Optimized prediction method and device for silicon carbide high-temperature vapor deposition, equipment and medium

Abstract

The invention relates to an optimized prediction method and device for silicon carbide high-temperature vapor deposition, equipment and a medium, and relates to the field of silicon carbide preparation.The method comprises the steps that grid division is conducted on a fluid area in a furnace body structure, and a computational domain is obtained; performing PCA analysis on the chemical mechanism model according to the reference working condition to obtain a mechanism simplified model; and based on the computational domain and the mechanism simplified model, solving the continuity equation, the momentum conservation equation and the energy conservation equation of the gas in the furnace according to the actual working condition, when the calculation of the flow field, the temperature field and the chemical field reaches convergence, outputting the growth rates of the flow field, the temperature field, the chemical field and the seed crystal surface, and otherwise, continuing the calculation. According to the optimization simulation method provided by the invention, the multi-physical field calculation model for growing the silicon carbide by the high-temperature chemical vapor deposition method is established, and the growth condition of the SiC crystal is quickly simulated to provide guidance for engineering application, so that the iterative upgrading of equipment is accelerated, and the time and economic cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of silicon carbide preparation, specifically to an optimized prediction method, apparatus, equipment, and medium for high-temperature chemical vapor deposition of silicon carbide, and more particularly to an optimized prediction method, apparatus, equipment, and medium for growing silicon carbide crystals by high-temperature chemical vapor deposition. Background Technology

[0002] Silicon carbide (SiC) is a typical representative of third-generation semiconductor materials, possessing excellent properties such as high breakdown voltage, wide bandgap, high temperature resistance, and good thermal conductivity, making it an important component in energy production and electronic products. Compared with existing silicon power devices, SiC power devices can significantly reduce power losses during conduction and switching, contributing to improved energy efficiency and thus reducing fuel consumption and carbon dioxide emissions.

[0003] Currently, silicon carbide semiconductor materials have been practically applied in various applications such as power regulators for photovoltaic systems, industrial power supplies, electric vehicle chargers and inverters, and vehicle drive systems. They also have broad application prospects in onshore and offshore wind power generation systems, power storage systems, and power transmission and distribution systems.

[0004] Traditional silicon carbide (SiC) growth relies on physical vapor transport (PVT) technology, where SiC powder sublimates at approximately 2200°C or higher and recrystallizes on a seed crystal. However, PVT-based SiC crystal growth suffers from challenges such as difficulty in replenishing source powder and low growth rates, with a growth rate less than 1 / 100th that of crystalline silicon ingots. High-temperature chemical vapor deposition (HTCVD), on the other hand, allows for the continuous introduction of source gas to react at high temperatures and deposit crystals on a seed crystal. This significantly improves the growth rate and wafer quality of SiC, thereby reducing its production costs.

[0005] However, high-temperature chemical vapor deposition (HTCVD) is still in the development stage, and its practical engineering applications are not yet mature. The specific process and furnace structure for growing SiC crystals still need to be adjusted. In addition, unlike general chemical vapor deposition, the chemical reaction mechanism of SiC crystal growth by HTCVD is very complex, involving more than 100 kinds of gas-phase chemical reactions and surface chemical reactions, which poses a great challenge to the convergence, accuracy and computational efficiency of numerical simulation. At present, there is no efficient numerical simulation method available for reference.

[0006] Therefore, developing an efficient simulation method for growing silicon carbide using high-temperature chemical vapor deposition can provide guidance for the growth of SiC crystals in engineering, enabling rapid iteration of reactor structure and process parameters, thereby effectively reducing time and economic costs. Summary of the Invention

[0007] In view of the problems existing in the prior art, the purpose of the present invention is to provide an optimized prediction method for high-temperature chemical vapor deposition of silicon carbide, so as to achieve efficient optimization of silicon carbide growth by high-temperature chemical vapor deposition.

[0008] To achieve this objective, the present invention adopts the following technical solution:

[0009] In a first aspect, the present invention provides an optimized prediction method for high-temperature vapor deposition of silicon carbide, the optimized prediction method comprising:

[0010] Geometric modeling of the furnace structure used in high-temperature chemical vapor deposition was performed, and the fluid region within the reactor structure was meshed to obtain the computational domain.

[0011] The chemical mechanism model was calculated for a zero-dimensional fully mixed reaction device based on the baseline operating conditions to obtain an initial database of reaction kinetics. Then, PCA analysis was performed to obtain a simplified mechanism model.

