A mask parameter optimization method and device

Abstract

The application discloses a mask parameter optimization method, which comprises the following steps: obtaining a test pattern, light source parameters and initial mask parameters, wherein the initial mask parameters comprise a mask thickness and an initial mask sidewall angle; generating a plurality of groups of candidate mask parameters according to the initial mask sidewall angle in the initial mask parameters; the plurality of groups of candidate mask parameters comprise different mask sidewall angles and the same mask thickness; obtaining the imaging contrast of each group of candidate mask parameters based on the test pattern and the light source parameters; and selecting an optimal mask sidewall angle from the plurality of groups of candidate mask parameters according to the imaging contrast. The plurality of groups of mask parameters comprising different mask sidewall angles and the same mask thickness are generated, and the imaging contrast of each group of mask parameters is obtained by simulating the plurality of groups of mask parameters respectively, so that the optimal mask sidewall angle is found according to the imaging contrast. Therefore, the imaging contrast can be significantly improved and the imaging resolution can be improved by optimizing the mask parameters of the multi-layer film lens structure.

Description

Technical Field

[0001] This invention relates to the field of semiconductor technology, and more specifically to a method and apparatus for optimizing mask parameters. Background Technology

[0002] In the semiconductor field, traditional photolithography typically achieves the fabrication of smaller feature sizes by continuously reducing the exposure wavelength and increasing the numerical aperture. However, this is limited by the diffraction limit, presenting a significant obstacle. Surface plasmons, on the other hand, can utilize specially designed metal thin films to couple and transmit evanescent waves, enabling the imaging of fine structures of objects with feature sizes far smaller than the wavelength. This provides a feasible method for overcoming the optical diffraction limit and manipulating the light field.

[0003] Currently, plasma-based metal-dielectric multilayer lens structures are used. Improving imaging resolution is primarily achieved by modifying the structural parameters of these multilayer lens structures, thereby enhancing imaging contrast.

[0004] However, there is no research on the optimization of mask parameters related to this multilayer lens structure. Summary of the Invention

[0005] The purpose of this invention is to provide a mask parameter optimization method and apparatus to address the shortcomings of the prior art. This purpose is achieved through the following technical solutions.

[0006] A first aspect of the present invention provides a mask parameter optimization method, the method comprising:

[0007] Obtain the test pattern, light source parameters, and initial mask parameters, including mask thickness and initial mask sidewall angles;

[0008] Based on the initial mask sidewall angle in the initial mask parameters, multiple sets of candidate mask parameters are generated; the multiple sets of candidate mask parameters include different mask sidewall angles and the same mask thickness;

[0009] Based on the test image and the light source parameters, the imaging contrast of each group of candidate mask parameters is obtained;

[0010] Based on the imaging contrast, the optimal mask sidewall angle is selected from multiple sets of candidate mask parameters.

[0011] Preferably, the step of generating multiple sets of candidate mask parameters based on the initial mask sidewall angle in the initial mask parameters includes: starting from the initial mask sidewall angle, generating a mask sidewall angle at preset step intervals until the generated mask sidewall angle is greater than the preset sidewall angle; and combining each generated mask sidewall angle with the mask thickness to form a set of candidate mask parameters.

[0012] Preferably, obtaining the imaging contrast of each set of candidate mask parameters based on the test pattern and the light source parameters includes: for each set of candidate mask parameters, under the conditions of the set of candidate mask parameters, using the test pattern and the light source parameters, obtaining the maximum light intensity and minimum light intensity of the spatial image formed in the photoresist coated on the substrate layer; and determining the imaging contrast of the set of candidate mask parameters based on the maximum light intensity and minimum light intensity.

[0013] Preferably, the step of selecting the optimal mask sidewall angle from multiple sets of candidate mask parameters based on the imaging contrast includes: determining the mask sidewall angle in the candidate mask parameters corresponding to the maximum value of the imaging contrast as the optimal mask sidewall angle.

