Simulation analysis method for influence of surface roughness of metal film layer on lithography imaging quality

Abstract

The application provides a simulation analysis method and device for the influence of the surface roughness of a metal film layer on the lithography imaging quality, and belongs to the field of micro-nano manufacturing and lithography technology. The simulation analysis method comprises the following steps: constructing a surface roughness model of a metal film layer based on preset roughness modeling parameters through a random spectrum function and a two-dimensional Fourier series superposition method; constructing a multi-layer lithography system model based on the surface roughness model of the metal film layer and a lithography geometric structure model; in the case that a simulation light source controls the simulation light in the form of a uniform plane wave to be vertically irradiated to the metal film layer of the multi-layer lithography system model, performing strict electromagnetic field simulation on the multi-layer lithography system model by using a finite element analysis method to obtain electric field distribution characteristics; obtaining lithography imaging performance parameters by quantitatively analyzing the electric field distribution characteristics; and determining the influence of the surface roughness of the metal film layer on the lithography imaging quality based on the corresponding relationship between the roughness modeling parameters and the lithography imaging performance parameters.

Description

Technical Field

[0001] This application relates to the fields of micro-nano manufacturing and photolithography, and more specifically, to a simulation analysis method and apparatus for the influence of surface roughness of metal films on photolithographic imaging quality. Background Technology

[0002] In plasma lithography systems, the surface roughness of the metal film layer has a significant impact on the near-field light field distribution and the final lithographic pattern quality.

[0003] Current research mainly analyzes the influence of metal film surface roughness on photolithography imaging based on experimental phenomena, but there is still a lack of an analytical method that can comprehensively consider the relationship between metal film surface roughness morphology parameters and imaging quality indicators. Summary of the Invention

[0004] In view of this, this application provides a simulation analysis method and apparatus for the influence of surface roughness of metal film on photolithography imaging quality.

[0005] One aspect of this application provides a simulation analysis method for the influence of metal film surface roughness on photolithography imaging quality, comprising: constructing a metal film surface roughness model based on preset roughness modeling parameters using a random spectral function and a two-dimensional Fourier series superposition method; constructing a multilayer photolithography system model based on the aforementioned metal film surface roughness model and photolithography geometric structure model; performing rigorous electromagnetic field simulation on the multilayer photolithography system model using the finite element analysis method under the condition that the simulation light source controls the uniform plane wave form of simulation light to be perpendicularly irradiated onto the metal film of the aforementioned multilayer photolithography system model, thereby obtaining electric field distribution characteristics; obtaining photolithography imaging performance parameters by quantitatively analyzing the aforementioned electric field distribution characteristics; and determining the influence of metal film surface roughness on photolithography imaging quality based on the correspondence between the aforementioned roughness modeling parameters and the aforementioned photolithography imaging performance parameters.

[0006] According to an embodiment of this application, the above-described method of constructing a surface roughness model of a metal film based on preset roughness modeling parameters using a random spectral function and a two-dimensional Fourier series superposition method includes: obtaining a preset spectral index; generating a random amplitude matrix that follows a standard normal distribution and a random phase matrix that follows a uniform distribution; obtaining a spectral amplitude matrix based on the spectral index and the random amplitude matrix; superimposing the spectral amplitude matrix and the random phase matrix using a two-dimensional Fourier series superposition method to obtain an initial roughness surface function; and obtaining a surface roughness model of the metal film based on the initial roughness surface function.

[0007] According to an embodiment of this application, obtaining a metal film surface roughness model based on the initial rough surface function includes: obtaining a preset root mean square roughness; performing numerical calculations based on the root mean square roughness and the spectral amplitude matrix to obtain a scaling factor; scaling the initial rough surface function based on the scaling factor to obtain a metal film surface height function; and constructing a metal film surface roughness model based on the metal film surface height function.

[0008] According to an embodiment of this application, the above-mentioned construction of a multilayer lithography system model based on the above-mentioned metal film surface roughness model and lithography geometry model includes: replacing the surface roughness parameters of each of the multiple interface layers included in the above-mentioned lithography geometry model with the surface roughness parameters of the above-mentioned metal film surface roughness model to obtain a multilayer lithography system model.

