Simulation method and apparatus, electronic device, and storage medium

Abstract

The present disclosure provides a simulation method and device, electronic equipment and storage medium, and relates to the technical field of semiconductors. The simulation method comprises: determining prediction data of a chemical vapor deposition process of a target device, the target device comprising a target surface, the prediction data comprising a predicted deposition rate of each of a plurality of surface nodes in the target surface, the prediction data being obtained through a steady-state CFD simulation process; dividing the plurality of surface nodes to obtain a plurality of point sets based on distances between each of the plurality of surface nodes and a central node of the target surface, the distance intervals corresponding to each of the plurality of point sets being mutually non-overlapping; determining an average deposition rate corresponding to each of the plurality of point sets based on the predicted deposition rates of the surface nodes included in each of the plurality of point sets; and obtaining a simulation result of the chemical vapor deposition process of the target device based on the average deposition rate corresponding to each of the plurality of point sets and the distance interval corresponding to each of the plurality of point sets.

Description

Technical Field

[0001] This disclosure relates to the field of semiconductor technology, and more specifically to a simulation method and apparatus, electronic devices, and storage media. Background Technology

[0002] Silicon carbide (SiC) coated graphite substrates are key consumables in semiconductor epitaxial growth equipment. During epitaxial growth, the SiC-coated graphite substrate is in direct contact with the wafer, therefore requiring a certain degree of surface flatness to prevent wafer deformation. Furthermore, the SiC-coated graphite substrate needs to provide a uniform thermal field to ensure temperature uniformity during wafer epitaxial growth. Therefore, ensuring the uniformity of the SiC coating on the graphite substrate surface is crucial.

[0003] However, the method of obtaining a uniform SiC coating on the graphite substrate surface by empirically adjusting process parameters often requires huge costs and development cycles. Summary of the Invention

[0004] In view of this, the present disclosure provides a simulation method and apparatus, an electronic device and a storage medium.

[0005] Firstly, a simulation method is provided, comprising: determining predicted data for a chemical vapor deposition process of a target device, wherein the target device includes a target surface, and the predicted data includes the predicted deposition rates of multiple surface nodes on the target surface, the predicted data being obtained through a steady-state CFD simulation process; dividing the multiple surface nodes into multiple point sets based on the distances between each of the multiple surface nodes and the center node of the target surface, wherein the distance intervals corresponding to each of the multiple point sets do not overlap; determining the average deposition rate corresponding to each of the multiple point sets based on the predicted deposition rates of the surface nodes included in each of the multiple point sets; and obtaining the simulation results of the chemical vapor deposition process of the target device based on the average deposition rates corresponding to each of the multiple point sets and the distance intervals corresponding to each of the multiple point sets.

[0006] In some embodiments, multiple surface nodes are divided into multiple point sets based on the distance between each surface node and the center node of the target surface, including: determining the height coordinate values ​​of each surface node; dividing the multiple surface nodes based on the height coordinate values ​​of each surface node to generate at least one group; determining the center node corresponding to each group based on the surface nodes included in each group, and determining the point set corresponding to the group based on the distance between the surface nodes included in the group and the center node corresponding to the group.

[0007] In some embodiments, multiple surface nodes are divided into at least one group based on their respective height coordinate values. This includes: counting the occurrences of the height coordinate values ​​of the multiple surface nodes and sorting them from highest to lowest frequency to obtain a sorting result; selecting the top N height values ​​in the sorting result based on the frequency of their height coordinate values; and assigning the surface node corresponding to each of the top N height values ​​to the same group.

[0008] In some embodiments, determining the point set corresponding to the group based on the distance between the surface nodes included in the group and the corresponding center node of the group includes: dividing the target surface corresponding to the group into multiple sub-regions along the radial direction of the target surface corresponding to the group, with the corresponding center node of the group as the center, wherein the target surface corresponding to the group is circular and the sub-regions are annular or circular; determining the sub-region into which each surface node of the group falls based on the distance between the surface nodes included in the group and the corresponding center node of the group, and defining the surface nodes falling into the same sub-region as a point set.

[0009] In some embodiments, determining the average deposition rate corresponding to each of the multiple point sets based on the predicted deposition rates of the surface nodes included in each point set includes: adding the predicted deposition rates of the surface nodes included in each point set to obtain the sum of the predicted deposition rates corresponding to the point set; and determining the average deposition rate corresponding to the point set based on the sum of the predicted deposition rates corresponding to the point set and the number of surface nodes included in the point set.

[0010] In some embodiments, simulation results of the chemical vapor deposition process of the target device are obtained based on the average deposition rate corresponding to each of the multiple point sets and the distance interval corresponding to each of the multiple point sets. This includes: establishing a target coordinate system, where the first coordinate axis of the target coordinate system represents the distance between the surface node and the center node, and the second coordinate axis of the target coordinate system represents the predicted deposition rate; determining the coordinate interval of the first coordinate axis corresponding to each of the multiple point sets based on the distance interval corresponding to each of the multiple point sets, and using the average deposition rate corresponding to each of the multiple point sets as the coordinate value of the second coordinate axis, and plotting the simulation results expressed based on a bar chart.