[0012] Based on the computational domain and simplified mechanism model, the continuity equation, momentum conservation equation, and energy conservation equation of the gas in the furnace are solved in the first step according to the actual working conditions to obtain the flow field and temperature field. Based on the flow field and temperature field, the gas phase chemical reaction equation and surface chemical reaction equation are solved in the second step to obtain the growth rate at each position in the flow field. When the calculation of the flow field, temperature field, and chemical field converges, the flow field, temperature field, chemical field, and growth rate at the seed crystal surface are output; otherwise, the calculation continues.

[0013] The optimized simulation method provided by this invention establishes a multiphysics calculation model for silicon carbide growth by high-temperature chemical vapor deposition, which can quickly simulate the growth of SiC crystals to provide guidance for engineering applications, thereby accelerating equipment iteration and upgrading, and reducing time and economic costs.

[0014] As a preferred technical solution of the present invention, the characteristic parameters of the computational domain include: the diameter of the reaction chamber and the height of the reaction chamber.

[0015] As a preferred technical solution of the present invention, the parameters of the reference operating condition include: pressure, temperature, gas flow rate, gas component ratio and rotation speed.

[0016] As a preferred technical solution of the present invention, the calculation of the zero-dimensional total mixed reaction device is based on the mass conservation equation.

[0017] As a preferred technical solution of the present invention, the PCA analysis adopts eigenvalue-eigenvector analysis.

[0018] As a preferred technical solution of the present invention, before the first solution is obtained, the flow rate and temperature of the inlet gas, the operating pressure inside the furnace, the furnace wall temperature, the pressure of the outlet gas, the rotation speed of the seed crystal, and the target calculation residual are specified in the calculation domain according to the actual working conditions.

[0019] As a preferred technical solution of the present invention, if the calculated residuals of the flow field, temperature field and chemical field are all less than the target calculated residuals, then convergence is achieved; otherwise, the calculation continues.

[0020] In a second aspect, the present invention provides an optimized prediction apparatus for high-temperature vapor deposition of silicon carbide, the optimized prediction apparatus comprising:

[0021] The computational domain module is used to perform geometric modeling of the furnace structure used in high-temperature chemical vapor deposition and to mesh the fluid region within the reactor structure to obtain the computational domain.

[0022] The mechanism simplification module uses a chemical mechanism model to perform zero-dimensional total mixed reaction equipment calculations based on baseline operating conditions to obtain an initial database of reaction kinetics. Then, PCA analysis is performed to obtain a simplified mechanism model.

[0023] The optimization prediction module is used to perform the first solution of the continuity equation, momentum conservation equation, and energy conservation equation of the gas in the furnace based on the simplified model of the computational domain and mechanism, according to the actual working conditions, to obtain the flow field and temperature field. Based on the flow field and temperature field, the second solution of the gas phase chemical reaction equation and the surface chemical reaction equation is performed to obtain the growth rate at each position in the flow field. When the calculation of the flow field, temperature field, and chemical field reaches convergence, the flow field, temperature field, chemical field, and growth rate at the seed crystal surface are output; otherwise, the calculation continues.

[0024] Thirdly, the present invention provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the optimized prediction method for high-temperature vapor deposition of silicon carbide as described in the first aspect.

[0025] Fourthly, the present invention provides a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the optimized prediction method for high-temperature vapor deposition of silicon carbide as described in the first aspect.

[0026] Compared with existing technical solutions, the present invention has the following beneficial effects:

[0027] (1) Sensitivity analysis and chemical mechanism simplification of the chemical kinetic model for silicon carbide growth by high temperature chemical vapor deposition were performed, and chemical reactions with no or little impact were ignored, which significantly improved the computational stability and accelerated the computational speed.

[0028] (2) Using high-dimensional (two-dimensional and three-dimensional) computational domain models can obtain more information and richer details of flow field, temperature field and chemical field than low-dimensional (zero-dimensional and one-dimensional) computational models.

[0029] (3) Simultaneously modeling the flow field, temperature field and chemical field, considering the feedback effect between flow, heat transfer and chemical reaction, the coupled calculation of multiple physics fields is realized, thereby improving the prediction accuracy.

