Simulation method and device of annealing equipment, computer equipment and readable storage medium

Abstract

The invention relates to a simulation method and device of annealing equipment, computer equipment and a readable storage medium. The method comprises the steps of obtaining a simulation target of annealing equipment; performing geometric modeling on the annealing equipment according to the simulation target to obtain a two-dimensional geometric model and a three-dimensional geometric model of the annealing equipment; performing two-dimensional simulation based on the two-dimensional geometric model to obtain a first simulation case meeting a simulation target; constructing a second simulation case of the three-dimensional geometric model according to the first simulation case; performing three-dimensional simulation based on the second simulation case and the three-dimensional geometric model to obtain simulation data; and analyzing the simulation data, and determining a process optimization strategy of the annealing equipment. By adopting the method, the simulation process can be simplified.

Description

Technical Field

[0001] The present application relates to the field of semiconductor manufacturing process technology, and in particular to a simulation method, device, computer equipment and readable storage medium for annealing equipment. Background Art

[0002] Semiconductor annealing equipment is used to heat semiconductor materials to modify their physical or chemical properties and optimize their electrical performance. Its operating principle is to use thermal energy to eliminate defects that cause internal stress within an object. To ensure a fast and uniform heating rate, the annealing reactor is surrounded by heaters, which then heat the annealing chamber using the thermal radiation released by the heaters. During heat treatment, these heaters can heat the wafers in the annealing chamber to the required process temperature within seconds at a heating rate exceeding 100°C per second. After the heat treatment phase is completed (approximately tens of seconds), the annealing chamber is cooled from the high temperature to its original temperature.

[0003] Traditional annealing equipment uses two heating methods: rapid thermal annealing (RTA) or furnace annealing. RTA uses halogen lamps, infrared lamps, or lasers to rapidly heat the sample to a high temperature (typically hundreds to thousands of degrees Celsius), followed by rapid cooling. Furnace annealing, on the other hand, places the sample in a high-temperature furnace tube, achieving a more uniform temperature and making it suitable for large-scale processing.

[0004] However, the optimization of the above annealing process relies on a trial-and-error approach, which is time-consuming and costly. Simulation technology has been introduced to address these issues. However, the simulation process in related simulation technologies is redundant, cumbersome, and costly. Summary of the Invention

[0005] Based on this, it is necessary to provide a simulation method, device, computer equipment, computer-readable storage medium and computer program product for annealing equipment that can simplify the simulation process and reduce simulation costs in order to address the above technical problems.

[0006] In a first aspect, the present application provides a simulation method for an annealing device, the method comprising:

[0007] Get the simulation target of the annealing equipment;

[0008] Performing geometric modeling on the annealing equipment according to the simulation target to obtain a two-dimensional geometric model and a three-dimensional geometric model of the annealing equipment;

[0009] Performing a two-dimensional simulation based on the two-dimensional geometric model to obtain a first simulation case that meets the simulation goal;

[0010] constructing a second simulation case of the three-dimensional geometric model based on the first simulation case;

[0011] Performing a three-dimensional simulation based on the second simulation case and the three-dimensional geometric model to obtain simulation data;

[0012] The simulation data is analyzed to determine a process optimization strategy for the annealing equipment.

[0013] In one embodiment, geometric modeling of the annealing equipment according to the simulation target to obtain a two-dimensional geometric model and a three-dimensional geometric model of the annealing equipment includes:

[0014] According to the simulation target, determining a target physical component affecting the physical and chemical phenomena in the simulation domain from the annealing device;

[0015] constructing a two-dimensional geometric model of the annealing equipment according to the first target component, and generating a first information file of the two-dimensional geometric model;

[0016] A three-dimensional geometric model of the annealing equipment is constructed according to the second target component of the annealing equipment, and a second information file of the three-dimensional geometric model is generated.

[0017] In one embodiment, performing a two-dimensional simulation based on the two-dimensional geometric model to obtain a first simulation case that meets the simulation goal includes:

[0018] generating a base mesh of the two-dimensional geometric model;

[0019] Importing the first information file into a preset open source framework, and reading the device parameters of the target physical components in the first information file and the relative position information between the target physical components through the preset open source framework;

[0020] Meshing the base mesh according to the device parameters and the relative position information, deleting meshes irrelevant to the first information file, and obtaining a simulation mesh of the two-dimensional geometric model;

[0021] Determining a simulation physical model, initial conditions, boundary conditions, and simulation calculation setting data for simulation based on the simulation target;

[0022] Physical simulation is performed according to the simulation grid, the simulation physical model, the initial conditions, the boundary conditions and the simulation calculation setting data to obtain a first simulation case that meets the simulation goal.

[0023] In one embodiment, determining a simulation physical model, initial conditions, boundary conditions, and simulation calculation setting data for simulation based on the simulation target includes:

[0024] Determining a simulation physical model for simulation based on the simulation target;

[0025] Determining a simulation physical model setting of the simulation physical model and a physical property parameter setting that enables the simulation physical model to reflect the physical behavior of a material or fluid;

[0026] Determining initial conditions and boundary conditions based on the boundaries determined by the two-dimensional geometric model and the physical properties of the simulated physical model;

[0027] Simulation calculation setting parameters are determined according to the simulation physical model, the simulation target and simulation calculation resources.

[0028] In one embodiment, determining a simulation physical model for simulation based on the simulation target includes:

[0029] In a case where the simulation target is a coupled simulation of a flow field and a temperature field, determining that the simulation physical model corresponding to the coupled simulation of the flow field and the temperature field includes a turbulence model and a radiation model;

[0030] The turbulence model includes a laminar flow model and / or a kEpsilon turbulence model, and the radiation model includes a P1 model and / or an FvDOM radiation model.

[0031] In one embodiment, constructing a second simulation case of the three-dimensional geometric model based on the first simulation case includes:

[0032] Determine a first simulation setting dimension that is the same as that of the two-dimensional simulation and a second simulation setting dimension that is different from that of the three-dimensional simulation;

[0033] Determining first simulation setting parameters corresponding to the first simulation setting dimension from a first simulation case; the first simulation setting parameters include a simulation physical model, initial conditions, boundary conditions, and simulation calculation setting parameters;

[0034] A second simulation case of the three-dimensional geometric model is constructed according to the first simulation setting parameters and second simulation setting parameters corresponding to the second simulation setting dimensions.

[0035] In one embodiment, the method further comprises:

[0036] generating a base mesh of the three-dimensional geometric model;

[0037] Importing the second information file into a preset open source framework, and reading the device parameters of the second target physical component in the second information file and the relative position information between the physical components through the preset open source framework;

[0038] The base grid is meshed according to the device parameters and the relative position information, and grids irrelevant to the second information file are deleted to obtain a simulation grid of the three-dimensional geometric model.