[0014] A second aspect of the present invention provides a mask parameter optimization apparatus, the apparatus comprising:

[0015] The acquisition module is used to acquire the test pattern, light source parameters, and initial mask parameters, including mask thickness and initial mask sidewall angles.

[0016] The candidate parameter generation module is used to generate multiple sets of candidate mask parameters based on the initial mask sidewall angles in the initial mask parameters; the multiple sets of candidate mask parameters include different mask sidewall angles and the same mask thickness;

[0017] The simulation module is used to obtain the imaging contrast of each set of candidate mask parameters based on the test image and the light source parameters;

[0018] The optimal value selection module is used to select the optimal mask sidewall angle from multiple sets of candidate mask parameters based on the imaging contrast.

[0019] Preferably, the candidate parameter generation module is specifically used to generate a mask sidewall corner every preset step length, starting from the initial mask sidewall corner, until the generated mask sidewall corner is greater than the preset sidewall corner; and to form a set of candidate mask parameters by combining each generated mask sidewall corner with the mask thickness.

[0020] Preferably, the simulation module is specifically used to obtain the maximum and minimum light intensity of the spatial image formed in the photoresist coated on the substrate layer under the test pattern and the light source parameters for each set of candidate mask parameters; and to determine the imaging contrast of the set of candidate mask parameters based on the maximum and minimum light intensity.

[0021] Preferably, the optimal value selection module is specifically used to determine the mask sidewall angle in the candidate mask parameters corresponding to the maximum value of the imaging contrast as the optimal mask sidewall angle.

[0022] Based on the mask parameter optimization method and apparatus described in the first and second aspects above, the present invention has at least the following beneficial effects or advantages:

[0023] By generating multiple sets of candidate mask parameters, including different mask sidewall angles and the same mask thickness, and simulating each set of candidate mask parameters separately to obtain the imaging contrast of each set, the optimal mask sidewall angle can be found based on the imaging contrast. Therefore, by optimizing the mask parameters of the multilayer lens structure, the imaging contrast can be significantly improved, and the imaging resolution can be enhanced. Attached Figure Description

[0024] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0025] Figure 1 This is a flowchart illustrating an embodiment of a mask parameter optimization method according to an exemplary embodiment of the present invention;

[0026] Figure 2 According to the present invention Figure 1 The illustrated embodiment shows a schematic diagram of an imaging structure composed of a multilayer film lens structure with alternating metal and dielectric components;

[0027] Figure 3 According to the present invention Figure 1 The illustrated embodiment shows a schematic diagram of the mask sidewall angle parameters;

[0028] Figure 4 According to the present invention Figure 1 The illustrated embodiment shows a schematic diagram of the imaging contrast variation curve as a function of the mask sidewall angle;

[0029] Figure 5 According to the present invention Figure 1 The illustrated embodiment shows a comparison of light intensity curves of spatial images formed in photoresist by different mask sidewall angles.

[0030] Figure 6 This is a schematic diagram of a mask parameter optimization device according to an exemplary embodiment of the present invention. Detailed Implementation

[0031] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0032] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular forms “a,” “the,” and “the” used in this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0033] It should be understood that although the terms first, second, third, etc., may be used in this invention to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of this invention, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0034] In existing technologies, super-diffraction imaging is achieved by using superlenses. These lenses, due to their negative refractive index, can couple and transmit high-frequency evanescent wave components, thus achieving the effect of super-diffraction optical imaging. In recent years, super-diffraction imaging has seen extensive research and progress in breaking through optical limits and improving imaging resolution.

[0035] A series of research activities have been carried out on the impact of structural and material parameters on the imaging quality of superlenses, such as metal loss, film thickness and roughness, illumination patterns, temporal characteristics of light, bandwidth characteristics, and metal reflective layers. Extensive research has been conducted primarily on the lens portion of super-diffraction imaging systems, specifically the multilayer thin-film lens structure of metal-dielectric components. Improving and optimizing the structural parameters of super-diffraction lenses has enhanced their imaging resolution and significantly improved imaging contrast.

