Analog simulation method and device, electronic equipment and storage medium

Abstract

The invention provides an analog simulation method and device, electronic equipment and a storage medium, and relates to the technical field of semiconductors. The analog simulation method comprises the steps that prediction data of the chemical vapor deposition process of a target device are determined, the target device comprises a target surface, the prediction data comprise respective prediction deposition rates of a plurality of surface nodes in the target surface, and the prediction data are obtained through a steady-state CFD simulation process; dividing the plurality of surface nodes to obtain a plurality of point sets based on the distances between the plurality of surface nodes and the center node of the target surface, the distance intervals corresponding to the plurality of point sets being not overlapped; based on the predicted deposition rates of the surface nodes included in the multiple point sets, determining average deposition rates corresponding to the multiple point sets; and obtaining a simulation result of the chemical vapor deposition process of the target device based on the average deposition rates corresponding to the plurality of point sets and the distance intervals corresponding to the plurality of point sets.

Description

Technical Field

[0001] The present disclosure relates to the field of semiconductor technology, and in particular to a simulation method and device, an electronic device, and a storage medium. Background Art

[0002] Silicon carbide (SiC)-coated graphite susceptors are key consumables in semiconductor epitaxial growth equipment. During the epitaxial growth process, the SiC-coated graphite susceptor is in direct contact with the wafer, so it must maintain a certain surface flatness to prevent wafer deformation. Furthermore, the SiC-coated graphite susceptor must provide a uniform thermal field to ensure temperature uniformity during epitaxial growth. Therefore, ensuring the uniformity of the SiC coating on the graphite susceptor surface is crucial.

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

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

[0005] In a first aspect, a simulation method is provided, comprising: determining predicted data of a chemical vapor deposition process of a target device, the target device comprising a target surface, the predicted data comprising predicted deposition rates of respective multiple surface nodes in 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 distances between each of the multiple surface nodes and a central node of the target surface, the multiple point sets corresponding to respective distance intervals not overlapping with each other; determining an 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 a simulation result 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, based on the distance between each of the multiple surface nodes and the central node of the target surface, the multiple surface nodes are divided to obtain multiple point sets, including: determining the height coordinate value of each of the multiple surface nodes; dividing the multiple surface nodes based on the height coordinate value of each of the multiple surface nodes to generate at least one group; determining the central node corresponding to each group based on the surface nodes included in the group, and determining the point set corresponding to the group based on the distance between the surface nodes included in the group and the central node corresponding to the group.

[0007] In some embodiments, multiple surface nodes are divided based on their respective height coordinate values ​​to generate at least one group, including: counting the number of occurrences of the height coordinate values ​​of the multiple surface nodes, and sorting them from high to low according to the number of occurrences to obtain a sorting result; selecting the top N height values ​​in terms of the number of occurrences of the height coordinate values ​​in the sorting result; for each of the top N height values, dividing the surface node corresponding to the height value into the same group.

[0008] In some embodiments, based on the distance between the surface nodes included in the group and the corresponding center node of the group, the point set corresponding to the group is determined, including: with the corresponding center node of the group as the center, along the radial direction of the target surface corresponding to the group, the target surface corresponding to the group is divided into multiple sub-areas, wherein the target surface corresponding to the group is circular and the sub-areas 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-areas into which each surface node included in the group falls are determined, and the surface nodes falling into the same sub-area are determined as a point set.

[0009] In some embodiments, based on the predicted deposition rates 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, including: 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, 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, a simulation result of the chemical vapor deposition process of the target device is obtained, including: establishing a target coordinate system, 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 interval corresponding to each of the multiple point sets, the coordinate interval of the first coordinate axis corresponding to each of the multiple point sets is 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 draw the simulation results expressed based on a bar chart.

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

[0012] In a second aspect, a simulation device is provided, including: 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, and the predicted data being obtained through a steady-state CFD simulation process; a division module, configured to divide the multiple surface nodes into multiple point sets based on the distances between each of the multiple surface nodes and the central node of the target surface, and the distance intervals corresponding to the multiple point sets do not overlap with each other; a determination module, configured to determine an 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 a simulation result 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] In a third aspect, 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 above by executing the executable instructions.

[0014] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the simulation method provided in the first aspect is implemented.

