Simulation method and device for coating deposition, storage medium and electronic equipment

Abstract

The application provides a coating deposition simulation method and device, a storage medium and an electronic device, and relates to the technical field of simulation. Specifically, the method comprises the following steps: importing a gridding model into a solver; at least setting 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 chemical reactions, and the material properties include 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; obtaining an initial flow field result matrix in the solver by using an initialization method; setting a result convergence standard according to the initial flow field result matrix, and performing iterative calculation on the initial flow field result matrix. Since the material properties in the component model are obtained by optimization calculation, the simulation method can improve the accuracy and calculation rate of simulation calculation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of simulation, in particular to a simulation method and device for coating deposition, a storage medium and an electronic device. BACKGROUND

[0002] Silicon carbide is an important inorganic non-metallic material, and parts covered with silicon carbide coating are often used in metal-organic chemical vapor deposition (MOCVD) equipment. The parts with silicon carbide coating are used to support the substrate in the MOCVD reaction chamber, and the thermal stability and thermal uniformity of the silicon carbide coating have a decisive effect on the quality of the epitaxial material growth of the substrate. The thermal stability and thermal uniformity of the silicon carbide coating are affected by the uniformity of the coating thickness, and the higher the uniformity of the coating thickness, the better the quality of the epitaxial material growth on the substrate supported by the parts. In order to improve the uniformity of the silicon carbide coating thickness, shorten the product development cycle and improve the production yield, a simulation model is used to predict the deposition rate of different parts of the parts in the deposition process, and then the structure and process parameters in the reaction chamber are adjusted through the simulation model, so that the parts obtain a coating with more uniform thickness. However, the simulation results of the current simulation model have low precision, and the simulation calculation takes a long time, which cannot meet the demand for high production yield of the products with silicon carbide coating. SUMMARY

[0003] Therefore, 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 simulation calculation precision and calculation rate of the coating deposition cannot meet the demand.

[0004] In a first aspect, an embodiment of the present application provides a simulation method for coating deposition, comprising: importing a mesh model into a solver, the mesh model comprising a meshed model of a reaction furnace having a reaction chamber and 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, wherein the setting parameters of the component model include material properties of molecules and active free radicals participating in chemical reactions, and the material properties include characteristic lengths and energy parameters of the molecules and the active free radicals in the MTS reaction system calculated by the density functional theory of the first principle; obtaining an initial flow field result matrix in the solver by using an initialization method; setting a result convergence standard according to the initial flow field result matrix, and iteratively calculating the initial flow field result matrix to obtain a deposition rate cloud picture of the component to be simulated in the reaction furnace, the deposition rate cloud picture being used to represent a flow field result matrix obtained by the last iteration.

[0005] In some embodiments, before the at least setting the turbulence model, the energy model, the radiation model and the component model in the solver, the method further comprises: creating two group models according to the geometry structure of the molecule or the active free radical by using quantum chemistry software; selecting one of the group models as a 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 a plurality of potential energy points; and based on the potential energy equation: fitting calculation is performed on the plurality of potential energy points in the coordinate system to obtain a characteristic length and an energy parameter of the molecule or the active free radical; wherein V is the potential energy, r is the distance between the two group models, is the characteristic length, is the energy parameter, k is the Boltzmann constant, is the proportion factor of the potential energy.

[0006] In some embodiments, the initial distance between the two group models is in the range of 2.5 Å-5 Å, and the distance moved each time the other group model is moved is in the range of 0.1 Å-0.2 Å.

[0007] In some embodiments, before the at least setting the turbulence model, the energy model, the radiation model and the component model in the solver, the method further comprises: based on the relationship among the coating deposition thickness, the temperature and the inlet gas velocity, simplifying the chemical reaction equation in the component model to obtain a simplified chemical reaction equation; removing the influence of chemical reaction heat in the component model on the actual temperature field; and / or the method further comprises: setting the working conditions in the solver, the working conditions including setting the working gas pressure in the range of 8000 Pa-20000 Pa, the gravitational acceleration to 9.81 m / s 2 , and the working temperature in the range of 900℃-1500℃.

[0008] In combination with the first aspect of the present application, in some embodiments, before the grid model is imported into the solver, the three-dimensional model is divided to obtain an initial grid model, wherein the three-dimensional model includes the reaction furnace with the reaction chamber and the component to be simulated loaded into the reaction chamber; the quality of the divided grid cells in the initial grid model is checked, and if the quality requirement is not met, the initial grid model is subjected to local grid cell refinement or grid reconstruction processing until the quality requirement is met, and the grid model is obtained; wherein the quality requirement includes that the maximum skewness of the grid cell is less than 0.7, the minimum orthogonal quality of the grid cell is between 0.1-0.15, and the maximum aspect ratio of the grid cell is between 50-100.