[0009] According to an embodiment of this application, the above-mentioned photolithography imaging performance parameters include imaging contrast. The above-mentioned method of obtaining photolithography imaging performance parameters by quantitatively analyzing the above-mentioned electric field distribution characteristics includes: determining the light intensity distribution characteristics of the photolithography region of the above-mentioned multilayer photolithography system model based on the above-mentioned electric field distribution characteristics; determining the maximum and minimum light intensity values ​​based on the light intensity distribution characteristics; and obtaining the above-mentioned imaging contrast by performing numerical calculations on the above-mentioned maximum and minimum light intensity values.

[0010] According to an embodiment of this application, the above-mentioned photolithography imaging performance parameters include key dimensions. The above-mentioned method of obtaining photolithography imaging performance parameters by quantitatively analyzing the above-mentioned electric field distribution characteristics includes: determining the light intensity distribution characteristics of the photolithography region of the above-mentioned multilayer photolithography system model based on the above-mentioned electric field distribution characteristics; extracting multiple edge position points of the above-mentioned multilayer photolithography system model based on the light intensity distribution characteristics and a preset light intensity threshold; and obtaining key dimensions based on the above-mentioned multiple edge position points.

[0011] According to an embodiment of this application, the above-mentioned photolithography imaging performance parameters include the normalized image logarithmic slope. The above-mentioned photolithography imaging performance parameters are obtained by quantifying and analyzing the above-mentioned electric field distribution characteristics, including: determining the light intensity distribution characteristics of the photolithography region of the above-mentioned multilayer photolithography system model based on the above-mentioned electric field distribution characteristics; determining the light intensity variation characteristics based on the light intensity distribution characteristics; and determining the above-mentioned normalized image logarithmic slope based on the above-mentioned light intensity distribution characteristics, the above-mentioned light intensity variation characteristics, and the above-mentioned key dimensions.

[0012] Another aspect of this application provides a simulation analysis apparatus for the influence of metal film surface roughness on photolithography imaging quality, comprising: a first model building module for constructing a metal film surface roughness model based on preset roughness modeling parameters using a random spectral function and a two-dimensional Fourier series superposition method; a second model building module for constructing a multilayer photolithography system model based on the aforementioned metal film surface roughness model and photolithography geometric structure model; a simulation module for performing rigorous electromagnetic field simulation on the aforementioned multilayer photolithography system model using finite element analysis method, under the condition that the simulation light source is controlled to perpendicularly irradiate the metal film of the aforementioned multilayer photolithography system model with uniform plane wave form simulation light, to obtain electric field distribution characteristics; a performance parameter determination module for obtaining photolithography imaging performance parameters by performing data processing and quantitative analysis on the aforementioned electric field distribution characteristics; and a result module for determining the influence of metal film surface roughness on photolithography imaging quality based on the correspondence between the aforementioned roughness modeling parameters and the aforementioned photolithography imaging performance parameters.

[0013] Another aspect of this application provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the methods described above.

[0014] Another aspect of this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the methods described above.

[0015] According to embodiments of this application, a surface roughness model of a metal film is constructed based on preset roughness modeling parameters using a random spectrum function and a two-dimensional Fourier series superposition method. This achieves a controllable description of the statistical characteristics of the surface morphology of the metal film, thereby establishing a roughness model that can better reflect the surface morphology characteristics during the actual thin film deposition process. Subsequently, the surface roughness model of the metal film is applied to the photolithography geometric structure model to obtain a multilayer photolithography system model. The finite element analysis method is used to perform rigorous electromagnetic field simulation on the multilayer photolithography system model to obtain electric field distribution characteristics that are closer to the actual situation. Furthermore, by quantitatively analyzing the electric field distribution characteristics, photolithography imaging performance parameters are obtained. Thus, based on the correspondence between the roughness modeling parameters and the photolithography imaging performance parameters, the influence of the surface roughness of the metal film on the photolithography imaging quality can be determined. This helps to evaluate the potential impact of the surface roughness of the metal film during the photolithography system design stage, thereby reducing experimental trial and error costs to a certain extent and providing technical reference for related micro-nano manufacturing technology research. Attached Figure Description

[0016] The above and other objects, features and advantages of this application will become clearer from the following description of embodiments of this application with reference to the accompanying drawings.

