Method for screening and evaluating optical proximity correction optical models

Abstract

The application provides a method for screening and evaluating an optical proximity correction optical model, provides test patterns and an optical model for optical proximity correction; obtains fitting images of all test patterns needed to be evaluated under an optical model and post-exposure images on a wafer; obtains contrast images according to the fitting images and the post-exposure images; obtains various parameter differences between the fitting images and the post-exposure images according to the contrast images; sets a weighted evaluation function according to the various parameter differences, sets a threshold of the weighted evaluation function, and then judges whether the test patterns meet the threshold; the test patterns meeting the threshold are suitable for the optical model; the test patterns not meeting the threshold are corrected, and the above steps are repeated until the test patterns not meeting the threshold meet the threshold; and the optical model suitable for each test pattern is imported into an optical model pattern library. The application can shorten an iteration time of optical proximity correction and improve correction accuracy of optical proximity correction on all patterns.

Description

Technical Field

[0001] This invention relates to the field of semiconductor technology, and in particular to a method for screening and evaluating optical proximity correction optical models. Background Technology

[0002] As integrated circuit manufacturing processes shrink, the critical dimensions requiring exposure are increasingly approaching the limits of photolithography. Due to interference and diffraction effects in the mask pattern, distortion occurs in the exposed pattern. OPC (Optical Proximity Correction) has become an indispensable process step below the 90nm node. By pre-correcting the pattern structure on the mask, exposure using an OPC-corrected mask ensures that the pattern structure formed in the photoresist matches the designed pattern structure, meeting semiconductor manufacturing requirements.

[0003] Model-based OPC (MBOPC) establishes an accurate computational model through optical simulation, adjusting the edges of the graphic and iterating through simulation to approximate the target graphic. As technology advances, graphic sizes become smaller and smaller, and graphic designs become increasingly complex. The impact of minute physical effects on lithography dimensions is becoming increasingly significant. An MBOPC optical model has a certain scope of application, such as a specific CD range, a specific pattern density range, and a specific substrate environment.

[0004] Currently, the main method for verifying the applicability of an optical model is to compare the difference between the simulated value of the linewidth (CD) of a specific design pattern under the optical model and the actual measured value after silicon wafer exposure. However, with the miniaturization of process nodes, the applicability of an optical model depends not only on whether the CD is close to the actual value on the silicon wafer, but also on many other factors that affect the stability, reliability, and even feasibility of the lithography process.

[0005] To address the aforementioned issues, a novel method for screening and evaluating optical proximity correction optical models is needed. Summary of the Invention

[0006] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method for screening and evaluating optical proximity correction optical models. This method addresses the problem that existing methods for verifying the applicability of optical models primarily rely on comparing the difference between the simulated value of the linewidth (CD) of a specific design pattern under the optical model and the actual measured value after silicon wafer exposure to determine the applicability of the optical model. Figure 1 As shown. However, with the miniaturization of process nodes, the applicability of optical models, besides whether CD is close to the actual value on the silicon wafer, involves many other factors that affect the stability, reliability, and even feasibility of the photolithography process.

[0007] To achieve the above and other related objectives, the present invention provides a method for screening and evaluating optical proximity correction optical models, comprising:

[0008] Step 1: Provide test graphics and an optical model for optical proximity correction;

[0009] Step 2: Obtain fitted images of all the test patterns to be evaluated under an optical model, as well as post-exposure images on the wafer;

[0010] Step 3: Obtain a comparison image based on the fitted image and the exposed image; obtain various parameter differences between the fitted image and the exposed image based on the comparison image;

[0011] Step 4: Set a weighted evaluation function based on the differences in the various parameters, set a threshold for the weighted evaluation function, and then determine whether the test graph meets the threshold.

[0012] Step 5: The test pattern that meets the threshold is adapted to this optical model;

[0013] If the test pattern does not meet the threshold, the optical model is corrected, and steps two to four are repeated until the test pattern that does not meet the threshold meets the threshold.

[0014] Step 6: Import the optical model applicable to each of the test graphics into the optical model graphics library.

[0015] Preferably, the optical model in step one is a model-based optical proximity correction model established based on the actual exposure and film conditions of the test pattern in the process flow.