[0009] In combination with the first aspect of the present application, in some embodiments, the three-dimensional model is divided to obtain an initial grid model, including: 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 deposition surface of the component to be simulated is parallel to the air inlet direction; the simplified three-dimensional model is divided by using a hexahedral solid cell type to obtain an initial grid model, wherein the minimum size in the simplified three-dimensional model includes at least three grid cells.

[0010] In combination with the first aspect of the present application, in some embodiments, the result convergence standard 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 method for obtaining the initial flow field result matrix in the solver includes: setting the region condition and the boundary condition of the grid model, wherein the region condition includes setting the normal airflow region in the reaction furnace as a fluid region and setting the region in the reaction furnace where no airflow passes through as a dead zone, and the boundary condition includes the flow rate and temperature of the air inlet, the type of the air outlet being a pressure outlet, the pressure value and temperature of the air outlet, and the temperature or heat power of the heat source; mixing the data in the solver according to the region condition and the boundary condition to obtain the initial flow field result matrix.

[0012] In a second aspect, an embodiment of the present application provides a simulation device for coating deposition, comprising: an import module configured to import a mesh model into a solver, the mesh model comprising a meshed model of a reaction furnace having a reaction chamber and 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, wherein a setting parameter of the component model comprises material properties of molecules and active radicals participating in chemical reactions, the material properties comprising characteristic length and energy parameters of the molecules and the active radicals in an MTS reaction system calculated by using a density functional theory of a first principle; an acquisition module configured to acquire an initial flow field result matrix in the solver by using an initialization method; and a second setting module configured to set a result convergence standard according to the initial flow field result matrix, and perform iterative calculation on the initial flow field result matrix to obtain a deposition rate cloud picture of the component to be simulated in the reaction furnace, the deposition rate cloud picture being used to represent a flow field result matrix obtained in the last iteration.

[0013] In a third aspect, an embodiment of the present application provides a computer readable storage medium, the storage medium storing instructions, when the instructions are executed by a processor of an electronic device, the electronic device is enabled to perform the method mentioned in the first aspect.

[0014] In a fourth aspect, an embodiment of the present application provides an electronic device, the electronic device comprising: a processor; a memory for storing computer executable instructions; and the processor is configured to execute the computer executable instructions to implement the method mentioned in the first aspect.

[0015] The simulation method for coating deposition provided by the embodiments of the present application can utilize the characteristic length and energy parameters of molecules and active radicals in an MTS reaction system calculated by using a density functional theory of a first principle when setting a turbulence model, an energy model, a radiation model and a component model in a solver, and add the material properties of the characteristic length and energy parameters of each molecule and active radical calculated to the setting of the component model, so that the complexity of the model in subsequent iterative calculation is reduced and the calculation accuracy is improved. Therefore, 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 the component and the production yield. BRIEF DESCRIPTION OF DRAWINGS

[0016] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description thereof taken in conjunction with the accompanying drawings, in which: The accompanying drawings provide a further understanding of the present application and constitute a part of this specification, illustrate embodiments of the present application and together with the description serve to explain the present application. In the drawings, like reference numerals refer to like elements or steps throughout.

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

[0018] Figure 2 Fig. 1 shows a schematic diagram of an application scenario of a simulation method for coating deposition provided by an embodiment of the present application.

[0019] Figure 3 Fig. 1 shows a schematic diagram of an application scenario of a simulation method for coating deposition provided by an embodiment of the present application.

[0020] Figure 4 Fig. 1 shows a schematic diagram of an application scenario of a simulation method for coating deposition provided by an embodiment of the present application.

[0021] Figure 5 Fig. 1 shows a schematic diagram of an application scenario of a simulation method for coating deposition provided by an embodiment of the present application.

[0022] Figure 6 Fig. 1 shows a schematic diagram of an application scenario of a simulation method for coating deposition provided by an embodiment of the present application.

[0023] Figure 7 Fig. 1 shows a schematic diagram of an application scenario of a simulation method for coating deposition provided by an embodiment of the present application.

[0024] Figure 8 Fig. 1 shows a schematic diagram of an application scenario of a simulation method for coating deposition provided by an embodiment of the present application.

[0025] Figure 9 Fig. 1 shows a schematic diagram of an application scenario of a simulation method for coating deposition provided by an embodiment of the present application.

[0026] Figure 10 Fig. 1 shows a schematic diagram of an application scenario of a simulation method for coating deposition provided by an embodiment of the present application.

[0027] Figure 11 Fig. 1 shows a schematic diagram of an application scenario of a simulation method for coating deposition provided by an embodiment of the present application. DETAILED DESCRIPTION

[0028] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0029] SUMMARY

[0030] Silicon carbide coating is widely used in the surface of parts due to its high wear resistance, high temperature resistance and high stability. Silicon carbide coating is generally deposited on the surface of parts by a chemical vapor deposition (CVD) device. The uniformity of the thickness of the silicon carbide coating directly affects the flatness, thermal conductivity uniformity and other properties of the parts. Therefore, for some parts with high requirements in these aspects, the uniformity of the thickness of the silicon carbide coating is higher, which requires a more uniform deposition rate during deposition of the parts. However, in actual production and processing, the CVD reaction furnace and the parts loaded in the reaction chamber of the CVD reaction furnace need to be repeatedly adjusted several times to make the parts have a relatively uniform deposition rate during deposition. This method makes the production yield of the parts low and the research and development cycle long.