[0039] In a second aspect, the present application provides a simulation device for an annealing device, the device comprising:

[0040] A data acquisition module is used to obtain the simulation target of the annealing equipment;

[0041] A geometric modeling module, configured to perform geometric modeling on the annealing equipment according to the simulation target, and obtain a two-dimensional geometric model and a three-dimensional geometric model of the annealing equipment;

[0042] a simulation case generating module, configured to perform a two-dimensional simulation based on the two-dimensional geometric model to obtain a first simulation case that meets the simulation objective;

[0043] constructing a second simulation case of the three-dimensional geometric model based on the first simulation case;

[0044] a simulation module, configured to perform a three-dimensional simulation based on the second simulation case and the three-dimensional geometric model to obtain simulation data;

[0045] The data processing module is used to analyze the simulation data and determine the process optimization strategy of the annealing equipment.

[0046] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-described method when executing the computer program.

[0047] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described above when the computer program is executed by a processor.

[0048] In a fifth aspect, the present application also provides a computer program product, comprising a computer program, which implements the steps of the method described above when executed by a processor.

[0049] The simulation method, device, computer device, computer-readable storage medium and computer program product of the annealing equipment above construct a two-dimensional geometric model of the simulation object, perform two-dimensional simulation based on the two-dimensional geometric model, and obtain a first simulation case that meets the simulation goal. On this basis, a second simulation case of the three-dimensional geometric model is constructed according to the first simulation case; three-dimensional simulation is performed based on the second simulation case and the three-dimensional geometric model to obtain simulation data; the simulation data is analyzed to determine the process optimization strategy of the annealing equipment, that is, the three-dimensional simulation inherits the simulation parameter settings of the two-dimensional simulation for simulation, and the two-dimensional simulation has a fast calculation speed. Even when the calculation results diverge, the cause of the divergence can be locked more quickly, and adjustments can be made in time and conveniently, transitioning from a simple two-dimensional simulation to a complex three-dimensional simulation. On the one hand, the probability of divergence of the calculation results of the three-dimensional scene simulation is reduced. On the other hand, by inheriting the two-dimensional simulation settings, the simulation process can be simplified, and reasonable simulation settings can be obtained more quickly, thereby ensuring simulation reliability and reducing simulation costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 Schematic diagram of a flow chart of a simulation method for an annealing device in one embodiment;

[0052] Figure 2 is a schematic diagram of a target physical component in a two-dimensional geometric model in one embodiment;

[0053] Figure 3 A schematic diagram of the main components of a three-dimensional geometric model in one embodiment;

[0054] Figure 4 A schematic flow chart of a method for determining a first simulation case in one embodiment;

[0055] Figure 5 A schematic diagram of a simulation grid of a two-dimensional geometric model in one embodiment;

[0056] Figure 6 A schematic diagram of a portion of simulation results of a two-dimensional simulation in one embodiment;

[0057] Figure 7 is a schematic diagram of a geometric outline and surface mesh of a three-dimensional geometric model in one embodiment;

[0058] Figure 8A schematic diagram of a simulation grid of a three-dimensional geometric model in one embodiment;

[0059] Figure 9 Schematic diagram of simulation results of three-dimensional simulation in one embodiment;

[0060] Figure 10 A structural block diagram of a simulation device for an annealing device in one embodiment;

[0061] Figure 11 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0063] Semiconductor annealing equipment is used to heat semiconductor materials to modify their physical or chemical properties and optimize their electrical performance. Its operating principle is to use thermal energy to eliminate defects that cause internal stress. To ensure rapid and uniform heating, the annealing reactor is surrounded by heaters, which heat the annealing chamber using thermal radiation. During heat treatment, these heaters can heat the wafers in the annealing chamber to the required process temperature within seconds at a rate exceeding 100°C per second. After the heat treatment phase is complete (approximately tens of seconds), the annealing chamber is cooled back to its original temperature. Common annealing equipment uses two heating methods: rapid thermal annealing (RTA) and furnace annealing. RTA uses halogen lamps, infrared lamps, or lasers to rapidly heat the sample to a high temperature (typically hundreds to thousands of degrees Celsius), followed by rapid cooling. Furnace annealing involves placing the sample in a high-temperature furnace tube, achieving a more uniform temperature and suitable for large-scale processing. Annealing equipment requires atmosphere control. Specific atmospheres, such as nitrogen (N2), oxygen (O2), and hydrogen (H2), can be used to control redox reactions or reduce contamination. Precise control of heating and cooling rates is crucial during the annealing process to prevent thermal stress from causing material damage. Semiconductor thermal annealing processes are commonly used for doping activation, defect repair, thin film stress relief, and interface optimization.

[0064] In the production and actual application of semiconductor annealing equipment, there are many technical challenges and practical problems, which directly affect the process efficiency, equipment performance and the final chip yield. The main problems include temperature control and uniformity problems, insufficient process repeatability and stability, and the complexity of multi-physical field coupling. During the annealing process, it is necessary to reach a high temperature (above 1000°C) in a very short time (such as seconds), but the uneven distribution of the thermal field of the equipment leads to a large temperature gradient on the surface of the wafer, causing material stress and lattice defects. Factors such as equipment aging, power fluctuations of heating elements (such as halogen lamps and lasers), and changes in the gas environment lead to drift in process parameters between batches. The annealing process involves the coupling of multiple physical fields such as heat conduction, radiation, material phase change, and stress relaxation. Existing equipment is difficult to monitor and control these complex interactions in real time.

[0065] To address these optimization challenges, simulation software, particularly computational fluid dynamics (CFD) simulation and multiphysics coupled simulation, can play a crucial role. CFD simulation can simulate the airflow and heat transfer processes within the annealing equipment, accurately predicting the temperature distribution within different regions of the furnace chamber. Simulation can help optimize the furnace chamber's airflow and heating system to ensure uniform heat distribution, thereby avoiding localized overheating or uneven cooling. Simulation can also be used to optimize the layout and power output of heating sources, ensuring a uniform temperature field during the heating process. This is particularly important for the rapid thermal annealing (RTA) technology used in annealing furnaces, as the rapid heating and cooling processes place high demands on temperature control. Temperature control involves more than just heat conduction; it is also closely related to factors such as airflow and radiative heat transfer. By coupling fluid dynamics and heat transfer processes, simulation can optimize heating and cooling systems to ensure efficient and uniform operation across the entire system. Simulation can also provide detailed analysis of process sensitivity under varying operating conditions. For example, simulation can predict process stability and repeatability under varying airflow, temperature, and atmosphere conditions, providing data support for process optimization. Through simulation, sensitivity analysis of the process is performed to evaluate the impact of different operating parameters (such as heating rate, cooling rate, atmosphere type, etc.) on the annealing effect. This helps engineers identify and eliminate factors that may cause instability in the process, thereby improving the repeatability and stability of the process. Through simulation, the automated control system of the annealing equipment can also be designed and optimized to ensure that parameters such as temperature, atmosphere, and airflow are always kept within the appropriate range, reduce human interference, and improve the stability of the process. Simulation technology can comprehensively analyze multiple physical processes inside the annealing furnace, such as fluid flow, thermal radiation, the performance of heating elements, the interaction between gas and materials, etc., and couple these physical phenomena into the same simulation model for analysis. This integrated simulation method can help design a more efficient, uniform and stable annealing process.

