Photoresist evaluation method

Abstract

The invention discloses a photoresist evaluation method, and particularly relates to evaluation of photoresist capability and hot spots. An optical model and a photoresist model based on a target photoresist are established, the difference between the optical model and the photoresist model is calculated, and the available photoresist is screened out by judging whether the difference meets a preset range or not. And predicting hot spots in the photoetching layer by judging whether the difference of different design structures in the photoetching layout meets a preset range or not. According to the photoresist evaluation method provided by the invention, the capability of the photoresist can be qualitatively judged theoretically, and compared with a method in the current industry, the photoresist evaluation method is higher in judgment result accuracy, more persuasive, short in time consumption and low in cost.

Description

Technical Field

[0001] The invention relates to the field of integrated circuit manufacturing, and in particular to a photoresist evaluation method. Background Art

[0002] Photoresist is a key material in integrated circuit technology, and the quality of photoresist directly determines the process quality and defects. In order to improve the resolution of optical imaging, some existing technologies use OPC (Optical Proximity Correction) to correct the mask to ensure the work quality. For example, the patent with publication number CN104570585A discloses the best correction parameter value of the test mask pattern based on the optical proximity correction model (OPC model), ensures the physical properties of the optical proximity correction model, and ensures the process quality by making the mask through the OPC pattern.

[0003] However, there are many types of photoresists on the market according to different needs. Some photoresists with very different indicators will greatly increase production costs if used in mass production. Therefore, in order to make photoresists suitable and high-quality for full use in the actual integrated circuit manufacturing process, the industry generally conducts a simple process quality evaluation on the photoresist before mass production, and selects the appropriate photoresist for mass production through evaluation. However, the current evaluation method is to evaluate the morphology of the one-dimensional pattern of the photoresist layer after exposure and development of the photoresist layer, and calculate the exposure energy (EL) and depth of focus (DOF) of the photoresist through the morphology resolution value (CD). DOF and EL are generally determined by measuring the exposure focus-energy matrix (FEM) data using a CD-SEM machine. Figure 1 As shown in the figure, it is an exposure focus-energy matrix diagram. By subjectively observing the exposure focus-energy matrix diagram, it is subjectively predicted whether the photoresist meets the process window. However, the actual mass-produced layout structures are various and complex, and the subjective observation results will also vary depending on different people's feelings. The method of evaluating the photoresist capability only by analyzing the exposure energy (EL) and depth of focus (DOF) results of limited graphics has few measurement parameters, and there are often large errors in subjective judgments, which cannot accurately screen out qualified photoresist products.

[0004] In order to accurately screen out the appropriate photoresist products corresponding to the photoresist layer, it has always been the research direction of the industry. For example, the patent with publication number WO2021 / 217972 A1 discloses that the characteristic parameters of all electron beam microscopic images are calculated, and a photoresist quality evaluation table is established according to the characteristic values ​​of the photoresist to screen the best photoresist products. However, such a screening method is not intuitive enough and the efficiency is not high enough. At the same time, it is impossible to predict the hot spots in the photoresist layer, so there is still the disadvantage of inaccurate photoresist evaluation. Once the photoresist is evaluated inaccurately, it is difficult to replace the photoresist and the mask during mass production when the photoresist has been selected and the mask has been completed. This will cause a lot of waste of manpower and material resources, and the rework cycle is very long, which significantly increases the production cost.

[0005] In summary, the industry is in urgent need of improving photoresist evaluation methods to ensure process quality, reduce production costs and improve production efficiency. Summary of the invention

[0006] Based on the above-mentioned problems, the present invention proposes a photoresist evaluation method for screening out qualified photoresists and estimating the hot spots of target photoresists to ensure process quality, reduce production costs, and improve production efficiency. Among them, by establishing an optical model and a photoresist model based on the target photoresist, comparing the target evaluation index values ​​calculated by the optical model and the photoresist model to evaluate the target photoresist, and by judging whether the comparison result value of the target evaluation index value of the optical model and the photoresist model meets a preset range to evaluate the photoresist capability, thereby screening and determining whether the photoresist is qualified; and by judging whether the comparison result value of different design structures in the photolithography layout meets a preset range to predict the hot spots in the photolithography layer. By accurately screening out qualified photoresists and estimating the hot spots of the target photoresist, the use of unqualified photoresists can be avoided to cause a large number of defective products, resulting in a large amount of waste of manpower and material resources, and significantly increasing production costs.