[0031] With the gradual development of simulation technology, a CVD simulation model is established by using computational fluid dynamics (CFD) technology to predict the deposition rate of different parts of the parts 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 parts.

[0032] However, in the CVD simulation model established by the existing research, more than 50 kinds of chemical molecules and 104 kinds of chemical reactions are involved, which are various and have no physical property limitation for related reaction molecules, resulting in slow simulation calculation speed, unstable calculation result and low calculation accuracy of the existing CVD simulation model, which cannot meet the high thickness uniformity requirement of the silicon carbide coating of the parts in actual production.

[0033] To solve the above technical problems, the embodiment of the present application provides a simulation method for coating deposition, a grid model is introduced into a solver, the grid model includes a reaction furnace with a reaction chamber and a grid 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 set parameters of the component model include material properties of molecules and active radicals participating in chemical reactions, and the material properties include characteristic lengths and energy parameters of the molecules and active radicals in a methyltrichlorosilane (MTS) reaction system calculated by a density functional theory of a first principle; an initial flow field result matrix in the solver is obtained by an initialization method; a 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 picture of the component to be simulated in the reaction furnace, and the deposition rate cloud picture is used to represent a flow field result matrix obtained by the last iteration.

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

[0035] Example scenarios

[0036] Figure 1 As shown in the figure, the application scenario includes a server 110 and a user terminal 120 with a display screen, wherein the server 110 and the user terminal 120 are in communication connection. Figure 1

[0037] The server 110 can be a stand-alone physical server, a server cluster or a distributed system composed of multiple servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content distribution networks (CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0038] ​The user terminal 120 can be a device providing data connectivity to a user, such as a target user, or a handheld device with wireless connection capability, or other processing device connected to a wireless modem. It can also be a portable, pocket, hand-held, computer- built-in or car-mounted mobile device which exchanges data with the wireless access network. In addition, it can also be a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA), and the like.

[0039] Exemplarily, in actual application, the user terminal 120 can present the CVD simulation model of the to-be-simulated component after being assembled in the CVD device to the target user by using the display screen in response to a request of the target user for simulation of the to-be-simulated component in the CVD device, so that the target user inputs relevant model data based on the CVD simulation model. After obtaining the relevant model data input by the target user, the user terminal 120 sends the relevant model data to the server 110, so that the server 110 performs simulation calculation on the CVD simulation model to obtain a deposition rate cloud atlas of the to-be-simulated component in the CVD device, thereby providing decision support for subsequent adjustment of the CVD device and the position of the to-be-simulated component loaded into the CVD device and other related structures and process parameters.

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

[0041] After obtaining the relevant model data, the server 110 first obtains an initial flow field result matrix in the solver by using an initialization method, and then sets a result convergence standard according to the initial flow field result matrix to perform iterative calculation on the initial flow field result matrix to obtain a deposition rate cloud atlas of the to-be-simulated component in the CVD device, and the deposition rate cloud atlas is used to represent a flow field result matrix obtained by the last iteration. That is, the server 110 performs the simulation and emulation method of the coating deposition mentioned in the application. It can be understood that the simulation and emulation of the coating deposition performed by the server 110 can be based on code implementation. That is, after the code corresponding to the simulation and emulation method of the coating deposition is compiled, the code is stored in the server 110.

[0042] The above-mentioned Figure 1The application scenario shown can be applied to wafer surface coating, graphite base surface coating, ceramic surface coating and other more specific application environments, thereby providing strong decision support for the CVD simulation model. The following will be described by taking the graphite base surface coating as an example.

[0043] Taking the graphite base surface coating as an example, the component to be simulated is a graphite base, which needs to be loaded into the reaction chamber of the CVD reaction furnace to deposit a coating on the surface of the graphite base. The graphite base with a silicon carbide coating is used to support the substrate in the MOCVD equipment, so that the surface of the substrate grows an epitaxial layer in the MOCVD equipment. 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 surface of the graphite base determines the quality of the epitaxial layer growth on the surface of the substrate.

[0044] Example methods

[0045] Figure 2 The flowchart shown is a simulation method for coating deposition provided by an embodiment of the present application. As shown in the flowchart, the simulation method for coating deposition provided by the embodiment of the present application includes the following steps. Figure 2 The simulation method for coating deposition provided by the embodiment of the present application includes the following steps.

[0046] In step 210, the meshed model is imported into the solver, and the meshed model includes a meshed model of the reaction furnace with the reaction chamber and the component to be simulated loaded into the reaction chamber.

