Mask structure parameter optimization method and device and computer readable storage medium

Abstract

The invention provides a mask structure parameter optimization method and device and a computer readable storage medium. According to the optimization method of the mask structure parameters, first diffraction field distribution associated with to-be-optimized first mask structure parameters is constructed based on a propagation path after incident light encounters a mask, and according to an imaging model of an imaging system, imaging intensity distribution of the first diffraction field distribution after the first diffraction field distribution passes through the imaging system is constructed; according to the correlation information between the intensity distribution and the imaging quality parameter, the first mask structure parameter is optimized by taking the imaging quality parameter reaching the first preset condition as an optimization target, and the optimized first mask structure parameter is obtained, so that the imaging quality parameter of the mask structure is close to the first preset condition; the imaging quality parameter of the mask structure is improved, and the influence of the diffraction effect of the mask structure on the incident light on the imaging quality is reduced.

Description

Technical Field

[0001] The present application relates to the field of photolithography technology, and more specifically, to a method, device, and computer-readable storage medium for optimizing mask structure parameters. Background Art

[0002] Photolithography technology uses optical projection to transfer the circuit device pattern on a mask to the photoresist coated on the surface of a silicon wafer. Due to the small size of the integrated circuit pattern on the mask, the incident light is severely diffracted after passing through the mask structure, resulting in a difference between the image of the incident light after passing through the mask and the integrated circuit pattern on the mask. Summary of the Invention

[0003] Based on the above-mentioned technical defects, the present application proposes a method, device and computer-readable storage medium for optimizing mask structure parameters. The mask structure parameters are optimized with the quality parameters of the image formed by the diffraction field distribution of the mask reaching preset conditions as the optimization goal, thereby improving the imaging quality of the mask structure and reducing the influence of the diffraction effect of the mask structure on the incident light on the imaging quality.

[0004] In a first aspect, the present application provides a method for optimizing mask structure parameters, comprising:

[0005] Based on the propagation path of the incident light after encountering the mask, a first diffraction field distribution associated with the first mask structural parameter to be optimized is constructed; the first diffraction field distribution is the distribution of the diffraction field on a side outside the mask close to the imaging system; the mask includes an absorption layer, and the first mask structural parameter includes a film thickness of the absorption layer;

[0006] Constructing an intensity distribution of the first diffraction field distribution imaged by the imaging system according to an imaging model of the imaging system;

[0007] According to the correlation information between the intensity distribution and the imaging quality parameters, the first mask structure parameters are optimized with the imaging quality parameters reaching a first preset condition as the optimization goal to obtain the optimized first mask structure parameters.

[0008] A second aspect of the present application provides a device for optimizing mask structure parameters, comprising:

[0009] A first construction module is configured to construct a first diffraction field distribution associated with a first mask structural parameter to be optimized based on a propagation path of incident light after encountering the mask; the first diffraction field distribution is a distribution of the diffraction field on a side of the mask that is close to the imaging system; the mask includes an absorption layer, and the first mask structural parameter includes a film thickness of the absorption layer;

[0010] A second construction module is used to construct an intensity distribution of the first diffraction field distribution after imaging by the imaging system according to an imaging model of the imaging system;

[0011] The optimization module is used to optimize the first mask structure parameters according to the correlation information between the intensity distribution and the imaging quality parameters, with the imaging quality parameters meeting the first preset condition as the optimization goal, to obtain the optimized first mask structure parameters.

[0012] The third aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method provided in the first aspect of the present application.

[0013] The beneficial effects of this application include at least:

[0014] In an embodiment of the present application, after constructing the imaging quality parameters of the first mask diffraction field distribution imaged in association with the first mask structure parameters to be optimized, the first mask structure parameters are optimized with the imaging quality parameters reaching the first preset conditions as the optimization goal, and the optimal mask structure parameters can be found, so that the imaging quality parameters of the mask structure are close to the first preset conditions, thereby improving the imaging quality parameters of the mask structure and reducing the influence of the diffraction effect of the mask structure on the incident light on the imaging quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present application and, together with the description, serve to explain the principles of the present application.

[0016] The present application can be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0017] Figure 1 A schematic diagram showing the imaging principle of a lithography projection system in an embodiment of the present application is shown;

[0018] Figure 2 A schematic diagram showing a reflective mask structure and its light propagation path in an embodiment of the present application is shown;

[0019] Figure 3 A schematic flow chart of a method for optimizing mask structure parameters in an embodiment of the present application is shown;

[0020] Figure 4 A schematic diagram of a reflective mask structure in an embodiment of the present application is shown;

[0021] Figure 5 Shown Figure 4 A schematic diagram of a curve showing the relationship between reflectivity and incident angle after optimization of the reflective mask structure of the example shown;

[0022] Figure 6 A comparative schematic diagram of a reflective mask structure in an embodiment of the present application under illumination by a central light source at different incident angles is shown;

[0023] Figure 7 Shown Figure 6 Schematic diagram comparing the imaging distribution of the reflective mask structure before and after optimization under illumination from a central light source at different incident angles;

[0024] Figure 8 Schematic diagram showing a comparison of a reflective mask structure under diode illumination at different incident angles in an embodiment of the present application;

[0025] Figure 9 Shown Figure 8 Schematic diagram comparing the imaging distribution of the reflective mask structure before and after optimization under diode illumination at different incident angles;

[0026] Figure 10 A schematic diagram of a mask structure parameter optimization device according to an embodiment of the present application is shown;

[0027] Figure 11 A schematic structural diagram of an electronic device in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0028] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present application. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present application. It is obvious to those skilled in the art that the present application can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present application, some technical features known in the art are not described.

[0029] Some terms in the exemplary embodiments of the present application are explained below.

[0030] Extreme ultraviolet, or EUV for short, covers the range of electromagnetic radiation with wavelengths from 121 nanometers to 10 nanometers in the electromagnetic spectrum.