[0007] In a first aspect, an embodiment of the present invention provides a photoresist evaluation method, comprising: establishing an optical model and a photoresist model based on a target photoresist; and evaluating the target photoresist by comparing target evaluation index values ​​calculated by the optical model and the photoresist model with photoresist-related factors added.

[0008] In some embodiments, a comparison result value is obtained by comparing the target evaluation index value, and whether the photoresist is qualified is evaluated by judging whether the comparison result value satisfies a preset range, so as to screen the photoresist.

[0009] In some embodiments, a comparison result value is obtained by comparing the target evaluation index value, and the hot spots in the lithography layer are predicted by judging whether the comparison result values ​​of different design structures in the lithography layout meet a preset range. If there is a position of the corresponding design structure that does not meet the preset range, it can be determined that the point may be a hot spot.

[0010] In some embodiments, the comparison method includes but is not limited to: difference method, quotient method, reciprocal comparison method, or logarithmic comparison method.

[0011] In some embodiments, the target evaluation index includes: an evaluation index characterizing a photolithography process window.

[0012] In some embodiments, the evaluation index characterizing the photolithography process window includes, but is not limited to: normalized image logarithmic slope, mask error factor, and / or depth of focus.

[0013] In some embodiments, the comparison result value includes but is not limited to: a normalized image logarithmic slope comparison value, a mask error factor comparison value, and / or a depth of focus comparison value.

[0014] In some embodiments, the photoresist model includes photoresist-related influencing factors.

[0015] In some embodiments, the influencing factors include, but are not limited to: diffusion effect, acid-base reaction.

[0016] In some embodiments, the calculation performs difference fitting under the same target resolution value and different period changes.

[0017] In a second aspect, the present invention further provides a photoresist evaluation method based on an optical proximity correction model, comprising: adding a comparison result value calculation entry in the optical proximity correction model, and executing the aforementioned method for evaluating the photoresist through the calculation entry.

[0018] In some embodiments, the comparison results of different photoresists are accumulated into a database to provide a reference comparison. Different photoresists can be compared based on the database.

[0019] In a third aspect, the present invention provides an optical proximity correction system, which includes computer instructions, characterized in that the computer instructions are used to execute the aforementioned photoresist evaluation method.

[0020] Compared with the prior art, the present invention provides a photoresist evaluation method, establishes an optical model and a photoresist model based on a target photoresist, compares the target evaluation index values ​​calculated by the optical model and the photoresist model with added photoresist-related factors to evaluate the target photoresist, and evaluates the photoresist capability by judging whether the comparison result values ​​of the target evaluation index values ​​of the optical model and the photoresist model meet a preset range, thereby screening out a suitable or optimal photoresist. In addition, by judging whether the comparison result values ​​of different design structures in the photolithography pattern meet a preset range, the hot spots in the photolithography layer can also be predicted. Compared with the current methods in the industry, this method can more accurately screen out qualified photoresists, and can also predict hot spots in the photolithography layer. Its judgment method is based on a large amount of actual wafer data, so it is more scientific, the results are more convincing, and it takes less time, greatly reducing the risk of a large number of defective products caused by unqualified photoresists.

