Method of predicting deposition thickness, storage medium, and electronic device

Abstract

The application provides a method for predicting deposition thickness, a storage medium and an electronic device, so as to realize the prediction of the deposition thickness, provide an important basis for optimizing the chemical reaction process and optimizing the structural design of the reaction cavity, and improve the uniformity of the deposition thickness of the target material in actual production. The method for predicting the deposition thickness comprises the following steps: performing grid division on a three-dimensional model to obtain a gridded model; importing the gridded model into a numerical solver, and setting the working conditions, the turbulence model, the energy model and the radiation model in the numerical solver; setting the calculation conditions in the transient solver, wherein the calculation conditions comprise a deposition time, a time step and a maximum iteration number; performing iterative solving, and processing the result of the iterative solving to obtain the deposition thickness of the target material. Through the transient solving of the gridded model, the states of the flow field and the chemical reaction at different times can be calculated in real time, and then the prediction of the deposition thickness can be realized.

Description

TECHNICAL FIELD

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

[0002] The parts coated with coating, such as graphite susceptor coated with silicon carbide coating, have an important function in metal organic chemical vapor deposition (MOCVD), which not only provides physical support for epitaxial reaction, but also provides high-temperature thermal field for epitaxial reaction. First, the poor flatness of the surface of the graphite susceptor will lead to uneven temperature field of the surface of the graphite susceptor, and then lead to the yield reduction of the epitaxial product; secondly, the silicon carbide coating of the graphite susceptor often cracks during use. Research shows that the root cause of the above problems is that the deposition thickness of the surface of the graphite susceptor is uneven.

[0003] Computational Fluid Dynamics (CFD) is a powerful tool and is widely used in actual industrial production. In the related art, the research on chemical vapor deposition (CVD) is to couple the flow field and the reaction chemical field to predict the coating deposition rate of the parts at different positions in the chemical vapor deposition process, which provides theoretical support for improving the design of the equipment and regulating the process parameters. However, the above method is limited to the prediction of the coating deposition rate, and cannot predict the deposition thickness, and the theoretical support for the actual production is insufficient. Therefore, in order to effectively improve the uniformity of the deposition thickness, it is necessary to provide a method for predicting the deposition thickness. SUMMARY

[0004] Therefore, the embodiments of the present application are committed to providing a method for predicting deposition thickness, a storage medium and an electronic device, so as to realize the prediction of the deposition thickness, provide an important basis for optimizing the chemical reaction process and optimizing the structural design of the reaction chamber, and improve the uniformity of the deposition thickness of the target material in the actual production.

[0005] In a first aspect, the present application provides a method for predicting deposition thickness, wherein the method for predicting deposition thickness comprises: meshing a three-dimensional model to obtain a meshed model, wherein the three-dimensional model comprises a furnace body having a reaction cavity, a rack rotatably installed in the reaction cavity, and a target loaded into the reaction cavity, and the meshed model comprises a plurality of mesh units, at least a part of the mesh units being dynamic mesh units corresponding to the rack; importing the meshed model into a numerical solver, and setting working conditions, a turbulence model, an energy model, and a radiation model in the numerical solver, wherein the working conditions comprise a reaction temperature, a reaction pressure, and properties of a gas; setting calculation conditions in the transient solver, the calculation conditions comprising a deposition time, a time step, and a maximum number of iterations; determining a discrete algebraic equation set for controlling flow, and setting region conditions and boundary conditions of the meshed model; iteratively solving, and processing a result of the iterative solving to obtain a deposition thickness of the target.

[0006] Optionally, before meshing the three-dimensional model, the method comprises: simplifying the three-dimensional model, wherein the simplified three-dimensional model comprises an inlet, an outlet, a chemical deposition reaction surface, and a heating source, and the chemical deposition reaction surface is a contact surface between the target and the gas.

[0007] Optionally, meshing the three-dimensional model to obtain the meshed model comprises: dividing the three-dimensional model into a moving region and a non-moving region, wherein the moving region is a region that moves during the chemical deposition process, and the non-moving region is a region that does not move during the chemical deposition process; and meshing the moving region and the non-moving region using a polyhedral element type to obtain the meshed model, wherein the mesh units corresponding to the moving region are dynamic mesh units.

