fig.11 is a layout constructed based on the obtained optimal SRAF rule in an embodiment of the SRAF rule construction method of the present invention;

[0037] Fig.12 A functional block diagram of an embodiment of a device for constructing SRAF rules of the present invention. DETAILED DESCRIPTION

[0038] As can be seen from the background technology, the method of constructing SRAF rules in the prior art has the problem that the constructed SRAF rules have limited effect on improving the process window and have a long running time.

[0039] At present, SRAF rules are generally constructed by designing experiments. After obtaining the main pattern, first, the shape of the auxiliary pattern and the distance between the auxiliary pattern and the main pattern are fixed, the range of variation of the distance between adjacent auxiliary patterns is designed, and the process window after the auxiliary pattern is inserted is simulated by the lithography model to obtain the distance between the auxiliary patterns corresponding to the maximum process window; then, the distance between the auxiliary patterns is fixed, the shape of the auxiliary pattern and the range of variation of the distance between the auxiliary pattern and the main pattern are designed, and the process window after the auxiliary pattern is inserted is simulated by the lithography model to determine the optimal auxiliary pattern width and the range of variation of the distance between the auxiliary pattern and the main pattern; finally, the SRAF rule is obtained.

[0040] This method of designing experiments is effective in finding the optimal solution to a single extreme value problem, but may not be ideal for finding the optimal solution to a multi-extreme value problem. The construction of SRAF rules is the solution to multi-extreme value problems. Therefore, the SRAF rules constructed by this method have limited effect on improving the process window and take a long time to run.

[0041] In order to solve the technical problem, the present invention provides a method for constructing SRAF rules, comprising:

[0042] Design a test pattern; establish a photolithography model; and construct an optimal SRAF rule through a simulated annealing algorithm based on the test pattern and the photolithography model.

[0043] In the technical solution of the present invention, the optimal SRAF rule is constructed by a simulated annealing algorithm according to the test pattern and the lithography model. The simulated annealing algorithm can obtain better results in solving the multi-extreme value problem, and the SRAF rule constructed by the simulated annealing algorithm can obtain better results in solving the multi-extreme value problem of the auxiliary pattern shape and the distance between the auxiliary pattern and the main pattern. The obtained SRAF rule has a better effect on improving the main pattern process window, and this method has higher calculation efficiency and shorter calculation time.

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0045] refer to Figure 1 , shows a flow chart of an embodiment of a SRAF rule construction method of the present invention.

[0046] The construction method comprises: step S110, designing a test pattern; step S120, establishing a photolithography model; step S130, constructing an optimal SRAF rule by a simulated annealing algorithm according to the test pattern and the photolithography model.

[0047] The technical solution of the SRAF rule construction method embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0048] like Figure 1 As shown, combined with reference Figure 2 The construction method includes: step S110, designing a test pattern 210.

[0049] Step S110 is a step of designing a test pattern 210, i.e., generating a main pattern into which an auxiliary pattern needs to be inserted for optical simulation. Specifically, the test pattern 210 is designed based on a layout to which the SRAF (Sub-Resolution Assist Features) rule applies.

[0050] In some embodiments of the present invention, the SRAF rule is applicable to inserting auxiliary graphics around isolated graphics; therefore, in step S110, in the step of designing test graphics, an isolated test graphic 210 is designed. Specifically, the isolated test graphic 210 means that the distance between the test graphic 210 and other surrounding graphics is greater than a preset distance, so as to ensure that the impact between the test graphic 210 and other surrounding graphics is weak or even no impact. Specifically, Figure 2 As shown, the test pattern 210 is an isolated long strip pattern.

[0051] Continue to refer Figure 1 The construction method further includes: step 120, establishing a photolithography model.

[0052] The lithography model can describe the entire lithography process including the optical system, mask, photoresist, and etching process. The lithography model can be a strict and accurate lithography model or a semi-empirical approximate lithography model. The semi-empirical approximate lithography model uses some simplified empirical formulas to replace some of the complex processes in the accurate lithography model, which can be obtained by fitting experimental data.

[0053] Specifically, step S120, the step of establishing a photolithography model, can refer to the existing method for establishing a photolithography model, or directly obtain the existing photolithography model, which will not be described in detail in the present invention.

[0054] After obtaining the test pattern 210 and the lithography model, the construction method further includes: step S130, constructing an optimal SRAF rule by a simulated annealing algorithm according to the test pattern and the lithography model.

[0055] SRAF rules include: the size of auxiliary patterns, the distance between adjacent auxiliary patterns, and the distance between auxiliary patterns and main patterns. Excellent SRAF rules can effectively enhance the positive effect of auxiliary patterns and effectively improve lithography effects.

[0056] The simulated annealing algorithm adds a time-varying probability jump that eventually tends to zero during the search for the global optimal solution, which can effectively prevent the search process from falling into a local minimum. The construction of the SRAF rule is a solution to the optimal solution of a multi-extreme value problem. The simulated annealing algorithm has a good effect in solving the optimal solution of the multi-extreme value problem and can effectively improve the effect of the obtained SRAF rule.

[0057] like Figure 3 In some embodiments of the present invention, step S130, the step of constructing the optimal SRAF rule by simulated annealing algorithm includes: step S131, setting the initial annealing parameters; step S132, obtaining the current temperature and performing rule perturbation iteration to obtain the optimized rule group; step S133, judging whether to execute the step of obtaining the current temperature and performing rule perturbation iteration again according to the current temperature; when it is judged that the step of obtaining the current temperature and performing rule perturbation iteration is not to be executed again, executing step S134, obtaining the optimal SRAF rule according to the optimized rule group obtained by the last rule perturbation iteration.

[0058] Among them, step S131, setting the initial annealing parameters. The initial annealing parameters are suitable for controlling the temperature change in the simulated annealing algorithm. The initial annealing parameters are the calculation basis of the simulated annealing algorithm. Specifically, the initial annealing parameters may include: initial temperature T0, termination temperature T end and the temperature attenuation coefficient α.

[0059] After the initial annealing parameters are set, step S132 is executed to obtain the current temperature and perform rule perturbation iteration to obtain the optimized rule group by gradually optimizing the SRAF rules to obtain the equilibrium state at the current temperature.

