Analog simulation method and device for coating deposition, storage medium and electronic equipment

Abstract

The invention provides an analog simulation method and device for coating deposition, a storage medium and electronic equipment, and relates to the technical field of analog simulation. The method specifically comprises the following steps: importing a gridding model into a solver; at least a turbulence model, an energy model, a radiation model and a component model in the solver are set, and setting parameters of the component model comprise material attributes of molecules and active free radicals participating in the chemical reaction; the material attributes comprise characteristic lengths and energy parameters of molecules and active free radicals in the MTS reaction system, which are calculated by using a density functional theory of a first principle; obtaining an initial flow field result matrix in the solver by using an initialization method; and setting a result convergence standard according to the initial flow field result matrix, and carrying out iterative calculation on the initial flow field result matrix. Due to the fact that the material attributes in the component model are obtained through optimization calculation, the accuracy and the calculation rate of simulation calculation can be improved through the simulation method.

Description

Technical Field

[0001] The present application relates to the field of simulation technology, and particularly to a simulation method and device for coating deposition, a storage medium, and an electronic device. Background Art

[0002] Silicon carbide is an important inorganic non-metallic material. Parts coated with silicon carbide are commonly used in Metal-Organic Chemical Vapor Deposition (MOCVD) equipment. In the MOCVD reaction chamber, a substrate is supported by a part with a silicon carbide coating. Among them, the thermal stability, thermal uniformity, and other properties of the silicon carbide coating play a decisive role in the quality of the epitaxial material growth on the substrate. The thermal stability, thermal uniformity, and other properties of the silicon carbide coating are affected by the coating thickness uniformity. The higher the coating thickness uniformity, the better the quality of the epitaxial material growth on the substrate carried by the part. In order to improve the uniformity of the silicon carbide coating thickness, shorten the product R & D cycle, and improve the production yield, a simulation model is used to predict the deposition rate of different parts of the part during the deposition process, and then the structure and process parameters in the reaction chamber are adjusted through the simulation model, so that the part can obtain a more uniform coating thickness. However, the accuracy of the simulation results of the current simulation model is low, and the time consumed for simulation calculation is long, which cannot meet the requirements for high production yield of products with silicon carbide coatings. Summary of the Invention

[0003] In view of this, the present application provides a simulation method and device for coating deposition, a storage medium, and an electronic device, which solve the problem that the accuracy and calculation rate of the simulation calculation for coating deposition cannot meet the requirements.

[0004] In a first aspect, an embodiment of the present application provides a simulation method for coating deposition, including: importing a meshed model into a solver, where the meshed model includes a reaction furnace with a reaction chamber and a meshed model of a component to be simulated loaded into the reaction chamber; setting at least a turbulence model, an energy model, a radiation model, and a component model in the solver, where the set parameters of the component model include the material properties of molecules and active radicals participating in the chemical reaction, and the material properties include the characteristic lengths and energy parameters of the molecules and the active radicals in the MTS reaction system calculated using the density functional theory of the first principles; obtaining an initial flow field result matrix in the solver by using an initialization method; setting a result convergence criterion according to the initial flow field result matrix, and performing iterative calculation on the initial flow field result matrix to obtain a deposition rate cloud map of the component to be simulated in the reaction furnace, where the deposition rate cloud map is used to characterize the flow field result matrix obtained in the last iteration.

[0005] In combination with the first aspect of the present application, in some embodiments, before setting the turbulence model, energy model, radiation model, and component model in the solver, it further includes: creating two group models according to the geometric structures of the molecules or the active free radicals by using quantum chemistry software; selecting one of the group models as the reference group, and moving the other group model, recording the potential energy between the two group models at different distances, to obtain a coordinate system including multiple potential energy points; based on the potential energy equation: Performing fitting calculations on multiple potential energy points in the coordinate system to obtain the characteristic length and energy parameters of the molecules or the active free radicals; where V is the potential energy, r is the distance between the two group models, σ is the characteristic length, is the energy parameter, k is the Boltzmann constant, and ε is the proportionality factor of the potential energy.

[0006] In combination with the first aspect of the present application, in some embodiments, there is an initial distance between the two group models, and the range of the initial distance is The range of the distance moved each time when moving the other group model is

[0007] In combination with the first aspect of the present application, in some embodiments, before setting the turbulence model, energy model, radiation model, and component model in the solver, the simulation method of coating deposition further includes: simplifying the chemical reaction equation in the component model based on the relationship among the coating deposition thickness, temperature, and intake air velocity to obtain a simplified chemical reaction equation; removing the influence of the chemical reaction heat in the component model on the actual temperature field; and / or, the simulation method of coating deposition further includes: setting the working conditions in the solver, and the working conditions include setting the working air pressure between 8000 Pa and 20000 Pa, setting the gravitational acceleration to 9.81 m / s², and setting the working temperature between 900 °C and 1500 °C.

[0008] In combination with the first aspect of the present application, in some embodiments, before importing the meshed model into the solver, it further includes: dividing the three-dimensional model to obtain an initial meshed model, where the three-dimensional model includes the reaction furnace having the reaction chamber and the component to be simulated loaded into the reaction chamber; performing a quality check on the mesh cells divided in the initial meshed model, and if the quality requirements are not met, performing local mesh cell refinement or mesh reconstruction on the initial meshed model until the quality requirements are met, and then obtaining the meshed model; where the quality requirements include that the maximum skewness of the mesh cells is less than 0.7, the minimum orthogonal quality of the mesh cells is between 0.1 and 0.15, and the maximum aspect ratio of the mesh cells is between 50 and 100.