[0031] Beyond Extreme Ultraviolet (BEUV). The wavelength of an EUV light source is shorter than that of EUV light. In some implementations, the EUV light source uses electromagnetic waves of 6.x nanometers. Because the wavelength of EUV is shorter than that of EUV, theoretically, lithography systems using EUV as a light source can achieve resolution superior to that of EUV systems. Therefore, EUV lithography is a technological path forward, following EUV lithography, to further reduce the resolution of integrated circuit patterning.

[0032] Diffraction near field (DNF) is the electric field distribution beneath the mask in lithography. For BEUV lithography masks, incident light is incident obliquely on the mask's three-dimensional structure. Even the tiny pattern size on the mask causes severe diffraction and shadowing effects in the mask's diffraction near field, resulting in discrepancies between the diffraction near field distribution and the designed mask layout.

[0033] Numerical aperture (NA) is a term used in optics to describe the angle of the cone of light a lens receives. This determines the lens's light-collecting capacity and spatial resolution. NA measures the angular range of light that an optical system can collect.

[0034] Critical Dimension (CD) refers to the physical size of a chip. The minimum feature size on a silicon wafer, or the minimum line width, is defined as the critical dimension.

[0035] Particle Swarm Optimization (PSO) is a technique that was developed by researchers who developed a simplified algorithm model inspired by the foraging behavior of bird flocks. This simplified model was later refined to form the PSO algorithm. PSO features fast convergence, a small number of parameters, and a simple, easy-to-implement algorithm. Furthermore, as a heuristic optimization algorithm, PSO does not require gradient calculations; it is a non-gradient optimization algorithm that avoids complex gradient calculations and has a wide range of applications. For high-dimensional optimization problems, PSO can converge to the optimal solution faster than genetic algorithms.

[0036] Transfer Matrix Method (TMM) is an algorithm based on the wave nature of electromagnetic fields that can quickly calculate the transmittance and scattering of one-dimensional multilayer film structures.

[0037] Born's series is an algorithm based on Maxwell's equations that can realize fast calculation of time-harmonic electromagnetic wave scattering fields.

[0038] It should be noted that the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should also be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of the features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0039] Now, exemplary embodiments according to the present application will be described in more detail with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in a variety of different forms and should not be construed as being limited to the embodiments described herein. The accompanying drawings are not drawn to scale, and certain details may be magnified and omitted for the purpose of clarity. The shapes of the various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may further design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0040] The following describes exemplary embodiments of the present application. It should be noted that the following embodiments are merely provided to facilitate understanding of the spirit and principles of the present application, and the embodiments of the present application are not limited in this respect. On the contrary, the embodiments of the present application can be applied to any applicable scenario.

[0041] Photolithography is a crucial step in the manufacturing process of very large-scale integrated circuits (VLSI). Using optical projection, photolithography precisely transfers the circuit device pattern from a mask onto a photoresist coated on the surface of a silicon wafer. The goal of photolithography (including extreme ultraviolet and extreme ultraviolet lithography) is to produce extremely small circuit patterns, making image quality crucial. The mask structure, a crucial optical structure in photolithography, significantly impacts image quality.

[0042] The resolution of photolithography is proportional to the wavelength of the light source used. Extreme ultraviolet (EUV) lithography (e.g., a light source with a wavelength of 13.5 nanometers) is currently being applied in production. Because the wavelength of the light source used in EUV lithography is shorter (e.g., a wavelength of 6.x nanometers), theoretically, a lithography system using EUV as a light source can achieve a resolution superior to that of an EUV lithography system. Extreme ultraviolet (BEUV) lithography is a technological route for improving the resolution of integrated circuit patterning, following EUV lithography.

[0043] Figure 1 The following figure shows a schematic diagram of the imaging principle of a projection system based on BEUV lithography technology. Figure 1 As shown, after incident light 11 strikes reflective mask 101, the reflected light serves as the light source for projection system 102. After passing through the optical path structure within projection system 102, it forms an image on wafer 103, resulting in an aerial image 13. Furthermore, after incident light 11 strikes the surface of reflective mask 101, it generates a diffraction near-field 12 near the reflective surface. This is because the BEUV wavelength is extremely short, much smaller than the size of the mask structure.

[0044] A mask is an optical element that can transmit or reflect incident light. Accordingly, masks can be divided into transmissive masks and reflective masks. Figure 2 The structure of a reflective mask according to an embodiment of the present application is shown. It can be understood that Figure 2 The description of the mask structure is only for illustrative purposes and is not intended to limit the embodiments of the present application. Figure 2 The reflective mask structure shown includes an absorption layer 201 and a multilayer reflective film 202 .

[0045] The following is based on Figure 2 The reflective mask structure shown illustrates the propagation path of incident light after it encounters the mask. There are gaps 2011 in the absorption layer 201, and the integrated circuit pattern on the mask can be formed based on the gaps in the absorption layer. Since the wavelength of the incident light (e.g., extreme ultraviolet light, ultraviolet light) is relatively short, almost any substance can strongly absorb the incident light. Therefore, the incident light is absorbed after entering the material of the absorption layer 201, and after entering the gaps 2011, it will pass through the gaps and enter the multilayer reflective film 202 below. Since the wavelength of the incident light is relatively short, much smaller than the size of the gaps 2011, the incident light passing through the gaps 2011 will produce a diffraction effect below the absorption layer 201, forming a diffraction near-field of the incident light below the absorption layer 201.

[0046] The reflective multilayer film 202 includes a composite multilayer reflective film. Figure 2 The reflective multilayer film 202 is formed by repeatedly stacking two different materials, including a first material layer 2021, a second material layer 2022, a first material layer 2023, a second material layer 2024, a first material layer 2025, and a second material layer 2026. The reflective multilayer film 202 is capable of reflecting incident light that passes through the slits 2011 in the absorption layer 201. After reflection, the reflected light passes through the slits in the absorption layer 201, forming a diffraction near-field of the reflected light above the absorption layer 201.