[0060] like Figure 4 As shown, in some embodiments, step S132, the step of obtaining the current temperature and performing rule perturbation iteration to obtain the optimized rule group includes: step S132a, obtaining the current temperature according to the annealing initial parameters; step S132b, obtaining the pre-perturbation rule group, the pre-perturbation rule group including: multiple groups of pre-perturbation SRAF rules; step S132c, performing lithography simulation according to the pre-perturbation rule group, combining the test pattern and the lithography model, and obtaining the process window of the pre-perturbation SRAF rule; step S132d, perturbing the pre-perturbation SRAF rule in the pre-perturbation rule group to obtain the post-perturbation rule group, the post-perturbation rule group including : multiple groups of post-disturbance SRAF rules, the multiple groups of post-disturbance SRAF rules of the post-disturbance rule group correspond one to one with the multiple groups of pre-disturbance SRAF rules of the pre-disturbance rule group; step S132e, according to the post-disturbance rule group, combined with the test pattern and the lithography model, perform lithography simulation to obtain the process window of the post-disturbance SRAF rules; step S132f, compare the process window of the post-disturbance SRAF rules with the corresponding process window of the pre-disturbance SRAF rules, and determine whether to accept the post-disturbance SRAF rules; step S132g, according to the result of determining whether to accept the post-disturbance SRAF rules, obtain the optimized rule group.

[0061] Wherein, step S132a, the step of obtaining the current temperature according to the annealing initial parameters is suitable for obtaining the current temperature corresponding to the equilibrium state. Specifically, the current temperature is obtained according to the initial temperature T0 and the temperature attenuation coefficient α.

[0062] It should be noted that, in some embodiments, when step S132 is executed for the first time, step S132a in the process of obtaining the current temperature and performing rule perturbation iteration to obtain the optimized rule group is used as the initial temperature T0 when obtaining the current temperature based on the annealing initial parameters.

[0063] like Figure 4 As shown, step S132, obtaining the current temperature and performing rule perturbation iteration to obtain the optimized rule group, also includes: step S132b, obtaining the pre-perturbation rule group.

[0064] The pre-disturbance rule group is the basis for optimizing and obtaining the optimal SRAF rule. The pre-disturbance rule group includes: multiple groups of pre-disturbance SRAF rules. In the pre-disturbance rule group, each group of pre-disturbance SRAF rules includes: the size of the auxiliary pattern, the distance between adjacent auxiliary patterns, and the distance between the auxiliary pattern and the main pattern.

[0065] In some embodiments, when the step of obtaining the current temperature and performing rule perturbation iteration to obtain the optimized rule group is executed for the first time, before obtaining the pre-disturbance rule group, an initial rule group is generated, and the initial rule group includes: multiple groups of initial SRAF rules; step S132b, in the step of obtaining the pre-disturbance rule group, the pre-disturbance rule group is obtained according to the initial rule group.

[0066] The initial SRAF rule is the basis for subsequent optimization. Specifically, in the process of obtaining the current temperature and performing rule perturbation iteration to obtain the optimized rule group for the first time, in the step of obtaining the pre-perturbation rule group, the initial rule group is used as the pre-perturbation rule group.

[0067] In the step of generating an initial rule group, the more initial SRAF rules there are in the generated initial rule group, the more effective the optimal SRAF rule is, but the greater the computational load is, the longer the computation time is. In some exemplary embodiments, in the step of generating an initial rule group, the generated initial rule group has 100 groups of initial SRAF rules.

[0068] In some embodiments, in the step of generating the initial rule group, the initial rule group is generated based on the applicability of the SRAF rule. The setting of the SRAF rule is related to the main graphic to which the SRAF rule is applied; the initial rule group is generated based on the applicability of the SRAF rule, that is, the initial rule group is generated based on the characteristics of the main graphic to which the SRAF rule is applied.

[0069] Specifically, in some embodiments, the SRAF rule is applicable to inserting auxiliary graphics around isolated graphics; Figure 2 As shown, the test pattern 210 is an isolated long strip pattern, and the auxiliary pattern applicable to the test pattern 210 is a long strip pattern parallel to and equal in length to the test pattern 210; accordingly, the SRFA rules to be established include: the width SW of the auxiliary pattern, the distance STS between adjacent auxiliary patterns, and the distance STM between the auxiliary pattern and the main pattern.

[0070] In some embodiments, in the step of generating the initial rule group, the initial rule group is randomly generated. Randomly generating the initial rule group means that the number of initial SRAF rules in the generated initial rule group is a random number, and the rules in any initial SRAF rule are also random, that is, the values ​​of the width SW of the auxiliary pattern, the distance STS between adjacent auxiliary patterns, and the distance STM between the auxiliary pattern and the main pattern in any initial SRAF rule are all random numbers.

[0071] It should be noted that, in order to accelerate computational convergence and improve computational efficiency, in some embodiments, in the step of generating an initial rule group, in the step of randomly generating the initial rule group, the initial rule group is randomly generated within a preset experience range, wherein the experience range can be preset based on a priori experience summary.

[0072] Specifically, in the step of randomly generating the initial rule group, different initial SRAF rules in the generated initial rule group may be different or the same. Specifically, in different initial SRAF rules, the size SW of the auxiliary pattern, the distance STS between adjacent auxiliary patterns, and the distance STM between the auxiliary pattern and the main pattern may be at least partially different or may be equal.

[0073] For example, in the generated initial rule group, Figure 5 As shown, the first initial SRAF rule includes: the size SW10 of the auxiliary pattern 221 is 28nm, the distance STS10 between adjacent auxiliary patterns 221 is 98nm, and the distance STM10 between the auxiliary pattern 221 and the main pattern 211 is 103nm; Figure 6 As shown, the second initial SRAF rule includes: the size SW20 of the auxiliary pattern 222 is 40nm, the distance STS20 between adjacent auxiliary patterns 222 is 90nm, and the distance STM20 between the auxiliary pattern 222 and the main pattern 212 is 90nm; Figure 7 As shown, the third initial SRAF rule includes: a size SW30 of the auxiliary pattern 223 is 25 nm, a distance STS30 between adjacent auxiliary patterns 223 is 95 nm, and a distance STM30 between the auxiliary pattern 223 and the main pattern 213 is 107 nm.

[0074] Continue to refer Figure 4 After obtaining the pre-disturbance rule group, execute step S132c, perform lithography simulation according to the pre-disturbance rule group, combined with the test pattern and the lithography model, and obtain the process window of the pre-disturbance SRAF rule.