[0009] In combination with the first aspect of the present application, in some embodiments, the dividing the three-dimensional model to obtain an initial meshed model includes: performing a simplification process on the three-dimensional model to obtain a simplified three-dimensional model, where in the simplified three-dimensional model, the reaction furnace at least includes an air inlet, an air outlet, and a heat source, and making the deposition surface to be simulated of the component to be simulated parallel to the air inlet direction of the air inlet; dividing the simplified three-dimensional model using a hexahedral solid element type to obtain an initial meshed model, where the minimum size in the simplified three-dimensional model includes at least three mesh cells.

[0010] In combination with the first aspect of the present application, in some embodiments, the result convergence criterion includes that the residual of each equation in the solver is less than or equal to a preset value.

[0011] In combination with the first aspect of the present application, in some embodiments, the obtaining the initial flow field result matrix in the solver using an initialization method includes: setting the regional conditions and boundary conditions of the meshed model, where the regional conditions include setting the normal air flow region in the reaction furnace as a fluid region and the region in the reaction furnace where no air flow passes as a dead zone, and the boundary conditions include the flow rate and temperature of the air inlet, the type of the air outlet as a pressure outlet, the pressure value and temperature of the air outlet, and the temperature or heating power of the heat source; performing a mixed initialization on the data in the solver according to the regional conditions and the boundary conditions to obtain the initial flow field result matrix.

[0012] Second aspect, an embodiment of the present application provides a simulation device for coating deposition, including: an import module configured to import a meshed model into a solver, where the meshed model includes a reaction furnace having a reaction chamber and a meshed model of a component to be simulated loaded into the reaction chamber; a first setting module configured to set at least a turbulence model, an energy model, a radiation model, and a component model in the solver, where the set parameters of the component model include the material properties of molecules and active free radicals participating in chemical reactions, and the material properties include the characteristic lengths and energy parameters of the molecules and the active free radicals in the MTS reaction system calculated using the density functional theory of first principles; an acquisition module configured to obtain an initial flow field result matrix in the solver using an initialization method; a second setting module configured to set a result convergence criterion according to the initial flow field result matrix, perform iterative calculations on the initial flow field result matrix, and obtain a deposition rate contour map of the component to be simulated in the reaction furnace, where the deposition rate contour map is used to characterize the flow field result matrix obtained in the last iteration.

[0013] Third aspect, an embodiment of the present application provides a computer-readable storage medium storing instructions that, when executed by a processor of an electronic device, enable the electronic device to execute the method mentioned in the first aspect above.

[0014] Fourth aspect, an embodiment of the present application provides an electronic device, including: a processor; a memory for storing computer-executable instructions; the processor for executing the computer-executable instructions to implement the method mentioned in the first aspect above.

[0015] When setting the turbulence model, energy model, radiation model, and component model in the solver in the simulation method for coating deposition provided by the embodiments of the present application, the characteristic lengths and energy parameters of molecules and active free radicals in the MTS reaction system calculated using the density functional theory of first principles are used, and the material properties of the characteristic lengths and energy parameters of each molecule and active free radical obtained by calculation are added to the setting of the component model, so that the complexity of the model in subsequent iterative calculations is reduced and the calculation accuracy is improved. Therefore, using the simulation method provided by the embodiments of the present application can improve the model calculation speed and calculation accuracy, and further improve the coating thickness uniformity of components and the production yield. Description of the Drawings

[0016] The above and other objects, features, and advantages of the present application will become more apparent by describing the embodiments of the present application in more detail with reference to the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. They are used together with the embodiments of the present application to explain the present application and do not constitute a limitation to the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0017] Figure 1 The following is a schematic diagram of the application scenario of the simulation method for coating deposition provided by an embodiment of the present application.

[0018] Figure 2 The following is a schematic flow diagram of the simulation method for coating deposition provided by an embodiment of the present application.

[0019] Figure 3 The following is a schematic flow diagram of the simulation method for coating deposition provided by another embodiment of the present application.

[0020] Figure 4 The following is a schematic flow diagram of the simulation method for coating deposition provided by another embodiment of the present application.

[0021] Figure 5 The following is a schematic flow diagram of the simulation method for coating deposition provided by another embodiment of the present application.

[0022] Figure 6 The following is a schematic flow diagram of the simulation method for coating deposition provided by another embodiment of the present application.

[0023] Figure 7 The following is a schematic flow diagram of the simulation method for coating deposition provided by another embodiment of the present application.

[0024] Figure 8 The following is a schematic diagram of the deposition rate at different positions of the graphite base provided by an embodiment of the present application.

[0025] Figure 9 The following is a curve graph of the deposition rate at different positions of the graphite base provided by an embodiment of the present application.

[0026] Figure 10 The following is a schematic structural diagram of the simulation device for coating deposition provided by an embodiment of the present application.

[0027] Figure 11 The following is a schematic structural diagram of the electronic device provided by an embodiment of the present application. Detailed implementation manners

[0028] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts belong to the scope of protection of the present application.

[0029] Application Overview

[0030] Silicon carbide coatings are widely used on the surfaces of components due to their high wear resistance, high temperature resistance, and high stability. Generally, silicon carbide coatings are deposited on the surfaces of components through Chemical Vapor Deposition (CVD) equipment. Among them, the uniformity of the thickness of the silicon carbide coating will directly affect the flatness, heat conduction uniformity, and other properties of the components. Therefore, for some components with higher requirements in these aspects, the requirement for the thickness uniformity of the silicon carbide coating is higher, which requires a more uniform deposition rate when depositing on the components. However, in actual production and processing, it is often necessary to repeatedly debug the CVD reaction furnace and the components loaded in the reaction chamber of the CVD reaction furnace many times to make the components have a relatively uniform deposition rate during the deposition process. This method results in a low production yield of the components and a long R & D cycle.

[0031] With the gradual development of simulation technology, Computational Fluid Dynamics (CFD) technology is used to establish a CVD simulation model to predict the deposition rates of different parts of the components in the reaction chamber, and the structure and process parameters in the reaction furnace are adjusted through the simulation model to obtain a more uniform deposition rate of the components.