[0066] There are also numerous simulation tools to choose from. For semiconductor device simulation, numerous specialized software packages are available for simulating processes such as airflow, heat transfer, chemical reactions, and plasma behavior. Examples include COMSOL Multiphysics, ANSYS Fluent, and Synopsys Sentaurus. However, these are mostly commercial software and are relatively expensive to use. Therefore, a simulation method that simplifies the simulation process is needed.

[0067] Based on this, a method is proposed that innovatively integrates production data-driven modeling technology, constructs a two-dimensional simplified model for rapid verification and a three-dimensional fine model for multi-physics field coupling analysis, and uses the open source computing platform OpenFOAM to establish a complete simulation system, thereby reducing the cost of process line experiments, simplifying the simulation process, and getting rid of dependence on commercial software, while achieving optimization of equipment structure and process.

[0068] It's important to note that the open-source computing platform OpenFOAM offers significant advantages over commercial simulation software in certain areas, such as lower cost-effectiveness, strong flexibility and customizability, and excellent transparency and understandability. These advantages make it attractive in certain fields, particularly in research, education, and specific industrial applications. In the field of CFD simulation, OpenFOAM is a mature open-source software package. In this embodiment of the present invention, OpenFOAM will be used to simulate semiconductor annealing equipment.

[0069] Within the OpenFOAM software framework, users can fully access and modify the source code to customize it according to specific needs. This means that simulation models can be adjusted or optimized based on the specific requirements of the semiconductor annealing process (such as temperature distribution, airflow pattern, distribution of reactive gases, etc.). New physical models or boundary conditions can also be customized based on the characteristics of the annealing equipment, and even existing solvers and algorithms can be expanded, which is a significant advantage for handling complex annealing processes and equipment. The annealing process involves the coupling of multiple physical fields such as heat conduction, gas flow, radiation heat transfer, and chemical reactions.

[0070] OpenFOAM provides powerful multi-physics simulation capabilities that can handle coupled problems involving multiple physical processes such as heat, fluid, radiation, and chemical reactions. Annealing equipment typically has complex geometries, such as furnace chambers, heating elements, and airflow distribution systems. OpenFOAM's powerful mesh generation tools can handle complex geometries and provide high-quality meshes to ensure accurate simulation results. The core of the annealing process is the precise heating of the wafer. OpenFOAM can simulate processes such as heat conduction, convection, and radiation to accurately predict the temperature field distribution within the furnace chamber. By simulating changes in the temperature field, heating and cooling strategies can be optimized to avoid overheating or uneven cooling.

[0071] In an exemplary embodiment, Figure 1 As shown, a simulation method for annealing equipment is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0072] Step 102: Acquire a simulation target of the annealing equipment.

[0073] The simulation objective can be a specific requirement or desired result that requires clarification, such as optimizing the heating rate, temperature uniformity, or energy efficiency of an annealing device. Alternatively, for a simple annealing process, the gas flow and temperature distribution within the chamber under a specific scenario can be simulated. The mutual influence between the two, i.e., the coupling of the flow and temperature fields, is described in this embodiment. Based on the simulation results, the boundary conditions can be appropriately modified to simulate and explore the impact of experimental conditions on the temperature field.

[0074] For example, the simulation target can be determined by matching it with actual application requirements and analyzing parameters in the annealing process (such as temperature distribution, time-temperature curve, etc.).

[0075] Step 104 : geometrically modeling the annealing equipment according to the simulation target to obtain a two-dimensional geometric model and a three-dimensional geometric model of the annealing equipment.

[0076] Geometric modeling refers to the process of abstracting physical objects into mathematical descriptions, which is used to define the spatial structure of objects. Geometric modeling includes two-dimensional geometric modeling and three-dimensional geometric modeling. It should be noted that when establishing the geometric model of the annealing equipment, only the geometric model of the simulation object (i.e., the gas in the chamber) is considered. Special boundary types are used to replace entities such as the chamber wall, wafer, and heater. The core components used to construct two-dimensional and three-dimensional geometric models are determined according to the simulation objectives.

[0077] For example, if the simulation scenario corresponding to the simulation target only focuses on the airflow, heat exchange, and chemical reactions within the cavity, then all structural components within the cavity that can affect these issues are considered core components. In other words, the common characteristic of core components is that they significantly influence the physical and chemical phenomena in the simulation domain. Small components whose influence can be ignored within an appropriate range are not considered core components in the simulation.

[0078] 2D geometry can be created using OpenFOAM's built-in mesh generation commands. When creating the 2D geometry of the RTA equipment, the circular cavity can be simplified into a radial-height 2D model. Key structures such as the gas inlet and outlet, wafer, and heater must be positioned appropriately. The main components of the 2D geometry are the top heating wall, left and right side walls, and bottom wall, gas inlet, gas outlet, and rectangular wafer cross section. Modeling 2D and 3D geometries can be accomplished using existing methods and will not be detailed here.

[0079] Creating a 3D geometry model using only OpenFOAM's native commands is time-consuming, so in most cases, you'll need to use other geometry modeling software, such as SpaceClaim 2022R2. The main components of the 3D geometry model are the top heating wall, the side cylindrical wall, the bottom wall, the gas inlet and outlet, and the cylindrical wafer wall.

[0080] It should also be noted that after modeling is completed on the modeling software, a corresponding information file will be generated, and the information file is used to be imported into the simulation software for simulation.

[0081] Step 106 , performing a two-dimensional simulation based on the two-dimensional geometric model to obtain a first simulation case that meets the simulation goal.

[0082] Among them, the first simulation case includes initial conditions, boundary conditions, simulation physical model settings, physical property parameter settings, numerical discrete format and calculation settings that meet the requirements.

[0083] For example, two-dimensional simulation can be achieved by setting boundary conditions (such as initial temperature, heat flux, etc.), selecting an appropriate simulation algorithm (such as finite element method or finite difference method), and running the simulation program. Through two-dimensional simulation, the performance of the annealing equipment can be preliminarily evaluated and the parameter combination that meets the requirements can be screened out, that is, the first simulation case is obtained, thereby providing a reference for subsequent three-dimensional simulation. Among them, the simulation algorithm can be, but is not limited to, the simple algorithm, the piso algorithm, or the pimple algorithm.