[0075] Step S132c, the step of performing photolithography simulation, can predict the light field distribution inside the photoresist when the mask composed of the test pattern and the auxiliary pattern is exposed, and further predict the morphology of the photoresist after development.

[0076] Specifically, the pre-disturbance rule group has multiple groups of pre-disturbance SRAF rules; in step S132c, in the step of performing lithography simulation, according to each group of pre-disturbance SRAF rules in the pre-disturbance rule group, auxiliary patterns are set around the test pattern to form a corresponding layout to be simulated; lithography simulation is performed on each layout to be simulated to obtain the process window of the layout to be simulated; therefore, in step S132c, in the step of performing lithography simulation, multiple process windows are obtained, and the multiple process windows correspond one-to-one to the multiple groups of pre-disturbance SRAF rules of the pre-disturbance rule group.

[0077] It should be noted that step S132c, the step of performing photolithography simulation, can be performed with reference to the existing photolithography simulation method, and the present invention will not elaborate on it here.

[0078] like Figure 4 As shown, after obtaining the pre-disturbance rule group, step S132, the step of obtaining the current temperature and performing rule perturbation iteration to obtain the optimized rule group also includes: step S132d, perturbing the pre-disturbance SRAF rules in the pre-disturbance rule group to obtain the post-disturbance rule group, the post-disturbance rule group including: multiple groups of post-disturbance SRAF rules, the multiple groups of post-disturbance SRAF rules in the post-disturbance rule group correspond one-to-one to the multiple groups of pre-disturbance SRAF rules in the pre-disturbance rule group.

[0079] Step S132d, the step of disturbing the pre-disturbance SRAF rule in the pre-disturbance rule group is suitable for modifying the pre-disturbance SRAF rule, thereby providing a basis and direction for optimization.

[0080] In some embodiments, in the step of perturbing the pre-disturbance SRAF rules in the pre-disturbance rule group to obtain the post-disturbance rule group, the pre-disturbance SRAF rules in the pre-disturbance rule group are randomly perturbed to obtain the post-disturbance rule group.

[0081] Specifically, randomly perturbing the pre-disturbance SRAF rules in the pre-disturbance rule group means that, for multiple pre-disturbance SRAF rules in the pre-disturbance rule group, the number of disturbance objects, the disturbance objects and the disturbance amplitude are all random, that is, for multiple pre-disturbance SRAF rules in the pre-disturbance rule group, the number of changed pre-disturbance SRAF rules is random, the changed pre-disturbance SRAF rules are random, and the amplitude of the change of the pre-disturbance SRAF rules is also random.

[0082] In some embodiments, in the step of randomly perturbing the pre-disturbance SRAF rules in the pre-disturbance rule group to obtain the post-disturbance rule group, the pre-disturbance SRAF rules in the pre-disturbance rule group are randomly perturbed according to the current temperature to obtain the post-disturbance rule group.

[0083] Specifically, randomly perturbing the pre-disturbance SRAF rule in the pre-disturbance rule group according to the current temperature means randomly perturbing the pre-disturbance SRAF rule in the pre-disturbance rule group based on the current temperature.

[0084] For example, in the step of randomly perturbing the pre-disturbance SRAF rules in the pre-disturbance rule group, the disturbance amplitude of the random perturbation of the pre-disturbance SRAF rules in the pre-disturbance rule group is determined based on the current temperature, that is, for multiple pre-disturbance SRAF rules in the pre-disturbance rule group, the number of changed pre-disturbance SRAF rules is random, the changed pre-disturbance SRAF rules are random, and the amplitude of the change of the pre-disturbance SRAF rules is a random number related to the current temperature.

[0085] It should be noted that, in step S132d, the disturbance performed on the pre-disturbance SRAF rules in the pre-disturbance rule group is a random disturbance, that is, the disturbance quantity, disturbance object and disturbance amplitude are all random, so after the disturbance performed on the pre-disturbance SRAF rules in the pre-disturbance rule group, the obtained post-disturbance rule group includes the disturbed pre-disturbance SRAF rules and the unperturbed pre-disturbance SRAF rules, that is, for the multiple groups of post-disturbance SRAF rules in the obtained post-disturbance rule group, the post-disturbance SRAF rules can be the same as or different from the pre-disturbance SRAF rules. However, the number of post-disturbance SRAF rules in the post-disturbance rule group is equal to the number of pre-disturbance SRAF rules in the pre-disturbance rule group, and they correspond one to one.

[0086] For example, the obtained perturbation rule group is as follows: Figure 8 As shown, the SRAF rule after the first disturbance includes: the size SW11 of the auxiliary pattern 221 is 28nm, the distance STS11 between adjacent auxiliary patterns 221 is 98nm, and the distance STM11 between the auxiliary pattern 221 and the main pattern 211 is 103nm; Fig. 9 As shown, the second post-perturbation SRAF rule includes: the size SW21 of the auxiliary pattern 222 is 32nm, the distance STS21 between adjacent auxiliary patterns 222 is 102nm, and the distance STM21 between the auxiliary pattern 222 and the main pattern 212 is 125nm; Fig.10As shown, the third post-disturbance SRAF rule includes: the size SW31 of the auxiliary pattern 223 is 25 nm, the distance STS31 between adjacent auxiliary patterns 223 is 93 nm, and the distance STM31 between the auxiliary pattern 223 and the main pattern 213 is 107 nm.

[0087] After obtaining the post-perturbation rule group, step S132e is executed to perform lithography simulation according to the post-perturbation rule group in combination with the test pattern and the lithography model to obtain the process window of the post-perturbation SRAF rule.

[0088] Specifically, the number of post-disturbance SRAF rules in the post-disturbance rule group is equal to the number of pre-disturbance SRAF rules in the pre-disturbance rule group; therefore, the post-disturbance rule group also has a plurality of post-disturbance SRAF rules.

[0089] In step S132e, in the step of performing lithography simulation, the same method as in the step of performing lithography simulation according to the pre-disturbance rule group is adopted in step S132c. Specifically, in step S132e, in the step of performing lithography simulation, according to each group of post-disturbance SRAF rules in the post-disturbance rule group, auxiliary patterns are set around the test pattern to form a corresponding layout to be simulated; lithography simulation is performed on each layout to be simulated to obtain the process window of the layout to be simulated; therefore, in step S132e, in the step of performing lithography simulation, multiple process windows are obtained, and the multiple process windows correspond one-to-one to the multiple groups of post-disturbance SRAF rules in the post-disturbance rule group.