[0036] However, there is currently no research on the optimization of mask parameters, which are closely related to the imaging structure.

[0037] Based on this, this application proposes a mask parameter optimization method, which involves obtaining a test pattern, light source parameters, and initial mask parameters. The initial mask parameters include mask thickness and initial mask sidewall angles. Then, based on the initial mask sidewall angles in the initial mask parameters, multiple sets of candidate mask parameters are generated. The multiple sets of candidate mask parameters include different mask sidewall angles and the same mask thickness. Based on the test pattern and the light source parameters, the imaging contrast of each set of candidate mask parameters is obtained. Finally, based on the imaging contrast, the optimal mask sidewall angle is selected from the multiple sets of candidate mask parameters.

[0038] Based on the above description, the proposed solution has the following beneficial effects:

[0039] By generating multiple sets of candidate mask parameters, including different mask sidewall angles and the same mask thickness, and simulating each set of candidate mask parameters separately to obtain the imaging contrast of each set, the optimal mask sidewall angle can be found based on the imaging contrast. Therefore, by optimizing the mask parameters of the multilayer lens structure, the imaging contrast can be significantly improved, and the imaging resolution can be enhanced.

[0040] Figure 1 This is a flowchart illustrating an embodiment of a mask parameter optimization method according to an exemplary embodiment of the present invention. In this embodiment, the mask parameters of a multilayer lens structure are optimized, specifically the mask sidewall angles, to improve the imaging contrast and resolution of the lens structure. Figure 1 As shown, the mask parameter optimization method includes the following steps:

[0041] Step 101: Obtain the test image, light source parameters, and initial mask parameters.

[0042] The light source parameters can be 365nm TM polarized light incident on the mask surface under off-axis illumination conditions.

[0043] The multilayer lens structure used in this invention is specifically a metal-dielectric alternating multilayer film structure, such as... Figure 2 As shown, the mask pattern (i.e., the test pattern) is a chromium (Cr) mask fabricated on quartz glass, with a slit width of 150 nm and a period of 300 nm. The entire multilayer film structure consists of five pairs of silver (Ag) / titanium dioxide (TiO2) composite films of equal thickness, each 30 nm thick, totaling 300 nm. In terms of fabrication, a 150 nm thick PMMA (polymethyl methacrylate) material matching the real part of the dielectric constant of the Ag film is applied over the mask to fill the Cr mask slits.

[0044] exist Figure 2 The photoresist on the substrate shown is 50 nm thick.

[0045] Mask parameters include mask thickness d and mask sidewall angle θ. For example... Figure 3 As shown, the mask thickness d in the mask pattern used in this invention is 60nm, and the initial mask sidewall angle θ = 0 degrees.

[0046] Step 102: Generate multiple sets of candidate mask parameters based on the initial mask sidewall angles in the initial mask parameters.

[0047] In the prior art, the mask sidewall angle used is a vertical sidewall angle, i.e., θ = 0 degrees. In order to optimize the mask sidewall angle, the present invention changes it to an inclined sidewall angle (greater than 0 degrees).

[0048] In one optional specific embodiment, starting from the initial mask sidewall corner, a mask sidewall corner can be generated at preset step intervals until the generated mask sidewall corner is greater than the preset sidewall corner, and then each generated mask sidewall corner and the mask thickness are combined to form a set of candidate mask parameters.

[0049] For example, with the range of mask sidewall angles to be optimized being 0 degrees to 50 degrees and a step size of 2 degrees, mask sidewall angles of 2 degrees, 4 degrees, 6 degrees...50 degrees can be generated sequentially.

[0050] During the simulation, the mask thickness remains unchanged, thus allowing the generation of multiple sets of candidate mask parameters, including different mask sidewall angles and the same mask thickness.