[0047] Therefore, when the incident light is BEUV, the complex diffraction effect and electromagnetic field effect between the incident light and the mask structure will cause the diffraction near field of the mask to deviate seriously from the ideal 0, 1 distribution. This phenomenon is called the mask three-dimensional effect. Severe mask three-dimensional effect will have an adverse effect on lithography imaging, resulting in Figure 1 There is a significant difference between the aerial image 13 in FIG. 1 and the design drawing of the integrated circuit pattern of the reflective mask 101 .

[0048] The embodiment of the present application provides a method for optimizing mask structural parameters, which can optimize the mask structural parameters and thus improve the imaging quality of the mask. Figure 3FIG. 1 is a flow chart of a method for optimizing mask structure parameters provided by an exemplary embodiment of the present application. The method includes steps 301 to 303 , and each step is described in detail below.

[0049] Step 301: constructing a first diffraction field distribution associated with a first mask structural parameter to be optimized based on a propagation path of the incident light after encountering the mask.

[0050] Incident light refers to the light that enters the mask. The propagation path of the incident light after encountering the mask is the optical path propagation process of the incident light after encountering the mask.

[0051] The first diffraction field distribution is the distribution of the diffraction field outside the mask close to the imaging system side, which is the electric field distribution of the diffraction field formed outside the mask. The imaging system is used to image the light incident to the imaging system on the image plane to be imaged, for example, Figure 1 The projection system 102 in FIG. 1 is an example of an imaging system. For a transmissive mask, the imaging system and the incident light are on both sides of the mask. For a reflective mask, the imaging system and the incident light are on the same side of the mask.

[0052] According to the above Figure 2 From the description of the mask structure shown, it can be seen that the propagation path of the incident light after encountering the mask determines the diffraction near-field distribution and is related to the mask structure parameters. Among them, the absorption layer material, thickness, gap width, etc. have an impact on the diffraction near-field distribution of the incident light, and further affect the diffraction near-field of the reflected light. The reflective layer material, thickness and number of layers will also have an impact on the diffraction near-field of the reflected light.

[0053] Therefore, based on the propagation path of the incident light after encountering the mask, the first diffraction field distribution can be derived, thereby constructing a function expression for expressing the first diffraction field distribution. The first diffraction field distribution is associated with the first mask structure parameter to be optimized. In the mask structure parameter optimization method provided in the embodiment of the present application, the mask structure parameters that need to be optimized include at least the first mask structure parameter. The mask structure parameter is a parameter used to describe the mask structure. For example, the mask structure parameter may include the film thickness, film material, film period, superposition relationship between film layers, etc. of one or more film layers in the mask. For example, the mask structure parameter may include at least one of the following: the material, film thickness, gap width of the absorption layer, the material, film thickness and number of film layers of the reflection layer, etc. Optionally, the first mask structure parameter associated with the first diffraction field distribution may include the film thickness of the absorption layer.

[0054] In some embodiments, the first diffraction field distribution can be solved using the Born series method. In other embodiments, the first diffraction field distribution can also be constructed using electromagnetic wave simulation technology, such as the Finite-Difference Time-Domain (FDTD) method, the waveguide method, the finite element method, etc.

[0055] Step 302: constructing an intensity distribution of the first diffraction field distribution imaged by the imaging system according to an imaging model of the imaging system.

[0056] The imaging model of an imaging system is used to describe the spatial distribution of the image formed by light incident on the imaging system after passing through the imaging system. Specifically, it can be represented by the spatial distribution of light intensity. Based on the imaging model of the imaging system, a functional expression for the spatial distribution of light intensity, i.e., the intensity distribution, formed by the first diffraction field distribution after passing through the imaging system can be constructed. Optionally, the imaging model can be established based on the Abbe model or the Hopkins model.

[0057] Step 303: Based on the correlation information between the intensity distribution and the imaging quality parameters, and with the imaging quality parameters meeting the first preset condition as the optimization goal, the first mask structure parameters are optimized to obtain optimized first mask structure parameters.

[0058] Imaging quality parameters are used to evaluate imaging quality. For example, the imaging quality parameters may include at least one of the following: normalized logarithmic slope (NILS), aerial image peak intensity, etc. The imaging quality parameters are related to the intensity distribution. Specifically, the imaging quality parameters can be expressed using an expression associated with the intensity distribution.

[0059] Therefore, achieving the first preset condition for the imaging quality parameter can be used as an optimization goal. Based on the correlation information between the imaging quality parameter and the intensity distribution, the correlation information between the intensity distribution and the first diffraction field distribution, and the correlation relationship between the first diffraction field distribution and the first mask structure parameters, the first mask structure parameters are optimized to obtain the optimized first mask structure parameters. Preferably, the optimization algorithm can use a PSO algorithm.

[0060] In this way, after constructing the imaging quality parameters of the first mask diffraction field distribution associated with the first mask structure parameters to be optimized, the first mask structure parameters are optimized with the imaging quality parameters reaching the first preset condition as the optimization goal. This can optimize the mask structure, thereby changing the diffraction field distribution, and thus making the imaging quality parameters of the mask structure close to the first preset condition, thereby improving the imaging quality parameters of the mask structure and reducing the impact of the diffraction effect of the mask structure on the incident light on the imaging quality. The mask structure parameter optimization method provided in the embodiments of the present application is applied in the context of a BEUV lithography system. By optimizing the BEUV lithography mask structure, its DNF distribution can be changed, thereby optimizing the imaging quality of the BEUV lithography system.