[0011] In some embodiments, determining the predicted data for the chemical vapor deposition process of the target device includes: determining a three-dimensional geometric model of the target device and the chemical vapor deposition reactor; dividing the three-dimensional geometric model into multiple solid mesh elements to obtain a meshed three-dimensional geometric model; importing the meshed three-dimensional geometric model into a CFD solver to establish a CFD simulation model; and using the CFD simulation model to perform steady-state calculations to obtain the predicted data.

[0012] Secondly, a simulation device is provided, comprising: an acquisition module configured to determine predicted data of a chemical vapor deposition process of a target device, the target device including a target surface, the predicted data including predicted deposition rates of multiple surface nodes in the target surface, the predicted data being obtained through a steady-state CFD simulation process; a partitioning module configured to partition the multiple surface nodes into multiple point sets based on the distances between each of the multiple surface nodes and the center node of the target surface, the distance intervals corresponding to each of the multiple point sets not overlapping; a determination module configured to determine the average deposition rate corresponding to each of the multiple point sets based on the predicted deposition rates of the surface nodes included in each of the multiple point sets; and a simulation module configured to obtain simulation results of the chemical vapor deposition process of the target device based on the average deposition rate corresponding to each of the multiple point sets and the distance intervals corresponding to each of the multiple point sets.

[0013] Thirdly, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the simulation method provided in the first aspect by executing the executable instructions.

[0014] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the simulation method provided in the first aspect.

[0015] The simulation method disclosed herein uses the ring integral method to process the predicted data obtained based on the CFD steady-state algorithm, and simulates the dynamic deposition process of the circumferential position of the target device surface changing over time. While ensuring low computational load, it improves the accuracy of simulation results and post-processing efficiency. Attached Figure Description

[0016] Figure 1 The diagram shown is a schematic flowchart of a simulation method provided in an embodiment of this disclosure.

[0017] Figure 2 The diagram shows a flowchart of an embodiment of the present disclosure, illustrating the steps of dividing multiple surface nodes into multiple point sets based on the distances between each surface node and the center node of the target surface.

[0018] Figure 3 The diagram shown is a flowchart illustrating the steps of dividing multiple surface nodes based on their respective height coordinate values ​​to generate at least one group, according to an embodiment of this disclosure.

[0019] Figure 4 The diagram shown is a flowchart illustrating the steps of determining the point set corresponding to a group based on the distance between the surface nodes included in the group and the corresponding center node of the group, according to an embodiment of this disclosure.

[0020] Figure 5 The diagram shows a flowchart illustrating the steps of determining the average deposition rate of each of the multiple point sets based on the predicted deposition rate of the surface nodes included in each of the multiple point sets, according to an embodiment of this disclosure.

[0021] Figure 6 The diagram shows a flowchart illustrating the steps of obtaining the simulation results of the chemical vapor deposition process of the target device based on the average deposition rate corresponding to each of multiple point sets and the distance interval corresponding to each of the multiple point sets, according to an embodiment of this disclosure.

[0022] Figure 7 The figure shown is a simulation result diagram provided by an embodiment of this disclosure.

[0023] Figure 8 The figure shown is a simulation result obtained by using transient CFD simulation in related technologies.

[0024] Figure 9 The diagram shown is a flowchart illustrating the steps for predicting data in the chemical vapor deposition process of a target device according to an embodiment of this disclosure.

[0025] Figure 10 The diagram shown is a structural schematic of a simulation device provided in an embodiment of this disclosure.

[0026] Figure 11 The diagram shown is a structural schematic of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0027] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0028] SiC-coated graphite substrates have significant application value in the surface modification of key components in semiconductor manufacturing equipment due to their excellent high-temperature stability, thermal shock resistance, and corrosion resistance. Statistics show that SiC-coated graphite substrates can improve the thickness non-uniformity of GaN epitaxial wafers from ±6% to ±3%. Simultaneously, the SiC coating can also extend the service life of the graphite substrate. Uncoated graphite substrates need to be replaced every 200 hours in SiC epitaxial processes, while the service life of SiC-coated graphite substrates is extended to 2000 hours, significantly reducing consumable costs. The advantages of SiC-coated graphite substrates are widely recognized.

[0029] However, due to stringent surface flatness standards, the production difficulty and yield of SiC-coated graphite substrates are difficult to improve. Furthermore, the cracking of the SiC coating during service also limits the lifespan of the SiC-coated graphite substrates. According to existing research, the uneven distribution of the SiC coating is the cause of abnormal flatness and thermal stress cracking in the graphite substrate. Therefore, improving the uniformity of the SiC coating has become an urgent problem to be solved.

[0030] Chemical vapor deposition (CVD) is currently the main technology for preparing SiC coatings on graphite substrates. To save on R&D costs and shorten the development cycle, researchers have attempted to couple the flow field and chemical field during the CVD process using computational fluid dynamics (CFD) to simulate the deposition process of SiC coatings on graphite substrates and adjust the CVD process parameters based on predicted data.

[0031] In the CVD production process of graphite substrates, the graphite substrates typically need to rotate at a constant speed to ensure the uniformity of SiC coating deposition. However, the rotation of the graphite substrate and the support rack alters the flow field distribution to some extent. CFD steady-state algorithms cannot accurately simulate the dynamic effects caused by the rotation process, and consequently, cannot accurately simulate the dynamic deposition process of the circumferential position of the graphite substrate surface changing over time, resulting in large errors in the prediction of deposition rate distribution. While CFD transient algorithms can capture the dynamic effects caused by the rotation of the graphite substrate, simulating a single complete rotation in an industrial-grade furnace model requires tens of thousands of CPU hours, and this massive computational load severely restricts the efficiency of process optimization.