[0090] After obtaining the process window of the SRAF rule after the disturbance and the process window of the SRAF rule before the disturbance, execute step S132f to compare the process window of the SRAF rule after the disturbance and the corresponding process window of the SRAF rule before the disturbance to determine whether to accept the SRAF rule after the disturbance; then, execute step S132g to obtain the optimized rule group based on the result of determining whether to accept the SRAF rule after the disturbance.

[0091] The process window can determine the influence of the auxiliary graphics on the lithography effect: the stronger the positive effect of the auxiliary graphics, the larger the process window; the weaker the positive effect of the auxiliary graphics, the smaller the process window; therefore, step S132f, by comparing the process windows of the SRAF rules before and after the disturbance, can determine whether the disturbance to the SRAF rules is an optimization or degradation of the SRAF rules.

[0092] In some embodiments, in the step of comparing the process window of the post-disturbance SRAF rule with the corresponding process window of the pre-disturbance SRAF rule, and determining whether to accept the post-disturbance SRAF rule, the relative sizes of the process window of the post-disturbance SRAF rule and the process window of the corresponding pre-disturbance SRAF rule are compared, and when the process window of the post-disturbance SRAF rule is larger, the post-disturbance SRAF rule is accepted.

[0093] For example, in the step of obtaining the optimized rule group according to the result of determining whether to accept the post-disturbance SRAF rules, the optimized rule group includes: the accepted SRAF rules of the post-disturbance rule group and the unaccepted SRAF rules of the pre-disturbance rule group.

[0094] Based on the process window size of the SRAF rules before and after the disturbance, determine whether to accept the SRAF rules after the disturbance; accept the SRAF rules with a larger process window; therefore, the rule group after optimization has multiple SRAF rules, and the process windows of the SRAF rules of the rule group after optimization are larger than the process windows of the SRAF rules of the rule group before the disturbance; retain the disturbance that expands the process window, and eliminate the disturbance that reduces the process window, so that each disturbance is carried out in the direction of expanding the process window to achieve optimization of the SRAF rules.

[0095] Continue to refer Figure 3 , after obtaining the optimized rule group, step S130, the step of constructing the optimal SRAF rule by a simulated annealing algorithm according to the test pattern and the lithography model also includes: step S133, according to the current temperature, determining whether to execute the step of obtaining the current temperature and performing rule perturbation iteration again;

[0096] Obtaining the optimized rule group, that is, obtaining the equilibrium state at the current temperature, is the local optimal solution at the current temperature; step S133, judging whether to execute the step of obtaining the current temperature and performing rule perturbation iteration again to judge whether annealing is completed.

[0097] Specifically, compare the current temperature with the termination temperature T in the annealing initial parameters. end The current temperature is lower than the termination temperature T end , it is determined that the steps of obtaining the current temperature and performing regular disturbance iteration are executed again, that is, Figure 4 Steps shown.

[0098] In some embodiments, in step S133, when determining to execute the step of obtaining the current temperature and performing rule perturbation iteration again, in the process of executing the step of obtaining the current temperature and performing rule perturbation iteration again, in step S132b, in the step of obtaining the rule group before perturbation, the rule group before perturbation is obtained according to the optimized rule group obtained by the previous execution of rule perturbation iteration.

[0099] Specifically, in the process of obtaining the current temperature and performing rule perturbation iteration again, in step S132b, in the step of obtaining the rule group before perturbation, the optimized rule group obtained by the previous execution of rule perturbation iteration is used as the rule group before perturbation.

[0100] Continue to refer Figure 3 When it is determined that the step of obtaining the current temperature and performing rule perturbation iteration is not to be executed again, step S134 is executed to obtain the optimal SRAF rule according to the optimization rule group obtained in the last step of obtaining the current temperature and performing rule perturbation iteration.

[0101] For example, in the step of obtaining the optimal SRAF rule according to the optimization rule group obtained by the last execution of the rule perturbation iteration, the SRAF rule with the largest corresponding process window in the optimization rule group obtained by the last execution of the rule perturbation iteration is used as the optimal SRAF rule.

[0102] like Fig.11 As shown, the obtained optimal SRAF rule includes: the size SW of the auxiliary pattern 221 e The distance STS between adjacent auxiliary patterns 221 is 35nm. e is 78nm and the distance between the auxiliary pattern 221 and the main pattern 211 STM e It is 105nm.

[0103] Correspondingly, the present invention also provides a device for constructing SRAF rules.

[0104] refer to Fig.12 , showing a functional block diagram of an embodiment of a device for constructing SRAF rules of the present invention.

[0105] The construction device comprises:

[0106] A design module 310, wherein the design module 310 is suitable for designing a test pattern;

[0107] A model module 320, wherein the model module 320 is suitable for establishing a lithography model;

[0108] The annealing module 330 is adapted to construct an optimal SRAF rule through a simulated annealing algorithm according to the test pattern and the lithography model.

[0109] Combined with reference Figure 2 The design module 310 is used to generate a main pattern that needs to be inserted with an auxiliary pattern for optical simulation. Specifically, the design module 310 designs the test pattern 210 based on a layout to which the SRAF (Sub-Resolution Assist Features) rule applies.

[0110] In some embodiments of the present invention, the SRAF rule is applicable to inserting auxiliary graphics around isolated graphics; therefore, the design module 310 designs isolated test graphics 210. Specifically, the isolated test graphics 210 refers to the distance between the test graphics 210 and other surrounding graphics being greater than a preset distance, so as to ensure that the impact between the test graphics 210 and other surrounding graphics is weak or even non-existent. Figure 2 As shown, the test pattern 210 is an isolated long strip pattern.

[0111] Continue to refer Fig.12 The construction device also has a model module 320 for establishing a lithography model.

[0112] The lithography model can describe the entire lithography process including the optical system, mask, photoresist, and etching process. The lithography model can be a strict and accurate lithography model or a semi-empirical approximate lithography model. The semi-empirical approximate lithography model uses some simplified empirical formulas to replace some of the complex processes in the accurate lithography model, which can be obtained by fitting experimental data.

[0113] The model module 320 may pre-store an existing photolithography model, or establish a photolithography model using an existing photolithography model establishment method, which will not be described in detail in the present invention.

[0114] like Fig.12 As shown, the construction device further includes: an annealing module 330 connected to both the design module 310 and the model module 320, for constructing an optimal SRAF rule through a simulated annealing algorithm.