[0061] In some embodiments, in addition to optimizing the first mask structure parameters including the thickness of the absorption layer, the second mask structure parameters including the thickness of the multilayer reflective film can also be optimized. The mask structure includes an absorption layer and a multilayer reflective film. An example structure is as follows: Figure 2 As shown, the film thickness of the multilayer reflective film may include the thickness of each reflective film layer. The film thickness of the multilayer reflective film may affect the reflective parameters of the multilayer reflective film. For example, the reflective parameters may include reflectivity. Furthermore, a functional expression for the reflective parameters of the multilayer reflective film associated with the second mask structure parameters may be constructed. Optionally, the correlation between the reflective parameters of the multilayer reflective film and the second mask structure parameters may be solved using a transfer matrix method (TMM). Using the TMM, the transmittance and scattering rate of light passing through the reflective multilayer film may be quickly calculated, thereby obtaining the reflectivity of the reflective multilayer film.

[0062] Specifically, before constructing the first diffraction field distribution associated with the mask structure parameters to be optimized, the second mask structure parameters can be optimized based on the association information between the reflection parameters of the multilayer reflective film and the second mask structure parameters, with the reflection parameters of the multilayer reflective film meeting a second preset condition as the optimization goal, to obtain the optimized second mask structure parameters. Preferably, the optimization algorithm can use a PSO algorithm.

[0063] In this way, by optimizing the second mask structure parameters, the structure of the reflective multilayer film can be optimized, bringing the reflective parameters of the multilayer reflective film closer to the second preset condition. By setting the second preset condition, the reflectivity of the incident light can be maximized, thereby improving reflection efficiency. For BEUV lithography systems, reflection efficiency directly determines the system's energy loss, which in turn affects a series of key indicators such as imaging and productivity of the BEUV lithography machine. Therefore, improving reflection efficiency helps reduce system energy loss, improve imaging quality, and increase productivity.

[0064] Furthermore, the mask structure parameters to be optimized include the absorption layer thickness, optimized based on the imaging quality optimization objective, and the reflective layer thickness, optimized based on the reflection parameter optimization objective. This fully considers the close correlation between different mask structure parameters and different optimization objectives. Furthermore, the practice of optimizing different mask structure parameters separately—first optimizing the thickness of the multilayer reflective film, then optimizing the thickness of the absorption layer—can also simplify the complexity of the optimization algorithm.

[0065] Furthermore, before constructing the first diffraction field distribution associated with the first mask structure parameter to be optimized based on the propagation path of the incident light after encountering the mask, the reflection parameters of the optimized multilayer reflective film can be calculated based on the optimized second mask structure parameters. Figure 2 The propagation path of the example mask structure is described. The first diffraction field distribution is also related to the reflection parameters of the multilayer reflective film. Therefore, when constructing the first diffraction field distribution associated with the first mask structure parameters to be optimized, the calculated reflection parameters can be substituted into the function expression of the first diffraction field distribution. In this way, the algorithm complexity of optimizing the first mask structure parameters can be simplified.

[0066] In a specific embodiment, step 301: constructing a first diffraction field distribution associated with a first mask structural parameter to be optimized based on a propagation path of incident light after encountering a mask, may include the following steps 3011 to 3013:

[0067] Step 3011: Construct a second diffraction field distribution associated with the first mask structure parameters.

[0068] The second diffraction field distribution is the distribution of the diffraction field generated in the multilayer reflective film after the incident light is diffracted by the absorption layer. Figure 2 In the example shown, the second diffraction field distribution is the diffraction near-field distribution of the incident light formed below the absorption layer 201 after the incident light passes through the slits 2011 of the absorption layer 201 .

[0069] Step 3012: Using the optimized reflection parameters of the multilayer reflective film, construct a reflection field distribution generated after the second diffraction field distribution is reflected by the multilayer reflective film.

[0070] Step 3013: Construct a first diffraction field distribution associated with the first mask structure parameters.

[0071] The first diffraction field distribution is the distribution of the diffraction field generated outside the mask near the imaging system after the reflection field is diffracted by the absorption layer. Figure 2In the example shown, the first diffraction field distribution is: after the reflected light of the multilayer reflective film passes through the gap 2011 of the absorption layer 201 from bottom to top, a diffraction near-field distribution of the reflected light is formed above the absorption layer 201 .

[0072] Through steps 3011 to 3013, a first diffraction field distribution can be constructed based on the propagation path of light. This construction method is simple in calculation and can more accurately represent the diffraction near-field distribution of the absorption layer on the side close to the imaging system.

[0073] Furthermore, step 3012: using the optimized reflection parameters of the multilayer reflective film to construct a reflection field distribution generated after the second diffraction field distribution is reflected by the multilayer reflective film, may include the following steps 3121 to 3123:

[0074] Step 3121: Convert the second diffraction field distribution into the frequency domain using a first algorithm.

[0075] Each component of the second diffraction field distribution in the frequency domain represents the incident light corresponding to the incident angle. Exemplarily, the first algorithm may be a Fourier transform, or other algorithms capable of converting to the frequency domain.

[0076] Step 3122: Obtain the reflection parameters of the multilayer reflective film for each component corresponding to the incident angle.

[0077] Step 3123: Multiply each component of the second diffraction field distribution in the frequency domain by the reflection parameter corresponding to the incident angle, and perform the inverse operation of the first algorithm on the obtained result to obtain the reflection field distribution.

[0078] For example, the first algorithm may be a Fourier transform, and the inverse operation of the first algorithm may be an inverse Fourier transform.

[0079] By converting the second diffraction field distribution to the frequency domain, we can obtain the various components of the second diffraction field distribution in the frequency domain. In the frequency domain, each component of the second diffraction field distribution represents a plane wave at a certain incident angle. Therefore, by multiplying each component of the second diffraction field distribution in the frequency domain with the reflection parameter corresponding to the incident angle, we can obtain the frequency domain component after the light incident at the corresponding incident angle is reflected, which simplifies the process of calculating the reflection field distribution.