[0084] Step 108 : constructing a second simulation case of the three-dimensional geometric model based on the first simulation case.

[0085] It should be noted that 2D simulations can be implemented by simplifying the 3D mesh (i.e., using a single-layer mesh and setting the boundary conditions to sliding or periodic). This allows for rapid simulation case creation, significantly reducing computing resource requirements and increasing simulation speed. Furthermore, the boundary type, model selection, and solver settings for 3D simulations can be inherited from the 2D simulation case.

[0086] Among them, the second simulation case is a new simulation scheme generated based on the three-dimensional geometric model and the parameter combination screened out in the first simulation case. It should be noted that the two-dimensional simulation settings inherited by the three-dimensional simulation include: initial conditions, boundary conditions, turbulence model settings, radiation model settings, physical property parameter settings, numerical discrete format, and calculation settings. However, there are some differences in the initial conditions and boundary conditions between the three-dimensional simulation and the two-dimensional simulation. The reason is that there are more boundary surfaces in the three-dimensional simulation than in the two-dimensional case, such as the wafer surface, the top heating surface, and the bottom wall. In the three-dimensional simulation, all boundary surfaces need to set the initial conditions and boundary conditions of the corresponding physical quantities. For example, in the three-dimensional simulation, all boundary surfaces need to set the initial conditions and boundary conditions of physical quantities such as velocity, temperature, and radiation intensity.

[0087] For example, the second simulation case can be constructed by mapping the target parameters selected in the first simulation case to a 3D geometric model and adjusting the boundary conditions of the 3D model to accommodate the simulation requirements of a higher dimension. This approach avoids designing a 3D simulation case from scratch, thereby optimizing the simulation process and saving time and computing resources.

[0088] Step 110 : Perform a three-dimensional simulation based on the second simulation case and the three-dimensional geometric model to obtain simulation data.

[0089] 3D simulation is a comprehensive numerical simulation based on a 3D geometric model, capable of reflecting the complete spatial behavior of the annealing equipment. Simulation data refers to the various output information generated during the simulation process, including temperature field distribution, stress changes, etc.

[0090] For example, the three-dimensional simulation can be implemented by loading the parameter configuration of the second simulation case, running the three-dimensional simulation program, and recording the simulation results. Through three-dimensional simulation, a more accurate performance evaluation of the annealing equipment can be obtained, providing a reliable basis for subsequent process optimization. The specific simulation steps can be implemented using existing simulation steps and are not detailed here.

[0091] Step 112: Analyze the simulation data to determine the process optimization strategy for the annealing equipment.

[0092] Simulation data analysis is the process of processing and interpreting simulation data to extract useful information. Process optimization strategies are improvement measures developed based on the analysis results, aiming to improve the performance of annealing equipment.

[0093] For example, simulation data analysis can be achieved by using statistical methods or machine learning algorithms (such as regression analysis and support vector machines) to mine patterns in the simulation data and identify factors that affect the annealing effect. Based on the analysis results, specific process optimization suggestions (such as adjusting heater power distribution, changing wafer placement, etc.) are proposed to achieve better annealing results.

[0094] The above-mentioned simulation method for annealing equipment involves geometrically modeling the annealing equipment to obtain two-dimensional and three-dimensional geometric models. A two-dimensional simulation is then performed based on the two-dimensional geometric model to preliminarily evaluate equipment performance and screen parameter combinations. A second simulation case is constructed based on the three-dimensional geometric model based on the first simulation case, avoiding the need to design a three-dimensional simulation case from scratch, thereby saving time and computing resources. A three-dimensional simulation is then performed based on the second simulation case and the three-dimensional geometric model to obtain a more accurate performance evaluation. Finally, the simulation data is analyzed to determine the process optimization strategy for the annealing equipment. In other words, by inheriting the simulation parameters of the two-dimensional simulation through three-dimensional simulation, the advantages of high-precision simulation are retained while unnecessary computation is reduced, effectively lowering simulation costs and promoting improved performance and stability of the annealing equipment.

[0095] In an exemplary embodiment, geometric modeling is performed on the annealing equipment according to the simulation target to obtain a two-dimensional geometric model and a three-dimensional geometric model of the annealing equipment, including:

[0096] According to the simulation target, a target physical component that affects the physical and chemical phenomena in the simulation domain is determined from the annealing equipment; a two-dimensional geometric model of the annealing equipment is constructed according to the first target component, and a first information file of the two-dimensional geometric model is generated; according to the second target component of the annealing equipment, a three-dimensional geometric model of the annealing equipment is constructed, and a second information file of the three-dimensional geometric model is generated.

[0097] Among them, the determination of the target physical components depends on the corresponding simulation scenario. For example, if the simulation scenario only focuses on the airflow, heat exchange, chemical reaction, etc. in the cavity, then all structural components in the cavity that can affect airflow, heat exchange, chemical reaction and other issues are core components. For example, the target physical components include heating surfaces, side walls, gas inlets, carriers, wafers, bottom walls, gas in the cavity and gas outlets. The common feature of the target physical components is that they have a great influence on the physical and chemical phenomena in the simulation domain. Small components whose influence can be ignored within an appropriate range are not the target physical components in the simulation. Figure 2 As shown, in an exemplary embodiment, a schematic diagram of the target physical components in the two-dimensional geometric model is shown. The target physical components of the two-dimensional geometric model include a heating surface 1, a side wall surface 2, a gas inlet 3, a supporting platform 4, a wafer 5, a bottom wall surface 6, an intracavity gas 7 and a gas outlet 8.

[0098] It should also be noted that the purpose of building a two-dimensional geometric model of the annealing equipment based on the first target component is to quickly establish a simulation case with simple geometry. Figure 2 In the two-dimensional geometric model shown, the heating surface 1 is used to simulate the heating lamp, where a fixed heating power or a fixed temperature can be used as the heat source. Other walls, including the side wall 2, the bottom wall 6, and all the walls of the wafer 5, are set as special wall functions in the simulation, with different settings for temperature T, airflow velocity v, pressure p, turbulence parameters, and radiation parameters. The simulation scenario is that a certain gas (which can be oxygen, nitrogen, argon, etc.) enters the cavity through the gas inlet 3 at a certain flow rate, and then flows out from the gas outlet 8, so that the interior of the cavity is filled with intracavity gas 7. In the process of forming a stable flow field, the heating surface 1 will exchange heat with the intracavity gas 7, and the thermal radiation of the heating surface 1 to each component must also be considered. Using this simple geometry to construct a simulation case can greatly shorten the pre-simulation calculation time.