[0008] Optionally, meshing the moving region and the non-moving region using the polyhedral element type to obtain the meshed model comprises: initially meshing the moving region and the non-moving region using the polyhedral element type to obtain an initial meshed model; performing quality checking on the mesh units in the initial meshed model, and when the mesh units do not meet quality requirements, processing the initial meshed model to obtain a meshed model that meets the quality requirements, wherein the quality checking comprises checking whether the shape of the mesh units, the size of the mesh units, and the internal angles of the mesh units meet requirements.

[0009] Optionally, setting the turbulence model in the numerical solver comprises: setting the turbulence model in the numerical solver as a k-ε model; and / or, setting the radiation model in the numerical solver comprises: setting the radiation model in the numerical solver as a d-o model.

[0010] Optionally, the region conditions of the meshed model are set, including: setting the inlet flow rate of the gas, the temperature of the gas, and the physical and chemical properties of different regions; and / or the boundary conditions of the meshed model are set, including: setting the heat insulation condition or the heat conduction condition of the furnace body.

[0011] Optionally, the iterative solving is performed, including: performing the steady-state simulation, iteratively solving the steady-state field to obtain the flow field, the temperature field and the chemical reaction field of the steady-state field; performing the transient simulation, taking the flow field, the temperature field and the chemical reaction field of the steady-state field as the initial flow field conditions of the transient solving, iteratively solving the transient field to obtain the flow field, the temperature field and the chemical reaction field of the transient field.

[0012] Optionally, the result of the iterative solving is processed to obtain the deposition thickness of the target material, including: processing the chemical reaction field of the transient field to obtain the deposition rate of each grid unit in each time step; and obtaining the deposition thickness of each grid unit based on the deposition rate of each grid unit in each time step, the time step and the deposition time.

[0013] In a second aspect, the present application provides a storage medium, wherein the storage medium stores computer instructions, and when the computer instructions are called by a processor, the computer instructions are used to execute the method for predicting the deposition thickness according to any one of the above aspects.

[0014] In a third aspect, the present application provides an electronic device, wherein the electronic device comprises a processor and a memory, the memory stores computer instructions; and the processor is used to call the computer instructions to execute the method for predicting the deposition thickness according to any one of the above aspects.

[0015] The method for predicting the deposition thickness according to the present application, when the three-dimensional model is meshed, the grid units corresponding to the material rack are set as dynamic grid units by considering the rotation of the material rack, so that the flow field and the chemical reaction in the process of coating deposition of the target material can be more accurately simulated. By performing the transient solving on the meshed model, the states of the flow field and the chemical reaction at different times can be calculated in real time, and then the prediction of the deposition thickness is realized, which provides an important basis for optimizing the chemical reaction process and optimizing the structure design of the reaction chamber, so as to improve the uniformity of the deposition thickness of the target material in actual production. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 Fig. 1 shows a flowchart of a method for predicting a deposition thickness according to an embodiment of the present application.

[0017] Figure 2 Fig. 2 shows a flowchart of a method for predicting a deposition thickness according to another embodiment of the present application.

[0018] Figure 3Fig. 1 shows a flowchart of a method for predicting deposition thickness according to an embodiment of the present application.

[0019] Figure 4 Fig. 2 shows a flowchart of a method for predicting deposition thickness according to another embodiment of the present application.

[0020] Figure 5 Fig. 3 shows a flowchart of a method for predicting deposition thickness according to another embodiment of the present application.

[0021] Figure 6 Fig. 4 shows a schematic diagram of deposition thickness at different positions of a target according to an embodiment of the present application.

[0022] Figure 7 Fig. 5 shows a curve diagram of deposition thickness at different positions of a target according to an embodiment of the present application.

[0023] Figure 8 Fig. 6 shows a structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. 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 those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0025] With the gradual development of simulation technology, a CVD simulation model is established by using CFD technology to predict the deposition rate of a target at different positions in a reaction chamber, and the structure and process parameters of the furnace body are adjusted through the simulation model to obtain a more uniform deposition rate on the surface of the target.