[0017] Figure 1 A flowchart illustrating the simulation analysis method for the influence of metal film surface roughness on photolithography imaging quality according to an embodiment of this application is shown.

[0018] Figure 2 A schematic diagram of a modeling method for a surface roughness model of a metal film layer according to an embodiment of this application is shown.

[0019] Figure 3 A schematic diagram showing the modeling results of a surface roughness model of a metal film layer according to an embodiment of this application is illustrated.

[0020] Figure 4 A schematic diagram of the structure of a multilayer lithography system model according to an embodiment of this application is shown.

[0021] Figure 5 A schematic diagram showing the effect of spectral index on lithographic imaging quality according to an embodiment of this application is illustrated.

[0022] Figure 6 A schematic diagram showing the effect of spatial frequency resolution on lithographic imaging quality according to an embodiment of this application is illustrated.

[0023] Figure 7 A schematic diagram showing the effect of root mean square roughness on lithographic imaging quality according to an embodiment of this application is illustrated.

[0024] Figure 8 A schematic diagram of a simulation analysis apparatus for the influence of surface roughness of a metal film layer on photolithography imaging quality according to an embodiment of this application is shown.

[0025] Figure 9 A block diagram of an electronic device according to an embodiment of this application is shown schematically. Detailed Implementation

[0026] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0027] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms "comprising," "including," etc., as used herein indicate the presence of the above-described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0028] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0029] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0030] Figure 1 A flowchart illustrating the simulation analysis method for the influence of metal film surface roughness on photolithography imaging quality according to an embodiment of this application is shown.

[0031] like Figure 1 As shown, the simulation analysis method for the influence of metal film surface roughness on photolithography imaging quality includes operations S110~S150.

[0032] In operation S110, a surface roughness model of the metal film is constructed based on preset roughness modeling parameters by using a random spectrum function and a two-dimensional Fourier series superposition method.

[0033] Constructing a surface roughness model for a metal film refers to the process of mathematically representing and statistically modeling the surface roughness structure of a metal film actually deposited in a photolithography system. Specifically, after a metal thin film (such as an aluminum film, silver film, or gold film) is formed through evaporation, sputtering, or other thin film deposition processes, its surface usually exhibits irregular undulations at the nanoscale. These undulations can be described by a surface height distribution function, and their statistical characteristics are characterized using parameters such as root mean square roughness, correlation length, and power spectral density.

[0034] The above modeling method can parameterize the statistical characteristics of rough surfaces. By establishing a surface roughness model of metal film that can reflect the micro-morphological characteristics of the metal film surface, the real film interface structure can be introduced in the subsequent plasma lithography electromagnetic propagation analysis, thereby providing basic data and model support for analyzing the influence of surface roughness of metal film on surface plasmon polaron propagation characteristics and light field distribution.

[0035] In operation S120, a multilayer lithography system model is constructed based on the surface roughness model of the metal film and the lithography geometry model.

[0036] In operation S130, under the condition that the simulation light source controls the simulation light in the form of uniform plane wave to be perpendicularly irradiated onto the metal film layer of the multilayer lithography system model, the finite element analysis method is used to perform a rigorous electromagnetic field simulation on the multilayer lithography system model to obtain the electric field distribution characteristics.

[0037] Rigorous electromagnetic field simulation refers to a technique that uses numerical calculation methods (such as the finite element method, finite-difference time-domain method, and method of moments) to perform high-precision, full-wave modeling and solution of the physical processes of electromagnetic field distribution, propagation, radiation, and coupling under a given structure and excitation, based on Maxwell's equations and their constitutive relations. It does not rely on equivalent circuits or approximate analytical formulas, but directly discretizes space and time, strictly satisfying boundary conditions and material properties, thereby accurately predicting electromagnetic behavior in complex geometries, non-homogeneous media, dispersive / anisotropic materials, and broadband scenarios. In the embodiments of this application, by solving Maxwell's equations, the propagation and scattering of electromagnetic waves in a metal-dielectric structure with a rough interface, as well as the excitation and coupling processes of surface plasmon polaritons, can be calculated, thereby obtaining the electric field distribution in the near-field region.