[0099] The determination of the main components of the 3D geometric model is based entirely on the establishment of the geometric model according to the physical object, because in the 2D simulation, the 3D problem is completely simplified into 2D, which is definitely based on not considering the influence of non-axisymmetry. In fact, most physical objects are non-axisymmetric, so the 3D geometric model is based on the physical object to establish a simplified geometric model. That is, the 3D geometric model of the annealing equipment is constructed according to the second target component of the annealing equipment. In an exemplary embodiment, Figure 3 As shown, it is a schematic diagram of the main components of the three-dimensional geometric model, including the top heating wall 9, the air inlet 10, the internal gas 11, the bottom wall 12, the cylindrical wafer wall 13, the side cylindrical wall 14, and the air outlet 15. Similarly, the simulation scenario is that while the top heating wall 9 is heated at a fixed heating power or a fixed temperature, the internal gas 11 flows into the cavity from the air inlet 10 and wraps the cylindrical wafer wall 13, and then flows out from the air outlet 15. When the entire system reaches a steady state, a stable flow field and temperature field will be formed. In the simulation, the bottom wall 12, the cylindrical wafer wall 13 and the side cylindrical wall 14 are all set as special wall boundary conditions similar to the two-dimensional simulation.

[0100] The first information file may be a data file used to store information related to a two-dimensional geometric model. Its content typically includes the model's geometric parameters, material properties, and boundary conditions. The second information file may be a data file used to store information related to a three-dimensional geometric model. Its content is more detailed and comprehensive than the first information file, covering the second target component of the entire annealing equipment and its related properties.

[0101] In the above method, by constructing a two-dimensional geometric model of the annealing equipment and generating a first information file, and constructing a three-dimensional geometric model of the annealing equipment and generating a second information file, the efficiency of modeling and simulation can be effectively improved while ensuring simulation accuracy.

[0102] In an exemplary embodiment, Figure 4 , provides a method for determining a first simulation case, comprising the following steps:

[0103] Step 402: Generate a base mesh of the two-dimensional geometric model.

[0104] It should be noted that the above-described geometric model generation does not involve dimensioning, but dimensioning is crucial in simulation, as demonstrated in the generation of the base mesh, the topology of the gas inlet and outlet meshes, and the densification of the mesh close to the wafer in a 2D simulation. In one specific embodiment, the base mesh can be determined by initially discretizing the geometric model.

[0105] Step 404: import the first information file into a preset open source framework, and read the device parameters of the target physical components and the relative position information between the target physical components in the first information file through the preset open source framework.

[0106] The preset open source framework may be OpenFOAM, an open source software tool widely used in the field of computational fluid dynamics that supports a variety of physical simulation tasks. The device parameters may be specific numerical values or attributes describing the characteristics of the target physical components, such as heater power or reaction chamber thermal conductivity, and may be obtained by parsing the first information file. The relative position information may be data representing the spatial relationship between different target physical components.

[0107] Exemplarily, this process can be achieved by loading the first information file into the OpenFOAM environment and using its built-in functions to parse the file content, so that the data generated in the modeling phase can be seamlessly integrated into the simulation phase to ensure the accuracy and consistency of information transmission.

[0108] Step 406 , meshing the base mesh according to the device parameters and the relative position information, deleting meshes irrelevant to the first information file, and obtaining a simulation mesh of the two-dimensional geometric model.

[0109] Meshing is the process of further refining the base mesh into smaller units to accommodate the specific characteristics and distribution of the target physical component. A simulation mesh is an optimized mesh structure that accurately reflects the details of the geometric model and meets simulation requirements. This is achieved by removing mesh components not relevant to the simulation, thereby reducing unnecessary computational resource consumption and improving simulation accuracy and efficiency.

[0110] For example, referring to the actual dimensions of fire protection equipment, the base mesh (i.e., cavity) dimensions in the x, y, and z directions are 500mm*200mm*10mm. When generating the mesh using the blockMeshDict dictionary file, the x and y dimensions should be close to square to facilitate mesh refinement. The z-direction mesh is not used in the simulation and is customarily set to a single 0.01m grid. When creating the base mesh, define and name each boundary to facilitate subsequent boundary condition setting.

[0111] In the 2D geometry model, the hollowed wafer position uses snappyHexMeshDict, and the coordinates are freely selected (i.e. the wafer can be located anywhere in the cavity). Optionally, the wafer size can be set to 300mm*0.8mm. In the snappyHexMeshDict setting, the boundary layer can be defined. The boundary layer is suitable for turbulence models to improve simulation accuracy. If the inlet and outlet boundaries are not set at the beginning, you can use topoSetDict to select the grid position to reset it. Figure 5 As shown, in an exemplary embodiment, a simulation grid of a two-dimensional geometric model is shown. The simulation grid is a two-dimensional grid, including a heating surface 1 , a side wall surface 2 , a grid densification area 16 at the wafer, and a bottom surface 17 .

[0112] Step 408 : Determine the simulation physical model, initial conditions, boundary conditions, and simulation calculation setting data used for the simulation based on the simulation target.

[0113] The simulation physics model can be a mathematical equation or algorithm that describes the physical phenomena during the simulation, and can be selected based on the simulation objective. Initial conditions can be the state parameters of the system at the start of the simulation, such as the initial temperature distribution, and can be set based on the simulation objective. Boundary conditions can be constraints on the system boundaries, such as constant heat flux or adiabatic boundaries, and can also be defined based on the simulation objective. Simulation calculation setup data can include control parameters such as the solver type and time step.

[0114] When the simulation grid is determined, the boundary conditions and initial conditions need to be set. The selection of boundary conditions and initial conditions in three-dimensional and two-dimensional simulations is based on the actual situation and the simulation physical model. Based on the actual situation means setting certain boundaries (such as the heating surface 1, side wall 2, wafer 5, bottom wall 6 in the two-dimensional geometric model, and the top heating wall 9, bottom wall 12, and cylindrical wafer wall 13 in the three-dimensional geometric model) to fixed values, fixed functions, or fixed types according to the actual situation. The so-called based on the simulation physical model means setting the parameters involved in the simulation physical model, such as the turbulence model and the radiation model, to the boundary conditions and initial conditions suitable for the model.

[0115] The simulation calculation setting data includes the discrete format fvSchemes, the solution algorithm fvSolution and the calculation output setting controlDict, the number of iteration steps, the output data file at a specific step, whether to print coefficients during the calculation process, the output format and the residual display, etc. The discretization format in OpenFOAM is mainly used for the processing of convection, diffusion and source terms in numerical calculations. According to different flow characteristics (such as flow rate, turbulence level, etc.) and grid types, reasonable adjustment of the discretization method can greatly improve the quality of simulation results. The number of iteration steps and intervals in these operations have a certain impact on the simulation calculation. Other operations actually have no effect on the numerical calculation and are only for the convenience of viewing and subsequent data processing. The specific settings can be set according to actual conditions and will not be described here.