[0030] (4) It can realize the rapid simulation of SiC crystal growth by high temperature chemical vapor deposition, thereby providing a reference for engineering applications and design, accelerating equipment iteration and upgrading, and reducing time and economic costs. Attached Figure Description

[0031] Figure 1 This is a flowchart of an optimized prediction method for high-temperature vapor deposition of silicon carbide provided in an embodiment of the present invention;

[0032] Figure 2 This is a schematic diagram of an optimized prediction device for high-temperature silicon carbide vapor deposition provided in an embodiment of the present invention;

[0033] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention;

[0034] Figure 4 This is a flowchart of the optimized prediction method for high-temperature vapor deposition of silicon carbide in Embodiment 1 of the present invention;

[0035] Figure 5 This is a schematic diagram of the furnace body structure in Embodiment 1 of the present invention;

[0036] Figure 6 These are growth rate results under different working conditions in Embodiment 1 of the present invention;

[0037] Figure 7 This is a concentration distribution diagram of CH3, a chemical component, inside the reactor in Embodiment 1 of the present invention;

[0038] Figure 8 This is a distribution diagram of the SiC crystal growth rate on the seed crystal surface in Embodiment 1 of the present invention.

[0039] In the diagram: 100 - Computational domain module, 200 - Mechanism simplification module, 300 - Optimization and prediction module;

[0040] 10-Electronic device, 11-Processor, 12-ROM, 13-RAM, 14-Bus, 15-I / O interface, 16-Input unit, 17-Output unit, 18-Storage unit, 19-Communication unit.

[0041] The present invention will now be described in further detail. However, the examples described below are merely simplified examples of the present invention and do not represent or limit the scope of protection of the present invention. The scope of protection of the present invention is determined by the claims. Detailed Implementation

[0042] To better illustrate the present invention and facilitate understanding of its technical solutions, typical but non-limiting embodiments of the present invention are as follows:

[0043] I. This embodiment provides an optimized prediction method for high-temperature silicon carbide vapor deposition, the process of which is as follows: Figure 1 As shown, the optimized prediction method includes:

[0044] Geometric modeling of the furnace structure used in high-temperature chemical vapor deposition was performed, and the fluid region within the reactor structure was meshed to obtain the computational domain.

[0045] The chemical mechanism model was calculated for a zero-dimensional fully mixed reaction device based on the baseline operating conditions to obtain an initial database of reaction kinetics. Then, PCA analysis was performed to obtain a simplified mechanism model.

[0046] Based on the computational domain and simplified mechanism model, the continuity equation, momentum conservation equation, and energy conservation equation of the gas in the furnace are solved in the first step according to the actual working conditions to obtain the flow field and temperature field. Based on the flow field and temperature field, the gas phase chemical reaction equation and surface chemical reaction equation are solved in the second step to obtain the growth rate at each position in the flow field. When the calculation of the flow field, temperature field, and chemical field converges, the flow field, temperature field, chemical field, and growth rate at the seed crystal surface are output; otherwise, the calculation continues.

[0047] In this invention, an example of geometric modeling the furnace structure used in high-temperature chemical vapor deposition is as follows: The furnace body can be approximated as a cylinder, with the bottom as the inlet, the top center as the seed crystal, and the top two sides as the outlets. The main structural parameters are the furnace diameter, height, and seed crystal diameter. Geometric modeling is performed, and based on this, the reaction furnace geometry is meshed. The specific design can be reasonably selected according to the conventional requirements in this field.

[0048] The characteristic parameters of the computational domain include the diameter of the reaction chamber and the height of the reaction chamber.

[0049] In this invention, the furnace structure is specifically based on the reactor used in actual high-temperature chemical vapor deposition. The specific high-temperature chemical vapor deposition reactor can be a commercially available product or a reactor designed and manufactured based on existing technology.

[0050] In this invention, when conducting multiphysics coupling simulation, the reactor is first simplified, and only the fluid region inside the furnace is considered. The fluid region is modeled and meshed to obtain the computational domain.

[0051] The parameters of the reference operating condition include: pressure, temperature, gas flow rate, gas component ratio, and rotation speed.

[0052] In this invention, the chemical mechanism model refers to the main chemical reactions involved in the growth of silicon carbide crystals by high-temperature chemical vapor deposition, which can be obtained based on existing technology (Allendorf MD, Kee R J. A model of silicon carbide chemical vapor deposition[J]. Journal of the Electrochemical Society, 1991, 138(3): 841.).

[0053] The calculations for the zero-dimensional total mixed reaction device are based on the mass conservation equation.