[0080] In a specific embodiment, step 3011: constructing a second diffraction field distribution associated with the first mask structure parameters may include the following steps 3111 and 3112:

[0081] Step 3111: Establish a first equation satisfied by the second diffraction field distribution.

[0082] In one embodiment, the first equation can be established based on the Helmholtz equation. In the first equation, the parameters include incident light and a wave number associated with the first mask structure parameter. Specifically, the source term of the Helmholtz equation is the incident light.

[0083] Step 3112: Solve the second diffraction field distribution in the first equation. When solving, the Born's series method can be used to obtain a function expression of the second diffraction field distribution represented by the Born's series.

[0084] By establishing the first equation, an expression for the second diffraction field distribution related to the first mask structure parameters can be solved. The Born series method can be used to quickly calculate the DNF.

[0085] Similar to step 3011, step 3013: constructing a first diffraction field distribution associated with the first mask structure parameter may include the following steps 3131 and 3132:

[0086] Step 3131: Establish a second equation satisfied by the first diffraction field distribution.

[0087] In one embodiment, the second equation can be established based on the Helmholtz equation. In the second equation, the parameters include the reflected field distribution and the wave number associated with the first mask structure parameter. Specifically, the source term of the Helmholtz equation is the reflected field distribution. The reflected field distribution is obtained by calculating the field distribution after the second diffraction field distribution is reflected by the multilayer reflective film.

[0088] Step 3132: Solve the first diffraction field distribution in the second equation. When solving, the Born series method can be used to obtain a function expression of the first diffraction field distribution represented by the Born series.

[0089] In some embodiments, the imaging quality parameters are further associated with third mask structure parameters, which include a critical dimension of the mask. Furthermore, during step 303, based on the association between the intensity distribution and the imaging quality parameters, the first and third mask structure parameters can be optimized with the goal of achieving a first preset condition for the imaging quality parameters, thereby obtaining optimized first and third mask structure parameters.

[0090] Therefore, step 303 can simultaneously optimize at least two mask structural parameters, namely, the absorption layer thickness and the critical dimension of the mask.

[0091] In some embodiments, the mask structure parameter optimization method provided in the embodiments of the present application is applied to a lithography system based on BEUV lithography technology. BEUV lithography technology is similar to EUV lithography technology. In this embodiment, BEUV lithography technology employs a reflective optical design and mask structure. The reflective mask structure includes a reflective multilayer film, which is a very important optical structure in BEUV lithography technology.

[0092] To optimize the structure of reflective multilayer films, the thickness of each layer is selected as one of the parameters to be optimized (the second mask structure parameter). The transfer matrix method (TMM) can be used to calculate the reflectivity of one-dimensional optical multilayer films. TMM is based on Maxwell's equations and is characterized by its efficiency and simplicity.

[0093] In addition to the film thickness of the reflective multilayer film, other structural parameters of the reflective multilayer film are also required when using TMM to solve the reflectivity, such as the film material, repetition period, etc. In a specific example, the basic structure of the reflective multilayer film includes the following determined structural parameters: a lanthanum La / boron B binary film (i.e., a composite film of two materials) is used as the basic structural design of the BEUV reflective multilayer film, and the repetition period (i.e., the number of times the structure of the two composite films is repeatedly superimposed) is 200, such as Figure 4 Among them, for the materials of BEUV reflective multilayer films, previous studies have proposed many possible material combination schemes, but there is currently no consensus on the material selection of BEUV reflective multilayer films, and the relevant technologies rarely provide accurate specific structural parameters of reflective multilayer films.

[0094] Then, based on Figure 4 The infrastructure shown is built using TMM Figure 4 The expression of the reflectivity of the reflective multilayer film is obtained. Then, the film thickness is taken as the parameter to be optimized and optimized by the PSO algorithm. The optimization goal is to maximize the reflectivity of the reflective multilayer film. After the optimization calculation of the PSO algorithm, an example result is obtained: when the thickness d of the La film layer is La The thickness of the B film layer is 1.29nm. B When the wavelength is 2.09 nm, the reflective multilayer film can maximize the reflectivity near the normal incident angle.

[0095] In the above example, after the thickness of each layer of the reflective multilayer film is obtained, the structure of the reflective multilayer film is determined, and the reflection coefficient of the reflective multilayer film to incident light at different incident angles can be calculated accordingly. Figure 5 Shown in Figure 4 After optimizing the film thickness of the reflective multilayer film under the basic structure of the BEUV reflective multilayer film shown, the reflectivity curve of the optimized reflective multilayer film structure for incident light at different incident angles.

[0096] After optimizing the film thickness of the reflective multilayer film based on the reflectivity, the film thickness and critical dimensions of the absorption layer are optimized.

[0097] based on Figure 2 The mask structure shown in FIG. 1 is a block diagram of a method for quickly calculating DNF.

[0098] First, calculate the diffraction field distribution of the incident light after it passes through the absorption layer, that is, the second diffraction field distribution. Here, the Born series method can be used to solve the second diffraction field distribution. The specific solution process is as follows:

[0099] According to the lighting settings of the BEUV system, the plane wave of the incident light angle is set as the source term S(r). The wave number k(r) is related to the thickness of the absorption layer. Solving the diffraction field distribution E(r) of the incident light is reduced to the time-harmonic diffraction problem of electromagnetic waves. The first equation that satisfies the Helmholtz equation is established:

[0100]

[0101] In formula 1, E(r), k(r) and S(r) represent the D The diffraction field electric field, wave number and source term at the position, D is the dimension of space.