[0015] The simulation method disclosed in the present invention utilizes a circular integration method to process the prediction data obtained based on the CFD steady-state algorithm, simulating the dynamic deposition process in which the circumferential position on the surface of the target device changes with time. While ensuring a low amount of calculation, it improves the accuracy of the simulation results and the efficiency of post-processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Shown is a flowchart of a simulation method provided by an embodiment of the present disclosure.

[0017] Figure 2 The figure shows a flow chart of the steps of dividing a plurality of surface nodes into a plurality of point sets based on the distances between each of the plurality of surface nodes and the central node of the target surface, provided by an embodiment of the present disclosure.

[0018] Figure 3 The figure shows a flow chart of dividing a plurality of surface nodes based on their respective height coordinate values ​​to generate at least one group step provided by an embodiment of the present disclosure.

[0019] Figure 4 The figure shows a flow chart of 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 central node corresponding to the group, provided by an embodiment of the present disclosure.

[0020] Figure 5 FIG2 is a flow chart of a step of determining an average deposition rate corresponding to each of a plurality of point sets based on predicted deposition rates of surface nodes included in each of the plurality of point sets, provided by an embodiment of the present disclosure.

[0021] Figure 6 1. A flow chart of steps for obtaining simulation results of a chemical vapor deposition process of a target device based on average deposition rates corresponding to a plurality of point sets and distance intervals corresponding to a plurality of point sets according to an embodiment of the present disclosure is shown.

[0022] Figure 7 Shown is a simulation result diagram provided by an embodiment of the present disclosure.

[0023] Figure 8 Shown is a simulation result diagram obtained by using a transient CFD simulation process in related technology.

[0024] Figure 9 FIG2 is a flow chart of the steps of determining prediction data of a chemical vapor deposition process of a target device provided by an embodiment of the present disclosure.

[0025] Figure 10 Shown is a schematic structural diagram of a simulation device provided by an embodiment of the present disclosure.

[0026] Figure 11 Shown is a structural schematic diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present disclosure in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present disclosure without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0028] SiC-coated graphite susceptors, due to their excellent high-temperature stability, thermal shock resistance, and corrosion resistance, have significant application value in the surface modification of key components in semiconductor manufacturing equipment. Statistics show that the use of SiC-coated graphite susceptors can improve the thickness nonuniformity of GaN epitaxial wafers from ±6% to ±3%. Furthermore, SiC coatings can extend the lifespan of graphite susceptors. Uncoated graphite susceptors require replacement every 200 hours during the SiC epitaxial process, while SiC-coated graphite susceptors have a lifespan extended to 2,000 hours, significantly reducing consumable costs. The advantages of SiC-coated graphite susceptors are widely recognized.

[0029] However, due to stringent surface flatness standards, the production of SiC-coated graphite susceptors is difficult and yield is difficult to improve. Furthermore, cracking of the SiC coating during service limits the service life of SiC-coated graphite susceptors. Existing research has shown that uneven SiC coating distribution contributes to abnormal graphite susceptor flatness and thermal stress cracking. Therefore, improving the uniformity of the SiC coating has become a pressing issue.

[0030] Chemical vapor deposition (CVD) is currently the primary technique for depositing SiC coatings on graphite susceptors. To reduce R&D costs and shorten development cycles, researchers are attempting to use computational fluid dynamics (CFD) to couple the flow and chemical fields during the CVD process. This allows them to simulate the deposition of SiC coatings on graphite susceptors and adjust CVD process parameters based on the predicted data.

[0031] During the CVD production process of graphite susceptors, the graphite susceptor usually needs to maintain a constant rotation speed to ensure the uniformity of the SiC coating deposition. However, the rotation of the graphite susceptor and the material rack supporting the graphite susceptor will change the flow field distribution to a certain extent. The CFD steady-state algorithm cannot accurately simulate the dynamic effects caused by the rotation process, and thus cannot accurately simulate the dynamic deposition process in which the circumferential position of the graphite susceptor surface changes with time, resulting in large errors in the prediction of the deposition rate distribution. Although the CFD transient algorithm can capture the dynamic effects caused by the rotation of the graphite susceptor, when simulating and calculating an industrial-grade furnace model, it takes tens of thousands of CPU hours to complete a single full rotation. The huge amount of calculation seriously restricts the efficiency of process optimization.