[0038] Based on the established multilayer lithography system model with surface roughness of the gold film layer, the incident conditions of the simulated light are set so that the beam is incident into the lithographic structure in a direction perpendicular to the metal film layer surface and propagates in the form of parallel light, thus simulating the incident light situation in actual plasma lithography or near-field lithography. Specifically, the wavelength, polarization state, and propagation direction of the incident light are set in the multilayer lithography system model, so that the incident light is perpendicularly irradiated into the interface between the mask structure and the metal film layer in the form of a uniform plane wave, exciting surface plasmon polaritons at the metal-dielectric interface and generating a near-field enhancement effect. Through this incident method, while maintaining the controllability of the simulation conditions, the propagation, scattering, and energy distribution changes of electromagnetic waves in the structure with metal film layer roughness can be analyzed, thereby obtaining the electric field distribution characteristics of the lithographic region. This step provides the basic simulation conditions for subsequent extraction of lithographic imaging distribution and analysis of the impact of metal film layer roughness on imaging quality.

[0039] By introducing a rough film interface with statistical characteristics into the multilayer lithography system model, and using the finite element method to perform rigorous electromagnetic field simulation calculations on the lithography structure containing the rough interface, the propagation and scattering of electromagnetic waves in the metal-dielectric interface and the excitation process of surface plasmon polaritons can be simulated more closely to the actual situation. This provides effective technical support for analyzing the impact of metal film roughness on lithography imaging quality.

[0040] By operating S140, the performance parameters of photolithography imaging are obtained through quantitative analysis of the electric field distribution characteristics.

[0041] In operation S150, based on the correspondence between roughness modeling parameters and lithography imaging performance parameters, the influence of metal film surface roughness on lithography imaging quality is determined.

[0042] After completing the electromagnetic field simulation calculations for the multilayer lithography system model, the electromagnetic field distribution at the center of the photoresist layer can be extracted, and the lithography imaging quality can be quantitatively analyzed to evaluate the impact of metal film surface roughness on plasma lithography imaging performance. Specifically, firstly, the electric field distribution data of the lithography region is extracted from the electromagnetic simulation results, and the corresponding light intensity distribution is calculated through electric field strength calculation. Then, the lithography imaging performance parameters are determined based on the light intensity distribution.

[0043] Through the embodiments of this application, a surface roughness model of a metal film is constructed based on preset roughness modeling parameters using a random spectral function and a two-dimensional Fourier series superposition method. This achieves a controllable description of the statistical characteristics of the surface morphology of the metal film, thereby establishing a roughness model that can better reflect the surface morphology characteristics during the actual thin film deposition process. Subsequently, the surface roughness model of the metal film is applied to the photolithography geometric structure model to obtain a multilayer photolithography system model. The finite element analysis method is used to perform rigorous electromagnetic field simulation on the multilayer photolithography system model to obtain electric field distribution characteristics that are closer to the actual situation. Furthermore, by quantitatively analyzing the electric field distribution characteristics, photolithography imaging performance parameters are obtained. Thus, based on the correspondence between the roughness modeling parameters and the photolithography imaging performance parameters, the influence of the surface roughness of the metal film on the photolithography imaging quality can be determined. This helps to evaluate the potential impact of the surface roughness of the metal film during the photolithography system design stage, thereby reducing experimental trial and error costs to a certain extent and providing technical reference for related micro-nano manufacturing technology research.

[0044] In one specific embodiment of this application, the surface roughness model of a metal film layer can be constructed based on preset roughness modeling parameters using the following method: obtaining a preset spectral index; generating a random amplitude matrix that follows a standard normal distribution and a random phase matrix that follows a uniform distribution; obtaining a spectral amplitude matrix based on the spectral index and the random amplitude matrix; using a two-dimensional Fourier series superposition method, superimposing the spectral amplitude matrix and the random phase matrix to obtain an initial rough surface function; obtaining a preset root mean square roughness; performing numerical calculations based on the root mean square roughness and the spectral amplitude matrix to obtain a scaling factor; scaling the initial rough surface function based on the scaling factor to obtain a metal film layer surface height function; and constructing a metal film layer surface roughness model based on the metal film layer surface height function.

[0045] In embodiments of this application, roughness modeling parameters may include the root mean square roughness (RMS) R. q Surface dimensions of the film layer Spectral index Spatial frequency resolution Number of discrete surface grids Surface root mean square roughness R q It can be defined as:

[0046] (1);

[0047] in, The surface height of the metal film is a function of its height. denoted as the surface area of ​​the metal film.