[0021] The present invention also provides a photoresist evaluation method based on an optical proximity correction model, and an optical proximity correction system based thereon. The photoresist evaluation method is executed through a calculation entry module to further improve the evaluation efficiency. The difference values ​​calculated by different photoresists are accumulated to form a database to facilitate reference comparison in the subsequent evaluation of the same type of photoresist needs, avoiding the waste of re-evaluation costs. At the same time, it provides learning data for difference comparison when evaluating the needs of other types of photoresists, which is convenient for promoting process optimization and updating. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings of the present invention constitute a part of this specification and are used to further understand the present invention. The drawings illustrate embodiments of the present invention and together with the description are used to explain the principle of the present invention.

[0023] Figure 1 Focus-energy matrix for exposure.

[0024] Figure 2 Flowchart for photoresist screening and hotspot assessment.

[0025] Figure 3 Flowchart for photoresist screening.

[0026] Figure 4 The process of photoresist hotspot risk prediction method is presented.

[0027] Figure 5 For photoresist 1 evaluated based on ORD_NILS.

[0028] Figure 6 For photoresist 2 evaluated based on ORD_NILS.

[0029] Figure 7 Evaluate photoresist A based on ORD_DOF.

[0030] Figure 8 Evaluate photoresist B based on ORD_DOF.

[0031] Fig. 9 This is the hotspot prediction based on ORD_NILS.

[0032] Fig.10 Indicates hot spots. DETAILED DESCRIPTION

[0033] The following detailed descriptions are all illustrative and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0034] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention.

[0035] like Figure 2 As shown, it is a photoresist screening and hot spot evaluation flow chart, which provides a photoresist evaluation method, which includes the following steps: Step S1: Determine photolithography conditions based on target photoresist.

[0036] Step S2: establishing an optical model and a photoresist model based on the target photoresist based on OPC (Optical Proximity Correction) software.

[0037] Step S3: respectively using the optical model and the photoresist model to simulate and calculate the target evaluation index values ​​of the one-dimensional pattern of the photoresist layer and / or the pattern of different designed structures.

[0038] Step S4: Compare the target evaluation index value obtained by the optical model with the target evaluation index value obtained by the photoresist model and obtain a comparison result value ORD (Optical Resist Delta).

[0039] Step S5: determining whether the comparison result value satisfies a preset range. If so, it is determined that the target photoresist has passed the screening; if not, it is determined that the target photoresist has not passed the screening.

[0040] The following describes each step separately.

[0041] In step S1 , the target photoresist may be photoresist provided by different vendors, or may be photoresist that has been corrected at different angles, but is not limited to the aforementioned types of photoresist.

[0042] In step S2, OPC software is common software in this field, wherein the optical model and the resist model belong to the optical proximity correction model (OPC model), and the optical model is a model established based on the exposure conditions of the photoresist layer and only considers the optical effect. The photoresist model is a model established on the basis of the optical model by mainly adding factors such as the photoacid-photobase diffusion effect and the acid-base reaction effect related to the photoresist, wherein the influencing factors may vary depending on the focus of the process. Among them, corresponding optical models and photoresist models need to be established for different photoresists, and different photoresists may be photoresists with different parameters such as refractive index, thickness reflectivity, etc.

[0043] In step S3, the target evaluation index may be a commonly used evaluation index such as normalized image logarithmic slope (NILS), mask error factor (MEEF), depth of focus (DOF), etc., but the evaluation index is not limited thereto, and the evaluation index may also be an evaluation index formed by a combination of multiple existing evaluation indexes, but is not limited thereto. Among them, the one-dimensional pattern of the lithography layer may be a pattern obtained by other lithography layer drawing software, and the different design structure patterns are design structures of specific lithography layout structures provided by semiconductor device design merchants.

[0044] In step S4, the comparison method may be a difference method, a quotient method, a reciprocal comparison method, a logarithmic comparison method, etc., but is not limited thereto, and any method that can qualitatively characterize the comparison difference may be used.