[0116] Furthermore, the PIMPLE algorithm can be selected as the solution algorithm. As the standard transient solution algorithm in OpenFOAM, the PIMPLE algorithm is highly flexible and stable, making it particularly suitable for dynamic flow problems. By combining the advantages of the PISO and SIMPLE algorithms, it not only improves numerical accuracy during time stepping but also effectively avoids numerical oscillation and instability. Regarding the calculation output settings, they only need to be adjusted according to the actual simulation results. The settings here will have a certain impact on the simulation results. Figure 6 Part of the simulation results of the two-dimensional simulation is shown, that is, part of the temperature field cloud map. In fact, corresponding data can be obtained in the entire simulation area, including the heating surface 1, the temperature cloud map 18 and the wafer 5.

[0117] In an exemplary embodiment, a simulation physical model, initial conditions, boundary conditions and simulation calculation setting data for simulation are determined based on a simulation target, including: determining a simulation physical model for simulation based on a simulation target; determining a simulation physical model setting of the simulation physical model and a physical parameter setting that enables the simulation physical model to reflect the physical behavior of a material or fluid; determining initial conditions and boundary conditions based on boundaries determined by a two-dimensional geometric model and physical properties of the simulation physical model; and determining simulation calculation setting parameters according to the simulation physical model, simulation target and simulation calculation resources.

[0118] Furthermore, when the simulation target is the coupled simulation of the flow field and the temperature field, the simulation physical model corresponding to the coupled simulation of the flow field and the temperature field is determined to include a turbulence model and a radiation model; wherein the turbulence model includes a laminar model and / or a kEpsilon turbulence model, and the radiation model includes a P1 model and / or an FvDOM radiation model.

[0119] For example, when the simulation target is the coupled simulation of the flow field and the temperature field, the corresponding simulation physical model can be a laminar flow model or a kEpsilon turbulence model, and for radiation simulation it can be a P1 model or an FvDOM radiation model. Furthermore, such a choice will result in the boundary conditions and initial conditions having additional parameters related to the simulation physical model in addition to the settings of temperature, velocity and pressure, such as turbulent kinetic energy k, turbulent thermal diffusivity alphat, turbulent energy dissipation rate epsilon, turbulent viscosity nut, and radiation intensity I. The additional parameters must be set as initial conditions and boundary conditions on all simulation boundaries. For example, the settings of some parameters such as turbulent kinetic energy k and turbulent energy dissipation rate epsilon need to depend on the gas flow rate and characteristic length at the gas inlet.

[0120] Step 410 , performing a two-dimensional simulation based on the simulation grid, the simulation physical model, the initial conditions, the boundary conditions and the simulation calculation setting data to obtain a first simulation case that meets the simulation goal.

[0121] For example, a two-dimensional simulation is performed based on the simulation grid, simulation physical model, initial conditions, boundary conditions, and simulation calculation setting data of the two-dimensional simulation. If no errors or anomalies occur during the simulation process and the simulation results meet the expected results of the simulation objectives, a first simulation case is determined and used as reference data for setting simulation cases for the three-dimensional simulation. For example, the simulation objective is a coupled simulation of the flow field and temperature field. The expected result of the simulation objective can be that the simulation results of temperature and velocity are reasonable and conform to physical laws and engineering requirements.

[0122] In the above embodiment, by generating a base mesh adapted to the target physical component to cover the entire model range, the device parameters and relative position information in the first information file are imported into the OpenFOAM framework to achieve seamless data integration, the base mesh is finely adjusted to generate a simulation mesh to improve resolution and computational efficiency, and the physical model, initial conditions, boundary conditions and calculation settings are clarified in combination with the simulation objectives to ensure the simulation targeting and accuracy, and a complete physical simulation process is executed based on all input data to obtain result data that meets the target. This can achieve the technical effect of reducing simulation complexity, improving result reliability and practicality, and laying the foundation for subsequent three-dimensional simulation and process optimization.

[0123] Based on the above 2D simulation, to achieve realistic simulation results, a 3D simulation case was constructed based on the 2D simulation case. It should be noted that the simulation steps for 3D and 2D simulations are the same. For 3D simulation, the corresponding 3D geometric model must be generated, the simulation grid must be set, the simulation physical model must be determined, and initial and boundary conditions, as well as simulation calculation data, must be set based on the simulation physical model.

[0124] In an exemplary embodiment, a base mesh of a three-dimensional geometric model is generated; a second information file is imported into a preset open source framework, and the device parameters of the second target physical component in the second information file and the relative position information between the physical components are read through the preset open source framework; the base mesh is meshed according to the device parameters and the relative position information, and meshes unrelated to the second information file are deleted to obtain a simulation mesh of the three-dimensional geometric model.

[0125] Among them, such as Figure 7 As shown in FIG. 1 , a schematic diagram of the geometric outline and surface mesh of a three-dimensional geometric model is provided in an exemplary embodiment. The geometric outline includes the top heating wall 9, the air inlet 10, the cylindrical wafer wall 13, and the air outlet 15. The surface mesh includes the top heating wall 9, the cylindrical wafer wall 13, and the side cylindrical wall 14. On this basis, we obtain Figure 8 The simulation grid diagram of the three-dimensional geometric model shown includes a gas inlet 10, a grid encryption area 16 at the wafer, a gas outlet 15, a central cross section 19 of the three-dimensional model, and a top heating surface 9.

[0126] Among them, the implementation method of obtaining the simulation mesh of the three-dimensional geometric model based on the three-dimensional geometric model can be implemented by existing methods, which will not be elaborated here. The simulation mesh of the three-dimensional geometric model can be understood as completely wrapping the prepared three-dimensional geometric model in a specific area, and drawing the base mesh (which can be a hexahedral mesh of exactly the same size) in this specific area. Then, using the three-dimensional geometric model as the outline, remove the redundant meshes to obtain the mesh required for the simulation. It should also be noted that when using SpaceClaim 2022R2 to generate and export a three-dimensional structure, that is, the second information file, the encoding of the exported second information file needs to be set to ASCII and the file type needs to be set to stl format, so that setting the mesh encryption area in OpenFOAM is greatly convenient and compatibility issues of different software can be avoided.