[0054] In this invention, the mass conservation equation is as follows:

[0055] ;

[0056] In the formula, ρ is the density, kg / m³ 3 V is the volume, m 3 ; The inlet mass flow rate is kg / s; The outflow mass flow rate is kg / s; N inlet The number of reactor inlets; A m Let m be the surface area of ​​the m-th material. 2 ; Let be the molar surface formation rate per unit surface area of ​​the k-th component on the m-th material, mol / (m 2 s); W k Let be the molar mass of the kth component, in kg / mol.

[0057] In this invention, zero-dimensional total mixed reaction equipment calculation refers to a calculation process based on an ideal reactor model, which assumes that the materials in the reaction equipment are completely mixed, with uniform concentration and temperature. This process involves less computation and is beneficial for rapid analysis of large-scale chemical reaction mechanisms.

[0058] In this invention, the initial reaction kinetics database includes the concentrations and growth rates of different chemical components under different operating conditions, calculated by a zero-dimensional fully mixed reaction apparatus.

[0059] The PCA analysis described herein employs eigenvalue-eigenvector analysis.

[0060] In this invention, PCA analysis can extract chemical kinetic information from the linear sensitivity calculations of each component in a chemical reaction model. Changes in the rate constant along the principal axis corresponding to the largest eigenvalue in the sensitivity matrix are most sensitive, while changes along the principal axis corresponding to the smallest eigenvalue are least sensitive. Therefore, chemical reactions corresponding to principal components with smaller eigenvalues ​​(e.g., considering only the top 10% of eigenvalues ​​and ignoring the remaining 90%) can be neglected.

[0061] In this invention, the exemplary calculation process of PCA analysis is as follows:

[0062] The sensitivity matrix S for calculating chemical reaction mechanisms:

[0063] ;

[0064] In the formula, y i Let be the mass fraction of the i-th component, %; k j Let a be the rate constant of the j-th chemical reaction, in mol / L; j =lnk j ,use This indicates the baseline operating condition.

[0065] Due to the change in rate constant ( The effect of this on the calculation of the reaction mechanism can be expressed as follows:

[0066] ;

[0067] Using Taylor expansion, it can be simplified to:

[0068] ;

[0069] In the formula, H is the Hessian matrix of Q, and the relationship between the sensitivity matrix S and H = 2S is given by the formula H = 2S. T S+R, where R is the second derivative matrix of the component concentration with respect to the rate constant, which can be ignored using the Gaussian approximation.

[0070] Therefore, the effect of rate constant variation on reaction mechanism calculations can be represented by a sensitivity matrix as follows:

[0071] ;

[0072] Further analysis of this symmetric matrix (S) TS) Perform eigenvalue-eigenvector decomposition:

[0073] ;

[0074] In the formula, ∧ represents the result of (S) T The eigenvalues ​​of S) are {λ} i The diagonal matrix formed by}, where U is a matrix composed of (S T The normalized eigenvector u of S) i The matrix formed.

[0075] Therefore, the principal element of matrix Q can be further denoted as ∆Ψ=U T If ∆a, then Q can be represented by pivoting as:

[0076] ;

[0077] in: This equation shows that the influence on the chemical reaction mechanism can be approximated as occurring along each reaction path. This represents the cumulative effect of the rate constant change along the principal axis.

[0078] Therefore, the calculation of reaction mechanisms is most sensitive to changes in the rate constant along the principal axis corresponding to the largest eigenvalue of the sensitivity matrix, and least sensitive to changes along the principal axis corresponding to the smallest eigenvalue. Chemical reactions belonging to principal elements with small eigenvalues ​​can be ignored, and the corresponding reactants or products can also be removed, because their contribution to the overall chemical reaction system is very small.

[0079] For example, the process of obtaining the simplified mechanism model is as follows: Based on the complete chemical reactions involved in the growth of silicon carbide crystals by high-temperature chemical vapor deposition in the existing technology (Allendorf MD, Kee RJ. A model of silicon carbide chemical vapor deposition[J]. Journal of the Electrochemical Society, 1991, 138(3): 841.), a mechanism model is established. The initial database is obtained by calculating different benchmark conditions using a zero-dimensional fully mixed reaction device. The initial database is analyzed and screened by the PCA method, and finally a simplified chemical mechanism model is obtained.

[0080] In the first solution, the flow rate and temperature of the inlet gas, the operating pressure inside the furnace, the furnace wall temperature, the pressure of the outlet gas, the rotation speed of the seed crystal, and the target calculation residual are specified in the calculation domain according to the actual working conditions.