[0032] Furthermore, existing commercial CFD software typically only provides basic visualization functions and lacks dedicated analysis tools for rotationally symmetric systems. After the simulation, engineers need to manually extract the predicted data and import it into external data analysis software for post-processing. This discretization operation is not only time-consuming but also prone to introducing human error, making it difficult to meet the needs of industrial production for rapid feedback of process parameters.

[0033] To address the aforementioned technical problems, this disclosure provides a simulation method and apparatus, electronic device, and storage medium that simulate the dynamic deposition process on the surface of a graphite substrate based on prediction data from a CFD steady-state algorithm, thereby improving the accuracy of simulation results and post-processing efficiency.

[0034] The following is combined with Figures 1 to 9 Examples illustrate the simulation method provided in the embodiments of this disclosure.

[0035] Figure 1The diagram shown is a schematic flowchart of a simulation method provided in an embodiment of this disclosure. Figure 1 As shown, the simulation method provided in this embodiment includes the following steps.

[0036] S110, determine the predicted data for the chemical vapor deposition process of the target device.

[0037] Chemical vapor deposition (CVD) is a thin-film process that involves heating and decomposing one or more gases to produce reaction products, which are then deposited onto a substrate surface to form a solid thin film. The target device is a device that achieves a specific function through thin-film deposition via CVD. For example, a target device may include a graphite substrate, and depositing a SiC coating on the graphite substrate via CVD can improve the process performance and lifespan of the graphite substrate.

[0038] The target device includes a target surface, which is at least one outer surface of the target device. During the CVD process, reactant gases are deposited on the target surface, and the uniformity of the deposited film layer on the target surface needs to be ensured. For example, if the target device is a graphite substrate, the target surface can be the upper and / or lower surface of the graphite substrate.

[0039] A steady-state CFD simulation process is used to simulate the CVD process of the target device in the reactor chamber, obtaining predicted data. This predicted data includes the three-dimensional coordinates of various locations on the target device surface and the predicted deposition rate. Specifically, the target device surface is uniformly divided into multiple grids, each corresponding to a surface node. Therefore, the predicted data includes the predicted deposition rate of each surface node on the target surface, which is the thin film deposition rate at the corresponding location of the surface node, predicted through a steady-state CFD simulation process.

[0040] S120: Based on the distance between each surface node and the center node of the target surface, the multiple surface nodes are divided into multiple point sets.

[0041] There can be one or more target surfaces, each corresponding to a central node. The central node is one of the multiple surface nodes on the target surface. Since the target device rotates during the CVD process, the central node can be understood as the rotation center of the target device, typically located at the geometric center of the target surface (or a central location generally accepted by those skilled in the art). For example, if the target surface is circular, the central node is located at the center of the circle.

[0042] After determining the center node, the distances between multiple surface nodes of the target surface and the center node can be calculated separately. These surface nodes are then divided into multiple point sets based on these distances. Each point set corresponds to a different distance interval, and the distance intervals for each point set do not overlap.

[0043] For example, multiple surface nodes are divided into three point sets: point set A, point set B, and point set C. The distance interval corresponding to point set A is (0, a], the distance interval corresponding to point set B is (a, b], and the distance interval corresponding to point set C is (b, c). If the distance between surface node P1 and center node P0 is d1, and a < d1 < b, then surface node P1 should be assigned to point set A. The number of point sets or the range of distance intervals are merely illustrative; those skilled in the art can set them according to actual conditions.

[0044] S130, based on the predicted deposition rates of the surface nodes included in each of the multiple point sets, determine the average deposition rate corresponding to each of the multiple point sets.

[0045] Each point set includes multiple surface nodes. Based on the prediction data, the predicted deposition rate for each of the multiple surface nodes can be obtained.

[0046] For any given set of points, calculate the average of the predicted deposition rates for each of the multiple surface nodes within that set, and use this average deposition rate as the corresponding average deposition rate for that set. In this way, the average deposition rate for each set of points can be obtained.

[0047] S140, based on the average deposition rate corresponding to each of the multiple point sets and the distance interval corresponding to each of the multiple point sets, obtain the simulation results of the chemical vapor deposition process of the target device.

[0048] Each point set corresponds to a different sub-region of the target surface, and the sub-region corresponding to each point set can be determined by the distance interval corresponding to that point set. The average deposition rate corresponding to each point set is determined as the predicted deposition rate of the sub-region corresponding to that point set. Based on the predicted deposition rates of each sub-region of the target surface in the target device, the simulation results of the chemical vapor deposition process of the target device are determined. The simulation results include the correspondence between different locations of the target device and the predicted deposition rates. For example, the simulation results can be presented in the form of contour plots, contour maps, bar charts, line charts, tables, etc.

[0049] This disclosure provides a simulation method that uses a ring integral method to process predicted data obtained from a CFD steady-state algorithm to simulate the dynamic deposition process of the circumferential position of a target device surface changing over time. During the CVD process, the target device rotates at a constant speed. Therefore, after one rotation, the film thickness deposited at any surface node of the target device should be equal to the sum of the film thicknesses deposited by multiple surface nodes at the same distance from the center node per unit time. Based on this, the average deposition rate corresponding to each point set can be used as the predicted deposition rate of the corresponding sub-region. Thus, the method of this disclosure improves the accuracy of simulation results and post-processing efficiency while maintaining low computational complexity.