[0127] On this basis, a second simulation case of a three-dimensional geometric model is constructed according to the first simulation case, including:

[0128] Determine a first simulation setting dimension that is the same as that of the two-dimensional simulation and a second simulation setting dimension that is different from that of the three-dimensional simulation; determine first simulation setting parameters corresponding to the first simulation setting dimension from the first simulation case; the first simulation setting parameters include a simulation physical model, initial conditions, boundary conditions, and simulation calculation setting parameters; and construct a second simulation case of the three-dimensional geometric model based on the first simulation setting parameters and the second simulation setting parameters corresponding to the second simulation setting dimension.

[0129] Building the second simulation case based on the first simulation case can be understood as the 3D simulation inheriting the simulation parameter settings of the 2D simulation. The 2D simulation settings inherited by the 3D simulation include: initial conditions, boundary conditions, simulation physical model settings, physical property parameter settings, numerical discretization format, and calculation settings.

[0130] It should also be noted that the simulation physical model settings (such as turbulence model settings, radiation model settings), physical parameter settings, numerical discrete formats and calculation settings in three-dimensional simulation and two-dimensional simulation can be set to be exactly the same. This will not affect the simulation, it is just an expansion from two-dimensional to three-dimensional. However, since there are more boundaries in three-dimensional simulation than in two-dimensional cases (such as wafer surface, top heating surface and bottom wall), the settings of initial conditions and boundary conditions are different. On the basis of the two-dimensional settings, the boundary surfaces need to set the initial conditions and boundary conditions of physical quantities such as velocity, temperature, radiation intensity, etc., which can inherit the settings in the two-dimensional case.

[0131] Furthermore, a second simulation case of a three-dimensional geometric model is constructed based on the first simulation case, and a three-dimensional simulation is performed based on the second simulation case and the three-dimensional geometric model to obtain simulation data. Figure 9 As shown in the figure, a schematic diagram of the simulation results of a three-dimensional simulation is provided. Figure 9 The result graph shown shows the wafer profile 901, the cross-sectional temperature cloud 902, the bottom profile 903, and the top heating surface profile 904. Figure 9 Part of the simulation results are displayed, and the corresponding data in the entire simulation area can be obtained.

[0132] In the above-mentioned embodiment, a two-stage simulation method based on actual production data is used to construct a simplified geometric model using historical production data for rapid simulation. On this basis, simulation parameter settings are inherited from the complex three-dimensional model to gradually achieve coordinated optimization of the flow and temperature fields. Compared to related technologies that use direct three-dimensional simulation, which leads to computational divergence and fails to produce expected simulation results, this method, through data-driven dimensionality reduction modeling technology, achieves iterative optimization of the entire process from basic models to sophisticated models. This achieves efficient and accurate solutions to engineering problems while simultaneously achieving process cost intensification and optimizing computing resources.

[0133] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0134] Based on the same inventive concept, the present application also provides an annealing device simulation device for implementing the aforementioned annealing device simulation method. The solution to the problem provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations in the embodiments of the annealing device simulation device provided below can be found in the above-mentioned limitations on the annealing device simulation method, and will not be repeated here.

[0135] In an exemplary embodiment, Figure 10 As shown, a simulation device for annealing equipment is provided, comprising: a data acquisition module 1002, a geometric modeling module 1004, a simulation case generation module 1006, a simulation module 1008 and a data processing module 1010, wherein:

[0136] The data acquisition module 1002 is used to acquire the simulation target of the annealing equipment.

[0137] The geometric modeling module 1004 is used to perform geometric modeling on the annealing equipment according to the simulation target, and obtain a two-dimensional geometric model and a three-dimensional geometric model of the annealing equipment.

[0138] The simulation case generation module 1006 is configured to perform a two-dimensional simulation based on the two-dimensional geometric model to obtain a first simulation case that meets the simulation target; and construct a second simulation case of the three-dimensional geometric model based on the first simulation case.

[0139] The simulation module 1008 is configured to perform a three-dimensional simulation based on the second simulation case and the three-dimensional geometric model to obtain simulation data.

[0140] The data processing module 1010 is used to analyze the simulation data and determine the process optimization strategy of the annealing equipment.

[0141] The simulation device of the above-mentioned annealing equipment constructs a two-dimensional geometric model of the simulation object, performs two-dimensional simulation based on the two-dimensional geometric model, and obtains a first simulation case that meets the simulation target. On this basis, a second simulation case of the three-dimensional geometric model is constructed according to the first simulation case; a three-dimensional simulation is performed based on the second simulation case and the three-dimensional geometric model to obtain simulation data; the simulation data is analyzed to determine the process optimization strategy of the annealing equipment, that is, the three-dimensional simulation inherits the simulation parameter settings of the two-dimensional simulation for simulation, and the two-dimensional simulation has a fast calculation speed. Even when the calculation results diverge, the cause of the divergence can be locked more quickly, and adjustments can be made in time and conveniently, transitioning from a simple two-dimensional simulation to a complex three-dimensional simulation. On the one hand, it reduces the probability of divergence of the calculation results of the three-dimensional scene simulation. On the other hand, by inheriting the two-dimensional simulation settings, the simulation process can be simplified, and reasonable simulation settings can be obtained more quickly, thereby ensuring simulation reliability and reducing simulation costs.

[0142] The geometric modeling module 1004 is further used to determine the target physical component that affects the physical and chemical phenomena in the simulation domain from the annealing equipment according to the simulation target; construct a two-dimensional geometric model of the annealing equipment according to the first target component and generate a first information file of the two-dimensional geometric model; and construct a three-dimensional geometric model of the annealing equipment according to the second target component of the annealing equipment and generate a second information file of the three-dimensional geometric model.

[0143] The simulation case generation module 1006 is also used to generate a base mesh of the two-dimensional geometric model;

[0144] Importing the first information file into a preset open source framework OpenFOAM, and reading device parameters of target physical components and relative position information between target physical components in the first information file through the preset open source framework OpenFOAM;

[0145] Meshing the base mesh according to the device parameters and the relative position information, deleting meshes irrelevant to the first information file, and obtaining a simulation mesh of the two-dimensional geometric model;

[0146] Determine the simulation physical model, initial conditions, boundary conditions and simulation calculation setting data for simulation based on the simulation goal;

[0147] Physical simulation is performed according to the simulation grid, the simulation physical model, the initial conditions, the boundary conditions and the simulation calculation setting data to obtain a first simulation case that meets the simulation objectives.

[0148] The simulation case generation module 1006 is further used to determine a simulation physical model for simulation based on the simulation target;

[0149] Determining simulation physical model settings of the simulation physical model and physical property parameter settings that enable the simulation physical model to reflect the physical behavior of the material or fluid;

[0150] Determine initial conditions and boundary conditions based on the boundaries determined by the two-dimensional geometric model and the physical properties of the simulated physical model;

[0151] Determine simulation calculation setting parameters based on simulation physical model, simulation target and simulation computing resources.