[0045] In step S5, the preset range for determination can be obtained through experience, or the preset optimal determination range can be obtained through big data learning and training based on existing model training methods. Figure 3 As shown in FIG. 1 , it is a flow chart of a photoresist screening method, by judging whether the ORD value of the one-dimensional pattern of the photoresist layer of different photoresists meets a preset range, a suitable photoresist can be screened out from a variety of photoresists. Figure 4 As shown, it is a flow chart of the hotspot risk prediction method. By judging whether the ORD values ​​at different positions of different design structure graphics meet a preset range, it is possible to evaluate whether the photoresist is suitable and estimate the possible hotspots in the photolithography layer. If there is a hotspot risk, it means that the target photoresist is not suitable for application in the specific photolithography layout. Based on this, it can also be determined that the photoresist does not pass the screening.

[0046] According to the photoresist evaluation method of the aforementioned steps S1-S5, it is possible to quickly and accurately screen out qualified or optimal photoresists from a wide variety of photoresists for various types of photolithography patterns provided by customers for use in process production, so as to achieve the effects of improving process quality, saving production costs, and improving production efficiency. Example

[0047] In a specific embodiment, the normalized image logarithmic slope (NILS) is used as an evaluation index, and the difference method is used as an example to illustrate as follows: the target evaluation index value of the one-dimensional pattern of the photolithography layer is calculated by simulating the optical model and the photoresist model to obtain the evaluation index value NILS optical NILS resist , the NILS difference ORD (OpticalResist Delta) is obtained by simulation calculation, and the calculation formula is as follows: ORD_NILS = NILS resist -NILS optical The evaluation index of normalized image logarithmic slope (NILS) is an important parameter for measuring the lithography process window. It characterizes the steepness of the light intensity distribution at the edge of the target feature size. The higher the NILS value, the higher the quality of the spatial image. The calculation formula of NILS is:

[0048] I( x ) is the cross-sectional distribution of the light intensity, x are the spatial coordinates of the cross section, w is the width of the target image, NILS is the spatial derivative of the logarithm of the intensity according to w Normalize.

[0049] The ultimate goal of the photolithography process is to create a photoresist pattern with a rapidly changing photoresist thickness at the edge of the target graphic. In order to achieve the required rapid change in photoresist thickness at the edge of the target graphic, a photoresist with a high NILS spatial image and a high contrast is required. By simultaneously establishing the optical model and the photoresist model of the photolithography layer, simulating the NILS of the one-dimensional pattern of the photolithography layer, and obtaining the NILS difference (ORD_NILS) between the optical model and the photoresist model, the effect of increasing the photoresist effect on the NILS parameters can be obtained. When ORD_NILS is greater than or equal to the minimum specific value required for the photolithography layer of the current photolithography process, it indicates that this photoresist has a low NILS spatial image and its capacity cannot meet the current process, and the photoresist needs to be replaced. Figure 5As shown, the ORD_NILS evaluates photoresist 1, and the calculation is performed under the same target resolution value and different period changes. The horizontal axis represents different periods, the vertical axis represents the NILS value, and the specific value is the empirical value of 0.5, that is, the preset range is less than or equal to 0.5. According to the ORD_NILS fitting curve obtained under the same resolution value and different periods, it can be seen that photoresist 1 does not meet the preset range and has a low NILS spatial image. The ability of photoresist 1 does not meet the current process and is an unqualified photoresist. When ORD_NILS is less than the specific value, that is, it meets the preset range, it indicates that the photoresist has a high NILS spatial image and can meet the current process development, such as Figure 6 As shown in FIG. 1 , it is a ORD_NILS fitting curve diagram of photoresist 2, the horizontal axis represents different periods, the vertical axis represents NILS value, and the specific value is 0.5, that is, the preset range is less than or equal to 0.5. According to the ORD_NILS fitting curve obtained under the same resolution value and different periods, it can be known that photoresist 2 can meet the current process requirements and is a qualified photoresist. In addition, according to the attached Figure 5 , Attachment Figure 6 The fitted curve can more intuitively evaluate the ability of the target photoresist under different cycles. It can be seen that by adding the changes in the spatial image before and after the photoresist effect through NILS difference analysis, the photoresist ability can be qualitatively judged theoretically and the appropriate photoresist can be quickly screened out. This method is more accurate and convincing than the current industry methods, and it is time-saving and low-cost.