[0115] SRAF rules include: the size of auxiliary patterns, the distance between adjacent auxiliary patterns, and the distance between auxiliary patterns and main patterns. Excellent SRAF rules can effectively enhance the positive effect of auxiliary patterns and effectively improve lithography effects.

[0116] The simulated annealing algorithm adds a time-varying probability jump that eventually tends to zero during the search process for the global optimal solution, which can effectively prevent the search process from falling into a local minimum. The construction of the SRAF rule is a solution to the optimal solution of a multi-extreme value problem. The simulated annealing algorithm has a better effect in solving the optimal solution of the multi-extreme value problem. Therefore, the annealing module 330 can effectively improve the effect of the obtained SRAF rule.

[0117] like Fig.12 As shown, in some embodiments of the present invention, the annealing module 330 includes: an initialization unit 331, wherein the initialization unit 331 is suitable for setting the initial annealing parameters; an external loop unit 332, wherein the external loop unit 332 is suitable for obtaining the current temperature and performing rule perturbation iteration to obtain the optimized rule group; an external control unit 333, wherein the external control unit 333 is suitable for controlling whether the external loop unit 33 executes the steps of obtaining the current temperature and performing rule perturbation iteration again according to the current temperature; when the external control unit 333 determines not to execute the steps of obtaining the current temperature and performing rule perturbation iteration again, the external loop unit 332 obtains the optimal SRAF rule according to the optimized rule group obtained by the last rule perturbation iteration.

[0118] The annealing initial parameters are suitable for controlling the temperature change in the simulated annealing algorithm. The annealing initial parameters are the calculation basis of the simulated annealing algorithm. The initialization unit 331 is used to set the annealing initial parameters. Specifically, the annealing initial parameters may include: initial temperature T0, termination temperature T end and the temperature attenuation coefficient α.

[0119] The outer loop unit 332 is adapted to obtain the current temperature and perform rule perturbation iteration to obtain the optimized rule group by gradually optimizing the SRAF rules to obtain the equilibrium state at the current temperature.

[0120] In some embodiments, the external circulation unit 332 includes: a temperature controller 332a, wherein the temperature controller 332a is suitable for obtaining the current temperature according to the annealing initial parameters; an initializer 332b, wherein the initializer 332b is suitable for obtaining a pre-disturbance rule group, wherein the pre-disturbance rule group includes: a plurality of pre-disturbance SRAF rules; a simulator 332c, wherein the simulator 332c is suitable for performing lithography simulation according to the pre-disturbance rule group, in combination with the test pattern and the lithography model, to obtain a process window of the pre-disturbance SRAF rule; a perturbator 332d, wherein the perturbator 332d is suitable for perturbing the pre-disturbance SRAF rule in the pre-disturbance rule group to obtain a post-disturbance rule group, wherein the post-disturbance rule group includes: a plurality of post-disturbance SRAF rules; Then, the multiple groups of post-disturbance SRAF rules of the post-disturbance rule group correspond one to one with the multiple groups of pre-disturbance SRAF rules of the pre-disturbance rule group; the simulator 332c is also suitable for performing lithography simulation according to the post-disturbance rule group in combination with the test pattern and the lithography model to obtain the process window of the post-disturbance SRAF rules; the comparator 332e, the comparator 332e is suitable for comparing the process window of the post-disturbance SRAF rules obtained by the simulator with the corresponding process window of the pre-disturbance SRAF rules to determine whether to accept the post-disturbance SRAF rules; the resulter 332f, the resulter 332e is suitable for determining whether to accept the post-disturbance SRAF rules according to the result of the comparator to obtain the optimized rule group.

[0121] The step of obtaining the current temperature by the temperature controller 332a according to the annealing initial parameters is suitable for obtaining the current temperature corresponding to the equilibrium state. Specifically, the current temperature is obtained according to the initial temperature T0 and the temperature attenuation coefficient α.

[0122] It should be noted that, in some embodiments, when the external circulation unit 332 obtains the current temperature for the first time and performs rule perturbation iteration to obtain the optimized rule group, the temperature controller 332a uses the initial temperature T0 as the current temperature.

[0123] The initiator 332b is used to obtain a pre-disturbance rule set.

[0124] The pre-disturbance rule group is the basis for optimizing and obtaining the optimal SRAF rule. The pre-disturbance rule group includes: multiple groups of pre-disturbance SRAF rules. In the pre-disturbance rule group, each group of pre-disturbance SRAF rules includes: the size of the auxiliary graphics, the distance between adjacent auxiliary graphics, and the distance between the auxiliary graphics and the main graphics.

[0125] In some embodiments, when the external loop unit 332 executes for the first time to obtain the current temperature and perform rule perturbation iteration to obtain the optimized rule group, the initializer 332b also generates an initial rule group, and the initial rule group includes: multiple groups of initial SRAF rules; step S132b, in the step of obtaining the pre-disturbance rule group, the pre-disturbance rule group is obtained according to the initial rule group.

[0126] The initial SRAF rule is the basis for subsequent optimization. Specifically, when obtaining the current temperature and performing rule perturbation iteration to obtain the optimized rule group for the first time, the initializer 332b uses the initial rule group as the pre-perturbation rule group.

[0127] The more initial SRAF rules there are in the initial rule group generated by the initiator 332b, the more effective the optimal SRAF rule is, but the greater the computational load is and the longer the computation time is. In some exemplary embodiments, the initial rule group generated by the initiator 332b has 100 groups of initial SRAF rules.

[0128] In some embodiments, the initiator 332b generates the initial rule group based on the applicability of the SRAF rule. The setting of the SRAF rule is related to the main graphic to which the SRAF rule is applied; the initiator 332b generates the initial rule group based on the applicability of the SRAF rule, that is, generates the initial rule group based on the characteristics of the main graphic to which the SRAF rule is applied.

[0129] Specifically, in some embodiments, the SRAF rule is applicable to inserting auxiliary graphics around isolated graphics; Figure 2 As shown, the test pattern 210 is an isolated long strip pattern, and the auxiliary pattern applicable to the test pattern 210 is a long strip pattern parallel to and equal in length to the test pattern 210; accordingly, the SRFA rules to be established include: the width SW of the auxiliary pattern, the distance STS between adjacent auxiliary patterns, and the distance STM between the auxiliary pattern and the main pattern.