[0081] In this invention, actual operating conditions refer to the actual process conditions used in crystal growth, including the flow rate and temperature of the inlet gas, the operating pressure inside the furnace, the furnace wall temperature, the pressure of the outlet gas, and the rotation speed of the seed crystal.

[0082] In this invention, an exemplary process for solving the continuity equation, momentum conservation equation, and energy conservation equation for laminar flow is as follows:

[0083] Continuity equation:

[0084] ;

[0085] In the formula, ρ is the density, kg / m³ 3 t represents time, in seconds; For velocity, m / s; S m For mass source terms, kg / (m 3 ·s).

[0086] Momentum conservation equation:

[0087] ;

[0088] In the formula, p is the static pressure, in Pa; Let Pa be the stress tensor. and These are gravitational volume force and external volume force, respectively, in N.

[0089] Energy conservation equation:

[0090] ;

[0091] In the formula, e is the internal energy, J / kg; h is the enthalpy, J / kg; and k is the thermal conductivity, W / (m·K). Let m be the diffusion flux of component j. 2 ·s) -1 S h The heat production rate of the volumetric heat source and the chemical reaction, in W / m³. 3 .

[0092] For gas-phase chemical reactions, the reaction rate is defined on a volume basis, and the net rate of chemical substance production and consumption becomes the source term in the continuity equation.

[0093] Solve the component transport equations for the following gas-phase chemical reaction:

[0094] ;

[0095] In the formula, Y i Let R be the mass fraction of the i-th component, %; i The net formation rate of component i during the chemical reaction is expressed in mol / (m).3 ·s); S i To define the generation rate in the custom source phase, mol / (m 3 ·s).

[0096] For laminar flow, the diffusion flux of component i It can be represented as:

[0097] ;

[0098] In the formula, D i,m D is the mass diffusion coefficient of component i in the mixture; t,i Let be the thermal diffusivity of component i in the mixture, and T be the temperature in K.

[0099] For surface chemical reactions, the adsorption and desorption rates are controlled by chemical kinetics and diffusion into and out of the surface.

[0100] Therefore, surface reactions generate sources and sinks of chemical substances in both the gas phase and at the reaction surface. The rate of the r-th surface reaction can be calculated using the following formula:

[0101] ;

[0102] In the formula, k f,r and k b,r These represent the forward and reverse reaction rates, respectively, in mol / (m 3 ·s); G i and S j Let i and j represent the i-th gaseous substance and the j-th site substance, respectively. wall N is the molar concentration of the adsorbed substance on the reaction surface. g and N s These represent the total amount of gaseous matter and site matter, respectively. and are the rate exponents of the i-th gaseous substance that is a reactant and a product, respectively. and denoted as the rate index of the j-th site substance that serves as both a reactant and a product.

[0103] Assuming the reaction rate is independent of the concentration of the bulk substances, the net molar rate of production or consumption of each substance can be determined by the following formula:

[0104] Net molar formation rate of gaseous substances, mol / (m 3 ·s):

[0105] ;

[0106] Net molar formation rate of bulk substance, mol / (m 3 ·s):

[0107] ;

[0108] Net molar formation rate of site-specific substances, mol / (m 3 ·s):

[0109] ;

[0110] In the formula, N rxn The total number of chemical reactions. , and These are the stoichiometric coefficients of each reactant i. , and , respectively, are the stoichiometric coefficients of each product i.

[0111] Update the velocity field, temperature field, and chemical field in the computational domain, and determine whether the computational residual is less than the set value, i.e., whether the computation has converged.

[0112] In this invention, the velocity field refers to the flow field at various locations within the computational domain, representing the gas flow velocity.

[0113] In this invention, the temperature field refers to the temperature field of the temperature distribution at various locations within the computational domain.

[0114] In this invention, the chemical field refers to the field that measures the distribution of gas components at various locations within the computational domain.

[0115] The calculation residuals of the flow field, temperature field, and chemical field are all less than the target calculation residual (e.g., exemplarily selected as less than 10). -3 (The specific choice can be made according to actual needs), then convergence is achieved; otherwise, the calculation continues.

[0116] II. This embodiment provides an optimized prediction device for high-temperature silicon carbide vapor deposition, such as... Figure 2 As shown, the optimized prediction device includes:

[0117] The computational domain module 100 is used to perform geometric modeling of the furnace structure used in the high-temperature chemical vapor deposition method and to mesh the fluid region within the reactor structure to obtain the computational domain.