[0026] In the related art, a steady-state analysis method is generally used when a simulation model is established, and only the coating deposition rate of the target at different positions in the deposition process can be obtained, and the influence of time variable on the coating thickness cannot be reflected in time. In addition, when the simulation model is simulated, the influence of the rotation of the material rack in the reaction chamber on the flow field is not considered, and there is a large gap between the simulation result and the actual production, and there is a deficiency in the theoretical support for the actual production.

[0027] The present application adds transient analysis on the basis of steady-state analysis, and combines with dynamic mesh technology to add the influence of the rotation of the material rack on the flow field, so that the simulation result is closer to the actual production situation. Not only can the gas flow and chemical reaction in the deposition process be truly reflected, but also the deposition thickness change on the surface of the target can be calculated in real time. By combining the relationship between the deposition rate and the deposition time, the prediction of the deposition thickness is realized, which can more intuitively guide the actual production and improve the uniformity of the deposition thickness on the surface of the target.

[0028] Figure 1 Fig. 1 shows a flowchart of a method for predicting deposition thickness according to an embodiment of the present application. Figure 2 Fig. 2 shows a flowchart of a method for predicting deposition thickness according to another embodiment of the present application. Figure 3 Fig. 3 shows a flowchart of a method for predicting deposition thickness according to yet another embodiment of the present application. Figure 4 Fig. 4 shows a flowchart of a method for predicting deposition thickness according to still another embodiment of the present application. Figure 5 Fig. 5 shows a flowchart of a method for predicting deposition thickness according to still another embodiment of the present application.

[0029] Reference will now be made to the drawings, and specific language will be used herein to describe the same. Figures 1 to 5 Embodiments of the method for predicting deposition thickness of the present application are described.

[0030] The method for predicting deposition thickness of the present application comprises:

[0031] In step S10, a three-dimensional model is meshed to obtain a meshed model, wherein the three-dimensional model comprises a furnace body having a reaction cavity, a rack rotatably installed in the reaction cavity, and a target material loaded into the reaction cavity, and the meshed model comprises a plurality of mesh units, at least a part of the mesh units being dynamic mesh units corresponding to the rack;

[0032] In step S20, the meshed model is imported into a numerical solver, and working conditions, a turbulence model, an energy model, and a radiation model in the numerical solver are set, wherein the working conditions comprise a reaction temperature, a reaction pressure, and properties of a gas;

[0033] In step S30, a calculation condition in the transient solver is set, the calculation condition comprising a deposition time, a time step, and a maximum number of iterations;

[0034] In step S40, a discrete algebraic equation set for controlling flow is determined, and a region condition and a boundary condition of the meshed model are set;

[0035] In step S50, an iterative solution is performed, and a result of the iterative solution is processed to obtain a deposition thickness of the target material.

[0036] The furnace body can be a CVD reaction furnace. The target material can be a graphite base, which is loaded into a reaction cavity of the furnace body and has a coating layer, such as a silicon carbide coating layer, deposited on a surface of the graphite base during chemical vapor deposition.

[0037] The deposition time refers to a total duration of the deposition process. The models and conditions set in steps S00, S30, and S40 are all conditions to be met in step S50.

[0038] The method for predicting the deposition thickness of the application, when meshing the three-dimensional model, considers the rotation of the rack, sets the grid unit corresponding to the rack as a dynamic grid unit, and can more accurately simulate the flow field and chemical reaction during the coating deposition process of the target material. By transiently solving the meshed model, the state of the flow field and chemical reaction at different times can be calculated in real time, and the prediction of the deposition thickness is realized, which provides an important basis for optimizing the chemical reaction process and optimizing the structure design of the reaction chamber, so as to improve the uniformity of the deposition thickness of the target material in actual production.

[0039] Exemplarily, the three-dimensional model can be obtained in the following way:

[0040] The three-dimensional model can be drawn by drawing software. Specifically, a plurality of parts can be drawn according to the actual sizes of the furnace body, the rack, the target material, the support seat, etc., and then the plurality of parts are assembled to obtain an initial three-dimensional geometric model. When drawing the initial three-dimensional geometric model by drawing software, it is necessary to ensure that the size of the drawn parts is consistent with the actual size, so as to simulate the real flow field and chemical reaction.