[0048] To construct a surface roughness model of the metal film, a two-dimensional surface coordinate model of the metal film needs to be established and normalized to form the spatial coordinate basis for subsequent spectral superposition calculations. The surface coordinates of the metal film are represented as follows:

[0049] (2);

[0050] After normalization, the coordinate range is [0, 1], which can be represented as:

[0051] (3).

[0052] Furthermore, in order to describe the random roughness characteristics of the metal film surface, a stochastic spectrum model is introduced.

[0053] First, generate random coefficients, where g(m,n) is a standard normally distributed random variable:

[0054] (4);

[0055] Then a random phase is generated, where φ(m,n) is a uniformly distributed random phase:

[0056] (5);

[0057] Next, we define a power spectrum function for rough surfaces to describe their spectral attenuation characteristics:

[0058] (6);

[0059] Where β is the spectral index.

[0060] Therefore, the spectral amplitude coefficient used to describe the contribution of different spatial frequency components to the surface morphology can be expressed as follows:

[0061] (7);

[0062] To construct a random rough surface, it is necessary to superimpose different spatial frequency components using a two-dimensional Fourier series. The initial rough surface function is expressed as:

[0063] (8);

[0064] At the same time, add the frequency term where m=0:

[0065] (9);

[0066] The initial rough surface function can be obtained by superimposing the above information.

[0067] Since the surface height distribution obtained after superimposing random spectra may not meet the preset roughness, amplitude scaling is required.

[0068] First, calculate the spectral energy, then calculate the scaling factor:

[0069] (10);

[0070] The final surface height function of the metal film is:

[0071] (11);

[0072] Through the above amplitude scaling operation, the root mean square roughness of the generated surface is ensured to meet the set value R. 𝑞 .

[0073] Figure 2 A schematic diagram of a modeling method for a surface roughness model of a metal film layer according to an embodiment of this application is shown.

[0074] like Figure 2 As shown, the modeling method for the surface roughness model of the metal film includes operations S210~S250.

[0075] In operation S210, set the roughness modeling parameters.

[0076] In operation S220, a two-dimensional surface coordinate model is established.

[0077] In operation S230, a random spectrum function is constructed.

[0078] In operation S240, perform two-dimensional Fourier superposition calculation.

[0079] In operation S250, a surface roughness model of the metal film layer that meets the target roughness is generated.

[0080] Figure 3 A schematic diagram showing the modeling results of a surface roughness model of a metal film layer according to an embodiment of this application is illustrated.

[0081] like Figure 3 As shown, X and Y are used to characterize the two-dimensional planar position of the metal film, and Z is used to characterize the height of the metal film corresponding to the planar point. Figure 3 In (a), the surface roughness model of the metal film has dimensions of 80 nm × 80 nm, a roughness of 2 nm, a spectral index of 0.6, and a spatial frequency resolution of 15. Figure 3 (b) Characterized roughness surface two-dimensional height contour plot and Figure 3 (c) The surface height statistical distribution histogram shows that the roughness of the metal film surface roughness model is consistent with the actual randomly generated rough surface.

[0082] By constructing a surface roughness modeling method for metal films based on the superposition of random spectral functions and two-dimensional Fourier series, a controllable description of the statistical characteristics of the surface morphology of metal films is achieved. By introducing parameters such as root mean square roughness, spectral exponent, and spatial frequency resolution, the spatial frequency characteristics and surface height distribution of the rough surface can be controlled, thereby establishing a metal film roughness model that can better reflect the surface morphology characteristics during actual thin film deposition, providing a reliable structural model foundation for subsequent electromagnetic field simulation analysis.

[0083] After constructing the surface roughness model of the metal film, it is necessary to apply the metal film surface roughness model to the existing photolithography geometry model to obtain a multilayer photolithography system model whose surface morphology is closer to reality. By simulating the multilayer photolithography system model, more accurate simulation results of photolithography imaging quality can be obtained. The method for constructing the multilayer photolithography system model is as follows: replace the surface roughness parameters of each of the multiple interface layers included in the photolithography geometry model with the surface roughness parameters of the metal film surface roughness model to obtain the multilayer photolithography system model.