[0130] In some embodiments, the initializer 332b randomly generates the initial rule group. Randomly generating the initial rule group means that the number of initial SRAF rules in the generated initial rule group is a random number, and the rules in any initial SRAF rule are also random, that is, the values ​​of the width SW of the auxiliary pattern, the distance STS between adjacent auxiliary patterns, and the distance STM between the auxiliary pattern and the main pattern in any initial SRAF rule are all random numbers.

[0131] It should be noted that, in order to accelerate computational convergence and improve computational efficiency, in some embodiments, the initializer 332b randomly generates the initial rule group within a preset experience range, wherein the experience range may be preset based on a priori experience summary.

[0132] Specifically, in the initial rule group generated by the initiator 332b, different initial SRAF rules may be different or the same. Specifically, in different initial SRAF rules, the size SW of the auxiliary pattern, the distance STS between adjacent auxiliary patterns, and the distance STM between the auxiliary pattern and the main pattern may be at least partially different or may be equal.

[0133] For example, in the generated initial rule group, Figure 5 As shown, the first initial SRAF rule includes: the size SW10 of the auxiliary pattern 221 is 28nm, the distance STS10 between adjacent auxiliary patterns 221 is 98nm, and the distance STM10 between the auxiliary pattern 221 and the main pattern 211 is 103nm; Figure 6 As shown, the second initial SRAF rule includes: the size SW20 of the auxiliary pattern 222 is 40nm, the distance STS20 between adjacent auxiliary patterns 222 is 90nm, and the distance STM20 between the auxiliary pattern 222 and the main pattern 212 is 90nm; Figure 7 As shown, the third initial SRAF rule includes: a size SW30 of the auxiliary pattern 223 is 25 nm, a distance STS30 between adjacent auxiliary patterns 223 is 95 nm, and a distance STM30 between the auxiliary pattern 223 and the main pattern 213 is 107 nm.

[0134] Continue to refer Fig.12 The outer loop unit also includes: a simulator 332c, which performs lithography simulation according to the pre-disturbance rule group, combined with the test pattern and the lithography model, to obtain the process window of the pre-disturbance SRAF rule.

[0135] The simulator 332c can predict the light field distribution inside the photoresist when the mask composed of the test pattern and the auxiliary pattern is exposed, and further can predict the morphology of the photoresist after development.

[0136] Specifically, the pre-disturbance rule group has multiple groups of pre-disturbance SRAF rules; the simulator 332c sets auxiliary graphics around the test graphics to form a corresponding layout to be simulated according to each group of pre-disturbance SRAF rules in the pre-disturbance rule group; performs lithography simulation on each layout to be simulated to obtain the process window of the layout to be simulated; therefore, the simulator 332c obtains multiple process windows, and the multiple process windows correspond one-to-one to the multiple groups of pre-disturbance SRAF rules in the pre-disturbance rule group.

[0137] It should be noted that the simulator 332c may be implemented by referring to the existing photolithography simulation method, which will not be elaborated in detail in the present invention.

[0138] The perturbator 332d in the outer loop unit 332 perturbs the pre-disturbance SRAF rules in the pre-disturbance rule group to obtain a post-disturbance rule group, thereby providing a basis and direction for optimization. The post-disturbance rule group includes: multiple groups of post-disturbance SRAF rules, and the multiple groups of post-disturbance SRAF rules in the post-disturbance rule group correspond one-to-one to the multiple groups of pre-disturbance SRAF rules in the pre-disturbance rule group.

[0139] In some embodiments, the perturbator 332d randomly perturbs the pre-disturbance SRAF rules in the pre-disturbance rule group to obtain a post-disturbance rule group.

[0140] Specifically, randomly perturbing the pre-disturbance SRAF rules in the pre-disturbance rule group means that, for multiple pre-disturbance SRAF rules in the pre-disturbance rule group, the number of disturbance objects, the disturbance objects and the disturbance amplitude are all random, that is, for multiple pre-disturbance SRAF rules in the pre-disturbance rule group, the number of changed pre-disturbance SRAF rules is random, the changed pre-disturbance SRAF rules are random, and the amplitude of the change of the pre-disturbance SRAF rules is also random.

[0141] In some embodiments, the perturbator 332d randomly perturbs the pre-disturbance SRAF rule in the pre-disturbance rule group according to the current temperature to obtain a post-disturbance rule group.

[0142] Specifically, randomly perturbing the pre-disturbance SRAF rule in the pre-disturbance rule group according to the current temperature means randomly perturbing the pre-disturbance SRAF rule in the pre-disturbance rule group based on the current temperature.

[0143] For example, the disturber 332d determines the disturbance amplitude of the random disturbance of the pre-disturbance SRAF rules in the pre-disturbance rule group based on the current temperature. That is to say, for multiple pre-disturbance SRAF rules in the pre-disturbance rule group, the number of changed pre-disturbance SRAF rules is random, the changed pre-disturbance SRAF rules are random, and the amplitude of the change of the pre-disturbance SRAF rules is a random number related to the current temperature.

[0144] It should be noted that the disturbance performed by the perturbator 332d on the pre-disturbance SRAF rules in the pre-disturbance rule group is a random disturbance, that is, the number of disturbances, the disturbance object, and the disturbance amplitude are all random. Therefore, after the disturbance of the pre-disturbance SRAF rules in the pre-disturbance rule group, the obtained post-disturbance rule group includes the disturbed pre-disturbance SRAF rules and the unperturbed pre-disturbance SRAF rules, that is, for the multiple groups of post-disturbance SRAF rules in the obtained post-disturbance rule group, the post-disturbance SRAF rules can be the same as or different from the pre-disturbance SRAF rules. However, the number of post-disturbance SRAF rules in the post-disturbance rule group is equal to the number of pre-disturbance SRAF rules in the pre-disturbance rule group, and they correspond one to one.

[0145] For example, the obtained perturbation rule group is as follows: Figure 8 As shown, the SRAF rule after the first disturbance includes: the size SW11 of the auxiliary pattern 221 is 28nm, the distance STS11 between adjacent auxiliary patterns 221 is 98nm, and the distance STM11 between the auxiliary pattern 221 and the main pattern 211 is 103nm; Fig. 9 As shown, the second post-perturbation SRAF rule includes: the size SW21 of the auxiliary pattern 222 is 32nm, the distance STS21 between adjacent auxiliary patterns 222 is 102nm, and the distance STM21 between the auxiliary pattern 222 and the main pattern 212 is 125nm; Fig.10 As shown, the third post-disturbance SRAF rule includes: the size SW31 of the auxiliary pattern 223 is 25 nm, the distance STS31 between adjacent auxiliary patterns 223 is 93 nm, and the distance STM31 between the auxiliary pattern 223 and the main pattern 213 is 107 nm.