[0032] However, in the CVD simulation models established in existing research, there are more than 50 chemical molecules and 104 chemical reactions involved. The types are numerous and there are no physical property limitations on the relevant reaction molecules, resulting in slow simulation calculation speed, unstable calculation results, and low calculation accuracy of the existing CVD simulation models, thus unable to meet the requirements for the relatively high thickness uniformity of the silicon carbide coatings of components in actual production.

[0033] Based on the above technical problems, an embodiment of the present application provides a simulation method for coating deposition. A meshed model is imported into a solver. The meshed model includes a reaction furnace with a reaction chamber and a meshed model of a component to be simulated loaded into the reaction chamber. At least the turbulence model, energy model, radiation model, and component model in the solver are set. Among them, the set parameters of the component model include the material properties of molecules and active free radicals participating in the chemical reaction. The material properties include the characteristic lengths and energy parameters of molecules and active free radicals in the methyltrichlorosilane (MTS) reaction system calculated using the density functional theory of first principles. The initial flow field result matrix in the solver is obtained by using an initialization method. According to the initial flow field result matrix, a result convergence criterion is set, and the initial flow field result matrix is iteratively calculated to obtain a deposition rate cloud map of the component to be simulated in the reaction furnace. The deposition rate cloud map is used to characterize the flow field result matrix obtained in the last iteration.

[0034] It can be seen that when setting the turbulence model, energy model, radiation model, and component model in the solver in an embodiment of the present application, the characteristic lengths and energy parameters of molecules and active free radicals in the MTS reaction system calculated using the density functional theory of first principles are used, and the material properties of the characteristic lengths and energy parameters of each molecule and active free radical obtained by calculation are added to the setting of the component model, so that the complexity of the model in subsequent iterative calculations is reduced and the calculation accuracy is improved. Therefore, using the simulation method provided by the embodiment of the present application can improve the model calculation speed and calculation accuracy, and further improve the coating thickness uniformity of components and the production yield.

[0035] Exemplary scenario

[0036] Figure 1 The following shows a schematic diagram of an application scenario of the simulation of coating deposition provided by an embodiment of the present application. As Figure 1 shown, this application scenario includes a server 110 and a client 120 with a display screen, where the server 110 and the client 120 are communicatively connected.

[0037] The server 110 can be an independent physical server, a server cluster or a distributed system composed of multiple servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms.

[0038] The client 120 can be a device that provides data connectivity to a user (such as a target user), or a handheld device with wireless connection capabilities, or other processing devices connected to a wireless modem. For example, a desktop computer, or it can also be a portable, pocket-sized, handheld, computer-integrated, or in-vehicle mobile device that exchanges data with the radio access network. In addition, it can also be devices such as a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA), etc.

[0039] Exemplarily, in practical applications, the client 120 can, in response to a request from the target user for an analog simulation of a component to be simulated loaded in a CVD device, present to the target user, via a display screen, a CVD simulation model of the component to be simulated after being assembled in the CVD device, so that the target user can input relevant model data based on the CVD simulation model. After obtaining the relevant model data input by the target user, the client 120 sends it to the server 110, so that the server 110 can perform analog calculations on the CVD simulation model to obtain a deposition rate cloud map of the component to be simulated in the CVD device, thereby providing decision-making support for subsequent adjustments of relevant structural and process parameters such as the CVD device and the position where the component to be simulated is loaded into the CVD device.

[0040] Among them, the relevant model data includes a turbulence model, an energy model, a radiation model, and a component model in the solver. The set parameters of the component model include the material properties of the molecules and reactive free radicals participating in the chemical reaction. The material properties include the characteristic lengths and energy parameters of the molecules and reactive free radicals in the MTS reaction system calculated using the density functional theory of first principles.

[0041] After obtaining the relevant model data, the server 110 first uses an initialization method to obtain an initial flow field result matrix in the solver, then sets a result convergence criterion based on the initial flow field result matrix, and performs iterative calculations on the initial flow field result matrix to obtain a deposition rate cloud map of the component to be simulated in the CVD device. The deposition rate cloud map is used to characterize the flow field result matrix obtained in the last iteration. That is to say, the server 110 executes the analog simulation method for coating deposition mentioned in the embodiments of the present application. It can be understood that the analog simulation of coating deposition executed by the server 110 can be implemented based on code. That is to say, after compiling the code corresponding to the analog simulation method for coating deposition, the code is stored in the server 110.

[0042] As mentioned above Figure 1The application scenarios shown can be applied to many more specific application environments such as coating the surface of wafers, coating the surface of graphite bases, coating the surface of ceramics, etc., thereby providing strong decision-making support for the CVD simulation model. Below, taking the coating of the graphite base surface as an example, an elaboration will be carried out.

[0043] Taking the coating of the graphite base surface as an example, the component to be simulated is the graphite base, and the graphite base needs to be loaded into the reaction chamber of the CVD reactor to deposit a coating on the surface of the graphite base. The graphite base with a silicon carbide coating is used for supporting the substrate in the MOCVD equipment, so that an epitaxial layer grows on the surface of the substrate in the MOCVD equipment. The properties such as the thermal stability and thermal uniformity of the silicon carbide coating on the surface of the graphite base in contact with the substrate play a decisive role in the quality of the epitaxial layer growth. Therefore, the thickness uniformity of the silicon carbide coating deposited on the graphite base surface determines the quality of the epitaxial layer growth on the substrate surface.

[0044] Exemplary method

[0045] Figure 2 The figure shows a schematic flow diagram of a simulation method for coating deposition provided by an embodiment of the present application. As Figure 2 shown, the simulation method for coating deposition provided by the embodiment of the present application includes the following steps.