[0118] The mechanism simplification module 200 uses the chemical mechanism model to perform zero-dimensional total mixed reaction equipment calculations based on the baseline operating conditions to obtain an initial database of reaction kinetics. Then, PCA analysis is performed to obtain a simplified mechanism model.

[0119] The optimization prediction module 300 is used to perform the first solution of the continuity equation, momentum conservation equation, and energy conservation equation of the gas in the furnace based on the simplified model of the computational domain and mechanism, according to the actual working conditions, to obtain the flow field and temperature field. Based on the flow field and temperature field, the second solution of the gas phase chemical reaction equation and the surface chemical reaction equation is performed to obtain the growth rate at each position in the flow field. When the calculation of the flow field, temperature field, and chemical field reaches convergence, the flow field, temperature field, chemical field, and growth rate at the seed crystal surface are output; otherwise, the calculation continues.

[0120] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0121] III. This embodiment provides an electronic device intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0122] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An I / O interface 15 is also connected to the bus 14.

[0123] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0124] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the optimized prediction method for silicon carbide high-temperature vapor deposition.

[0125] In some embodiments, the optimization prediction method for silicon carbide high-temperature vapor deposition can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the optimization prediction method for silicon carbide high-temperature vapor deposition described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the optimization prediction method for silicon carbide high-temperature vapor deposition by any other suitable means (e.g., by means of firmware).

[0126] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0127] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0128] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0129] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0130] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0131] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0132] The server provided in this embodiment includes: a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the program, it implements an optimized prediction method for high-temperature vapor deposition of silicon carbide.

[0133] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0134] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with embodiments of the present invention can all be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether this functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of protection of the present invention.

[0135] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0136] For software implementation, the techniques described in this invention can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or externally; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0137] IV. To illustrate the simulation effect achievable by the high-temperature chemical vapor deposition method for silicon carbide growth provided by this invention, a practical example is used as follows:

[0138] Example 1

[0139] This embodiment provides a simulation method for growing silicon carbide using high-temperature chemical vapor deposition, the process of which is as follows: Figure 4 As shown, the details are as follows:

[0140] Establish the reactor structure used in high-temperature chemical vapor deposition, such as Figure 5 As shown, the carrier gas and source gas enter from the bottom of the reactor, mix thoroughly in the crucible, and undergo a chemical reaction after heating. Finally, SiC crystals are deposited on the rotating seed crystal surface, and the exhaust gas after the reaction is discharged from the top of the furnace.

[0141] First, a complete chemical mechanism model for the growth of silicon carbide crystals by high-temperature chemical vapor deposition is established.

[0142] Operating conditions with varying pressures, temperatures, gas flow rates, gas component ratios, and rotational speeds were set up, and rapid analysis and calculations were performed on a zero-dimensional total mixed reactor under these conditions. Based on the calculation results from the zero-dimensional total mixed reactor, the concentrations and growth rates of each chemical component under different operating conditions were obtained, and an initial database of reaction kinetics was constructed. The growth rate results are shown below. Figure 6 As shown.

[0143] Sensitivity analysis was performed on the initial database using Principal Component Analysis (PCA). PCA employs eigenvalue-eigenvector analysis to extract chemical kinetic information from the linear sensitivity calculations of each component in the chemical reaction model. Changes in the rate constant along the principal axis corresponding to the largest eigenvalue in the sensitivity matrix are most sensitive, while changes along the principal axis corresponding to the smallest eigenvalue are least sensitive. Therefore, the chemical reactions corresponding to principal components with smaller eigenvalues ​​can be ignored.

[0144] The sensitivity matrix S for calculating chemical reaction mechanisms:

[0145] ;

[0146] In the formula, y i Let k be the mass fraction of the i-th component. j Let a be the rate constant of the j-th chemical reaction; j =lnk j ,use This indicates the baseline operating condition.

[0147] Due to the change in rate constant ( The effect of this on the calculation of the reaction mechanism can be expressed as follows:

[0148] ;

[0149] Using Taylor expansion, it can be simplified to:

[0150] ;

[0151] In the formula, H is the Hessian matrix of Q, and the relationship between the sensitivity matrix S and H = 2S is given by the formula H = 2S. T S+R, where R is the second derivative matrix of the component concentration with respect to the rate constant, which can be ignored using the Gaussian approximation.

[0152] Therefore, the effect of rate constant variation on reaction mechanism calculations can be represented by a sensitivity matrix as follows:

[0153] ;

[0154] Furthermore, regarding this symmetric matrix (S) T S) Perform eigenvalue-eigenvector decomposition:

[0155] ;

[0156] In the formula, ∧ represents the result of (S) T The eigenvalues ​​of S) are {λ} i The diagonal matrix formed by}, where U is a matrix composed of (S T The normalized eigenvector u of S) i The matrix formed.