[0146] The simulator 332c is also connected to the perturbator 332d to perform lithography simulation according to the post-perturbation rule group in combination with the test pattern and the lithography model to obtain the process window of the post-perturbation SRAF rule.

[0147] Specifically, the number of post-disturbance SRAF rules in the post-disturbance rule group is equal to the number of pre-disturbance SRAF rules in the pre-disturbance rule group; therefore, the post-disturbance rule group also has a plurality of post-disturbance SRAF rules.

[0148] Specifically, the simulator 332c sets auxiliary graphics around the test graphics to form a corresponding layout to be simulated according to each group of post-disturbance SRAF rules in the post-disturbance rule group; performs lithography simulation on each layout to be simulated to obtain a process window of the layout to be simulated; therefore, the simulator 332c obtains multiple process windows, and the multiple process windows correspond one-to-one to the multiple groups of post-disturbance SRAF rules in the post-disturbance rule group.

[0149] The external loop unit 332 also has a comparator 332e connected to the simulator 332c to compare the process window of the post-disturbance SRAF rule and the corresponding process window of the pre-disturbance SRAF rule to determine whether to accept the post-disturbance SRAF rule; based on the result of the comparator 332e determining whether to accept the post-disturbance SRAF rule, the result device 332f obtains the optimized rule group.

[0150] The process window can determine the impact of the auxiliary graphics on the lithography effect: the stronger the positive effect of the auxiliary graphics, the larger the process window; the weaker the positive effect of the auxiliary graphics, the smaller the process window; the comparator 332e compares the process windows of the SRAF rule before and after the disturbance to determine whether the disturbance to the SRAF rule is an optimization or degradation of the SRAF rule.

[0151] In some embodiments, the comparator 332e compares the relative sizes of the process window of the post-disturbance SRAF rule and the process window of the corresponding pre-disturbance SRAF rule, and accepts the post-disturbance SRAF rule when the process window of the post-disturbance SRAF rule is larger.

[0152] For example, the optimized rule group includes: the accepted SRAF rules of the post-disturbance rule group and the unaccepted SRAF rules of the pre-disturbance rule group.

[0153] The comparator 332e determines whether to accept the SRAF rule after the disturbance based on the process window size of the SRAF rule before and after the disturbance; accepts the SRAF rule with a larger process window; therefore, the rule group after optimization has multiple SRAF rules, and the process windows of the SRAF rules of the rule group after optimization are larger than the process windows of the SRAF rules of the rule group before the disturbance; retains the disturbance that expands the process window, and eliminates the disturbance that reduces the process window, so that each disturbance is carried out in the direction of expanding the process window to achieve optimization of the SRAF rule.

[0154] Continue to refer Fig.12 The annealing module 330 further includes: an external control unit 333, wherein the external control unit 333 is adapted to control the external circulation unit 332 to obtain the current temperature and perform regular perturbation iteration again according to the current temperature.

[0155] Obtaining the optimized rule group, that is, obtaining the equilibrium state at the current temperature, is the local optimal solution at the current temperature; the external control unit 333 can determine whether the annealing is completed.

[0156] Specifically, the external control unit 333 compares the current temperature with the termination temperature T in the annealing initial parameters. end The current temperature is lower than the termination temperature T end When , the external control unit 333 determines to execute again to obtain the current temperature and perform regular disturbance iteration.

[0157] In some embodiments, when the external control unit 333 determines to execute again the process of obtaining the current temperature and performing rule perturbation iteration, during the process of obtaining the current temperature and performing rule perturbation iteration again, the initializer 332b obtains the pre-disturbance rule group based on the optimized rule group obtained by the previous execution of rule perturbation iteration.

[0158] Specifically, in the process of obtaining the current temperature and performing rule perturbation iteration again, the initiator 332b uses the optimized rule group obtained by the previous rule perturbation iteration as the pre-perturbation rule group.

[0159] When the external control unit 333 determines not to execute obtaining the current temperature and performing rule perturbation iteration again, the external loop unit 332 obtains the optimal SRAF rule according to the optimization rule group obtained by the last rule perturbation iteration.

[0160] For example, the outer loop unit 332 uses the SRAF rule with the largest corresponding process window in the optimization rule group obtained by the last execution of the rule perturbation iteration as the optimal SRAF rule.

[0161] like Figure 8 As shown, the obtained optimal SRAF rule includes: the size SW of the auxiliary pattern 221 e The distance STS between adjacent auxiliary patterns 221 is 35nm. e is 78nm and the distance between the auxiliary pattern 221 and the main pattern 211 STM e It is 105nm.

[0162] In addition, the present invention also provides a readable storage medium on which a computer program is stored. The computer program is executed by a processor to implement the steps of the construction method of the present invention.

[0163] The computer program stored in the readable storage medium is executed by the processor to implement the steps of the construction method of the present invention. The specific technical solution of the readable storage medium can refer to the embodiment of the construction method mentioned above.

[0164] In summary, according to the test pattern and the lithography model, the optimal SRAF rule is constructed by the simulated annealing algorithm. The simulated annealing algorithm can achieve better results in solving the multi-extreme value problem, and the SRAF rule constructed by the simulated annealing algorithm can obtain better results in solving the multi-extreme value problem of the auxiliary pattern shape and the distance between the auxiliary pattern and the main pattern. The obtained SRAF rule has a better effect on improving the main pattern process window, and this method has higher calculation efficiency and shorter calculation time.

[0165] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the scope defined by the claims.

Claims

1. A method for constructing SRAF rules, characterized in that: include: Design test graphics; Build photolithography models; According to the test pattern and the lithography model, an optimal SRAF rule is constructed by a simulated annealing algorithm.

2. The construction method according to claim 1, characterized in that: The steps of constructing the optimal SRAF rule by simulated annealing algorithm include: Set the initial annealing parameters; Obtain the current temperature and perform rule perturbation iteration to obtain an optimized rule group; According to the current temperature, determining whether to execute the step of obtaining the current temperature and performing regular disturbance iteration again; When it is determined not to execute the step of obtaining the current temperature and performing rule perturbation iteration again, the optimal SRAF rule is obtained according to the optimization rule group obtained by performing rule perturbation iteration for the last time.