[0046] Step 210, import the meshed model into the solver. The meshed model includes a reactor with a reaction chamber and a meshed model of the component to be simulated loaded into the reaction chamber.

[0047] The component to be simulated can be a graphite base, the reactor can be a CVD reactor in the CVD equipment, and the meshed model can be drawn according to the actual dimensions of the reactor and the graphite base by 3D drawing and modeling software such as Pro / e, Solidworks, UG, and CAD. It can be understood that the 3D drawing and modeling software is installed in the computer. In other examples, the meshed model can also be obtained by other means, without specific limitation.

[0048] Meshing can be understood as dividing the geometric models of the drawn graphite base and reactor into a finite number of grids for dividing a complex solid model into a finite number of models that are interconnected and restricted. Among them, each grid is the smallest unit in the geometric model. The grid division tool can be selected from ICEM, Mesh, Gridgen, Gambit, HyperMesh, etc., without specific limitation. Similarly, the grid division tool is installed in the computer.

[0049] The solver, as the core part of the computational fluid dynamics software, is also installed in the computer. The computational fluid dynamics software can be CFX, Fluent, Phoenics, STAR-CCM+, Numeca, etc., and can be adaptively selected according to actual needs. In the embodiment of the present application, the simulation of coating deposition is to import the meshed model into the computational fluid dynamics software to complete the simulation, so as to simulate the flow field in the reaction chamber of the graphite base in the reaction furnace.

[0050] Step 220, at least set the turbulence model, energy model, radiation model and component model in the solver. Among them, the set parameters of the component model include the material properties of the molecules and active free radicals participating in the chemical reaction, and the material properties include the characteristic lengths and energy parameters of the molecules and active free radicals in the MTS reaction system calculated by using the density functional theory of the first principle.

[0051] In the solver of the computational fluid dynamics software, according to the situation of the imported meshed model, the turbulence model, energy model, radiation model and component model in the solver can be set. Among them, the material properties can be the physical properties possessed by all molecules and active free radicals in the gas participating in the chemical reaction.

[0052] Optionally, select the K-omega SST turbulence model with higher calculation accuracy for the near-wall boundary layer in the turbulence model setting. Select the discrete coordinate radiation model with a wider optical thickness coverage range and higher calculation accuracy in the radiation model setting.

[0053] It can be understood that the characteristic lengths and energy parameters of the molecules and active free radicals in the MTS reaction system can be calculated by using the density functional theory of the first principle through quantum chemistry software, data processing software, etc.

[0054] Step 230, use the initialization method to obtain the initial flow field result matrix in the solver.

[0055] The initialization method can be understood as that after inputting all relevant data into the solver, an initial calculation result, that is, the initial flow field result matrix, can be obtained.

[0056] Step 240, set the result convergence criterion according to the initial flow field result matrix, and perform iterative calculation on the initial flow field result matrix to obtain the deposition rate cloud map of the component to be simulated in the reaction furnace. The deposition rate cloud map is used to characterize the flow field result matrix obtained in the last iteration.

[0057] In the simulation method for coating deposition provided by the embodiments of the present application, when setting the turbulence model, energy model, radiation model, and component model in the solver, the characteristic lengths and energy parameters of molecules and active free radicals in the MTS reaction system calculated using the density functional theory of the first principle are utilized, and the material properties of the characteristic lengths and energy parameters of each molecule and active free radical obtained through calculation are added to the setting of the component model, so that the complexity during subsequent iterative calculations of the model is reduced and the calculation accuracy is improved. Therefore, by using the simulation method provided by the embodiments of the present application, the calculation speed and calculation accuracy of the model can be improved, thereby improving the coating thickness uniformity of components and the production yield.

[0058] The embodiments of the simulation method for coating deposition are described above. From the above content, it can be seen that the accuracy of setting the material properties of each molecule and active free radical in the component model has an important impact on the calculation speed and accuracy of the simulation. For this reason, the following will be combined with Figure 3 Examples are used to illustrate how to obtain more accurate data related to material properties.

[0059] Figure 3 The following shows a schematic flowchart of the simulation method for coating deposition provided by another embodiment of the present application. As Figure 3 shown, before at least setting the turbulence model, energy model, radiation model, and component model in the solver, the following steps are further included.

[0060] Step 310: Use quantum chemistry software to create two group models according to the geometric structure of the molecule or active free radical.

[0061] Step 320: Select one of the group models as the reference group, move the other group model, and record the potential energy between the two group models at different distances to obtain a coordinate system including multiple potential energy points.

[0062] Step 330: Based on the potential energy equation: Perform fitting calculations on multiple potential energy points in the coordinate system to obtain the characteristic length and energy parameters of the molecule or active free radical. Among them, V is the potential energy, r is the distance between the two group models, σ is the characteristic length, is the energy parameter, k is the Boltzmann constant, and ε is the proportionality factor of the potential energy.

[0063] The following takes the CH 3 free radical group as an example to illustrate the above steps in detail.

[0064] Based on the above step 310, in the quantum chemistry software, two CH 3 group models are created based on the geometric structure of the CH 3 free radical group.

[0065] In quantum chemistry software, the CCSD(T) / aug-cc-pVQZ basis set can be selected.

[0066] Based on the above step 320, select one of the CH 3 groups as the reference group, and move the other group to evaluate the interaction potential energy between them. By adjusting the spacing between the two groups, gradually change the distance between the two groups. When the distance between the two groups is less than , the repulsive force part between the groups dominates, which will cause the potential energy to increase sharply and approach infinity; when the distance between the two groups is within , the two groups interact most actively, and the attractive force between the groups comes into play, resulting in the repulsive force not dominating. Therefore, significant changes in potential energy during interaction can be captured within this distance range to improve the accuracy of potential energy calculation.