[0157] Therefore, the principal element of matrix Q can be further denoted as ∆Ψ=U T If ∆a, then Q can be represented by pivoting as:

[0158] ;

[0159] In the formula, This equation shows that the influence on the chemical reaction mechanism can be approximated as occurring along each reaction path. This represents the cumulative effect of the rate constant change along the principal axis.

[0160] Therefore, the calculation of reaction mechanisms is most sensitive to changes in the rate constant along the principal axis corresponding to the largest eigenvalue of the sensitivity matrix, and least sensitive to changes along the principal axis corresponding to the smallest eigenvalue. Chemical reactions belonging to principal elements with small eigenvalues ​​can be ignored, and the corresponding reactants or products can also be removed, because their contribution to the overall chemical reaction system is very small.

[0161] When conducting multiphysics coupled simulations, the reactor is simplified, and only the fluid region inside the furnace is considered. The fluid region is modeled and meshed to obtain the computational domain model.

[0162] A simplified gas and surface chemical kinetic model for SiC crystal growth was written using UDF and calculated. Based on the operating conditions, the inlet computational domain model was specified, including the inlet gas flow rate and temperature, furnace operating pressure, furnace wall temperature, outlet gas pressure, seed crystal rotation speed, and calculation residuals.

[0163] Solve the continuity equation, momentum conservation equation, and energy conservation equation for laminar flow:

[0164] Continuity equation:

[0165] ;

[0166] In the formula, ρ is density and t is time. For speed, S m This is a quality source item.

[0167] Momentum conservation equation:

[0168] ;

[0169] In the formula, p is the static pressure. For stress tensor, and These are gravitational volume force and external volume force, respectively.

[0170] Energy conservation equation:

[0171] ;

[0172] In the formula, e is the internal energy, h is the enthalpy, and k is the thermal conductivity. S is the diffusion flux of component j. h The heat production rate of the volumetric heat source and the chemical reaction.

[0173] For gas-phase chemical reactions, the reaction rate is defined on a volume basis, and the net rate of chemical substance production and consumption becomes the source term in the continuity equation.

[0174] Solve the component transport equations for the following gas-phase chemical reaction:

[0175] ;

[0176] In the formula, Y i R represents the mass fraction of substance component i. i S represents the net formation rate of component i during a chemical reaction. i To customize the generation rate in the source phase.

[0177] For laminar flow, the diffusion flux of component i It can be represented as:

[0178] ;

[0179] In the formula, D i,m D is the mass diffusion coefficient of component i in the mixture. t,i Let be the thermal diffusivity of component i in the mixture, and T be the temperature.

[0180] For surface chemical reactions, the adsorption and desorption rates are controlled by chemical kinetics and diffusion into and out of the surface.

[0181] Therefore, surface reactions generate sources and sinks of chemical substances in the gas phase and at the reaction surface. The rate of the r-th surface reaction can be obtained by the following formula:

[0182] ;

[0183] In the formula, k f,r and k b,r These represent the forward reaction rate and the reverse reaction rate, respectively, G i and S j Let i and j represent the i-th gaseous substance and the j-th site substance, respectively. wall N is the molar concentration of the adsorbed substance on the reaction surface. g and N s These represent the total amount of gaseous matter and site matter, respectively. and are the rate exponents of the i-th gaseous substance that is a reactant and a product, respectively. and denoted as the rate index of the j-th site substance that serves as both a reactant and a product.

[0184] Assuming the reaction rate is independent of the concentration of the bulk substances, the net molar rate of production or consumption of each substance can be determined by the following formula:

[0185] Net molar formation rate of gaseous substances:

[0186] ;

[0187] Net molar rate of bulk material formation:

[0188] ;

[0189] Net molar formation rate of site material:

[0190] ;

[0191] In the formula, N rxn The total number of chemical reactions. , and These are the stoichiometric coefficients of each reactant i. , and , respectively, are the stoichiometric coefficients of each product i.

[0192] Update the velocity, temperature, and chemical fields in the computational domain, and determine whether the computational residuals are less than the set values, i.e., whether the calculation has converged; if the residuals are not less than the set value of 10... -3 If the residual is less than the set value of 10, proceed to the next iteration step and repeat the calculation until convergence; -3 If the calculation converges, the calculation ends.