[0084] Figure 4 A schematic diagram of the structure of a multilayer lithography system model according to an embodiment of this application is shown.

[0085] like Figure 4 As shown, the multilayer photolithography system model includes an Al reflector layer, a PR (photoresist) layer, a SiO2 (silicon dioxide) layer, an Al grating layer, a PMMA (polymethyl methacrylate) layer, and a glass layer. This multilayer photolithography system model is primarily designed for plasmon interferometry lithography structures, allowing for the setting of corresponding material optical parameters and incident light conditions. The surface roughness parameters of the metal film surface roughness model described earlier can be applied to the upper surface of the Al layer on the PR layer (e.g., ...). Figure 4The location indicated by ① (as shown), and the upper surface of the Al reflective layer at the bottom (as shown by ①). Figure 4 (as shown in the ② label position).

[0086] In the embodiments of this application, in order to quantitatively characterize the quality of photolithography imaging, photolithography imaging performance parameters can be introduced. These parameters are obtained by quantitatively analyzing the electric field distribution characteristics. Based on the correspondence between roughness modeling parameters and photolithography imaging performance parameters, the influence of metal film surface roughness on photolithography imaging quality is determined. Photolithography imaging performance parameters include, but are not limited to: imaging contrast, critical dimension (CD), and normalized image log slope (NILS).

[0087] The imaging contrast is determined by the following method: the light intensity distribution characteristics of the lithography region of the multilayer lithography system model are determined based on the electric field distribution characteristics; the maximum and minimum light intensity values ​​are determined based on the light intensity distribution characteristics; and the imaging contrast is obtained by performing numerical calculations on the maximum and minimum light intensity values.

[0088] The critical dimensions are determined by the following method: the light intensity distribution characteristics of the lithography region of the multilayer lithography system model are determined based on the electric field distribution characteristics; multiple edge position points of the multilayer lithography system model are extracted based on the light intensity distribution characteristics and the preset light intensity threshold; and the critical dimensions are obtained based on the multiple edge position points.

[0089] The normalized image logarithmic slope is determined by the following method: determining the light intensity distribution characteristics of the lithography region of the multilayer lithography system model based on the electric field distribution characteristics; determining the light intensity variation characteristics based on the light intensity distribution characteristics; and determining the normalized image logarithmic slope based on the light intensity distribution characteristics, light intensity variation characteristics, and bond size.

[0090] Figure 5 A schematic diagram showing the effect of spectral index on lithographic imaging quality according to an embodiment of this application is illustrated.

[0091] exist Figure 5 In this context, RMS is used to characterize the root mean square roughness, and beta is used to characterize the spectral index. For example... Figure 5 As shown in (a), the root mean square roughness is consistent, but the spectral indices are different, resulting in different light intensities in the simulation results. Figure 5 As shown in (b), the root mean square roughness is consistent but the spectral index is different, resulting in different NILS values ​​in the simulation results. Figure 5 (c) and Figure 5(d) By extracting NILS and contrast from the simulation results, it was found that the plasmonic interference lithography structure is less affected by the roughness of the metal film layer. Its overall NILS and contrast do not change significantly. NILS is stable at around 3.0 and contrast is stable at above 0.85.

[0092] Figure 6 A schematic diagram showing the effect of spatial frequency resolution on lithographic imaging quality according to an embodiment of this application is illustrated.

[0093] exist Figure 6 In this context, RMS is used to characterize the root mean square roughness, and N is used to represent the spatial frequency resolution of the roughness. For example... Figure 6 As shown in (a), with consistent root mean square roughness but different spatial frequency resolutions, the corresponding light intensity in the simulation results differs. Figure 6 As shown in (b), with consistent root mean square roughness but different spatial frequency resolutions, the corresponding NILS values ​​in the simulation results are different. Figure 6 It can be concluded that spatial frequency resolution has virtually no impact on image quality.

[0094] Figure 7 A schematic diagram showing the effect of root mean square roughness on lithographic imaging quality according to an embodiment of this application is illustrated.

[0095] exist Figure 7 With a spectral index of 0.6 and a spatial frequency resolution of 15, the influence of root mean square roughness of different sizes on the lithographic imaging quality was analyzed. Simulation results show that within a certain range of roughness parameters, the surface roughness of the metal film has a relatively limited impact on the imaging quality indicators, indicating that the lithographic structure has a certain robustness to small-scale roughness disturbances.