[0051] Step 103: Based on the test image and light source parameters, obtain the imaging contrast of each group of candidate mask parameters.

[0052] In one optional embodiment, for each set of candidate mask parameters, under the conditions of the set of candidate mask parameters, the maximum and minimum light intensities of the spatial image formed in the photoresist coated on the substrate are obtained using test patterns and light source parameters, and then the imaging contrast of the set of candidate mask parameters is determined based on the maximum and minimum light intensities.

[0053] Among them, the maximum and minimum light intensities of the spatial image formed in the photoresist coated on the substrate layer after incident light passes through a multilayer lens structure can be modeled and simulated using software (FDTD solutions).

[0054] Preferably, since the light is usually focused specifically on the center of the photoresist, the maximum and minimum light intensities of the spatial image formed at the imaging position in the photoresist, i.e. the center position, can be obtained.

[0055] Specifically, the formula for calculating image contrast is as follows:

[0056] C = (I max -I min ) / (Imax +I min )

[0057] Where C represents the imaging contrast, I max I represents the maximum light intensity. min This represents the minimum light intensity.

[0058] Step 104: Select the optimal mask sidewall angle from multiple sets of candidate mask parameters based on the imaging contrast.

[0059] The higher the imaging contrast, the better the corresponding mask parameters.

[0060] Based on this, the mask sidewall angle in the candidate mask parameters corresponding to the maximum imaging contrast can be determined as the optimal mask sidewall angle.

[0061] like Figure 4 As shown, the image contrast varies with the mask sidewall angle within the range of 0-50 degrees. Figure 4 It can be seen that the optimal mask sidewall angle is 40 degrees.

[0062] For example Figure 5 As shown, the spatial image light intensity curves are for mask sidewall angles of 0 degrees, 16 degrees, 32 degrees, 40 degrees, and 48 degrees. The horizontal axis represents the horizontal position on the same layer of photoresist, and the vertical axis represents the light intensity. Figure 5 It can be seen that the light intensity curve has the largest amplitude when the mask sidewall angle is 40 degrees, indicating the highest imaging contrast. Therefore, it can be concluded that 40 degrees is the optimal mask sidewall angle.

[0063] This completes the above. Figure 1 The optimization process shown involves generating multiple sets of candidate mask parameters, including different mask sidewall angles and the same mask thickness, and simulating each set of candidate mask parameters to obtain the imaging contrast of each set. Based on the imaging contrast, the optimal mask sidewall angle is then found. Therefore, by optimizing the mask parameters of the multilayer lens structure, imaging contrast can be significantly improved, and imaging resolution can be enhanced.

[0064] Figure 6 This is a schematic diagram illustrating the structure of a mask parameter optimization device according to an exemplary embodiment of the present invention. The device includes:

[0065] The acquisition module 510 is used to acquire the test pattern, light source parameters, and initial mask parameters, wherein the initial mask parameters include mask thickness and initial mask sidewall angle;

[0066] The candidate parameter generation module 520 is used to generate multiple sets of candidate mask parameters based on the initial mask sidewall angle in the initial mask parameters; the multiple sets of candidate mask parameters include different mask sidewall angles and the same mask thickness;

[0067] The simulation module 530 is used to obtain the imaging contrast of each set of candidate mask parameters based on the test pattern and the light source parameters.

[0068] The optimal value selection module 540 is used to select the optimal mask sidewall angle from multiple sets of candidate mask parameters based on the imaging contrast.

[0069] In an optional implementation, the candidate parameter generation module 520 is specifically used to generate a mask sidewall corner every preset step length, starting from the initial mask sidewall corner, until the generated mask sidewall corner is greater than the preset sidewall corner; and to form a set of candidate mask parameters by combining each generated mask sidewall corner with the mask thickness.

[0070] In an optional implementation, the simulation module 530 is specifically used to obtain, for each set of candidate mask parameters, the maximum and minimum light intensities of the spatial image formed in the photoresist coated on the substrate layer using the test pattern and the light source parameters under the conditions of the set of candidate mask parameters; and to determine the imaging contrast of the set of candidate mask parameters based on the maximum and minimum light intensities.