[0102] Introducing Green's function, Formula 1 can be further rewritten as:

[0103] E(r)=∫g0(rr′)[V(r′)E(r′)+S(r′)]dr′ Formula 2

[0104] In formula 2, V(r)≡k(r) 2 -k0 2 -iε, ε is a real constant.

[0105] The convolution operation with g0 in Formula 2 can be replaced by the operator G, V is used to represent a diagonal matrix containing V(r′), and E and S are used to vectorize E(r) and S(r) respectively. Formula 2 can be further simplified to matrix form:

[0106] E=GVE+GS Formula 3

[0107] Formula 3 can be used to obtain the electric field solution of the incident light diffraction field distribution using the fixed point iteration method:

[0108]

[0109] Formula 4 is the solution to the diffraction field distribution of the incident light using the Born series method. However, the Born series is only applicable to weak diffraction structures and small simulation areas. For strong diffraction structures such as masks, it may not converge. In this embodiment, a correction algorithm can be used to introduce a pre-condition so that the Born series can converge for any diffraction structure, resulting in an expression for the diffraction field:

[0110]

[0111] The preconditions include: γ = (i / ε)V, and M≡γGV-γ+P, where P is the identity matrix.

[0112] After obtaining the expression of the diffraction field distribution of the incident light, the diffraction field distribution E(r) at the spatial position r below the absorption layer can be extracted to obtain the second diffraction field distribution.

[0113] Perform Fourier transform on the second diffraction field distribution below the absorption layer and convert it into frequency domain. Each component of the diffraction field in the frequency domain represents a plane wave at a certain incident angle. Figure 5 The reflection field distribution after reflection by the reflective multilayer film is obtained by multiplying the reflection coefficient of the reflective multilayer film at the corresponding incident angle and performing inverse Fourier transform.

[0114] Next, similar to the calculation of the diffraction field distribution of the incident light, the diffraction field distribution of the reflected field distribution is calculated. Specifically, the above-mentioned reflected field distribution is set as the source term of Formula 1. The Born series method is again used to calculate the diffraction field distribution generated by the reflected field distribution after passing through the absorption layer structure. The electric field distribution spatially located above the absorption layer structure is extracted to obtain the DNF under this mask structure, that is, the first diffraction field distribution.

[0115] The first diffraction field distribution is the input of the lithography projection system. Based on the Abbe model, the intensity distribution I of the first diffraction field distribution imaged by the lithography projection system is calculated:

[0116]

[0117] In formula 6, Indicates that at the light source point (x s ,y s ) The DNF generated by the scalar matrix Defined as the mask diffraction matrix, Corresponding to the point spread function under the light source, J(x s ,y s ) represents the intensity of the corresponding light source point, J sum is the normalization factor.

[0118] Imaging quality parameters used: Normalized Logarithmic Slope (NILS) and Aerial Image Peak Intensity. NILS is a commonly used metric for evaluating lithography imaging quality, used to evaluate the intensity slope at the edge of the aerial image.

[0119]

[0120] In Formula 7, I is the aerial image, x represents the position, and k is the critical dimension CD of the target pattern.

[0121] By maximizing NILS and the peak intensity of the aerial image as the optimization goal, the edge slope and overall intensity of the aerial image can be optimized simultaneously.

[0122] The optimization algorithm uses the PSO algorithm. The loss function loss is:

[0123]

[0124] In formula 8, w1 and w2 are two weight coefficients.

[0125] In one example, w1 is set to -1, w2 is set to -40, and PSO is used to optimize the thickness and CD parameters of the mask absorption layer (the absorption film material is tantalum Ta) structure.

[0126] Since the design of the BEUV lithography optical system has the Chief Ray Angle of Object side (CRAO), for the one-dimensional line pattern of the integrated circuit pattern on the mask, the diffraction near field of the mask has diffraction in two directions, that is, the one-dimensional line pattern is perpendicular or parallel to the CRAO, such as Figure 6 a1 in FIG shows a situation where the direction of the one-dimensional line pattern 601 is perpendicular to the CRAO direction (ie, the incident light), as shown in FIG. Figure 6 b1 in FIG. 6 shows the case where the direction of the one-dimensional line pattern 602 is parallel to the CRAO direction. Figure 6 A1 is a dotted line cross section, and we get Figure 6 In a2, CRAO is inclined at 6 degrees (the same as the EUV lithography projection system). Figure 6 b2 shows Figure 6 Schematic diagram of b1 on the cross section of the dotted line.

[0127] Under central light source illumination, the mask design layout was set to a 100-nanometer period, the initial thickness of the mask's absorption layer was set to 50 nanometers, and the critical dimension was set to a 40-nanometer line width. Using PSO optimization, the optimized mask absorption layer structure had a thickness of 66 nanometers and a line width of 62 nanometers.

[0128] The optimization results are as follows Figure 7As shown, the simulation results of imaging at different positions under the initial settings are represented by solid lines, and the simulation results of imaging at different positions after optimization are represented by dotted lines. Figure 7 Figures a3 and a4 represent the amplitude comparison of the diffraction near field and the intensity comparison of the aerial image before and after optimization when the one-dimensional line pattern is perpendicular to the incident light CRAO. Calculations show that the intensity loss of the image is reduced from -58 to -66, indicating that the image quality has been improved. Figure 7 Figures b3 and b4 show the amplitude comparison of the diffraction near field and the intensity comparison of the aerial image before and after optimization when the one-dimensional line pattern is parallel to the CRAO incident light. Calculations show that the intensity loss of the image is reduced from -58 to -70, indicating that the image quality has been improved.

[0129] Under diode illumination with partial coherence factors of ±0.8 at both light sources, the mask design pattern period was set to 50 nanometers and the line width to 20 nanometers, further exploring the imaging potential of the BEUV lithography system by reducing the size. The initial thickness of the mask's absorber layer structure was set to 50 nanometers, and the critical dimension line width was set to 20 nanometers. Simulation results for imaging the mask structure with the initial settings and optimized parameters are plotted using dashed lines. The optimized absorber layer thickness, after optimization, was 63 nanometers, and the critical dimension line width was 31 nanometers.