[0041] The drawing software can be CAD, Solidworks, Pro / e, UG, etc. It can be understood that the drawing software is installed in the computer.

[0042] Of course, in other embodiments, the three-dimensional model can also be obtained by other ways, which are not limited in the application.

[0043] In some embodiments, before meshing the three-dimensional model, it includes:

[0044] The three-dimensional model is simplified, wherein the simplified three-dimensional model includes an inlet, an outlet, a chemical deposition reaction surface and a heating source, and the chemical deposition reaction surface is the contact surface of the target material and the gas.

[0045] Exemplarily, the three-dimensional model is simplified, including:

[0046] Simplifying local small features, for example, deleting chamfer, fillet, etc.

[0047] The holes, grooves, gaps, etc. in the rack and target material that have little effect on the flow field are filled;

[0048] The holes and bolt structures in the reaction chamber are deleted or filled;

[0049] The roughness of the part surface is increased to replace the texture structure of the part surface, wherein the size of the roughness of the part surface is equivalent to the size of the roughness of the texture structure.

[0050] It should be noted that when simplifying the three-dimensional model, attention should be paid to retaining the key structural features of the parts, such as the boundary geometry of the main runner structure, the boundary geometry of the inlet and outlet, the boundary geometry of the rack, and the boundary geometry of the target material, etc. When simplifying the three-dimensional model, a suitable simplification strategy should be selected to reduce the computational complexity while retaining the key structural features of the parts.

[0051] It should be noted that in the simplified three-dimensional model, the diameter of the air inlet, the position of the air inlet, the diameter of the air outlet, the position of the air outlet, and the position of the heating source should be consistent with the actual situation to ensure that the simulation model can truly reflect the actual chemical reaction process. Among them, the air outlet is the exhaust port; the heating source can be an electric heating wire or an electric heating plate, etc.

[0052] By simplifying the three-dimensional model, the simulation model can truly reflect the actual chemical reaction process while improving the calculation speed of the simulation model and saving the simulation time.

[0053] In some embodiments, as shown in Figure 2 The three-dimensional model is meshed to obtain a meshed model, including:

[0054] Step S101, dividing the three-dimensional model into a moving region and a non-moving region, wherein the moving region is a region that moves during the chemical deposition process, and the non-moving region is a region that does not move during the chemical deposition process;

[0055] Step S102, meshing the moving region and the non-moving region using a polyhedral element type to obtain a meshed model, wherein the mesh element corresponding to the moving region is a dynamic mesh element.

[0056] The polyhedral element type can be a hexahedron or a tetrahedron. The size of the mesh element can be determined according to the complexity of the three-dimensional model and the computing resources. The moving region can include the rack area and the area near the rack that is affected by the rotation of the rack.

[0057] The above steps S101 and S102 facilitate the generation of a meshed model suitable for numerical calculation and stable calculation results, which is convenient for subsequent fluid dynamics calculation and analysis. In addition, in order to improve the accuracy of the simulation results, the grid of the moving region can also be encrypted.

[0058] Preferably, when meshing the moving region and the non-moving region, the grid at the interface between the moving region and the non-moving region is refined.

[0059] Exemplarily, the component located at the interface between the motion region and the non-motion region is an interface component, and the grid cell divided by the interface component is an interface grid cell. The interface component is divided into at least three interface grid cells, and the volume of each interface grid cell is less than or equal to one third of the volume of the interface component. In this way, the accuracy of the simulation result is further improved.

[0060] In some embodiments, as shown in Figure 3 The motion region and the non-motion region are meshed using a polyhedral cell type to obtain a meshed model, including:

[0061] In step S201, the motion region and the non-motion region are initially meshed using a polyhedral cell type to obtain an initial meshed model.

[0062] In step S202, the quality of the grid cells in the initial meshed model is checked, and when the grid cells do not meet the quality requirements, the initial meshed model is processed to obtain a meshed model that meets the quality requirements.

[0063] The quality check includes checking whether the shape, size, and internal angle of the grid cells meet the requirements.