[0050] In addition to the target surface, the target device may also include other external surfaces. The prediction data obtained from CFD simulation also includes the predicted deposition rates corresponding to the surface nodes of these other external surfaces. Therefore, after obtaining the prediction data for the target device, it is necessary to filter the predicted deposition rates for the target surface from the prediction data.

[0051] Figure 2 The diagram illustrates a process according to an embodiment of this disclosure, whereby multiple surface nodes are divided into multiple point sets based on the distances between each surface node and the center node of the target surface. Figure 2 As shown, the steps to divide multiple surface nodes into multiple point sets based on the distances between each surface node and the center node of the target surface include the following steps.

[0052] S121, determine the height coordinates of each of the multiple surface nodes.

[0053] Each surface node corresponds to a different three-dimensional coordinate, which includes a height coordinate value. The height coordinate value is the coordinate value of the surface node in the height direction, which can be understood as the direction perpendicular to the bottom surface of the reactor body.

[0054] S122, divide the multiple surface nodes based on their respective height coordinate values ​​to generate at least one group.

[0055] Surface nodes located on the same target surface should be at the same height, meaning that the height coordinate values ​​of surface nodes on the same target surface are the same. Taking a graphite base as an example, the height coordinate values ​​of all surface nodes located on the upper surface of the graphite base are the same; the height coordinate values ​​of all surface nodes located on the lower surface of the graphite base are the same, but different from the corresponding height coordinate values ​​on the upper surface of the graphite base.

[0056] Therefore, surface nodes belonging to the same target surface can be grouped together based on their respective height coordinates. Each target surface corresponds to a separate group of surface nodes.

[0057] S123, determine the center node corresponding to each group based on the surface nodes included in each group, and determine the point set corresponding to the group based on the distance between the surface nodes included in the group and the center node corresponding to the group.

[0058] For any one of the multiple groups of surface nodes, perform the following steps.

[0059] For the multiple surface nodes included in the group, the center node corresponding to the group is determined based on the coordinates of the multiple surface nodes. Then, based on the distances between each of the multiple surface nodes in the group and the center node, the multiple surface nodes in the group are divided to obtain multiple point sets corresponding to the surface nodes in the group.

[0060] In this way, the point sets corresponding to each group of surface nodes are obtained, which together constitute the multiple point sets mentioned above.

[0061] In this embodiment, the correspondence between the target surface and the surface nodes is determined based on the height of the surface nodes. For surface nodes belonging to the same target surface, a center node is determined and the nodes are divided according to the distance to obtain multiple point sets. In this way, target devices including multiple target surfaces can be processed simultaneously, or multiple target devices can be processed simultaneously, thereby improving processing efficiency.

[0062] Figure 3 The diagram illustrates a process according to an embodiment of this disclosure, involving the division of multiple surface nodes based on their respective height coordinates to generate at least one group of steps. Figure 3 As shown, the steps of dividing multiple surface nodes based on their respective height coordinates to generate at least one group include the following steps.

[0063] S1221: Count the number of occurrences of the height coordinate values ​​of multiple surface nodes, and sort them from highest to lowest frequency to obtain the sorting result.

[0064] Count the frequency of each height coordinate value among multiple surface nodes. Taking six surface nodes as an example, the coordinates of the six surface nodes are P1(x1, y1, z1), P2(x2, y2, z2), P3(x3, y3, z2), P4(x4, y4, z1), P5(x5, y5, z2), and P6(x5, y5, z3). Among them, the height coordinate value z1 appears 2 times, the height coordinate value z2 appears 3 times, and the height coordinate value z3 appears 1 time.

[0065] Sort the results by frequency of occurrence from highest to lowest. The sorted results can be written as z2, z1, z3.

[0066] S1222: Select the height values ​​that appear the most frequently in the sorting results.

[0067] The value of N can be determined based on the number of target surfaces.

[0068] For target devices such as graphite substrates, the target surface of the device is parallel to the bottom surface of the reactor body, meaning that multiple surface nodes on the same target surface have the same height coordinate value. Therefore, the target surface where a surface node is located can be determined based on the height coordinate value of the surface node.

[0069] If the number of target surfaces is N, then the height values ​​of the top N most frequent surfaces need to be determined. Each height value corresponds to one target surface.

[0070] For a graphite substrate, a graphite substrate includes two target surfaces: an upper surface and a lower surface. Therefore, the value of N can be determined based on the number of target surfaces included in the target device and the number of target devices.

[0071] Continuing with the example above, the six surface nodes belong to the same graphite base, and the number of target surfaces is 2. Therefore, we need to select the two height values ​​with the highest frequency of occurrence in the sorting results, which are z2 and z1, respectively. z2 and z1 are the height values ​​of the upper and lower surfaces of the graphite base, respectively.

[0072] S1223, for each height value among the top N height values, group the surface nodes corresponding to that height value into the same group.

[0073] For any group of the top N height values, perform the following steps.

[0074] Among multiple surface nodes, search for surface nodes whose height coordinates are equal to the given height value, and group the resulting surface nodes into the same group.