[0130] Figure 8 Figure c1 shows a schematic diagram of the mask cross-section when the one-dimensional line is perpendicular to the CRAO. In this case, the principal optical axis is inclined at a 6-degree angle to the line cross-section, and two plane waves generated by two light sources are incident on the mask plane. Figure 8 Figure d1 shows a schematic diagram of the mask cross section when the one-dimensional line is parallel to CRAO.

[0131] Figure 9 Figures c2 and c3 show the amplitude comparison of the diffraction near-field generated by the first and second light sources in the dipole illumination, respectively, before and after optimization, when a one-dimensional line is perpendicular to the CRAO. It can be seen that the second light source has a more severe shadowing effect, which seriously affects the diffraction near-field of the mask. Figure 9 Figure c4 shows the comparison of spatial imaging intensity before and after optimization. Although the imaging loss before and after optimization is -24, it can be seen from c4 that the spatial image intensity is improved after optimization.

[0132] Figure 9 Figures d2 and d3 show the comparison of the diffraction near-field amplitudes generated by the first and second light source points before and after optimization, respectively, when the one-dimensional line is parallel to the CRAO. Figure 9 The D4 in the figure shows the comparison of spatial imaging intensity before and after optimization. The imaging loss is reduced from -34 to -42, and the imaging quality is significantly improved.

[0133] Through this embodiment, the PSO algorithm is combined with the TMM algorithm to find the optimal film structure of the reflective multilayer film, thereby optimizing the reflectivity of the reflective multilayer film. With a given mask structure and the optimized parameters of the reflective multilayer film, based on the optical propagation path of "downward diffraction → multilayer film reflection → upward diffraction", a diffraction near-field based on the Born series is established, and the diffraction near-field of the mask is quickly simulated and calculated. Then, based on the BEUV imaging model, the optimization target of the imaging quality parameters is configured, and the thickness of the absorption layer of the mask structure is optimized, which can improve the imaging quality of the BEUV lithography system. This embodiment uses the PSO optimization algorithm to reversely optimize the film thickness of the reflective multilayer film and the absorption layer of the mask structure based on the optimization target of the imaging quality parameters, which can improve the lithography imaging quality and the energy utilization efficiency of the lithography system.

[0134] The embodiments of the present application do not limit the order in which the processes are executed. In actual applications, processes that do not have a dependency relationship can be executed in parallel or in any order. If the output of one process serves as the input of another process, the two processes are considered to have a dependency relationship.

[0135] The embodiment of the present application also provides a mask structure parameter optimization device, which is based on the same application concept as the mask structure parameter optimization method provided in the embodiment of the present application, and has the same beneficial effects as the method adopted, operated or implemented by it.

[0136] Please refer to the following Figure 10 , an embodiment of the present application also provides a mask structure parameter optimization device including a first construction module 1001 , a second construction module 1002 and an optimization module 1003 .

[0137] The first construction module 1001 is used to construct a first diffraction field distribution associated with a first mask structural parameter to be optimized based on a propagation path of incident light after encountering the mask. The first diffraction field distribution is a distribution of the diffraction field on a side of the mask close to the imaging system. The mask includes an absorption layer, and the first mask structural parameter includes a film thickness of the absorption layer.

[0138] The second construction module 1002 is used to construct the intensity distribution of the first diffraction field distribution after imaging by the imaging system according to the imaging model of the imaging system;

[0139] The optimization module 1003 is used to optimize the first mask structure parameters according to the correlation information between the intensity distribution and the imaging quality parameters, with the imaging quality parameters meeting the first preset condition as the optimization goal, to obtain the optimized first mask structure parameters.

[0140] Please refer to the following Figure 11, which shows a schematic diagram of an electronic device provided by some embodiments of the present application. Figure 11 As shown, the electronic device 7 includes: a processor 700, a memory 701, a bus 707 and a communication interface 703, and the processor 700, the communication interface 703 and the memory 701 are connected via the bus 707; the memory 701 stores a computer program that can be run on the processor 700, and when the processor 700 runs the computer program, it executes the mask structure parameter optimization method provided in any of the aforementioned embodiments of the present application.

[0141] Memory 701 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. Communication between the system network element and at least one other network element is achieved through at least one communication interface 703 (which may be wired or wireless), and may use the Internet, a wide area network, a local area network, a metropolitan area network, etc.

[0142] The bus 707 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 701 is used to store programs, and the processor 700 executes the programs after receiving execution instructions. The method for optimizing mask structure parameters disclosed in any of the aforementioned embodiments of the present application may be applied to the processor 700 or implemented by the processor 700.

[0143] The processor 700 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor 700 or by software instructions. The processor 700 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of this application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory 701 , and the processor 700 reads the information in the memory 701 and completes the steps of the above method in combination with its hardware.

[0144] The electronic device provided in the embodiment of the present application and the method for optimizing the mask structure parameters provided in the embodiment of the present application are based on the same application concept and have the same beneficial effects as the methods adopted, operated or implemented therein.

[0145] An embodiment of the present application also provides a computer-readable storage medium corresponding to the method for optimizing the mask structure parameters provided in the aforementioned embodiment. Optionally, the computer-readable storage medium may be a CD, and a computer program (i.e., a program product) is stored on the computer-readable storage medium. When the computer program is run by a processor, it will execute the method for optimizing the mask structure parameters provided in any of the aforementioned embodiments.

[0146] In addition, examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0147] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the method for optimizing the mask structure parameters provided in the embodiments of the present application are based on the same application concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0148] It should be noted that the algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other device. Various general-purpose devices may also be used in conjunction with the teachings herein. Based on the above description, the structure required to construct such devices is apparent. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using a variety of programming languages, and the above description of specific languages ​​is intended to disclose the best mode of implementation of this application.