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

[0033] In response to the above technical problems, the present disclosure provides a simulation method and device, an electronic device and a storage medium, which simulate the dynamic deposition process on the surface of a graphite base based on the prediction data of a CFD steady-state algorithm, thereby improving the accuracy of the simulation results and the post-processing efficiency.

[0034] The following combination Figures 1 to 9 The simulation method provided by the embodiment of the present disclosure is illustrated by way of example.

[0035] Figure 1FIG. 1 is a flow chart of a simulation method according to an embodiment of the present disclosure. Figure 1 As shown, the simulation method provided in the embodiment of the present disclosure includes the following steps.

[0036] S110 , determining prediction data of a chemical vapor deposition process of a target device.

[0037] Chemical vapor deposition (CVD) is a thin-film process that heats and decomposes one or more gases to produce reaction products that deposit a solid film on a substrate surface. Target devices are devices that achieve specific functions through thin-film deposition using CVD. For example, target devices may include graphite substrates. Depositing a SiC coating on a graphite substrate using CVD can improve the substrate's process performance and service life.

[0038] The target device includes a target surface, which is at least one external surface of the target device. During the CVD process, the reactant gas is deposited on the target surface, and the uniformity of the deposited film layer on the target surface must be ensured. For example, if the target device is a graphite susceptor, the target surface can be the upper and / or lower surface of the graphite susceptor.

[0039] Through a steady-state CFD simulation process, the CVD process of the target device in the reactor is simulated to obtain predicted data. The predicted data includes the three-dimensional coordinates of each location on the target device surface and the predicted deposition rate. Specifically, the target device surface is evenly divided into multiple grids, each corresponding to a surface node. Therefore, the predicted data includes the predicted deposition rate for each of the multiple surface nodes on the target surface. The predicted deposition rate is the film deposition rate at the corresponding location of the surface node, as predicted by the steady-state CFD simulation process.

[0040] S120 , dividing the plurality of surface nodes into a plurality of point sets based on the distances between each of the plurality of surface nodes and the central node of the target surface.

[0041] There can be one or more target surfaces, each corresponding to a central node. A central node is one of the multiple surface nodes of the target surface. Because the target device rotates during the CVD process, the central node can be understood as the rotation center of the target device. It is typically a surface node located at the geometric center of the target surface (or a central position generally recognized 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 central node, the distances between multiple surface nodes of the target surface and the central node of the target surface can be calculated, and the multiple surface nodes can be divided according to the distances to obtain multiple point sets. Each point set corresponds to a different distance interval, and the distance intervals corresponding to the multiple point sets do not overlap.

[0043] For example, multiple surface nodes are divided into three point sets, namely 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 A is (a, b], and the distance interval corresponding to point set A 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 divided into point set A. The number of point sets or the range of distance intervals are only illustrative, and those skilled in the art can set them according to actual conditions.

[0044] S130 , 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.

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

[0046] For any point set, the average predicted deposition rate of each of the multiple surface nodes in the point set is calculated as the average deposition rate corresponding to the point set. In this way, the average deposition rate corresponding to each point set is obtained.

[0047] S140 , obtaining a simulation result of a chemical vapor deposition process of a target device based on the average deposition rates corresponding to each of the plurality of point sets and the distance intervals corresponding to each of the plurality of point sets.

[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 the point set. The average deposition rate corresponding to each point set is determined as the predicted deposition rate of the sub-region corresponding to the point set, and based on the predicted deposition rate of each sub-region of the target surface in the target device, the simulation result of the chemical vapor deposition process of the target device is determined. The simulation results include the correspondence between different positions of the target device and the predicted deposition rate results. Exemplarily, the simulation results can be presented in the form of cloud maps, contour maps, bar graphs, line graphs, tables, etc.