[0016] Preferably, the test graphics in step one include line graphics and two-dimensional graphics.

[0017] Preferably, the post-exposure pattern in step two includes a top view from a scanning electron microscope and a longitudinal cross-sectional view from a scanning electron microscope.

[0018] Preferably, in step three, a scanning electron microscope is used to obtain the comparison image based on the fitted image and the exposed image.

[0019] Preferably, the types of parameter differences mentioned in step three include: differences in critical dimensions, line edge roughness, sidewall angles, critical dimension uniformity, and graphic area.

[0020] Preferably, the types of parameter differences mentioned in step three for hole patterns also include: differences in the area of ​​the hole patterns and differences in the axial length.

[0021] Preferably, the various parameter differences in step four are assigned corresponding weighting coefficients in the weighted evaluation function, the weighting coefficients being in the range of 0 to 1, and the sum of each weighting coefficient being 1.

[0022] Preferably, in step four, the scaling factor is set using the type and emphasis direction of each test pattern.

[0023] Preferably, the method for modifying the optical model in step five includes selecting other optical models or setting a corresponding menu according to the type of the test pattern.

[0024] As described above, the method for screening and evaluating optical proximity correction optical models of the present invention has the following beneficial effects:

[0025] This invention uses a weighted evaluation function to compare the fitted image under the optical model with the exposed image on the wafer from different aspects, such as the difference in critical dimensions, line edge roughness (LER), sidewall angle (SWA), critical dimension uniformity (CDU), and pattern area. Based on the results of the final evaluation function, the applicable range of the optical model is quickly screened, and applicable and unapplicable patterns are quickly classified and identified, and appropriate processing methods are applied to different patterns. This can significantly shorten the iteration time of optical proximity correction and improve the correction accuracy of optical proximity correction for all patterns. Attached Figure Description

[0026] Figure 1 The diagram shows a method for screening and evaluating optical models with optical proximity correction according to the present invention.

[0027] Figure 2 The image shown is a comparative schematic diagram of the present invention. Detailed Implementation

[0028] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention.

[0029] Please see Figure 1 This invention provides a method for screening and evaluating optical proximity correction optical models, comprising:

[0030] Step 1: Provide test graphics and an optical model for optical proximity correction;

[0031] In an embodiment of the present invention, the optical model in step one is a model-based optical proximity correction model established by the actual exposure and film conditions of the test pattern in the process flow.

[0032] In an embodiment of the present invention, the test graphics in step one include line graphics and two-dimensional graphics.

[0033] Step 2: Obtain fitted images of all test patterns to be evaluated under an optical model, as well as images of them after exposure on the wafer;

[0034] In an embodiment of the present invention, the post-exposure pattern in step two includes a top view from a scanning electron microscope and a longitudinal cross-sectional view from a scanning electron microscope.

[0035] Step 3, please refer to Figure 2 The process involves obtaining a comparison image based on the fitted image and the exposed image, and then identifying the various parameter differences between the fitted image and the exposed image based on the comparison image.

[0036] In an embodiment of the present invention, step three involves using a scanning electron microscope to obtain a comparison image based on the fitted image and the post-exposure image. It should be noted that other methods well-known to those skilled in the art can also be used to obtain the comparison image, and no specific limitations are imposed here.

[0037] In embodiments of the present invention, the types of parameter differences in step three include: differences in critical dimensions, line edge roughness (LER), side wall angle (SWA), critical dimension uniformity (CDU), and area. It should be noted that the items evaluated can be increased or decreased according to actual circumstances.

[0038] In embodiments of the present invention, the types of parameter differences in step three for hole patterns also include: differences in the area of ​​the hole patterns and differences in their axis lengths. It should be noted that the evaluation items can be added or reduced according to actual circumstances.

[0039] Step 4: Set a weighted evaluation function based on the differences in various parameters, set a threshold for the weighted evaluation function, and then determine whether the test graph meets the threshold.

[0040] In an embodiment of the present invention, the differences in various parameters in step four are assigned corresponding weighting coefficients in the weighted evaluation function. The weighting coefficients range from 0 to 1, and the sum of each weighting coefficient is 1.