[0075] Continuing with the example above, the surface nodes corresponding to the height value z2 are P2, P3, and P5, and these three surface nodes are grouped together. The surface nodes corresponding to the height value z1 are P1 and P4, and these two surface nodes are grouped together.

[0076] In this embodiment of the disclosure, a method for rapidly distinguishing and screening surface nodes belonging to different target surfaces is designed based on the characteristics of the target surface in the target device, thereby improving processing efficiency and meeting the needs of industrial production for rapid feedback of process parameters.

[0077] It is understood that the examples in the above embodiments are only for explaining the technical solutions of this disclosure. In actual simulation, the number of surface nodes is much greater than six.

[0078] Figure 4 The diagram shown is a flowchart illustrating the steps for determining the point set corresponding to a group based on the distance between the surface nodes included in the group and the corresponding center node of the group, according to an embodiment of this disclosure. Figure 4 As shown, for each group of surface nodes in the multiple groups of surface nodes, the following steps are performed respectively.

[0079] S1231, taking the center node corresponding to the group as the center, along the radial direction of the target surface corresponding to the group, divide the target surface corresponding to the group into multiple sub-regions.

[0080] For any set of surface nodes, the target surface corresponding to the set of surface nodes is divided into multiple sub-regions, with the center node of the set of surface nodes as the center.

[0081] In this embodiment, the target surface is circular, and the central node is located at the center of the target surface. During the division process, the target surface can be divided into multiple sub-regions that are circular or annular along the radial direction of the circle.

[0082] Specifically, the sub-region located at the center of the target surface is circular, while the remaining sub-regions are annular. The annular sub-regions are nested around the circular sub-regions. For example, the radius of the circular sub-region Q1 is r1; the inner radius of the annular sub-region Q2 adjacent to the circular sub-region is r1, and the outer radius is r2; the inner radius of the annular sub-region Q3 adjacent to the annular sub-region Q2 is r2, and the outer radius is r3, and so on.

[0083] S1232, based on the distance between the surface nodes included in the group and the corresponding center node of the group, determine the sub-regions into which each surface node in the group falls, and define the surface nodes that fall into the same sub-region as a point set.

[0084] For any sub-region, the range of distances between surface nodes and the center node falling within that sub-region is defined. Furthermore, the distance range for each sub-region is determined by its boundary, with the radius of the circular boundary of the sub-region serving as the endpoint value of its corresponding distance range. For example, the distance range for circular sub-region Q1 is (0, r1], for annular sub-region Q2 it is (r1, r2], and for annular sub-region Q3 it is (r2, r3).

[0085] After determining the distance interval for each sub-region, the sub-region in which a surface node falls can be determined based on the distance between the surface node and the center node. Then, surface nodes falling into the same sub-region are grouped into a single point set.

[0086] Furthermore, the difference between the inner and outer radii of each annular sub-region can be the same, meaning each annular sub-region has the same ring width, and the radius of the circular sub-region is equal to the ring width of the annular sub-region. This facilitates subsequent analysis of the differences in predicted deposition rates at different radius locations on the target surface. The ring width can be set according to actual needs; for example, it can be set to 4 mm.

[0087] This disclosure describes how to determine the distance interval corresponding to each point set when the target surface is circular. By dividing the target surface into sub-regions, the variation of deposition rate within different radius ranges can be evaluated more accurately, facilitating the optimization of process parameters based on the prediction results.

[0088] Figure 5 The diagram illustrates a flowchart of an embodiment of this disclosure, showing the steps for determining the average deposition rate corresponding to each of multiple point sets based on the predicted deposition rate of the surface nodes included in each point set. Figure 5 As shown, the following steps are performed for each of the multiple point sets.

[0089] S131, sum the predicted deposition rates of the surface nodes included in each point set to obtain the sum of the predicted deposition rates corresponding to that point set.

[0090] For any one of the multiple point sets, the predicted deposition rates of the multiple surface nodes included in the point set are added together to obtain the sum of the predicted deposition rates corresponding to that point set.

[0091] S132, Based on the sum of the predicted deposition rates corresponding to the point set and the number of surface nodes included in the point set, determine the average deposition rate corresponding to the point set.

[0092] Next, the number of surface nodes in the point set is determined, and the average deposition rate corresponding to the point set is calculated. In this way, the average deposition rate corresponding to each of the multiple point sets is determined.

[0093] In this embodiment, the average deposition rate of sub-regions with different radii is obtained by calculating the average deposition rate of each point in the point set. This facilitates comparison of deposition rate differences within different radii, providing data support for further optimization of process parameters. The prediction results calculated using this method improve the consistency between the steady-state algorithm and the rotation process, while also offering high computational efficiency.

[0094] After obtaining the average deposition rate for each sub-region, the simulation results for the target device can be further obtained. For example, the simulation results can be presented as a bar chart. This will be explained in detail below.

[0095] Figure 6 The diagram shows a flowchart illustrating the steps of obtaining simulation results of the chemical vapor deposition process of a target device based on the average deposition rate corresponding to multiple point sets and the distance interval corresponding to each point set, according to an embodiment of this disclosure. Figure 6 As shown, the simulation results of the chemical vapor deposition process of the target device are obtained based on the average deposition rate corresponding to each of the multiple point sets and the distance interval corresponding to each of the multiple point sets. The steps include the following steps.