3. The construction method according to claim 2, characterized in that: The steps of obtaining the current temperature and performing rule perturbation iteration to obtain the optimized rule group include: According to the annealing initial parameters, obtaining the current temperature; Obtaining a pre-disturbance rule group, the pre-disturbance rule group comprising: a plurality of pre-disturbance SRAF rules; According to the pre-disturbance rule group, in combination with the test pattern and the lithography model, lithography simulation is performed to obtain a process window of the pre-disturbance SRAF rule; Perturbing the pre-disturbance SRAF rules in the pre-disturbance rule group to obtain a post-disturbance rule group, wherein the post-disturbance rule group includes: a plurality of groups of post-disturbance SRAF rules, and the plurality of groups of post-disturbance SRAF rules in the post-disturbance rule group correspond one-to-one to the plurality of groups of pre-disturbance SRAF rules in the pre-disturbance rule group; According to the post-disturbance rule group, in combination with the test pattern and the lithography model, lithography simulation is performed to obtain a process window of the post-disturbance SRAF rule; Comparing the process window of the post-disturbance SRAF rule with the corresponding process window of the pre-disturbance SRAF rule, and determining whether to accept the post-disturbance SRAF rule; The optimized rule group is obtained according to the result of determining whether to accept the post-disturbance SRAF rule.

4. The construction method according to claim 3, characterized in that: In the step of perturbing the pre-disturbance SRAF rules in the pre-disturbance rule group to obtain the post-disturbance rule group, the pre-disturbance SRAF rules in the pre-disturbance rule group are randomly perturbed to obtain the post-disturbance rule group.

5. The construction method according to claim 4, characterized in that: In the step of randomly perturbing the pre-disturbance SRAF rule in the pre-disturbance rule group to obtain the post-disturbance rule group, the pre-disturbance SRAF rule in the pre-disturbance rule group is randomly perturbed according to the current temperature to obtain the post-disturbance rule group.

6. The construction method according to claim 3, characterized in that: In the step of comparing the process window of the SRAF rule after the disturbance with the corresponding process window of the SRAF rule before the disturbance and judging whether to accept the SRAF rule after the disturbance, the relative sizes of the process window of the SRAF rule after the disturbance and the process window of the SRAF rule before the disturbance are compared. When the process window of the SRAF rule after the disturbance is larger, the SRAF rule after the disturbance is accepted.

7. The construction method according to claim 3, characterized in that: In the step of obtaining the optimized rule group according to the result of determining whether to accept the post-disturbance SRAF rules, the optimized rule group includes: the accepted SRAF rules of the post-disturbance rule group and the unaccepted SRAF rules of the pre-disturbance rule group.

8. The construction method according to claim 3, characterized in that: When the step of obtaining the current temperature and performing rule perturbation iteration to obtain the optimized rule group is performed for the first time, before obtaining the pre-perturbation rule group, an initial rule group is generated, and the initial rule group includes: a plurality of initial SRAF rules; In the step of obtaining the pre-disturbance rule group, the pre-disturbance rule group is obtained according to the initial rule group.

9. The construction method according to claim 8, characterized in that: In the step of generating an initial rule group, the initial rule group is generated randomly.

10. The construction method according to claim 3, characterized in that: When determining to execute the step of obtaining the current temperature and performing rule perturbation iteration again, in the process of obtaining the current temperature and performing rule perturbation iteration again, in the step of obtaining the rule group before perturbation, the rule group before perturbation is obtained according to the optimized rule group obtained by the previous execution of rule perturbation iteration.

11. The construction method according to claim 2, characterized in that: In the step of obtaining the optimal SRAF rule according to the optimization rule group obtained in the last step of obtaining the current temperature and performing rule perturbation iteration, the SRAF rule with the largest corresponding process window in the optimization rule group obtained in the last step of performing rule perturbation iteration is used as the optimal SRAF rule.

12. The construction method according to claim 1, characterized in that: In the step of designing a test pattern, an isolated test pattern is designed.

13. A device for constructing SRAF rules, characterized in that: include: A design module, wherein the design module is suitable for designing a test pattern; A model module, wherein the model module is suitable for establishing a lithography model; An annealing module is adapted to construct an optimal SRAF rule through a simulated annealing algorithm according to the test pattern and the lithography model.

14. The construction device according to claim 13, characterized in that The annealing module comprises: an initialization unit, wherein the initialization unit is suitable for setting initial annealing parameters; An external circulation unit, wherein the external circulation unit is adapted to obtain a current temperature and perform rule perturbation iteration to obtain an optimized rule group; An external control unit, wherein the external control unit is adapted to control the external circulation unit to execute the steps of obtaining the current temperature and performing regular disturbance iteration again according to the current temperature; When the external control unit determines not to execute the step of obtaining the current temperature and performing rule perturbation iteration again, the external loop unit obtains the optimal SRAF rule according to the optimization rule group obtained by the last rule perturbation iteration.

15. The construction device according to claim 14, characterized in that The external circulation unit comprises: A temperature controller, wherein the temperature controller is adapted to obtain a current temperature according to the initial annealing parameters; An initiator, the initiator being adapted to obtain a pre-disturbance rule set, the pre-disturbance rule set comprising: a plurality of pre-disturbance SRAF rules; A simulator, wherein the simulator is adapted to perform lithography simulation according to the pre-disturbance rule group in combination with the test pattern and the lithography model to obtain a process window of the pre-disturbance SRAF rule; A perturbator, wherein the perturbator is adapted to perturb the pre-disturbance SRAF rules in the pre-disturbance rule group to obtain a post-disturbance rule group, wherein the post-disturbance rule group includes: a plurality of groups of post-disturbance SRAF rules, and the plurality of groups of post-disturbance SRAF rules in the post-disturbance rule group correspond one-to-one to the plurality of groups of pre-disturbance SRAF rules in the pre-disturbance rule group; The simulator is also adapted to perform lithography simulation according to the post-disturbance rule group in combination with the test pattern and the lithography model to obtain a process window of the post-disturbance SRAF rule; A comparator, the comparator being adapted to compare the process window of the post-disturbance SRAF rule obtained by the simulator with the process window of the corresponding pre-disturbance SRAF rule, and determining whether to accept the post-disturbance SRAF rule; A resulter is adapted to determine whether to accept the result of the disturbed SRAF rule according to the comparator to obtain the optimized rule group.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the steps of the method according to any one of claims 1 to 12.

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