[0064] When the grid cells do not meet the quality requirements, the initial meshed model can be processed for local grid cell refinement or grid reconstruction until the grid cells meet the quality requirements. The grid cells that meet the quality requirements have a maximum skewness of not greater than 0.7 and a maximum lateral-longitudinal arm of between 50 and 100, to ensure that no excessive shear or distortion is introduced.

[0065] The method of steps S201 and S202 can improve the stability and accuracy of subsequent numerical solving, thereby improving the prediction accuracy of the deposition thickness and further improving the uniformity of the deposition thickness of the target material in actual production.

[0066] Steps S201 and S202 can be implemented by meshing software, such as ICEM CFD, HyperMesh, Fluent Meshing, etc., to mesh the three-dimensional model.

[0067] In step S20, the reaction temperature can be 600-1400°C, the reaction pressure can be 10-100 Kpa, and the properties of the gas can include the density and viscosity of argon, hydrogen, and methane gas.

[0068] In some embodiments, the turbulent flow model in the numerical solver is set, including: setting the turbulent flow model in the numerical solver to a k-ε model.

[0069] By setting the turbulence model in the numerical solver as the k-ε model, the physical phenomena in the chemical reaction process can be simulated more realistically, the prediction accuracy of the deposition thickness is improved, and the uniformity of the deposition thickness of the target material in actual production is further improved.

[0070] In some embodiments, the radiation model in the numerical solver is set, including: setting the radiation model in the numerical solver as the d-o model.

[0071] By setting the radiation model in the numerical solver as the d-o model, the physical phenomena in the chemical reaction process can be simulated more realistically, the prediction accuracy of the deposition thickness is improved, and the uniformity of the deposition thickness of the target material in actual production is further improved.

[0072] In some embodiments, the region conditions of the gridding model are set, including: setting the inlet flow rate of the gas, the temperature of the gas, and the physical and chemical properties of different regions.

[0073] The above setting of the region conditions of the gridding model can realistically simulate the state of the fluid in the chemical reaction process, improve the prediction accuracy of the deposition thickness, and further improve the uniformity of the deposition thickness of the target material in actual production.

[0074] In some embodiments, the boundary conditions of the gridding model are set, including: setting the heat insulation condition or the heat conduction condition of the furnace body.

[0075] The above setting of the boundary conditions of the gridding model can more reasonably describe the heat exchange in the chemical reaction process, improve the prediction accuracy of the deposition thickness, and further improve the uniformity of the deposition thickness of the target material in actual production.

[0076] Preferably, in step S30, the deposition time can be set to 10-1000s; the time step is set to 0.01-0.1s, and the iteration number can be set to 20-200.

[0077] By setting the deposition time, the time step and the iteration number as described above, the dynamic changes in the chemical reaction process can be more accurately reflected, and it is ensured that the calculation result can capture the change of the reaction state. The prediction accuracy of the deposition thickness is improved, and the uniformity of the deposition thickness of the target material in actual production is further improved.

[0078] Preferably, in step S40, the discrete algebraic equation set includes energy conservation equation, momentum conservation equation and mass conservation equation. In the energy conservation equation, in order to improve the calculation efficiency and improve the stability of the numerical solution structure, the influence of the chemical reaction heat on the temperature field can be ignored, and only the influence of heat conduction, heat radiation and heat convection on the temperature field is considered.

[0079] Wherein, the energy conservation direction is as follows:

[0080]

[0081] Wherein, C p is the specific heat capacity; T is the temperature; k is the heat transfer coefficient of the fluid; S T is the part of the fluid mechanical energy converted into heat energy due to viscous action and the internal heat source of the fluid, and is the viscous dissipation phase coefficient.

[0082] The momentum conservation equation is established according to Newton's second law, which is expressed as the rate of change of fluid momentum with respect to time in all control micro-element bodies being equal to the sum of various forces acting on the micro-element bodies. The momentum conservation equation is as follows:

[0083]

[0084] Wherein, p is the static pressure, is the stress tensor; and are the gravity volume force and the external volume force in the i direction, respectively; is the source term containing other models, which is 0 here.