[0096] S141, Establish the target coordinate system.

[0097] The target coordinate system includes an orthogonal first coordinate axis and a second coordinate axis.

[0098] The first coordinate axis can represent the X-axis, the origin of the first coordinate axis represents the center node of the target surface, and the first coordinate on the first coordinate axis corresponds to the distance between the surface node and the center node of the target surface.

[0099] The second coordinate axis can represent the Y-axis, and the second coordinate on the second coordinate axis corresponds to the predicted deposition rate.

[0100] S142. Based on the distance intervals corresponding to each of the multiple point sets, determine the coordinate intervals of the first coordinate axis corresponding to each of the multiple point sets, and use the average deposition rate corresponding to each of the multiple point sets as the coordinate values ​​of the second coordinate axis to plot the simulation results expressed by the bar chart.

[0101] Specifically, the surface nodes of the target surface are divided into multiple point sets. In the above steps, the distance interval and average deposition rate corresponding to each point set are obtained.

[0102] Each point set corresponds to a bar in the histogram. Based on the endpoints of the distance interval, the coordinate interval of the first coordinate axis corresponding to the point set is determined, serving as the coordinate value of the bar on the first coordinate axis. The average deposition rate corresponding to the point set is used as the coordinate value of the bar on the second coordinate axis.

[0103] Simulation results can be plotted separately for each target surface. Alternatively, simulation results for multiple target surfaces of the same target device can be plotted on a single bar chart. For example, for a graphite substrate, simulation results for the upper and lower surfaces can be plotted on a single bar chart to more intuitively view the differences in deposition rates between the upper and lower surfaces in different radius regions.

[0104] Figure 7The figure shown is a simulation result diagram provided by an embodiment of this disclosure. Figure 7 The predicted deposition rates of the upper and lower surfaces of the graphite substrate are plotted based on the method of embodiments of this disclosure. Figure 8 The figure shown is a simulation result obtained by using transient CFD simulation in related technologies. Figure 8 It is a predicted deposition rate of the upper and lower surfaces of the graphite substrate, plotted based on transient prediction data.

[0105] contrast Figure 7 , Figure 8 It can be observed that the simulation results obtained by different methods show consistent trends in deposition rate for different radii. This indicates that the simulation results of the present invention can effectively capture the dynamic effects caused by the rotation of the target device, providing valuable reference for further analysis and optimization of process parameters. Furthermore, the computational load and time are much smaller than those of transient CFD simulation.

[0106] Furthermore, in this embodiment, the post-processing of the predicted data can be implemented by writing a program using the C++ / Qt framework, eliminating the need for engineers to manually extract the predicted data and import it into external data analysis software for post-processing, thus further improving post-processing efficiency.

[0107] The following is a brief introduction to the specific implementation method of predicting the CVD process of a target device using a steady-state CFD simulation algorithm.

[0108] Figure 9 The diagram shown is a flowchart illustrating the steps for determining predictive data in the chemical vapor deposition process of a target device according to an embodiment of this disclosure. Figure 9 As shown, the steps for determining the predicted data of the chemical vapor deposition process of the target device include the following steps.

[0109] S111, determine the three-dimensional geometric model of the target device and the chemical vapor deposition reactor.

[0110] Three-dimensional drawing and modeling software is used to construct three-dimensional geometric models of each part of the CVD reactor body and the target device, and then the various parts of the reactor body and the target device are assembled together.

[0111] During the modeling process, the dimensions of all parts of the reactor body and the target devices need to match those of the actual parts used. During assembly, gaps must be prevented between connected parts due to improper assembly.

[0112] Next, the three-dimensional geometric models of the assembled reactor body and target device are simplified to eliminate small features such as chamfers, small bevels, fine holes, and small steps, as well as grooves that will not have a significant impact on the flow field, thereby improving the efficiency of simulation calculations and the stability of results.

[0113] Feature structures such as air inlets, air outlets, and heating sources are set at corresponding positions in the reaction furnace body, and all surfaces inside the furnace cavity are set as CVD reaction surfaces, and all surfaces of the target device are also set as CVD reaction surfaces.

[0114] Check the simplified 3D geometric model of the reactor body and target device for redundant dividing edges, extra edges, extra faces, as well as small faces, inaccurate edges, and missing faces. If any are found, process and repair them using 3D drawing and modeling software.

[0115] S112 divides the three-dimensional geometric model into multiple solid mesh units to obtain a meshed three-dimensional geometric model.

[0116] A fluid domain is created using a volume extraction method. The fluid domain is then divided into multiple solid mesh elements to obtain a meshed 3D geometric model.

[0117] S113: Import the meshed 3D geometric model into the CFD solver to establish a CFD simulation model.

[0118] The meshed 3D geometric model is imported into the CFD solver, and parameters such as working conditions, turbulence model, and chemical reaction equations are set. Region conditions and boundary conditions are also set to obtain the final CFD simulation model.

[0119] It is understood that the parameters listed above can be adjusted by those skilled in the art according to the actual situation, and should not be construed as a limitation of this disclosure.

[0120] S114. Steady-state calculations are performed using a CFD simulation model to obtain predicted data.