[0050] It can be understood by those skilled in the art that the above specific embodiment is only an exemplary preferred embodiment, which only uses the normalized image logarithmic slope (NILS) as the evaluation index and the difference method as the comparison method as an example, but the evaluation index and the comparison method are not limited to this. The evaluation index can also be determined by the depth of focus (DOF) (such as Figure 7 and Figure 8 As shown in Figure 2), mask error enhancement factor (MEEF) and other common evaluation index parameters or their combinations. Figure 7-8 As shown in the figure, DOF is used as the evaluation index. Under the same resolution value and different periods, the ORD_DOF curves are fitted based on photoresist A and photoresist B respectively. Figure 7-8It can be intuitively concluded that the ability of photoresist B is better than that of photoresist A, but the abilities of photoresists A and B are weakened at large cycles, especially when the cycle of photoresist A is greater than 180, the ability degradation is quite obvious. It can be seen that the evaluation method of this patent using other common evaluation indicators can also qualitatively judge the ability of photoresists in theory, which is more accurate and convincing than the current methods in the industry, and is time-saving and low-cost. In addition, the ability of photoresists can also be qualitatively judged by combining at least one or more of the difference values ​​of common evaluation indicators such as ORD_NILS, ORD_DOF, and ORD_MEEF, so as to screen photoresists from various angles to further improve the accuracy of the judgment.

[0051] Those skilled in the art can understand that the comparison method involved in this specific embodiment is not limited to the difference method, and can also be selected from other common comparison methods, or cross-comparison analysis of multiple comparison methods, for example, a comparison method in combination of one or more of the difference method, the quotient method, the reciprocal comparison method, the logarithmic comparison method, etc. can be selected, all of which are within the scope of protection of this patent and will not be described in detail here. Example

[0052] The following is based on the Figure 4 Describe the hotspot risk prediction method in detail. Figure 4 As shown, it is a flow chart for realizing photoresist hotspot risk prediction based on optical model and photoresist model, and steps S1-S3 are consistent with the description of the previous embodiment, wherein, in order to predict the actual hotspot situation of the target photoresist in the photolithography layout with different design structure graphics provided by the customer, in step S3, the target evaluation index values ​​of different design structure graphics are simulated and calculated using the optical model and the photoresist model respectively, and in step S4, the target evaluation index values ​​obtained based on the optical model of different design structures in the photolithography layout are compared with the target evaluation index values ​​obtained based on the photoresist model to obtain the comparison result value. Thus, step S5 is: judging whether the comparison result value based on the different design structure graphics in the photolithography layout meets a preset range to predict the hotspot in the photolithography layer, and when the comparison result value of a certain design structure does not meet the preset range, it is determined that the structural position in the photolithography layer may be a hotspot. In addition, for NILS optical and NILS resist If the capacity of the corresponding photolithography layer is less than the lower limit, it will also be a hot spot, such as Fig. 9 The Hot Spot NO.1 shown in FIG. 1 is the Hot Spot NO.1. Otherwise, there is no hot spot. The preset range used for determination can be obtained through experience, or it can be the optimal value obtained through model learning and training as needed.

[0053] The following example is used to explain the photoresist hotspot prediction method in detail. The normalized image logarithmic slope (NILS) is used as the evaluation index and the difference method is used as the comparison method. The NILS of different design structures in the photolithography layout are calculated based on the optical model and the photoresist model. optical NILS resist , the difference between the two values ​​is used to obtain the ORD_NILS of different design structures on the lithography layout. Based on whether the ORD_NILS meets a specific preset range, the structure that may have hot spots is predicted. Fig. 9 As shown, the horizontal axis represents the different positions of different design structure graphics in the lithography layout. By calculating the ORD_NILS of the positions of different design structures on the lithography layout, the possible hot spots of the lithography layer can be predicted. When the ORD_NILS of a structure of the corresponding design on the layout is greater than or equal to a specific value, it indicates that this point may be a hot spot caused by the photoresist, and it is necessary to pay extra attention to the wafer performance of this structure in the process. In addition, for NILS optical and NILS resist If the capacity of the corresponding photolithography layer is less than the lower limit, it will also be a hot spot. Fig. 9 As shown, the location of Hot Spot NO.1, NILS optical and NILS resist are all smaller than the normal value, so it can be determined that this point is a process hotspot caused by insufficient photoresist capability, such as Fig.10 As shown, the arrows indicate possible hot spots in the photolithography layer.