[0149] Similarly, it should be understood that in order to streamline the present application and aid understanding of one or more of the various application aspects, in the above description of the exemplary embodiments of the present application, various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the claims below, aspects of the application lie in less than all the features of the individual embodiments disclosed above. Accordingly, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the present application.

[0150] The various component embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in the creation device of the virtual machine according to the embodiment of the present application. The application can also be implemented as a device or device program for executing part or all of the methods described herein. The program implementing the application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0151] The above description is merely a preferred embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for optimizing mask structure parameters, characterized in that: The method comprises: Based on the propagation path of the incident light after encountering the mask, a first diffraction field distribution associated with a first mask structural parameter to be optimized is constructed; the first diffraction field distribution is the distribution of the diffraction field on a side of the mask near the imaging system; the mask includes an absorption layer, and the first mask structural parameter includes a film thickness of the absorption layer; constructing, according to an imaging model of the imaging system, an intensity distribution of the first diffraction field distribution imaged by the imaging system; According to the correlation information between the intensity distribution and the imaging quality parameter, the first mask structure parameter is optimized with the imaging quality parameter meeting a first preset condition as an optimization goal to obtain the optimized first mask structure parameter.

2. The method for optimizing mask structure parameters according to claim 1, wherein: The mask further includes a multilayer reflective film; and before constructing a first diffraction field distribution associated with the mask structure parameters to be optimized based on the propagation path of the incident light after encountering the mask, the method further includes: Based on the correlation information between the reflection parameters of the multilayer reflective film and the second mask structure parameters, the second mask structure parameters are optimized with the reflection parameters of the multilayer reflective film reaching a second preset condition as the optimization goal to obtain the optimized second mask structure parameters; the second mask structure parameters include the film thickness of the multilayer reflective film.

3. The method for optimizing mask structure parameters according to claim 2, wherein: Before constructing a first diffraction field distribution associated with a first mask structural parameter to be optimized based on a propagation path of the incident light after encountering the mask, the method further includes: According to the optimized second mask structure parameters, the optimized reflection parameters of the multilayer reflective film are calculated.

4. The method for optimizing mask structure parameters according to claim 3, wherein: The step of constructing a first diffraction field distribution associated with a first mask structural parameter to be optimized based on a propagation path of the incident light after encountering the mask includes: Constructing a second diffraction field distribution associated with the first mask structure parameter; the second diffraction field distribution is a distribution of a diffraction field generated in the multilayer reflective film after the incident light is diffracted by the absorption layer; Using the optimized reflection parameters of the multilayer reflective film, constructing a reflection field distribution generated after the second diffraction field distribution is reflected by the multilayer reflective film; Constructing the first diffraction field distribution associated with the first mask structure parameters; the first diffraction field distribution is the distribution of the diffraction field generated outside the mask close to the imaging system side after the reflection field distribution is diffracted by the absorption layer.

5. The method for optimizing mask structure parameters according to claim 4, wherein: The step of constructing a reflection field distribution generated by the second diffraction field distribution being reflected by the multilayer reflection film using the optimized reflection parameters of the multilayer reflection film comprises: converting the second diffraction field distribution into a frequency domain using a first algorithm; each component of the second diffraction field distribution in the frequency domain represents incident light at a corresponding incident angle; Obtaining reflection parameters of the multilayer reflective film for each component corresponding to the incident angle; Each component of the second diffraction field distribution in the frequency domain is multiplied by a reflection parameter corresponding to the incident angle, and an inverse operation of the first algorithm is performed on the obtained result to obtain the reflection field distribution.

6. The method for optimizing mask structure parameters according to claim 4, wherein: The constructing a second diffraction field distribution associated with the first mask structure parameter comprises: Establishing a first equation satisfied by the second diffraction field distribution; parameters in the first equation include: the incident light, and a wave number associated with the first mask structure parameter; Solve the first equation for the second diffraction field distribution.

7. The method for optimizing mask structure parameters according to claim 4, wherein: The constructing the first diffraction field distribution associated with the first mask structure parameter comprises: Establishing a second equation satisfied by the first diffraction field distribution; parameters in the second equation include: the reflection field distribution, and a wave number associated with the first mask structure parameter; The second equation is solved for the first diffraction field distribution.

8. The method for optimizing mask structure parameters according to claim 1, wherein: The imaging quality parameter is associated with a third mask structure parameter; the third mask structure parameter includes a critical dimension of the mask; and the first mask structure parameter is optimized based on the association information between the intensity distribution and the imaging quality parameter, with the imaging quality parameter meeting a first preset condition as an optimization goal, to obtain the optimized first mask structure parameter, including: According to the correlation information between the intensity distribution and the imaging quality parameters, with the imaging quality parameters reaching the first preset condition as the optimization goal, the first mask structure parameters and the third mask structure parameters are optimized to obtain optimized first mask structure parameters and third mask structure parameters.

9. A mask structure parameter optimization device, characterized in that: The device comprises: A first construction module is configured to construct a first diffraction field distribution associated with a first mask structural parameter to be optimized based on a propagation path of incident light after encountering the mask; the first diffraction field distribution is a distribution of the diffraction field on a side of the mask that is close to the imaging system; the mask includes an absorption layer, and the first mask structural parameter includes a film thickness of the absorption layer; A second construction module is configured to construct, according to an imaging model of the imaging system, an intensity distribution of the first diffraction field distribution imaged by the imaging system; The optimization module is used to optimize the first mask structure parameters according to the correlation information between the intensity distribution and the imaging quality parameters, with the imaging quality parameters meeting the first preset condition as the optimization goal, to obtain the optimized first mask structure parameters.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

Images