[0121] The flow field equation matrix is ​​initialized, and the convergence criterion is set. The flow field result matrix is ​​calculated and solved using an iterative method. The calculation is considered complete when the residual size reaches the convergence criterion. Then, the calculated data results are processed, and the three-dimensional coordinates of the surface nodes of all target devices and the predicted deposition rate are exported as data files to obtain the predicted data.

[0122] The above text combined Figures 1 to 9 The method embodiments of this disclosure have been described in detail below, in conjunction with... Figure 10 The apparatus embodiments of this disclosure are described in detail below. It should be understood that the descriptions of the method embodiments correspond to the descriptions of the apparatus embodiments; therefore, any parts not described in detail can be referred to the foregoing method embodiments.

[0123] Figure 10 The diagram shown is a structural schematic of a simulation device provided in an embodiment of this disclosure. Figure 10As shown, the simulation device 1000 of this embodiment includes: an acquisition module 1010, a division module 1020, a determination module 1030, and a simulation module 1040.

[0124] Specifically, the acquisition module 1010 is configured to determine the predicted data of the chemical vapor deposition process of the target device. The target device includes a target surface, and the predicted data includes the predicted deposition rates of multiple surface nodes on the target surface. The predicted data is obtained through a steady-state CFD simulation process. The partitioning module 1020 is configured to partition the multiple surface nodes into multiple point sets based on the distances between each surface node and the center node of the target surface. The distance intervals of the multiple point sets do not overlap. The determination module 1030 is configured to determine the average deposition rate corresponding to each of the multiple point sets based on the predicted deposition rates of the surface nodes included in each point set. The simulation module 1040 is configured to obtain the simulation results of the chemical vapor deposition process of the target device based on the average deposition rates of the multiple point sets and the distance intervals corresponding to the multiple point sets.

[0125] In some embodiments, the partitioning module 1020 is further configured to: determine the height coordinate values ​​of each of the multiple surface nodes; partition the multiple surface nodes based on the height coordinate values ​​of each of the multiple surface nodes to generate at least one group; determine the center node corresponding to the group based on the surface nodes included in each group; and determine the point set corresponding to the group based on the distance between the surface nodes included in the group and the center node corresponding to the group.

[0126] In some embodiments, the partitioning module 1020 is further configured to: count the occurrence frequency of the height coordinate values ​​of each of the multiple surface nodes, sort them from high to low according to the occurrence frequency, and obtain a sorting result; select the height values ​​with the highest occurrence frequency of the height coordinate values ​​in the sorting result; and for each of the top N height values, partition the surface node corresponding to that height value into the same group.

[0127] In some embodiments, the partitioning module 1020 is further configured to divide the target surface corresponding to the group into multiple sub-regions along the radial direction of the target surface corresponding to the group, with the center node corresponding to the group as the center, wherein the target surface corresponding to the group is circular and the sub-regions are annular or circular; based on the distance between the surface nodes included in the group and the center node corresponding to the group, determine the sub-region in which each surface node included in the group falls, and determine the surface nodes falling into the same sub-region as a point set.

[0128] In some embodiments, the determining module 1030 is further configured to add the predicted deposition rates of the surface nodes included in each point set to obtain the sum of the predicted deposition rates corresponding to the point set; and to determine the average deposition rate corresponding to the point set based on the sum of the predicted deposition rates corresponding to the point set and the number of surface nodes included in the point set.

[0129] In some embodiments, the simulation module 1040 is further configured to: establish a target coordinate system, wherein the first coordinate axis of the target coordinate system represents the distance between the surface node and the center node, and the second coordinate axis of the target coordinate system represents the predicted deposition rate; determine the coordinate interval of the first coordinate axis corresponding to the multiple point sets based on the distance intervals corresponding to the multiple point sets, and use the average deposition rate corresponding to the multiple point sets as the coordinate value of the second coordinate axis to plot the simulation results expressed by a bar chart.

[0130] In some embodiments, the acquisition module 1010 is further configured to: determine the three-dimensional geometric model of the target device and the chemical vapor deposition reactor body; divide the three-dimensional geometric model into multiple solid mesh elements to obtain a meshed three-dimensional geometric model; import the meshed three-dimensional geometric model into a CFD solver to establish a CFD simulation model; and use the CFD simulation model to perform steady-state calculations to obtain prediction data.

[0131] Below, for reference Figure 11 To describe an electronic device according to embodiments of the present disclosure. Figure 11 The diagram shown is a structural schematic of an electronic device provided according to an embodiment of this disclosure. Figure 11 As shown, the electronic device 1100 includes one or more processors 1110 and memory 1120.

[0132] The processor 1110 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 1100 to perform desired functions.

[0133] The memory 1120 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 1110 may execute the program instructions to implement the simulation methods of the various embodiments of this disclosure described above and / or other desired functions.

[0134] In some embodiments, the electronic device 1100 may further include an input device 1130 and an output device 1140, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0135] The input device 1130 may include, for example, a touch screen, microphone, keyboard, mouse, etc. The output device 1140 may include, for example, a display, speaker, and communication network and its connected remote output devices, etc.

[0136] Of course, for the sake of simplicity, Figure 11 Only some of the components of the electronic device 1100 relevant to this disclosure are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device 1100 may include any other suitable components depending on the specific application.

[0137] In addition to the methods and apparatus described above, embodiments of this disclosure may also be computer program products comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the simulation methods according to various embodiments of this disclosure described above.