[0071] In an optional implementation, the optimal value selection module 540 is specifically used to determine the mask sidewall angle in the candidate mask parameters corresponding to the maximum value of the imaging contrast as the optimal mask sidewall angle.

[0072] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0073] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the present invention according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0074] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0075] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0076] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing mask parameters in a plasma-based metal-dielectric multilayer lens imaging system, characterized in that, The method includes: Obtain the test pattern, light source parameters, and initial mask parameters, including mask thickness and initial mask sidewall angles; Based on the initial mask sidewall angle in the initial mask parameters, multiple sets of candidate mask parameters are generated; the multiple sets of candidate mask parameters include different mask sidewall angles and the same mask thickness; Based on the test image and the light source parameters, the imaging contrast of each group of candidate mask parameters is simulated to obtain the imaging contrast of each group of candidate mask parameters. Based on the imaging contrast, the optimal mask sidewall angle is selected from multiple sets of candidate mask parameters.

2. The method according to claim 1, characterized in that, The step of generating multiple sets of candidate mask parameters based on the initial mask sidewall angles in the initial mask parameters includes: Starting from the initial mask sidewall corner, a mask sidewall corner is generated at preset step intervals until the generated mask sidewall corner is larger than the preset sidewall corner; Each generated mask sidewall corner and the mask thickness are combined to form a set of candidate mask parameters.

3. The method according to claim 1, characterized in that, The step of obtaining the imaging contrast of each set of candidate mask parameters based on the test image and the light source parameters includes: For each set of candidate mask parameters, under the conditions of that set of candidate mask parameters, the maximum and minimum light intensities of the spatial image formed in the photoresist coated on the substrate are obtained using the test pattern and the light source parameters. The imaging contrast of the candidate mask parameters is determined based on the maximum and minimum light intensities.

4. The method according to claim 1, characterized in that, The step of selecting the optimal mask sidewall angle from multiple candidate mask parameters based on the imaging contrast includes: The mask sidewall angle in the candidate mask parameters corresponding to the maximum value of the imaging contrast is determined as the optimal mask sidewall angle.

5. A mask parameter optimization device for a plasma-based metal-dielectric multilayer lens imaging system, characterized in that, The device includes: The acquisition module is used to acquire the test pattern, light source parameters, and initial mask parameters, including mask thickness and initial mask sidewall angles. The candidate parameter generation module is used to generate multiple sets of candidate mask parameters based on the initial mask sidewall angles in the initial mask parameters; the multiple sets of candidate mask parameters include different mask sidewall angles and the same mask thickness; The simulation module is used to simulate the candidate mask parameters of each group based on the test image and the light source parameters, so as to obtain the imaging contrast of each group of candidate mask parameters. The optimal value selection module is used to select the optimal mask sidewall angle from multiple sets of candidate mask parameters based on the imaging contrast.

6. The apparatus according to claim 5, characterized in that, The candidate parameter generation module is specifically used to generate a mask sidewall corner starting from the initial mask sidewall corner, at preset step intervals, until the generated mask sidewall corner is greater than the preset sidewall corner; and to form a set of candidate mask parameters by combining each generated mask sidewall corner with the mask thickness.

7. The apparatus according to claim 5, characterized in that, The simulation module is specifically used to obtain the maximum and minimum light intensity of the spatial image formed in the photoresist coated on the substrate layer under the test pattern and the light source parameters for each set of candidate mask parameters; and to determine the imaging contrast of the set of candidate mask parameters based on the maximum and minimum light intensity.

8. The apparatus according to claim 5, characterized in that, The optimal value selection module is specifically used to determine the mask sidewall angle in the candidate mask parameters corresponding to the maximum value of the imaging contrast as the optimal mask sidewall angle.

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