[0047] The component to be simulated can be a graphite base, the reaction furnace can be a CVD reaction furnace in a CVD equipment, and the meshed model can be obtained by Pro / e, Solidworks, UG, CAD and other three-dimensional drawing modeling software according to the actual size of the reaction furnace and the graphite base. It can be understood that the three-dimensional drawing modeling software is installed in the computer. In other examples, the meshed model can also be obtained by other means, which is not limited.

[0048] Meshing can be understood as dividing the geometric model of the graphite base and the reaction furnace drawn into a finite number of meshes, so as to divide the complex solid model into a finite number of models that exist in mutual contact and mutual restriction. Each mesh is the smallest unit in the geometric model. The meshing tool can be selected from ICEM, Mesh, Gridgen, Gambit, HyperMesh and the like, which is not limited. Similarly, the meshing tool is installed in the computer.

[0049] The solver is a core part of the computational fluid dynamics software, and is also installed in a computer. The computational fluid dynamics software can be selected from 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 the coating deposition is realized by importing the gridded model into the computational fluid dynamics software to simulate the flow field of the graphite susceptor in the reaction chamber of the reaction furnace.

[0050] In step 220, at least the turbulence model, the energy model, the radiation model and the component model in the solver are set, wherein the set parameters of the component model include the material properties of the molecules and active radicals participating in the chemical reaction, and the material properties include the characteristic length and energy parameters of the molecules and active radicals in the MTS reaction system calculated by the density functional theory of the first principle.

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

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

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

[0054] In step 230, the initial flow field result matrix in the solver is obtained by using an initialization method.

[0055] The initialization method can be understood as being able to obtain an initial calculation result, i.e., the initial flow field result matrix, after all related data are input into the solver.

[0056] In step 240, 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 picture of the component to be simulated in the reaction furnace. The deposition rate cloud picture is used to represent the flow field result matrix obtained by the last iteration.

[0057] The simulation method for coating deposition provided in the embodiments of the present application can utilize the characteristic length and energy parameter of the molecules and active radicals in the MTS reaction system calculated by the first-principle density functional theory when setting the turbulence model, energy model, radiation model and component model in the solver, and add the material properties of the characteristic length and energy parameter of each molecule and active radical calculated to the setting of the component model, so that the complexity of the model in subsequent iterative calculation is reduced and the calculation accuracy is improved. Therefore, the simulation method provided in the embodiments of the present application can improve the model calculation speed and calculation accuracy, and further improve the coating thickness uniformity of the parts and the production yield.

[0058] The embodiments of the simulation method for coating deposition are described above, and it can be known from the above that the accuracy of the setting of the material properties of each molecule and active radical in the component model has an important influence on the calculation speed and accuracy of the simulation. Therefore, the following will be combined with the embodiments of the present application to illustrate how to obtain the material property related data with higher accuracy. Figure 3 The embodiments of the simulation method for coating deposition are described above, and it can be known from the above that the accuracy of the setting of the material properties of each molecule and active radical in the component model has an important influence on the calculation speed and accuracy of the simulation. Therefore, the following will be combined with the embodiments of the present application to illustrate how to obtain the material property related data with higher accuracy.

[0059] Figure 3 The simulation method for coating deposition provided in the embodiments of the present application is shown in the flowchart. As shown in the figure, Figure 3 As shown in the figure, 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, utilizing quantum chemistry software to create two group models according to the geometric structure of the molecule or active radical.

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

[0062] Step 330, based on the potential energy equation: fitting calculation is performed on the plurality of potential energy points in the coordinate system to obtain the characteristic length and energy parameter of the molecule or active radical. Wherein, V is the potential energy, r is the distance between the two group models, is the characteristic length, is the energy parameter, k is the Boltzmann constant, is the proportion factor of the potential energy.

[0063] The above steps will be described in detail below taking the CH3 radical group as an example.

[0064] Based on step 310, two CH3 group models are created in the quantum chemistry software based on the geometric structure of the CH3 radical group.

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

[0066] Based on the step 320, one of the CH3 groups is selected as a reference group, and the other group is moved to evaluate the interaction potential energy therebetween, and the distance between the two groups is gradually changed by adjusting the distance between the two groups. When the distance between the two groups is less than 2.5 Å, the repulsive force between the groups is partially dominant, which can cause a sharp increase in potential energy, approaching infinity; when the distance between the two groups is between 2.5 Å and 5 Å, the two groups interact most actively, and the attractive force between the groups plays a role, so that the repulsive force does not dominate, and thus the distance range can capture the significant change in potential energy when the interaction occurs, thereby improving the accuracy of the potential energy calculation.

[0067] Therefore, the initial distance between the two initially selected CH3 groups is set to be in the range of 2.5 Å-5 Å, and the distance range for moving the other CH3 group each time is 0.1 Å-0.2 Å, so as to calculate the potential energy of the interaction between the two CH3 groups at different distances, respectively, and record the corresponding potential energy values. The distance between the two CH3 groups is taken as the X-axis, and the potential energy value is taken as the Y-axis, and the results are recorded in the coordinate system.