[0138] Computer program products can be written in any combination of one or more programming languages ​​to perform the operations of embodiments of this disclosure. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on a user's computing device, partially on a user's computing device, as a standalone software package, partially on a user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0139] Furthermore, embodiments of this disclosure may also be computer-readable storage media storing computer program instructions that, when executed by a processor, cause the processor to perform the steps in the simulation methods according to various embodiments of this disclosure described above.

[0140] Computer-readable storage media may take the form of any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable 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.

[0141] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0142] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0143] It should also be noted that in the systems, apparatus, and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0144] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0145] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A simulation method, characterized in that, include: Predictive data for the chemical vapor deposition process of a target device, the target device including a target surface, the predicted data including the predicted deposition rate of each of multiple surface nodes in the target surface, the predicted data being obtained through a steady-state CFD simulation process; Based on the distance between each of the multiple surface nodes and the center node of the target surface, the multiple surface nodes are divided into multiple point sets, and the distance intervals corresponding to the multiple point sets do not overlap with each other. Based on the predicted deposition rate of the surface nodes included in each of the multiple point sets, the average deposition rate corresponding to each of the multiple point sets is determined. Based on the average deposition rate corresponding to each of the multiple point sets and the distance range corresponding to each of the multiple point sets, the simulation results of the chemical vapor deposition process of the target device are obtained.

2. The simulation method according to claim 1, characterized in that, The method involves dividing the multiple surface nodes into multiple point sets based on the distances between each surface node and the center node of the target surface, including: Determine the height coordinate values ​​of each of the plurality of surface nodes; The multiple surface nodes are divided based on their respective height coordinate values ​​to generate at least one group; The center node corresponding to each group is determined based on the surface nodes included in each group, and the point set corresponding to the group is determined based on the distance between the surface nodes included in the group and the center node corresponding to the group.

3. The simulation method according to claim 2, characterized in that, The step of dividing the multiple surface nodes based on their respective height coordinate values ​​to generate at least one group includes: The frequency of occurrence of the height coordinate values ​​of each of the multiple surface nodes is counted, and the nodes are sorted from highest to lowest frequency to obtain the sorting result; Select the N height values ​​that appear the most frequently in the sorting results; For each of the top N height values, the surface node corresponding to that height value is grouped into the same group.

4. The simulation method according to claim 2, characterized in that, The step of determining the point set corresponding to the group based on the distance between the surface nodes included in the group and the corresponding center node of the group includes: Centered on the central node of the group, the target surface of the group is divided into multiple sub-regions along the radial direction of the target surface of the group. The target surface of the group is circular, and the sub-regions are annular or circular. Based on the distance between the surface nodes included in the group and the corresponding center node of the group, the sub-regions into which each surface node in the group falls are determined, and the surface nodes that fall into the same sub-region are defined as a point set.

5. The simulation method according to claim 1, characterized in that, The step of determining the average deposition rate corresponding to each of the multiple point sets based on the predicted deposition rates of the surface nodes included in each of the multiple point sets includes: The predicted deposition rates of the surface nodes included in each point set are added together to obtain the sum of the predicted deposition rates corresponding to the point set. The average deposition rate corresponding to the point set is determined based on the sum of the predicted deposition rates corresponding to the point set and the number of surface nodes included in the point set.

6. The simulation method according to claim 1, characterized in that, The simulation results of the chemical vapor deposition process of the target device, based on the average deposition rate corresponding to each of the multiple point sets and the distance interval corresponding to each of the multiple point sets, include: A target coordinate system is established, wherein the first coordinate axis of the target coordinate system represents the distance between the surface node and the center node, and the second coordinate axis of the target coordinate system represents the predicted deposition rate; Based on the distance intervals corresponding to each of the multiple point sets, the coordinate intervals of the first coordinate axis corresponding to each of the multiple point sets are determined, and the average deposition rate corresponding to each of the multiple point sets is used as the coordinate value of the second coordinate axis to plot the simulation results expressed by a bar chart.

7. The simulation method according to claim 1, characterized in that, The predicted data for determining the chemical vapor deposition process of the target device includes: Determine the three-dimensional geometric model of the target device and the chemical vapor deposition reactor body; The three-dimensional geometric model is divided into multiple solid mesh units to obtain a meshed three-dimensional geometric model. Import the meshed 3D geometric model into the CFD solver to establish a CFD simulation model; The predicted data are obtained by performing steady-state calculations using the CFD simulation model.

8. A simulation device, characterized in that, include: The acquisition module is configured to determine predicted data of a chemical vapor deposition process for a target device, the target device including a target surface, the predicted data including the predicted deposition rate of each of a plurality of surface nodes in the target surface, the predicted data being obtained through a steady-state CFD simulation process; The partitioning module is configured to partition the multiple surface nodes into multiple point sets based on the distance between each of the multiple surface nodes and the center node of the target surface, wherein the distance intervals corresponding to the multiple point sets do not overlap. The determination module is configured to determine the average deposition rate corresponding to each of the plurality of point sets based on the predicted deposition rate of the surface nodes included in each of the plurality of point sets. The simulation module is configured to obtain simulation results of the chemical vapor deposition process of the target device based on the average deposition rate corresponding to each of the multiple point sets and the distance interval corresponding to each of the multiple point sets.

9. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the simulation method according to any one of claims 1 to 7 by executing the executable instructions.

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

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