[0067] Therefore, the two initially selected CH 3 groups have an initial distance, and the range of the initial distance is set within Each time the other CH 3 group is moved, the distance range of movement is Calculate the interaction potential energy between the two CH 3 groups at different distances respectively, and record the corresponding potential energy values. Take the distance between the two CH 3 groups as the X-axis and the potential energy value as the Y-axis, and record the results in the coordinate system.

[0068] Based on the above step 330, input multiple potential energy points in the coordinate system into the data processing software, select non-linear curve fitting, and use the potential energy equation for fitting calculation. Through curve fitting, σ and ε can be approximately calculated: characteristic length = σ,

[0069] In the embodiments of the present application, by adjusting the distance between groups to obtain potential energies at different distances and using the potential energy equation for fitting calculation, the characteristic length and energy parameters of the corresponding molecules or active free radicals calculated are more accurate. Furthermore, the setting accuracy of the material properties in the component model is higher, and thus the calculation accuracy of the simulation model can be improved and the calculation complexity can be reduced.

[0070] Figure 4 The figure shows a schematic flow chart of a simulation method for coating deposition provided by another embodiment of the present application. As Figure 4 shown, before at least setting the turbulence model, energy model, radiation model, and component model in the solver, the following steps are further included.

[0071] Step 410: Based on the relationship among the coating deposition thickness, temperature, and intake air velocity, simplify the chemical reaction equations in the component model to obtain the simplified chemical reaction equations.

[0072] The component model includes a chemical reaction model, and the chemical reaction model includes multiple chemical reaction equations. The original chemical reaction model generally includes dozens or even hundreds of chemical reaction models for the reactions of dozens of chemical molecules and active free radicals, resulting in a large computational amount when calculating based on the chemical reaction model. Therefore, before setting the component model, first simplify the chemical equations in the chemical reaction model. The idea of the simplification process is as follows: When depositing in the temperature range of 850 °C to 1100 °C, as the temperature increases, the gas-phase reaction accelerates, and the deposition rate of silicon carbide also gradually increases. The deposition rate of silicon carbide first increases and then decreases along the intake air direction. This is because, on the one hand, MTS decomposes continuously upon heating, and the intermediate active components conducive to deposition in the gas phase accumulate continuously, and the surface reaction rate gradually increases; on the other hand, as the deposition progresses, more and more gas-phase product HCl that inhibits the deposition of silicon carbide is generated; at the same time, the deposition pedestal and the inner wall of the reaction furnace also consume a large amount of intermediate components, resulting in a decrease in the deposition rate along the gas flow direction. When depositing in the range of 1100 °C to 1400 °C, the deposition rate gradually decreases along the intake air direction. This is because MTS immediately reacts completely at high temperature to form intermediates, and the intermediates are gradually consumed during the flow process, resulting in a gradual decrease in the deposition rate along the flow direction. As the intake air velocity increases, the position of the maximum deposition rate gradually moves away from the intake position. This is because as the intake air velocity increases, the supply of MTS also increases, resulting in MTS not being completely converted into intermediate gas immediately, but being gradually completely converted during the flow process, resulting in an increase in the deposition rate in the flow direction. Therefore, based on the relationship changes among the coating deposition thickness, temperature, and intake air velocity during the actual processing process, some chemical equations in the original multiple chemical reaction equations in some component models that have little effect on the calculation accuracy can be removed, and the pre-exponential factor and temperature exponent are adjusted by comparing with the actual situation, so that the model is more in line with the actual situation, and thus the calculation accuracy of the simulation model can be improved. At the same time, the simplified chemical equations make the calculation simpler, reduce the complexity of the calculation, and improve the calculation speed. The following Table 1 shows the chemical reaction model in the component model.

[0073] Table 1 Chemical reaction model in the component model

[0074]

[0075] Step 420: Remove the influence of the chemical reaction heat in the component model on the actual temperature field.

[0076] In the embodiment of the present application, the chemical reaction equations in the chemical reaction model of the component model are simplified, and the relevant settings of the influence of the chemical reaction heat in the component model on the actual temperature field are removed, further improving the stability of the simulation results and the calculation speed.

[0077] It can be understood that before setting the turbulence model, energy model, radiation model, and component model in the solver, the simulation method for coating deposition further includes: setting the working conditions in the solver.

[0078] Optionally, the working conditions include setting the working pressure between 8000 Pa and 20000 Pa, setting the gravitational acceleration to 9.81 m / s², and setting the working temperature between 900 °C and 1500 °C.

[0079] In addition, it can be understood that the closer the mesh model imported into the solver is to the actual situation, the higher the accuracy of the simulation calculation. Therefore, the following will be combined with Figure 4 to illustrate how to obtain a mesh model closer to the actual situation.

[0080] Figure 5 The following shows a schematic flowchart of the simulation method for coating deposition provided by another embodiment of the present application. As Figure 5 shown, before importing the meshed model into the solver, the following steps are further included.

[0081] S510. Divide the three-dimensional model to obtain an initial meshed model, where the three-dimensional model includes a reaction furnace with a reaction chamber and a component to be simulated loaded into the reaction chamber.

[0082] When modeling the reaction furnace and the graphite base loaded into the reaction chamber, in order to improve the simulation accuracy, details such as small chamfers, rounded corners, relative position relationships in the reaction furnace and graphite base, and the specific positions of other components will be accurately reflected in the three-dimensional model to make the three-dimensional model closer to the actual situation.

[0083] As Figure 6 , S510 can specifically include the following steps.

[0084] S511. In the simplified three-dimensional model, the reaction furnace at least includes an air inlet, an air outlet, and a heat source, and the deposition surface to be simulated of the component to be simulated is parallel to the air inlet direction of the air inlet.

[0085] S512. Use the hexahedron solid element type to divide the simplified three-dimensional model to obtain an initial meshed model, where the minimum size in the simplified three-dimensional model includes at least three mesh elements.