[0085] The mass conservation equation is established, which is expressed as the increase of mass in the fluid micro-element body within a unit time being equal to the net mass flowing into the micro-element body within the same time interval. The mass conservation equation is as follows:

[0086]

[0087] Wherein, p is the density, u is the net inflow velocity of the fluid micro-element on both sides in the x direction, v is the net inflow velocity of the fluid micro-element on both sides in the y direction, and w is the net inflow velocity of the fluid micro-element on both sides in the z direction.

[0088] In some embodiments, as Figure 4 shown, iterative solving is performed, including:

[0089] Step S501, steady-state simulation is performed, and the flow field, temperature field and chemical reaction field of the steady-state field are obtained by iterative solving of the steady-state field;

[0090] Step S502, transient simulation is performed, and the flow field, temperature field and chemical reaction field of the steady-state field are taken as the initial flow field conditions of the transient solving, and the flow field, temperature field and chemical reaction field of the transient field are obtained by iterative solving of the transient field.

[0091] Wherein, the flow field includes the gas flow velocity vector in the reaction chamber; the temperature field includes the temperature distribution in the reaction chamber; and the chemical reaction field includes the reactant concentration distribution in the reaction chamber.

[0092] The steady-state solving calculates the state when the flow field is relatively stable, and the dynamic mesh remains static during the steady-state solving. The transient-state solving calculates the state at each time during the change of the flow field, and the dynamic mesh remains rotating during the transient-state solving, and the rotating speed of the dynamic mesh is the same as the rotating speed of the material rack. The influence of the rotating of the material rack on the flow field is obtained through the transient-state solving. Through the transient-state solving, the time-varying characteristics of the gas can be captured in time. The time-varying characteristics of the gas refer to the fact that the flow field and the chemical reaction field of the gas at each different time are changing when the gas is flowing and is affected by the rotation of the material rack.

[0093] The steady-state field is solved iteratively first, and then the result of the steady-state solving is used as the initial flow field condition of the transient-state solving, and the transient-state field is solved iteratively, so that the flow field, the temperature field and the chemical reaction field of the transient-state field are conveniently obtained.

[0094] In some other embodiments, the result of the steady-state solving can also not be used as the initial flow field condition of the transient-state solving.

[0095] In some embodiments, as shown in FIG. 5, the result of the iterative solving is processed to obtain the deposition thickness of the target material, including: Figure 5 In step S503, the chemical reaction field of the transient-state field is processed to obtain the deposition rate of each grid cell in each time step.

[0096] In step S504, the deposition thickness of each grid cell is obtained based on the deposition rate of each grid cell in each time step, the time step and the deposition time.

[0097] Each grid cell corresponds to a different position of the target material, and by obtaining the deposition thickness of each grid cell, the deposition thickness of the target material at different positions can be obtained. For example, the deposition thickness of the center, the edge and the side of the target material can be obtained.

[0098] For example, the position coordinates and the deposition thickness (or deposition rate) data of each grid cell are exported, and then the exported data are imported into a data analysis tool, so that the deposition thickness (or deposition rate) of the target material at different positions can be displayed in the data analysis tool. The data analysis tool can be Origin, Excel, WPS, matlab, etc.

[0099] For a single grid cell, the deposition rate of the grid cell multiplied by the time step is equal to the deposition thickness of the grid cell in the time step. The deposition thickness of the grid cell in the deposition time can be obtained by summing the deposition thickness in all time steps in the deposition time. That is, the deposition thickness of one part of the target material in the deposition time is obtained.

[0100] For a single grid cell, the deposition rate of the grid cell multiplied by the time step is equal to the deposition thickness of the grid cell in the time step. The deposition thickness of the grid cell in the deposition time can be obtained by summing the deposition thickness in all time steps in the deposition time. That is, the deposition thickness of one part of the target material in the deposition time is obtained.

[0101] Exemplarily, the deposition rate of the grid unit is v, the time step is tx, the deposition time is t, the deposition thickness of the grid unit in one time step is hx, and the deposition thickness of the grid unit in the deposition time is h, then wherein, , wherein, t1 is the first time step, t2 is the second time step, t3 is the third time step, tn is the n th time step, and ; h1 is the deposition thickness of the grid unit in the first time step, h2 is the deposition thickness of the grid unit in the second time step, h3 is the deposition thickness of the grid unit in the third time step, and hn is the deposition thickness of the grid unit in the n th time step.