[0068] Based on the step 330, the plurality of potential energy points in the coordinate system are input into the data processing software, a nonlinear curve fitting is selected, and the potential energy equation is calculated by fitting. Through the curve fitting, the and can be approximately calculated. .

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

[0070] Figure 4 As shown in the flowchart of the simulation method for coating deposition provided by another embodiment of the present application. As shown in Figure 4 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 between the coating deposition thickness, temperature and gas inlet speed, the chemical reaction equation in the component model is simplified to obtain the simplified chemical reaction equation.

[0072] The chemical reaction model includes a plurality of chemical reaction equations, and the original chemical reaction model generally includes dozens or even hundreds of chemical reaction equations for the reaction of dozens of chemical molecules and active free radicals, resulting in a large amount of calculation based on the chemical reaction model. Therefore, before setting the component model, the chemical equations in the chemical reaction model are simplified, and the idea of the simplification processing is as follows: when deposition is performed at a temperature range of 850°C to 1100°C, as the temperature increases, the gas phase reaction accelerates, and the deposition rate of silicon carbide gradually increases, and the deposition rate of silicon carbide will first increase and then decrease along the gas inlet direction. This is because, on the one hand, MTS is continuously decomposed by heat, and the intermediate active components conducive to deposition in the gas phase are continuously accumulated, and the surface reaction rate gradually increases; on the other hand, as the deposition proceeds, the gas phase product HCl that inhibits the deposition of silicon carbide becomes more and more; at the same time, the deposition base 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 deposition is performed at a temperature range of 1100°C to 1400°C, the deposition rate gradually decreases along the gas inlet direction. This is because MTS immediately reacts completely to form intermediates at high temperatures, 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 inlet gas velocity increases, the maximum deposition rate position gradually moves away from the inlet position. This is because as the inlet gas velocity increases, the supply of MTS also increases, resulting in that MTS does not completely convert into intermediate gas at the first time, but gradually completely converts during the flow process, resulting in that the deposition rate gradually increases in the flow direction. Therefore, based on the relationship between the coating deposition thickness, the temperature and the inlet gas velocity in the actual processing process, some chemical equations in the original plurality of chemical reaction equations in the component model that do not play a great role in the calculation accuracy can be removed, and the pre-coefficient and the temperature index are adjusted by comparison with the actual situation, so that the model is more matched with the actual situation, thereby improving the calculation accuracy of the simulation model. At the same time, the simplified chemical equation makes the calculation simpler, reduces the complexity of the calculation, and improves the calculation speed. Table 1 below is a chemical reaction model in the component model.

[0073] Table 1 Chemical reaction model in component model

[0074]

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

[0076] The embodiment of the present application simplifies the chemical reaction equations in the chemical reaction model in the component model, and removes the related settings of the influence of the chemical reaction heat in the component model on the actual temperature field, further improving the stability of the simulation result and the calculation speed.

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

[0078] Optionally, the working condition comprises that the working pressure is set to be between 8000 Pa and 20000 Pa, the gravity acceleration is set to be 9.81 m / s 2 , and the working temperature is set to be between 900 DEG C and 1500 DEG C.

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

[0080] Figure 5 Fig. 2 shows a flowchart of a simulation method of coating deposition according to another embodiment of the present application. As shown in Fig. 2, before importing the grid model into the solver, the method further comprises the following steps. Figure 5

[0081] S510, dividing the three-dimensional model to obtain an initial grid model, wherein the three-dimensional model comprises 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 susceptor loaded into the reaction chamber, in order to improve the simulation accuracy, the specific positions of the reaction furnace and the graphite susceptor such as the small chamfer, the round corner, the relative position relationship and other components are accurately reflected in the three-dimensional model, so that the three-dimensional model is closer to the actual situation.

[0083] S510 can specifically comprise the following steps. Figure 6

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

[0085] S512, dividing the simplified three-dimensional model by using a hexahedral solid element type to obtain an initial grid model, wherein the minimum size in the simplified three-dimensional model comprises at least three grid elements.

[0086] Before dividing the three-dimensional model, the three-dimensional model needs to be simplified, that is, the small chamfer, the round corner and other features in the reaction furnace and the graphite susceptor which do not affect the air flow movement and are irrelevant to the deposition surface of the graphite susceptor are deleted, so as to avoid that the three-dimensional geometric model which is too complex obtains poor quality grid after grid division. ​​​

[0087] S520, quality checking is performed on the divided grid cells in the initial meshing model, if the quality requirement is not met, local grid cell refinement or grid reconstruction processing is performed on the initial meshing model until the quality requirement is met, and then the meshing model is obtained.

[0088] The quality requirement includes that the maximum skewness of the grid cell is less than 0.7, the minimum orthogonal quality of the grid cell is between 0.1 and 0.15, and the maximum aspect ratio of the grid cell is between 50 and 100. For a qualified grid model, the solver can be directly imported. For an unqualified grid model, further local grid refinement or grid reconstruction processing is required at the unqualified place to further improve the quality of the grid model to meet the requirements of simulation calculation.