[0096] Figure 8 A schematic diagram of a simulation analysis apparatus for the influence of surface roughness of a metal film layer on photolithography imaging quality according to an embodiment of this application is shown.

[0097] like Figure 8 As shown, the simulation analysis device for the influence of metal film surface roughness on photolithography imaging quality includes a first model construction module 810, a second model construction module 820, a simulation module 830, a performance parameter determination module 840, and a result module 850.

[0098] The first model construction module 810 is used to construct a surface roughness model of a metal film layer based on preset roughness modeling parameters by using a random spectrum function and a two-dimensional Fourier series superposition method.

[0099] The second model construction module 820 constructs a multilayer lithography system model based on the surface roughness model of the metal film and the lithography geometry model.

[0100] The simulation module 830 is used to perform rigorous electromagnetic field simulation of the multilayer lithography system model using the finite element analysis method when the simulation light source is controlled to vertically irradiate the metal film layer of the multilayer lithography system model with uniform plane wave form simulation light, so as to obtain the electric field distribution characteristics.

[0101] The performance parameter determination module 840 is used to obtain the lithography imaging performance parameters by performing data processing and quantitative analysis on the electric field distribution characteristics.

[0102] The result module 850 is used to determine the influence of the surface roughness of the metal film on the photolithography imaging quality based on the correspondence between the roughness modeling parameters and the photolithography imaging performance parameters.

[0103] Figure 9 A block diagram of an electronic device according to an embodiment of this application is shown schematically.

[0104] like Figure 9 As shown, an electronic device 900 according to an embodiment of this application includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage portion 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this application.

[0105] RAM 903 stores various programs and data required for the operation of electronic device 900. Processor 901, ROM 902, and RAM 903 are interconnected via bus 904. Processor 901 executes various operations of the method flow according to embodiments of this application by executing programs in ROM 902 and / or RAM 903. It should be noted that the aforementioned programs may also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 may also execute various operations of the method flow according to embodiments of this application by executing programs stored in one or more of the aforementioned memories.

[0106] According to embodiments of this application, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to a bus 904. The electronic device 900 may also include one or more of the following components connected to the input / output (I / O) interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 910 as needed so that computer programs read from it can be installed into the storage section 908 as needed.

[0107] According to embodiments of this application, the method flow according to embodiments of this application can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 909, and / or installed from removable medium 911. When the computer program is executed by processor 901, it performs the functions defined in the system of embodiments of this application. According to embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0108] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0109] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0110] For example, according to embodiments of this application, a computer-readable storage medium may include the ROM 902 and / or RAM 903 described above and / or one or more memories other than ROM 902 and RAM 903.

[0111] Embodiments of this application also include a computer program product, which includes a computer program containing program code for executing the methods provided in the embodiments of this application. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the simulation analysis method for the influence of metal film surface roughness on photolithography imaging quality provided in the embodiments of this application.

[0112] When the computer program is executed by the processor 901, it performs the functions defined in the system / apparatus of this application embodiment. According to the embodiments of this application, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0113] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 909, and / or installed from a removable medium 911. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0114] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0115] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations are not explicitly described in this application. In particular, without departing from the spirit and teachings of this application, the features described in the various embodiments of this application can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of this application.

[0116] The embodiments of this application have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of this application. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Without departing from the scope of this application, those skilled in the art can make various substitutions and modifications, all of which should fall within the scope of this application.

Claims

1. A simulation analysis method for the influence of surface roughness of a metal film on photolithography imaging quality, comprising: A surface roughness model for a metal film is constructed based on preset roughness modeling parameters using a random spectral function and a two-dimensional Fourier series superposition method. Based on the surface roughness model of the metal film and the photolithography geometry model, a multilayer photolithography system model is constructed. By controlling the simulation light source to vertically irradiate the metal film layer of the multilayer lithography system model with uniform plane wave form simulation light, the finite element analysis method is used to perform rigorous electromagnetic field simulation on the multilayer lithography system model to obtain the electric field distribution characteristics. The lithographic imaging performance parameters are obtained by quantitatively analyzing the electric field distribution characteristics. Based on the correspondence between the roughness modeling parameters and the lithography imaging performance parameters, the influence of the surface roughness of the metal film on the lithography imaging quality is determined.