[0102] In addition, through numerical calculation, the deposition thickness of the target material at different positions when the rack rotates one circle can also be obtained. Exemplarily, the rotation speed of the rack is ω (unit: r / s), and the deposition thickness of the grid unit when the rack rotates one circle is h0, then .

[0103] Figure 6 FIG. 4 shows a schematic diagram of the deposition thickness of the target material at different positions according to an embodiment of the present application. Figure 7 FIG. 5 shows a curve diagram of the deposition thickness of the target material at different positions according to an embodiment of the present application.

[0104] Figure 6 FIG. 6 shows the deposition thickness of the target material at different positions when the rack rotates one circle in simulation, wherein the change from red to blue on the target material represents the difference in the deposition thickness of the target material at different positions, and the deposition thickness gradually decreases from red to blue, that is, the heavier the red color, the thicker the deposition thickness. Figure 7 FIG. 7 shows the comparison between the deposition thickness of the target material at different positions calculated by the method of the present application and the deposition thickness of the target material at different positions in actual production, and through Figure 7 it can be found that the deposition thickness of the target material at different positions calculated by the method of the present application is close to the deposition thickness of the target material at different positions in actual production, indicating that the simulation accuracy of the method of the present application is high.

[0105] The method for predicting the deposition thickness proposed in the present application increases the consideration of the influence of the rack rotation on the flow field and the deposition rate on the simulation model in the related art; through the calculation between the deposition rate and the deposition time, the deposition thickness of the target material at different positions on the surface can be obtained, so that more intuitive guidance can be provided for actual production, and the experimental development with high cost and long time in the actual production process can be avoided; through the observation and analysis of the flow and consumption process of the reaction gas and the generation and flow process of the intermediate gas, direct flow field analysis basis can be provided for the improvement and design of the process equipment.

[0106] The method for predicting deposition thickness provided in the present application is verified through a series of experiments, and the predicted deposition thickness distribution has good consistency with the actual experimental results. Through simulation of the target material under different flow fields and shelf rotation speeds, the best process parameters can be identified, and uniform coating deposition on the surface of the target material can be realized. In summary, through accurate prediction of the coating thickness during chemical vapor deposition, a reliable theoretical basis is provided for improving the uniformity of the coating thickness. This research result indicates that in the semiconductor manufacturing industry, through advanced numerical simulation technology, higher quality coating deposition can be achieved, and the reliability and performance of the device can be improved.

[0107] The storage medium of the present application stores computer instructions, which are called by the processor to execute the method for predicting deposition thickness of any of the above embodiments.

[0108] Figure 8 The structure of the electronic device provided by an embodiment of the present application is shown. The embodiments of the electronic device of the present application are described below with reference to the drawings.

[0109] As shown in Figure 8 The electronic device 1000 of the present application includes a processor 100 and a memory 300, and the memory 300 stores computer instructions. The processor 100 is configured to call the computer instructions to execute the method for predicting deposition thickness of any of the above embodiments.

[0110] The processor 100 can be one or more. The processor 100 can be a central processing unit (CPU) or other processing units, as long as the processor 100 has data processing capability and / or instruction execution capability, and can control other components in the electronic device 1000 to execute corresponding instructions.

[0111] The memory 300 can include one or more computer program products, which can be volatile memory, non-volatile memory, etc. The volatile memory can include random access memory (RAM), cache memory, etc. The non-volatile memory can include read-only memory (ROM), hard disk, flash memory, etc.

[0112] In some embodiments, the electronic device 1000 further includes an input device 200 and an output device 400, and the input device 200, the output device 400, the memory 300 and the processor 100 can be interconnected through a bus system. The input device 200 can include a keyboard, a mouse, a microphone, etc. The output device 400 can include a display, a printer, a remote output device, etc., to output various information to the outside.

[0113] In addition to the above method and electronic device, embodiments of the present application can also be a computer program product, including computer program instructions, which when executed by a processor, cause the processor to perform the steps of the method of predicting a deposition thickness according to any of the above embodiments.

[0114] 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++, etc., 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.