[0089] The embodiment of the application optimizes the grid model to make it closer to the actual situation while reducing the complexity of the model, which is beneficial to improve the accuracy of simulation calculation.

[0090] It can be understood that when the solver is solved, the more the set related data is close to the actual situation, the higher the simulation accuracy is. Therefore, the following will be illustrated by an example to show how to further obtain the related data in the solver which is closer to the actual situation. Figure 7

[0091] Figure 7 Fig. 1 shows a flowchart of a simulation method for coating deposition provided by another embodiment of the application. As shown in Fig. 1, the simulation method comprises the following steps. Figure 7 As shown in Fig. 1, the initial flow field result matrix in the solver is obtained by using an initialization method, which specifically comprises the following steps.

[0092] S610, the region condition and the boundary condition of the meshing model are set.

[0093] The region condition includes that the normal gas flow region in the reaction furnace is set as a fluid region, and the region in the reaction furnace where no gas flow passes through is set as a dead zone. The boundary condition includes the flow rate and temperature of the gas inlet, the type of the gas outlet which is a pressure outlet, the pressure value and temperature of the gas outlet, and the temperature or heating power of the heat source.

[0094] S610, the data in the solver is mixed initialized according to the region condition and the boundary condition, and the initial flow field result matrix is obtained.

[0095] It can be understood that after the initial flow field result matrix is obtained by using the above method, the iteration number and the convergence condition are set in the solver, wherein the convergence standard of the energy equation residual is 10 -6 , and the convergence standard of the residual of other equations is 10 -3 ​That is, the result convergence criterion, in the solving 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 judged that the calculation has converged, and the solver stops calculation.

[0096] It can be understood that after the solver calculates the final flow field result matrix obtained after the iterative calculation, the solver can represent the flow field result matrix in the form of an image to the deposition surface of the graphite susceptor in the three-dimensional model, such as Figure 8 The change from red to blue on the graphite susceptor represents the difference in deposition rate at different positions, and the deposition rate decreases 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 embodiment of the present application is compared with the scheme in which the chemical reaction model is not simplified, and the material properties of the energy parameters and the characteristic length are not set, that is, the scheme of the present application is compared with the scheme before optimization.

[0098] Among them, the comparison of the data between the scheme of the present application and the comparison scheme is as shown in the following table 2.

[0099] Table 2 Comparison of data between the scheme of the present application and the comparison scheme

[0100]

[0101] As Figure 9 , the comparison of the deposition rate at different positions on the graphite susceptor calculated by the scheme of the present application, the deposition rate at different positions on the graphite susceptor calculated by the comparison scheme before optimization and the actual deposition rate at different positions on the graphite susceptor can find that the deposition rate curve of the present application is closer to the actual one, which means that the simulation accuracy of the scheme of the embodiment of the present application is higher.

[0102] The method embodiment of the present application is described in detail above Figures 1-9 , the device embodiment of the present application is described in detail below. It should be understood that the description of the method embodiment corresponds to the description of the device embodiment, therefore, the parts not described in detail can be referred to the method embodiment. Figure 10

[0103] Example apparatuses

[0104] Figure 10 As shown in the structure schematic diagram of the simulation and simulation device for coating deposition provided by an embodiment of the present application. As Figure 10 , the simulation and 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 importing module 810 is configured to import a meshed model into a solver, the meshed model including a meshed model of a reaction furnace having a reaction chamber and 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, wherein a setting parameter of the component model includes material properties of molecules and active radicals participating in a chemical reaction, and the material properties include characteristic lengths and energy parameters of the molecules and the active radicals in the MTS reaction system calculated by using a density functional theory of a first principle. The obtaining module 830 is configured to obtain an initial flow field result matrix in the solver by 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, and iteratively calculate the initial flow field result matrix to obtain a deposition rate cloud picture of the component in the reaction furnace, the deposition rate cloud picture being used to represent a 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 the energy parameters of the molecules and the active radicals of different material properties of the component model. The characteristic lengths and the energy parameters of the molecules and the active radicals are calculated, and specifically include creating two group models according to a geometric structure of the molecule or the active radical by using quantum chemistry software; selecting one of the two group models as a reference group, and moving the other group model to record potential energy between the two group models at different distances to obtain a coordinate system including a plurality of potential energy points; and performing fitting calculation on the plurality of potential energy points in the coordinate system to obtain the characteristic lengths and the energy parameters of the molecule or the active radical, wherein V is the potential energy, r is a distance between the two group models, the characteristic length is the energy parameter is k is a Boltzmann constant, and a is a proportion factor of the potential energy.

[0107] In some embodiments, the two group models have an initial distance, and the initial distance ranges from 2.5 Å to 5 Å, and a distance range of each time of moving the other group model ranges from 0.1 Å to 0.2 Å.