[0086] Before dividing the three-dimensional model, it is necessary to simplify the three-dimensional model first, that is, to delete small chamfers, fillets and other features in the reactor and graphite base that do not affect the airflow movement and are irrelevant to the deposition surface of the graphite base, so as to avoid obtaining poor-quality meshes after meshing the overly complex three-dimensional geometric model.

[0087] S520. Check the quality of the mesh elements divided in the initial meshed model. If the quality requirements are not met, perform local mesh element refinement or mesh reconstruction on the initial meshed model until the quality requirements are met, and then obtain the meshed model.

[0088] Among them, the quality requirements include that the maximum skewness of the mesh element is less than 0.7, the minimum orthogonality quality of the mesh element is between 0.1 and 0.15, and the maximum aspect ratio of the mesh element is between 50 and 100. For a qualified mesh model, it can be directly imported into the solver; for an unqualified mesh model, further local mesh refinement or mesh reconstruction needs to be performed on the unqualified parts, so as to make the quality of the mesh model meet the requirements of simulation calculation.

[0089] Through the optimization of the mesh model in the embodiments of the present application, it is closer to the actual situation while reducing the complexity of the model, which is beneficial to improving the accuracy of simulation calculation.

[0090] It can be understood that when solving the solver, the closer the set relevant data is to the actual situation, the higher the accuracy of the simulation. Therefore, the following combines Figure 7 to illustrate how to further obtain more actual relevant data in the solver.

[0091] Figure 7 The figure shows a schematic flow chart of a simulation method for coating deposition provided by another embodiment of the present application. As Figure 7 shown, use the initialization method to obtain the initial flow field result matrix in the solver, which specifically includes the following steps.

[0092] S610. Set the regional conditions and boundary conditions of the meshed model.

[0093] Among them, the regional conditions include setting the normal airflow area in the reactor as the fluid area and the area where there is no airflow in the reactor as the dead area. The boundary conditions include the flow rate and temperature at the inlet, the type of the outlet as the pressure outlet, the pressure value and temperature at the outlet, and the temperature or heating power of the heat source.

[0094] S610. Mix and initialize the data in the solver according to the regional conditions and boundary conditions to obtain the initial flow field result matrix.

[0095] It can be understood that after obtaining the initial flow field result matrix by the above method, the number of iterations and convergence conditions are set in the solver. The convergence criterion for the residual of the energy equation is 10 -6 , and the convergence criterion for the residuals of other equations is 10 -3 , which is the result convergence criterion. During the solution process, when the residual of each equation is less than or equal to the preset value, or close to the preset value and no longer changes, it can be determined that the calculation has converged, and the solver stops calculating.

[0096] It can be understood that after the solver calculates the final flow field result matrix obtained through iterative calculation, the solver can represent the flow field result matrix in the form of an image on the deposition surface of the graphite base in the three-dimensional model, such as Figure 8 , the change from red to blue on the graphite base represents different deposition rates at different positions. The deposition rate decreases in turn from red to blue, that is, the heavier the red color, the higher the deposition rate.

[0097] In addition, the simulation-related data in the embodiments of the present application is compared with the scheme without simplifying the chemical reaction model and without setting the material properties of the energy parameter and characteristic length, that is, the scheme of the present application is compared with the scheme before optimization.

[0098] Among them, the comparison of the scheme of the present application and the comparative scheme in terms of data is shown in Table 2 below.

[0099] Table 2 Comparison of the scheme of the present application and the comparative scheme in terms of data

[0100]

[0101] Such as Figure 9 , by comparing the calculation results of the deposition rates at different positions on the graphite base of the present application, the calculation results of the deposition rates at different positions on the graphite base before optimization in the comparative scheme with the actual deposition rates at different positions on the graphite base, it can be found that the deposition rate curve of the present application is closer to the actual, indicating that the simulation accuracy of the scheme in the embodiments of the present application is higher.

[0102] As described above in conjunction with Figures 1 to 9 , the method embodiments of the present application have been described in detail. Below, in conjunction with Figure 10 , the device embodiments of the present application will be described in detail. It should be understood that the description of the method embodiments corresponds to the description of the device embodiments. Therefore, for the parts not described in detail, reference can be made to the previous method embodiments.

[0103] Exemplary device

[0104] Figure 10 The structural schematic diagram of a simulation device for coating deposition provided by an embodiment of the present application is shown as follows. Such as Figure 10, the simulation device 800 for coating deposition includes an import module 810, a first setting module 820, an acquisition module 830, and a second setting module 840.

[0105] Specifically, the import module 810 is configured to import a meshed model into a solver. The meshed model includes a reaction furnace having a reaction chamber and a meshed model of a component to be simulated loaded into the reaction chamber. The first setting module 820 is configured to set at least a turbulence model, an energy model, a radiation model, and a component model in the solver. Among them, the setting parameters of the component model include the material properties of molecules and active free radicals participating in chemical reactions. The material properties include the characteristic lengths and energy parameters of molecules and active free radicals in the MTS reaction system calculated using the density functional theory of first principles. The acquisition module 830 is configured to obtain an initial flow field result matrix in the solver using an initialization method. The second setting module 840 is configured to set a result convergence criterion according to the initial flow field result matrix, perform iterative calculations on the initial flow field result matrix, and obtain a deposition rate contour map of the component to be simulated in the reaction furnace. The deposition rate contour map is used to characterize the flow field result matrix obtained in the last iteration.