[0193] After the calculation, the concentration distribution of each chemical component in the reactor and the distribution of SiC crystal growth rate on the seed crystal surface can be obtained, such as... Figure 7 and Figure 8 As shown.

[0194] In summary, the high-temperature chemical vapor deposition (CVD) method for growing silicon carbide provided by this invention can rapidly simulate the growth of SiC crystals, assess the gas distribution within the reactor, and explore the influence of process and structural parameters on SiC crystal growth through numerical simulation. Compared to engineering experiments and existing simulation techniques, this method significantly reduces economic costs and shortens experimental time.

[0195] The present invention is described in detail through the above embodiments, but the present invention is not limited to the above detailed structural features, that is, it does not mean that the present invention must rely on the above detailed structural features to be implemented. Those skilled in the art should understand that any improvements to the present invention, equivalent substitutions for the components used in the present invention, additions of auxiliary components, and selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.

[0196] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

[0197] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

[0198] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

Claims

1. An optimized prediction method for high-temperature vapor deposition of silicon carbide, characterized in that, The optimized prediction method includes: Geometric modeling of the furnace structure used in high-temperature chemical vapor deposition was performed, and the fluid region within the reactor structure was meshed to obtain the computational domain. The chemical mechanism model was calculated for a zero-dimensional fully mixed reaction device based on the baseline operating conditions to obtain an initial database of reaction kinetics. Then, PCA analysis was performed to obtain a simplified mechanism model. Based on the computational domain and simplified mechanism model, the continuity equation, momentum conservation equation, and energy conservation equation of the gas in the furnace are solved in the first step according to the actual working conditions to obtain the flow field and temperature field. Based on the flow field and temperature field, the gas phase chemical reaction equation and surface chemical reaction equation are solved in the second step to obtain the growth rate at each position in the flow field. When the calculation of the flow field, temperature field, and chemical field converges, the flow field, temperature field, chemical field, and growth rate at the seed crystal surface are output; otherwise, the calculation continues.

2. The optimized prediction method as described in claim 1, characterized in that, The characteristic parameters of the computational domain include the diameter of the reaction chamber and the height of the reaction chamber.

3. The optimized prediction method as described in claim 1, characterized in that, The parameters of the reference operating condition include: pressure, temperature, gas flow rate, gas component ratio, and rotation speed.

4. The optimized prediction method as described in claim 1, characterized in that, The calculations for the zero-dimensional total mixed reaction apparatus are based on the mass conservation equation.

5. The optimized prediction method as described in claim 1, characterized in that, The PCA analysis used eigenvalue-eigenvector analysis.

6. The optimized prediction method as described in claim 1, characterized in that, Before the first solution is obtained, the flow rate and temperature of the inlet gas, the operating pressure inside the furnace, the furnace wall temperature, the pressure of the outlet gas, the rotation speed of the seed crystal, and the target calculation residual are specified in the calculation domain according to the actual working conditions.

7. The optimized prediction method as described in claim 6, characterized in that, If the calculated residuals of the flow field, temperature field, and chemical field are all less than the target calculated residuals, then convergence is achieved; otherwise, the calculation continues.

8. An optimized prediction device for high-temperature silicon carbide vapor deposition, characterized in that, The optimized prediction device includes: The computational domain module is used to perform geometric modeling of the furnace structure used in high-temperature chemical vapor deposition and to mesh the fluid region within the reactor structure to obtain the computational domain. The mechanism simplification module uses a chemical mechanism model to perform zero-dimensional total mixed reaction equipment calculations based on baseline operating conditions to obtain an initial database of reaction kinetics. Then, PCA analysis is performed to obtain a simplified mechanism model. The optimization prediction module is used to perform the first solution of the continuity equation, momentum conservation equation, and energy conservation equation of the gas in the furnace based on the simplified model of the computational domain and mechanism, according to the actual working conditions, to obtain the flow field and temperature field. Based on the flow field and temperature field, the second solution of the gas phase chemical reaction equation and the surface chemical reaction equation is performed to obtain the growth rate at each position in the flow field. When the calculation of the flow field, temperature field, and chemical field reaches convergence, the flow field, temperature field, chemical field, and growth rate at the seed crystal surface are output; otherwise, the calculation continues.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to said at least one processor; The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the optimized prediction method for silicon carbide high-temperature vapor deposition as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the optimized prediction method for high-temperature vapor deposition of silicon carbide as described in any one of claims 1-7.

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