[0041] In an embodiment of the present invention, step four involves setting a scaling factor based on the type and emphasis of each test pattern.

[0042] For example, for a hole pattern, its weighted evaluation function F(elva.) = a1f(ΔCD) + a2f(LER) + a3f(SWA) + a4f(CDU) + a5f(area) + a6f(axis) + ..., the items to be evaluated can be increased or decreased according to the actual situation.

[0043] Step 5: Test patterns that meet the threshold are suitable for this type of optical model;

[0044] If the test pattern does not meet the threshold, the optical model is corrected, and steps two to four are repeated until the test pattern that does not meet the threshold meets the threshold.

[0045] In an embodiment of the present invention, the method for correcting the optical model in step five includes selecting other optical models or setting the corresponding menu according to the type of the test pattern.

[0046] Step 6: Import the optical models applicable to each test graphic into the optical model graphics library.

[0047] It should be noted that the illustrations provided in this embodiment are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0048] In summary, this invention uses a weighted evaluation function to compare the fitted image under the optical model with the exposed image on the wafer from different aspects, such as the difference in critical dimensions, line edge roughness (LER), sidewall angle (SWA), critical dimension uniformity (CDU), and pattern area. Based on the results of the final evaluation function, the applicability range of the optical model is quickly screened, and applicable and inapplicable patterns are rapidly classified and identified, and appropriate processing methods are applied to different patterns. This can significantly shorten the iteration time of optical proximity correction and improve the correction accuracy of optical proximity correction for all patterns. Therefore, this invention effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0049] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for screening and evaluating optical proximity correction optical models, characterized in that, At least including: Step 1: Provide test graphics and an optical model for optical proximity correction; Step 2: Obtain fitted images of all the test patterns to be evaluated under an optical model, as well as post-exposure images on the wafer; Step 3: Obtain a comparison image based on the fitted image and the exposed image; obtain various parameter differences between the fitted image and the exposed image based on the comparison image. The types of parameter differences include: key dimension difference, line edge roughness, sidewall angle, key dimension uniformity, and graphic area. Step 4: Set a weighted evaluation function based on the various parameter differences described in Step 3, set a threshold for the weighted evaluation function, and then determine whether the test graph meets the threshold. Step 5: The test pattern that meets the threshold is adapted to this optical model; If the test pattern does not meet the threshold, the optical model is corrected, and steps two to four are repeated until the test pattern that does not meet the threshold meets the threshold. Step 6: Import the optical model applicable to each of the test graphics into the optical model graphics library.

2. The method for screening and evaluating optical proximity correction optical models according to claim 1, characterized in that: The optical model mentioned in step one is a model-based optical proximity correction model established for the actual exposure and film conditions of the test pattern in the process flow.

3. The method for screening and evaluating optical proximity correction optical models according to claim 1, characterized in that: The test graphics mentioned in step one include line graphics and two-dimensional graphics.

4. The method for screening and evaluating optical proximity correction optical models according to claim 1, characterized in that: The post-exposure images in step two include a top view from a scanning electron microscope and a longitudinal cross-sectional view from a scanning electron microscope.

5. The method for screening and evaluating optical proximity correction optical models according to claim 1, characterized in that: In step three, a scanning electron microscope is used to obtain the comparison image based on the fitted image and the exposed image.

6. The method for screening and evaluating optical proximity correction optical models according to claim 1, characterized in that: The types of parameter differences mentioned in step three also include, for hole patterns, differences in the area of ​​the hole patterns and differences in the axial length.

7. The method for screening and evaluating optical proximity correction optical models according to claim 1, characterized in that: The various parameter differences in step four are assigned corresponding weighting coefficients in the weighted evaluation function. The weighting coefficients range from 0 to 1, and the sum of each weighting coefficient is 1.

8. The method for screening and evaluating optical proximity correction optical models according to claim 7, characterized in that: In step four, the specific gravity coefficient is set using the type and emphasis direction of each test pattern.

9. The method for screening and evaluating optical proximity correction optical models according to claim 1, characterized in that: The method for correcting the optical model in step five includes selecting other optical models or setting the corresponding menu according to the type of the test pattern.

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