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

[0109] ​In some embodiments, the first setting module 820 is further configured to set the working conditions in the solver, the working conditions including that the working air pressure is set between 8000 Pa and 20000 Pa, the gravity acceleration is set to 9.81 m / s 2 , and the working temperature is set between 900 DEG C and 1500 DEG C.

[0110] In some embodiments, the importing module 810 is further configured to divide the three-dimensional model to obtain an initial gridding model, wherein the three-dimensional model includes a reaction furnace with a reaction chamber and a component to be simulated loaded into the reaction chamber; perform quality inspection on the divided grid cells in the initial gridding model, if the quality requirement is not met, perform local grid cell refinement or grid reconstruction processing on the initial gridding model until the quality requirement is met, and then obtain the gridding model; wherein the quality requirement includes that the maximum skewness of the grid cell is less than 0.7, the minimum orthogonal quality of the grid cell is between 0.1 and 0.15, and the maximum aspect ratio of the grid cell is between 50 and 100.

[0111] In some embodiments, the importing module 810 is further configured to perform simplification processing on 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 deposition surface of the component to be simulated is parallel to the air inlet direction; the simplified three-dimensional model is divided by using a hexahedral solid element type to obtain an initial gridding model, wherein the minimum size in the simplified three-dimensional model includes at least three grid cells.

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

[0113] In some embodiments, the obtaining module 830 is further configured to set the region condition and the boundary condition of the gridding model, wherein the region condition includes that the normal airflow region in the reaction furnace is set as a fluid region, and the region in the reaction furnace where no airflow passes through is set as a dead zone, the boundary condition includes the flow velocity and the temperature of the air inlet, the type of the air outlet is a pressure outlet, the pressure value and the temperature of the air outlet, and the temperature or the heat power of the heat source; and the data in the solver is mixedly initialized according to the region condition and the boundary condition to obtain an initial flow field result matrix.

[0114] Example electronic devices

[0115] Figure 11 Fig. 1 shows a structural schematic diagram of an electronic device provided by an embodiment of the present application. 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 perform the simulation method of coating deposition of any of the above embodiments.

[0116] The processor 901 can be a central processing unit (CPU) or other form of processing unit having data processing and / or instruction executing 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, which can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory, for example, can include random access memory (RAM), cache memory, and / or the like. The non-volatile memory, for example, can include read-only memory (ROM), hard disk, flash memory, and / or the like. One or more computer program instructions can be stored on the computer-readable storage media, which the processor 901 can run to implement the steps in the simulation method of coating deposition of 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, which are interconnected through a bus system and / or other forms of connection mechanisms (not shown in the figure). Figure 11

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

[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, a communication network and a remote output device connected thereto, and / or the like.

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

[0122] Example computer-readable storage media

[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 perform the steps in the simulation method of coating deposition of any of the above embodiments.​

[0124] The computer program product can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. The embodiments of the present application are not limited by the programming languages used to implement the routines.

[0125] In addition, the embodiments of the present application can also be a computer readable storage medium, which stores computer program instructions, and the computer program instructions make the processor execute the steps of the simulation method of coating deposition according to various embodiments of the present application described in the above "Exemplary Method" part of the specification when the processor runs.

[0126] The computer readable storage medium can take any combination of one or more of the readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any suitable combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include an electrical connection having one or more wires, a portable disc, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0127] The above describes the basic principles of the present application in combination with specific embodiments, but it should be noted that the advantages, advantages, effects and the like mentioned in the present application are only examples and are not limited, and these advantages, advantages, effects and the like cannot be considered as the must-have of each embodiment of the present application. In addition, the above specific details are only for the purpose of example and understanding, and the above details do not limit the present application to the must-have specific details.

[0128] The block diagrams of the devices, apparatuses, devices, systems referred to in this application are only illustrative examples and are not intended to require or imply that the connection, arrangement, configuration must be as shown in the block diagram. As those skilled in the art will recognize, these devices, apparatuses, devices, systems can be connected, arranged, configured in any manner. Words such as "include", "contain", "have" and the like are open-ended words, mean "including but not limited to", and can be used interchangeably. The words "or" and "and" used herein mean the word "and / or", and can be used interchangeably unless the context clearly indicates otherwise. The word "such as" used herein means the phrase "such as but not limited to", and can be used interchangeably.

[0129] It should also be noted that in the devices, apparatuses and methods of the present application, each of the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalents 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 generic principles defined herein can be applied to other aspects without departing from the scope of the present application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0131] The above description has been given for the purpose of illustration and description. Furthermore, this description does not intend to limit the embodiments of the present application to the forms disclosed herein. Although a number of 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 description is only the preferred embodiment of the present application and is not intended to limit the present application. Any modification, equivalent replacement and the like made within the spirit and principle of the present application should be included in the protection scope of the present application.