[0152] The simulation case generation module 1006 is further configured to determine, when the simulation target is a coupled simulation of a flow field and a temperature field, that the simulation physical models corresponding to the coupled simulation of the flow field and the temperature field include a turbulence model and a radiation model;

[0153] The turbulence model includes a laminar flow model and / or a kEpsilon turbulence model, and the radiation model includes a P1 model and / or an FvDOM radiation model.

[0154] The simulation case generation module 1006 is further configured to determine a first simulation setting dimension that is the same as that of the two-dimensional simulation and a second simulation setting dimension that is different from that of the three-dimensional simulation;

[0155] Determining first simulation setting parameters corresponding to the first simulation setting dimension from the first simulation case; the first simulation setting parameters include a simulation physical model, initial conditions, boundary conditions, and simulation calculation setting parameters;

[0156] A second simulation case of the three-dimensional geometric model is constructed according to the first simulation setting parameters and the second simulation setting parameters corresponding to the second simulation setting dimension.

[0157] The simulation case generation module 1006 is also used to generate a base mesh of the three-dimensional geometric model;

[0158] Importing the second information file into a preset open source framework, and reading the device parameters of the second target physical component and the relative position information between the physical components in the second information file through the preset open source framework;

[0159] The base grid is meshed according to the device parameters and the relative position information, and the grids irrelevant to the second information file are deleted to obtain the simulation grid of the three-dimensional geometric model.

[0160] Each module in the above-mentioned annealing equipment simulation device can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each of the above modules.

[0161] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 11As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a simulation method for an annealing device is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0162] Those skilled in the art will understand that Figure 11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0163] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0164] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0165] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0166] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0167] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile memory and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a programmable logic unit (PLC), a data processing logic unit based on quantum computing, an artificial intelligence (AI) processor, and the like.

[0168] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0169] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A simulation method for annealing equipment, characterized in that: The method comprises: Get the simulation target of the annealing equipment; Performing geometric modeling on the annealing equipment according to the simulation target to obtain a two-dimensional geometric model and a three-dimensional geometric model of the annealing equipment; Performing a two-dimensional simulation based on the two-dimensional geometric model to obtain a first simulation case that meets the simulation goal; constructing a second simulation case of the three-dimensional geometric model based on the first simulation case; Performing a three-dimensional simulation based on the second simulation case and the three-dimensional geometric model to obtain simulation data; The simulation data is analyzed to determine a process optimization strategy for the annealing equipment.

2. The method according to claim 1, characterized in that The step of geometrically modeling the annealing equipment according to the simulation target to obtain a two-dimensional geometric model and a three-dimensional geometric model of the annealing equipment includes: According to the simulation target, determining a target physical component affecting the physical and chemical phenomena in the simulation domain from the annealing device; constructing a two-dimensional geometric model of the annealing equipment according to a first target component of the annealing equipment, and generating a first information file of the two-dimensional geometric model; A three-dimensional geometric model of the annealing equipment is constructed according to the second target component of the annealing equipment, and a second information file of the three-dimensional geometric model is generated.

3. The method according to claim 2, characterized in that The performing of a two-dimensional simulation based on the two-dimensional geometric model to obtain a first simulation case that meets the simulation goal includes: generating a base mesh of the two-dimensional geometric model; Importing the first information file into a preset open source framework, and reading the device parameters of the target physical components and the relative position information between the target physical components in the first information file through the preset open source framework; Meshing the base mesh according to the device parameters and the relative position information, deleting meshes irrelevant to the first information file, and obtaining a simulation mesh of the two-dimensional geometric model; Determining a simulation physical model, initial conditions, boundary conditions, and simulation calculation setting data for simulation based on the simulation target; Physical simulation is performed according to the simulation grid, the simulation physical model, the initial conditions, the boundary conditions and the simulation calculation setting data to obtain a first simulation case that meets the simulation goal.

4. The method according to claim 3, characterized in that The step of determining a simulation physical model, initial conditions, boundary conditions, and simulation calculation setting data for simulation based on the simulation target includes: Determining a simulation physical model for simulation based on the simulation target; Determining a simulation physical model setting of the simulation physical model and a physical property parameter setting that enables the simulation physical model to reflect the physical behavior of a material or fluid; Determining initial conditions and boundary conditions based on the boundaries determined by the two-dimensional geometric model and the physical properties of the simulated physical model; Simulation calculation setting parameters are determined according to the simulation physical model, the simulation target and simulation calculation resources.

5. The method according to claim 4, characterized in that The step of determining a simulation physical model for simulation based on the simulation target includes: In a case where the simulation target is a coupled simulation of a flow field and a temperature field, determining that the simulation physical model corresponding to the coupled simulation of the flow field and the temperature field includes a turbulence model and a radiation model; The turbulence model includes a laminar flow model and / or a kEpsilon turbulence model, and the radiation model includes a P1 model and / or an FvDOM radiation model.

6. The method according to claim 4, characterized in that The second simulation case of constructing the three-dimensional geometric model according to the first simulation case includes: Determine a first simulation setting dimension that is the same as that of the two-dimensional simulation and a second simulation setting dimension that is different from that of the three-dimensional simulation; Determining first simulation setting parameters corresponding to the first simulation setting dimension from a first simulation case; the first simulation setting parameters include a simulation physical model, initial conditions, boundary conditions, and simulation calculation setting parameters; A second simulation case of the three-dimensional geometric model is constructed according to the first simulation setting parameters and second simulation setting parameters corresponding to the second simulation setting dimensions.

7. The method according to claim 2, characterized in that The method further comprises: generating a base mesh of the three-dimensional geometric model; Importing the second information file into a preset open source framework, and reading the device parameters of the second target physical component in the second information file and the relative position information between the physical components through the preset open source framework; The base grid is meshed according to the device parameters and the relative position information, and grids irrelevant to the second information file are deleted to obtain a simulation grid of the three-dimensional geometric model.

8. A simulation device for annealing equipment, characterized in that: The device comprises: A data acquisition module is used to obtain the simulation target of the annealing equipment; A geometric modeling module, configured to perform geometric modeling on the annealing equipment according to the simulation target, and obtain a two-dimensional geometric model and a three-dimensional geometric model of the annealing equipment; a simulation case generating module, configured to perform a two-dimensional simulation based on the two-dimensional geometric model to obtain a first simulation case that meets the simulation objective; constructing a second simulation case of the three-dimensional geometric model based on the first simulation case; a simulation module, configured to perform a three-dimensional simulation based on the second simulation case and the three-dimensional geometric model to obtain simulation data; The data processing module is used to analyze the simulation data and determine the process optimization strategy of the annealing equipment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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