[0106] In some embodiments, the first setting module 820 is further configured to set the characteristic lengths and energy parameters of molecules and active free radicals with different material properties in the component model. Among them, the characteristic lengths and energy parameters of molecules and active free radicals are calculated, specifically including creating two group models according to the geometric structures of molecules or the active free radicals using quantum chemical software; selecting one of the group models as a reference group, moving the other group model, and recording the potential energy between the two group models at different distances to obtain a coordinate system including multiple potential energy points; based on the potential energy equation: Performing fitting calculations on multiple potential energy points in the coordinate system to obtain the characteristic lengths and energy parameters of molecules or active free radicals; where V is the potential energy, r is the distance between the two group models, σ is the characteristic length, is the energy parameter, k is the Boltzmann constant, and ε is the proportionality factor of the potential energy.

[0107] In some embodiments, there is an initial distance between the two group models, and the range of the initial distance is The range of the distance moved each time when moving the other group model is

[0108] In some embodiments, the first setting module 820 is further configured to simplify the chemical reaction equation in the component model based on the relationship between the coating deposition thickness, temperature, and intake air velocity to obtain a simplified chemical reaction equation; and remove the influence of the chemical reaction heat in the component model on the actual temperature field.

[0109] In some embodiments, the first setting module 820 is further configured to set the working conditions in the solver, where the working conditions include that the working air pressure is set between 8000 Pa and 20000 Pa, the gravitational acceleration is set to 9.81 m / s², and the working temperature is set between 900 °C and 1500 °C.

[0110] In some embodiments, the import module 810 is further configured to divide the three-dimensional model to obtain an initial meshed model, where the three-dimensional model includes a reaction furnace having a reaction chamber and a component to be simulated loaded into the reaction chamber; perform a quality check on the mesh cells divided in the initial meshed model. If the quality requirements are not met, perform local mesh cell refinement or mesh reconstruction on the initial meshed model until the quality requirements are met, and then obtain the meshed model; where the quality requirements include that the maximum skewness of the mesh cells is less than 0.7, the minimum orthogonality quality of the mesh cells is between 0.1 and 0.15, and the maximum aspect ratio of the mesh cells is between 50 and 100.

[0111] In some embodiments, the import module 810 is further configured to simplify the three-dimensional model to obtain a simplified three-dimensional model. In the simplified three-dimensional model, the reaction furnace at least includes an air inlet, an air outlet, and a heat source, and the deposition surface to be simulated of the component to be simulated is parallel to the air inlet direction of the air inlet; use the hexahedron solid element type to divide the simplified three-dimensional model to obtain an initial meshed model, where the minimum size in the simplified three-dimensional model includes at least three mesh cells.

[0112] In some embodiments, the second setting module 840 is further configured to set the result convergence criterion to that the residual of each equation in the solver is less than or equal to a preset value.

[0113] In some embodiments, the acquisition module 830 is further configured to set the regional conditions and boundary conditions of the meshed model. The regional conditions include that the normal air flow region in the reaction furnace is set as the fluid region, and the region in the reaction furnace where no air flow passes is set as the dead zone. The boundary conditions include the flow rate and temperature of the air inlet, the type of the air outlet is the pressure outlet, the pressure value and temperature of the air outlet, and the temperature or heating power of the heat source; perform mixed initialization on the data in the solver according to the regional conditions and boundary conditions to obtain the initial flow field result matrix.

[0114] Exemplary electronic device

[0115] Figure 11 The following shows a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 11As shown, the electronic device 900 includes: one or more processors 901 and a memory 902; and computer program instructions stored in the memory 902, which, when run by the processor 901, cause the processor 901 to execute the simulation method of coating deposition as described in any of the above embodiments.

[0116] The processor 901 can be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and can control other components in the electronic device to perform desired functions.

[0117] The memory 902 can include one or more computer program products, and the computer program products can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory can include, for example, random access memory (RAM) and / or cache memory, etc. Non-volatile memory can include, for example, read-only memory (ROM), hard disks, flash memory, etc. One or more computer program instructions can be stored on the computer-readable storage media, and the processor 901 can run the program instructions to implement the steps in the simulation method of coating deposition in various embodiments of the present application above and / or other desired functions.

[0118] In one example, the electronic device 900 can further include: an input device 903 and an output device 904, and these components are interconnected through a bus system and / or other forms of connection mechanisms ( Figure 11 not shown in the figure).

[0119] In addition, the input device 903 can further include, for example, a keyboard, a mouse, a microphone, etc.

[0120] The output device 904 can output various information to the outside. The output device 904 can include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0121] Of course, for simplicity, Figure 11 only some of the components related to the present application in the electronic device 900 are shown in the figure, and components such as buses, input device / output interfaces, etc. are omitted. In addition, according to specific application scenarios, the electronic device 900 can further include any other appropriate components.

[0122] Exemplary computer-readable storage medium

[0123] In addition to the above methods and devices, embodiments of the present application can also be computer program products, including computer program instructions, which, when run by a processor, cause the processor to execute the steps in the simulation method of coating deposition as described in any of the above embodiments.

[0124] A computer program product may be written in any combination of one or more programming languages for programming code to perform the operations of the embodiments of the present application. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The programming code may be executed entirely on the user computing device, partially on the user device, executed as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0125] In addition, an embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are run by a processor, the processor is caused to execute the steps in the simulation method of coating deposition according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0126] The computer-readable storage medium may employ any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, include but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0127] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present application. In addition, the above-disclosed specific details are only for the purposes of illustration and facilitation of understanding, and not for limitation. The above details do not limit the present application to necessarily adopt the above specific details for implementation.

[0128] The block diagrams of the devices, apparatuses, equipment, and systems involved in the present application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open-ended terms that mean "including but not limited to" and can be used interchangeably with each other. The word "or" and "and" used herein refer to the phrase "and / or" and can be used interchangeably with it, unless the context clearly indicates otherwise. The phrase "such as" used herein refers to the phrase "such as but not limited to" and can be used interchangeably with it.

[0129] It should also be noted that in the apparatuses, equipment, and methods of the present application, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present application.