[0054] It can be understood by those skilled in the art that the above embodiments are only preferred embodiments for example, which only use the normalized image logarithmic slope (NILS) as an evaluation index and the difference method as a comparison method as an example, but are not limited to this. Among them, the evaluation index can also be replaced by evaluation indicators such as depth of focus (DOF), mask error enhancement factor (MEEF) or their combination. In addition, the comparison method is also not limited to the difference method, and other common comparison methods or cross-comparison analysis can be selected. For example, one or more comparison methods combined from the difference method, the quotient method, the reciprocal comparison method, the logarithmic comparison method, etc. can be selected, which are all within the scope of protection of this patent and will not be repeated here.

[0055] Furthermore, in a specific embodiment, the photoresist for which the hotspot is predicted can be a suitable or optimal photoresist selected according to the method in the first embodiment. After selecting the suitable or optimal photoresist, the hotspot prediction is directly performed on the photoresist. By judging whether the comparison result values ​​of different design structures in the photolithography pattern meet a preset range, the hotspot in the photolithography layer can also be predicted. This method can select the suitable or optimal photoresist and complete the hotspot prediction in the photolithography layer at the same time. The judgment results are faster, more scientific, more convincing, and low-cost than the current industry methods.

[0056] In another specific embodiment, the photoresist for which the hotspot is predicted can be any other photoresist, for example, it can be a photoresist provided by different merchants, or it can be a photoresist that has been corrected at different angles. The hotspot prediction method described above can be used to complete the hotspot prediction of any photoresist. This method has low cost, high accuracy, and high efficiency. Example

[0057] Based on the aforementioned embodiment one and / or embodiment two, this embodiment provides a photoresist evaluation method based on an optical proximity correction model, in which a calculation entry module for the comparison result value mentioned above is added to the optical proximity correction model, and the photoresist evaluation method of the aforementioned embodiment is executed through the calculation entry module to further improve the evaluation efficiency. Example

[0058] Based on the above-mentioned embodiment 1, embodiment 2, or / and embodiment 3, this embodiment provides an optical proximity correction system, which includes computer instructions, and the method in the above-mentioned embodiment is executed by the computer instructions to evaluate the photoresist, so as to screen out qualified or optimal photoresists, and predict the hot spots of the photoresist. The preset range used for judgment can be obtained through experience, or the optimal value can be obtained through model training and learning as needed. The preset range can also be set to different according to different actual conditions, so as to meet the selection of the best matching photoresist according to actual needs. At the same time, the difference values ​​calculated for different photoresists can be accumulated to form a database. Through the database, on the one hand, it can be convenient to provide reference comparisons when evaluating the needs of the same type of photoresists in the future, avoiding the waste of re-evaluation costs; on the other hand, it can also provide learning data for difference comparisons when evaluating the needs of other types of photoresists, which is convenient for promoting the optimization and updating of the process.

[0059] This patent mainly protects the photoresist evaluation and hotspot prediction based on the OPC method. By establishing an optical model and a photoresist model based on the target photoresist, comparing the target evaluation index values ​​calculated by the optical model and the photoresist model to evaluate the target photoresist, and judging whether the comparison result values ​​of the target evaluation index values ​​of the optical model and the photoresist model meet a preset range to evaluate the photoresist capability, thereby screening out suitable photoresists; and judging whether the comparison result values ​​of different design structures in the photolithography layout meet a preset range to predict the hotspots in the photolithography layer. This patent determines whether the photoresist capability is met and predicts the process hotspots through the difference between the optical model and the photoresist model, which can improve the accuracy of screening, and the process window can be guaranteed. At the same time, the method of this patent is efficient and low-cost.