[0115] In addition, embodiments of the present application can also be a storage medium, which stores computer program product instructions, which when executed by a processor, cause the processor to perform the steps of the method of predicting a deposition thickness according to various embodiments of the present application described in the above "Exemplary Methods" section of the specification.

[0116] The storage medium can be any combination of one or more of a readable medium or a readable storage medium. The readable storage medium can include, but is 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 of a readable storage medium include, but are not limited to, 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.

[0117] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method of predicting a deposition thickness, characterized by, The method comprises: grid division is performed on the three-dimensional model to obtain a meshed model, including: dividing the three-dimensional model into a motion region and a non-motion region, performing the grid division on the motion region and the non-motion region by using a polyhedral element type to obtain the meshed model, wherein the three-dimensional model comprises a furnace body having a reaction cavity, a rack rotatably installed in the reaction cavity, and a target material loaded into the reaction cavity, the motion region is a region that moves during a chemical deposition process, the non-motion region is a region that does not move during the chemical deposition process, and the meshed model comprises a plurality of grid elements, at least a part of the grid elements are dynamic grid elements corresponding to the rack, and the grid elements corresponding to the motion region are the dynamic grid elements; the meshed model is imported into a numerical solver, and working conditions, a turbulence model, an energy model, and a radiation model in the numerical solver are set, wherein the working conditions include a reaction temperature, a reaction pressure, and properties of a gas; a calculation condition in the transient solver is set, and the calculation condition includes a deposition time, a time step, and a maximum number of iterations; a discrete algebraic equation set for controlling flow is determined, and region conditions and boundary conditions of the meshed model are set; iterative solving is performed to obtain a transient field chemical reaction field, the transient field chemical reaction field is processed to obtain a deposition rate of each grid element in each time step; based on the deposition rate of each grid element in each time step, the time step, and the deposition time, a deposition thickness of each grid element is obtained.

2. The method of predicting a deposit thickness of claim 1, wherein, Before the grid division is performed on the three-dimensional model, the method further comprises: the three-dimensional model is simplified, wherein the simplified three-dimensional model comprises an air inlet, an air outlet, a chemical deposition reaction surface, and a heating source, and the chemical deposition reaction surface is a contact surface of the target material and the gas.

3. The method of predicting a deposit thickness of claim 1, wherein, The grid division on the motion region and the non-motion region by using the polyhedral element type to obtain the meshed model comprises: initial grid division is performed on the motion region and the non-motion region by using the polyhedral element type to obtain an initial meshed model; quality inspection is performed on the grid elements in the initial meshed model, and when the grid elements do not meet quality requirements, the initial meshed model is processed to obtain the meshed model meeting the quality requirements, wherein the quality inspection includes checking whether the shape of the grid elements, the size of the grid elements, and internal angles of the grid elements meet requirements.

4. The method for predicting a deposition thickness according to any one of claims 1-3, wherein: the turbulence model in the numerical solver is set, including: the turbulence model in the numerical solver is set as a k-ε model; and / or, the radiation model in the numerical solver is set, including: the radiation model in the numerical solver is set as a d-o model.

5. The method for predicting a deposition thickness according to any one of claims 1-3, wherein: setting a region condition of the meshed model, including: setting an inlet flow rate of the gas, a temperature of the gas, and physical and chemical properties of different regions; and / or, setting a boundary condition of the meshed model, including: setting an adiabatic condition or a heat conduction condition of the furnace body.

6. The method of predicting a deposited thickness according to any one of claims 1-3, wherein, the iterative solving, including: performing a steady-state simulation to iteratively solve a steady-state field to obtain a flow field, a temperature field, and a chemical reaction field of the steady-state field; performing a transient-state simulation to iteratively solve a transient-state field to obtain a flow field, a temperature field, and the chemical reaction field of the transient-state field, with the flow field, the temperature field, and the chemical reaction field of the steady-state field as initial flow field conditions of the transient-state solving.

7. A storage medium, characterized by The storage medium stores computer instructions, which, when called by the processor, execute the method for predicting the deposition thickness according to any one of claims 1-6.

8. An electronic device, comprising: including: a processor; a memory storing computer instructions; wherein the processor is configured to call the computer instructions to execute the method for predicting the deposition thickness according to any one of claims 1-6.

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