[0130] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

[0131] The above description has been given for purposes of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions, and sub-combinations thereof.

[0132] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

[0133] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

Claims

1. A method for simulating coating deposition, characterized in that: include: Importing a meshed model into a solver, the meshed model including a reaction furnace having a reaction chamber and a meshed model of a component to be simulated loaded into the reaction chamber; At least a turbulence model, an energy model, a radiation model and a component model in the solver are set, wherein the setting parameters of the component model include material properties of molecules and active free radicals participating in the chemical reaction, and the material properties include characteristic lengths and energy parameters of the molecules and active free radicals in the MTS reaction system calculated using density functional theory based on first principles; Obtaining an initial flow field result matrix in the solver using an initialization method; The result convergence standard is set according to the initial flow field result matrix, and the initial flow field result matrix is ​​iteratively calculated to obtain a deposition rate cloud map of the component to be simulated in the reactor. The deposition rate cloud map is used to characterize the flow field result matrix obtained by the last iteration.

2. The coating deposition simulation method according to claim 1, characterized in that: Before setting at least the turbulence model, energy model, radiation model and component model in the solver, the method further includes: Using quantum chemistry software to create two radical models based on the geometric structure of the molecule or the active free radical; Selecting one of the group models as a reference group, and moving another of the group models, recording the potential energy between the two group models at different distances, and obtaining a coordinate system including a plurality of potential energy points; Based on the potential energy equation: Performing fitting calculations on a plurality of potential energy points in the coordinate system to obtain characteristic lengths and energy parameters of the molecule or the active free radical; Where V is the potential energy, r is the distance between the two group models, σ is the characteristic length, is the energy parameter, k is the Boltzmann constant, and ε is the scaling factor of the potential energy.

3. The coating deposition simulation method according to claim 2, characterized in that: There is an initial distance between the two group models, and the range of the initial distance is Each time another group model is moved, the moving distance range is 4. The coating deposition simulation method according to claim 1, characterized in that: Before setting at least the turbulence model, energy model, radiation model and component model in the solver, the coating deposition simulation method further includes: Based on the relationship between the coating deposition thickness, temperature and air intake velocity, the chemical reaction equation in the component model is simplified to obtain a simplified chemical reaction equation; removing the influence of chemical reaction heat on the actual temperature field in the component model; and / or, The coating deposition simulation method also includes: The working conditions in the solver are set, and the working conditions include setting the working gas pressure between 8000Pa-20000Pa, setting the gravity acceleration to 9.81m / s2, and setting the working temperature between 900℃-1500℃.

5. The coating deposition simulation method according to any one of claims 1 to 4, characterized in that: Before importing the meshed model into the solver as described, also include: Dividing the three-dimensional model to obtain an initial mesh model, wherein the three-dimensional model includes the reaction furnace having the reaction chamber and the component to be simulated loaded into the reaction chamber; Performing a quality check on the grid units divided in the initial grid model, and if the quality requirements are not met, performing local grid unit refinement or grid reconstruction processing on the initial grid model until the quality requirements are met, thereby obtaining the grid model; The quality requirements include that the maximum skewness of the grid unit is less than 0.7, the minimum orthogonal quality of the grid unit is between 0.1-0.15, and the maximum aspect ratio of the grid unit is between 50-100; Preferably, dividing the three-dimensional model to obtain an initial meshed model includes: Simplifying the three-dimensional model to obtain a simplified three-dimensional model, wherein in the simplified three-dimensional model, the reaction furnace at least includes an air inlet, an air outlet, and a heat source, and the surface to be deposited of the component to be simulated is parallel to the air inlet direction of the air inlet; The simplified three-dimensional model is divided using a hexahedral solid unit type to obtain an initial meshed model, wherein the minimum size in the simplified three-dimensional model includes at least three mesh units.

6. The coating deposition simulation method according to any one of claims 1 to 4, characterized in that: The result convergence criteria include the residual of each equation in the solver being less than or equal to a preset value.

7. The coating deposition simulation method according to claim 5, characterized in that: The method of using initialization to obtain the initial flow field result matrix in the solver includes: The regional conditions and boundary conditions of the grid model are set, wherein the regional conditions include setting the normal airflow area in the reactor as the fluid area, setting the area in the reactor where no airflow passes as the dead zone, and the boundary conditions include the flow rate and temperature of the air inlet, the type of the air outlet as a pressure outlet, the pressure value and temperature of the air outlet, and the temperature or heating power of the heat source; The data in the solver is mixedly initialized according to the regional conditions and the boundary conditions to obtain the initial flow field result matrix.

8. A simulation device for coating deposition, characterized in that: include: An import module configured to import a gridded model into a solver, wherein the gridded model includes a reaction furnace having a reaction chamber and a gridded model of a component to be simulated loaded into the reaction chamber; A first setting module is configured to set at least a turbulence model, an energy model, a radiation model and a component model in the solver, wherein the setting parameters of the component model include material properties of molecules and active free radicals participating in the chemical reaction, and the material properties include characteristic lengths and energy parameters of the molecules and active free radicals in the MTS reaction system calculated using density functional theory based on first principles; An acquisition module is configured to acquire an initial flow field result matrix in the solver using an initialization method; The second setting module is configured to set the result convergence standard according to the initial flow field result matrix, iteratively calculate the initial flow field result matrix, and obtain a deposition rate cloud map of the component to be simulated in the reactor, wherein the deposition rate cloud map is used to characterize the flow field result matrix obtained in the last iteration.

9. A computer-readable storage medium, characterized in that: The storage medium stores instructions, and when the instructions are executed by a processor of an electronic device, the electronic device is enabled to execute the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The electronic device comprises: processor; memory for storing computer executable instructions; The processor is used to execute the computer executable instructions to implement the method described in any one of claims 1 to 7.

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