[0060] The main solution protected by this patent is a photoresist evaluation method based on an optical proximity correction model, and an optical proximity correction system based thereon. The photoresist evaluation method is executed through a calculation entry module to further improve the evaluation efficiency. The difference values ​​calculated by different photoresists are accumulated to form a database to facilitate reference comparison in the subsequent evaluation of the same type of photoresist needs, avoiding the cost of re-evaluation. At the same time, it provides learning data for difference comparison when evaluating the needs of other types of photoresists, so as to promote the optimization and update of the process.

[0061] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of the present invention. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and corrections to the present invention. Such modifications, improvements and corrections are suggested in the present invention, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of the present invention.

[0062] It should be understood that the embodiments described in the present invention are only used to illustrate the principles of the embodiments of the present invention. Other variations may also fall within the scope of the present invention. Therefore, as examples and not limitations, alternative configurations of the embodiments of the present invention may be considered consistent with the teachings of the present invention. Accordingly, the embodiments of the present invention are not limited to the embodiments explicitly introduced and described in the present invention.

Claims

1. A photoresist evaluation method, characterized in that: Establishing optical model and photoresist model based on target photoresist; The target photoresist is evaluated by comparing the target evaluation index values ​​calculated by the optical model and the photoresist model.

2. The photoresist evaluation method according to claim 1, characterized in that: A comparison result value is obtained by comparing the target evaluation index value, and whether the comparison result value satisfies a preset range is judged to evaluate whether the photoresist is qualified, so as to screen the photoresist.

3. The photoresist evaluation method according to any one of claims 1 to 2, characterized in that: A comparison result value is obtained by comparing the target evaluation index value, and a hot spot in the lithography layer is predicted by judging whether the comparison result values ​​of different design structures in the lithography layout meet a preset range.

4. The photoresist evaluation method according to any one of claims 1 to 3, characterized in that: The comparison method includes: a difference method; the comparison result value is a target evaluation index difference ORD (OpticalResist Delta).

5. The photoresist evaluation method according to any one of claims 1 to 3, characterized in that: The comparison method includes: quotient method, reciprocal comparison method, or logarithmic comparison method.

6. The photoresist evaluation method according to any one of claims 1 to 5, characterized in that: The target evaluation index includes: an evaluation index characterizing a photolithography process window.

7. The photoresist evaluation method according to claim 6, characterized in that: The evaluation index characterizing the photolithography process window includes: normalized image logarithmic slope, mask error factor, and / or focal depth.

8. The photoresist evaluation method according to claim 7, characterized in that: The comparison result value includes: a normalized image logarithmic slope comparison value, a mask error factor comparison value, and / or a depth of focus comparison value.

9. The photoresist evaluation method according to claim 1, characterized in that: The photoresist model includes photoresist-related influencing factors.

10. The photoresist evaluation method according to claim 10, characterized in that: The influencing factors include: diffusion effect and acid-base reaction.

11. The photoresist evaluation method according to claim 1, characterized in that: The calculation is performed under the same target resolution value and different period changes.

12. A photoresist evaluation method based on an optical proximity correction model, characterized in that: A comparison result value calculation entry is added to the optical proximity correction model, and the method described in any one of claims 1 to 11 is executed through the calculation entry to evaluate the photoresist.

13. The photoresist evaluation method according to claim 12, characterized in that: The comparison result values ​​of different photoresists are accumulated as a database to provide reference comparison.

14. The photoresist evaluation method according to claim 13, characterized in that: Different photoresists are compared with each other based on the database.

15. An optical proximity correction system comprising computer instructions, characterized in that: The computer instructions are used to execute the method according to any one of claims 1-14.

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