[0133] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. Any modification, equivalent replacement and the like made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method of simulation of coating deposition, characterized in that, The method comprises the following steps: Importing a meshed model into a solver, the meshed model comprising a reaction furnace having 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, wherein the setting parameters of the component model comprise material properties of molecules and active radicals participating in chemical reactions, the material properties comprising characteristic length and energy parameters of the molecules and the active radicals in the MTS reaction system calculated by density functional theory of first principles; Obtaining an initial flow field result matrix in the solver by an initialization method; Setting a result convergence standard according to the initial flow field result matrix, and iteratively calculating the initial flow field result matrix to obtain a deposition rate cloud picture of the component to be simulated in the reaction furnace, the deposition rate cloud picture being used to represent a flow field result matrix obtained in the last iteration.

2. The method of simulating coating deposition according to claim 1, characterized in that, Before the step of setting at least the turbulence model, the energy model, the radiation model and the component model in the solver, the method further comprises the following steps: Creating two group models according to the geometric structure of the molecules or the active radicals by quantum chemistry software; Selecting one of the group models as a reference group and moving the other group model, and recording potential energy between the two group models at different distances to obtain a coordinate system comprising a plurality of potential energy points; Based on the potential energy equation: A plurality of potential energy points in the coordinate system are fitted and calculated to obtain the characteristic length and energy parameters of the molecule or the active free radical; where V is the potential energy, r is the distance between the two groups models, L is the characteristic length, k is the Boltzmann constant, is the proportionality factor of the potential energy.

3. The method of simulating coating deposition according to claim 2, characterized in that, The two group models have an initial distance, and the initial distance ranges from 2.5 Å to 5 Å, and the distance range of each movement of the other group model ranges from 0.1 Å to 0.2 Å.

4. The method of simulating coating deposition according to claim 1, wherein, Before the step of setting at least the turbulence model, the energy model, the radiation model and the component model in the solver, the method further comprises the following steps: Based on the relationship among coating deposition thickness, temperature and gas inlet speed, simplifying the chemical reaction equation in the component model 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 method further comprises the following steps: The working conditions in the solver are set, including the working gas pressure is set between 8000 Pa-20000 Pa, the gravity acceleration is set to 9.81 m / s 2 , the working temperature is set between 900℃-1500℃.

5. The method of simulation of coating deposition according to any of claims 1-4, characterized in that, Before the step of importing the meshed model into the solver, the method further comprises the following steps: Dividing a three-dimensional model to obtain an initial meshed model, wherein the three-dimensional model comprises the reaction furnace having the reaction chamber and the component to be simulated loaded into the reaction chamber; Performing quality inspection on the divided grid cells in the initial meshed model, and if the quality requirement is not met, performing local grid cell refinement or grid reconstruction processing on the initial meshed model until the quality requirement is met, and then obtaining the meshed model; The quality requirement comprises that the maximum skewness of the grid cells is less than 0.7, the minimum orthogonal quality of the grid cells is between 0.1 and 0.15, and the maximum aspect ratio of the grid cells is between 50 and 100; The step of dividing the three-dimensional model to obtain the initial meshed model comprises the following steps: The three-dimensional model is simplified to obtain a simplified three-dimensional model, wherein the reaction furnace at least includes a gas inlet, a gas outlet and a heat source in the simplified three-dimensional model, and a deposition surface of the component to be simulated is parallel to a gas inlet direction of the gas inlet; The simplified three-dimensional model is divided by using a hexahedral solid element type to obtain an initial meshing model, wherein the minimum size in the simplified three-dimensional model includes at least three grid units.

6. The method of simulating coating deposition according to any of claims 1 to 4, characterized in that, The result convergence criterion includes that a residual of each equation in the solver is less than or equal to a preset value.

7. The method of simulating coating deposition according to claim 5, wherein, The method for obtaining the initial flow field result matrix in the solver by using initialization includes: The region condition and the boundary condition of the meshing model are set, wherein the region condition includes setting a normal gas flow region in the reaction furnace as a fluid region and setting a region in the reaction furnace where no gas flow passes through as a dead zone, and the boundary condition includes a flow velocity and a temperature of the gas inlet, a type of the gas outlet being a pressure outlet, a pressure value and a temperature of the gas outlet, and a temperature or a heat generation power of the heat source; The data in the solver is mixed initialized according to the region condition and the boundary condition to obtain the initial flow field result matrix.

8. A simulation apparatus for coating deposition, characterized by The method comprises: The import module is configured to import a meshing model into a solver, the meshing model including 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 is configured to set at least a turbulence model, an energy model, a radiation model and a component model in the solver, wherein a setting parameter of the component model includes material properties of molecules and active free radicals participating in chemical reactions, and the material properties include characteristic lengths and energy parameters of the molecules and the active free radicals in an MTS reaction system calculated by using a first-principle density functional theory; The acquisition module is configured to obtain an initial flow field result matrix in the solver by using a method for initialization; The second setting module is configured to set a result convergence criterion according to the initial flow field result matrix, and iteratively calculate the initial flow field result matrix to obtain a deposition rate cloud picture of the component to be simulated in the reaction furnace, the deposition rate cloud picture being used to represent a flow field result matrix obtained in the last iteration.

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

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

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