2. The simulation analysis method according to claim 1, characterized in that, The method of constructing a surface roughness model for a metal film based on preset roughness modeling parameters using a random spectral function and a two-dimensional Fourier series superposition method includes: Obtain the preset spectral index; Generate a random amplitude matrix that follows a standard normal distribution, and a random phase matrix that follows a uniform distribution; Based on the spectral index and the random amplitude matrix, the spectral amplitude matrix is ​​obtained; The initial rough surface function is obtained by superimposing the spectral amplitude matrix and the random phase matrix using the two-dimensional Fourier series superposition method. Based on the initial rough surface function, the surface roughness model of the metal film is obtained.

3. The simulation analysis method according to claim 2, characterized in that, The process of obtaining the surface roughness model of the metal film based on the initial roughness surface function includes: Obtain the preset root mean square roughness; Numerical calculations are performed based on the root mean square roughness and the spectral amplitude matrix to obtain the scaling factor; The initial rough surface function is scaled by the scaling factor to obtain the surface height function of the metal film layer. A surface roughness model for the metal film is constructed based on the surface height function of the metal film.

4. The simulation analysis method according to claim 1, characterized in that, The construction of a multilayer lithography system model based on the surface roughness model of the metal film and the photolithography geometry model includes: By replacing the surface roughness parameters of each of the multiple interface layers included in the photolithography geometry model with the surface roughness parameters of the metal film surface roughness model, a multilayer photolithography system model is obtained.

5. The simulation analysis method according to claim 1, characterized in that, The photolithography imaging performance parameters include imaging contrast. The process of obtaining these parameters through quantitative analysis of the electric field distribution characteristics includes: The light intensity distribution characteristics of the lithography region of the multilayer lithography system model are determined based on the electric field distribution characteristics. Based on the characteristics of light intensity distribution, determine the maximum and minimum light intensity values; The imaging contrast is obtained by performing numerical calculations on the maximum and minimum light intensity values.

6. The simulation analysis method according to claim 1, characterized in that, The photolithography imaging performance parameters include key dimensions. The photolithography imaging performance parameters are obtained by quantitatively analyzing the electric field distribution characteristics, including: The light intensity distribution characteristics of the lithography region of the multilayer lithography system model are determined based on the electric field distribution characteristics. Based on the light intensity distribution characteristics and the preset light intensity threshold, multiple edge position points of the multilayer lithography system model are extracted; Based on the multiple edge location points, the key dimensions are obtained.

7. The simulation analysis method according to claim 6, characterized in that, The photolithography imaging performance parameters include the normalized image logarithmic slope. The photolithography imaging performance parameters are obtained by quantifying and analyzing the electric field distribution characteristics, including: The light intensity distribution characteristics of the lithography region of the multilayer lithography system model are determined based on the electric field distribution characteristics. Determine the light intensity variation characteristics based on the light intensity distribution characteristics; Based on the light intensity distribution characteristics, the light intensity variation characteristics, and the key dimensions, the normalized image logarithmic slope is determined.

8. A simulation analysis device for the influence of surface roughness of a metal film on photolithography imaging quality, comprising: The first model building module is used to construct a surface roughness model of the metal film layer based on preset roughness modeling parameters by using a random spectrum function and a two-dimensional Fourier series superposition method. The second model construction module constructs a multilayer lithography system model based on the surface roughness model of the metal film and the lithography geometry model. The simulation module is used to perform rigorous electromagnetic field simulation of the multilayer lithography system model by using the finite element analysis method, under the condition that the simulation light source is used to vertically irradiate the metal film layer of the multilayer lithography system model with uniform plane wave form simulation light, and to obtain the electric field distribution characteristics. The performance parameter determination module is used to obtain the lithography imaging performance parameters by performing data processing and quantitative analysis on the electric field distribution characteristics. The results module is used to determine the influence of the surface roughness of the metal film on the lithographic imaging quality based on the correspondence between the roughness modeling parameters and the lithographic imaging performance parameters.

9. An electronic device, comprising: One or more processors; Memory, used to store one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 7.

10. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 7.

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