[0049] The embodiment of the present disclosure provides a simulation method, which uses a circular integration method to process the predicted data obtained based on the CFD steady-state algorithm to simulate the dynamic deposition process in which the circumferential position of the target device surface changes with time. During the CVD process, the target device rotates at a constant speed. Therefore, after the target device rotates one circle, the thickness of the film layer deposited by any surface node of the target device should be equivalent to the sum of the thickness of the film layer deposited by multiple surface nodes with the same distance from the center node as the surface node per unit time. Based on the above reasons, the average deposition rate corresponding to each point set can be used as the predicted result of the deposition rate of the sub-area corresponding to the point set. In this way, the method of the embodiment of the present disclosure improves the accuracy of the simulation results and the efficiency of post-processing while ensuring low computational complexity.

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

[0051] Figure 2 FIG. 1 is a flow chart of the steps of dividing a plurality of surface nodes into a plurality of point sets based on the distances between each of the plurality of surface nodes and the central node of the target surface provided by an embodiment of the present disclosure. Figure 2 As shown, based on the distance between each of the multiple surface nodes and the central node of the target surface, the step of dividing the multiple surface nodes into multiple point sets includes the following steps.

[0052] S121, determining the height coordinate values ​​of each of the plurality of surface nodes.

[0053] The plurality of surface nodes correspond to different three-dimensional coordinates, each of 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 , dividing the plurality of 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, that is, the height coordinate values ​​of the surface nodes located on the same target surface should be the same. Taking a graphite base as an example, the height coordinate values ​​of the surface nodes located on the upper surface of the graphite base are the same; the height coordinate values ​​of the surface nodes located on the lower surface of the graphite base are the same and different from the corresponding height coordinate values ​​of the upper surface of the graphite base.

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

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

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

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

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

[0061] In the disclosed embodiments, the correspondence between target surfaces and surface nodes is determined based on the surface node heights. For surface nodes belonging to the same target surface, a central node is determined and the nodes are divided based on distance to obtain multiple point sets. This allows for simultaneous processing of target devices including multiple target surfaces, or multiple target devices, improving processing efficiency.

[0062] Figure 3 FIG. 1 is a flow chart of dividing a plurality of surface nodes based on their respective height coordinate values ​​to generate at least one group step according to an embodiment of the present disclosure. Figure 3 As shown, the step of dividing the plurality of surface nodes based on their respective height coordinate values ​​and generating at least one group includes the following steps.

[0063] S1221: Count the number of occurrences of the height coordinate values ​​of the plurality of surface nodes, and sort them from high to low according to the number of occurrences to obtain a sorting result.

[0064] Count the number of times each height coordinate value appears in the height coordinate values ​​of multiple surface nodes. For example, for six surface nodes, their coordinates 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). 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 by the number of occurrences from high to low to get the sorting result. The sorting result can be written as z2, z1, z3.

[0066] S1222, selecting the top N height values ​​in terms of the number of occurrences of the height coordinate values ​​in the sorting results.

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

[0068] For target devices like graphite susceptors, the target surface of the target device is parallel to the bottom surface of the reactor body. This means that the height coordinates of multiple surface nodes on the same target surface are identical. Therefore, the target surface can be determined based on the height coordinates of the surface nodes.

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

[0070] As for a graphite base, a graphite base includes two target surfaces, an upper surface and a lower surface. Therefore, the value of N can also be determined according to the number of target surfaces included in the target device and the number of target devices.

[0071] Continuing with the above example, the six surface nodes belong to the same graphite base, and the number of target surfaces is 2. Therefore, we need to select the height values ​​z2 and z1 with the highest number of occurrences in the sorted results. 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 the height value into the same group.

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

[0074] Among the plurality of surface nodes, a surface node having a height coordinate value equal to the height value is searched, and the obtained surface nodes are divided into the same group.

[0075] Continuing with the above example, 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 the embodiment of the present disclosure, a method for quickly 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 to meet the demand for rapid feedback of process parameters in industrial production.

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

[0078] Figure 4 FIG. 1 is a flow chart of a step of determining a point set corresponding to a group based on the distance between the surface nodes included in the group and the center nodes corresponding to the group, provided by an embodiment of the present 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 central node corresponding to the group as the center and dividing the target surface corresponding to the group into a plurality of sub-areas along the radial direction of the target surface corresponding to the group.

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

[0081] In the embodiment of the present disclosure, 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 circular or annular sub-areas along the radial direction of the circle.

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

[0083] S1232: Based on the distances between the surface nodes included in the group and the central nodes corresponding to the group, determine the sub-regions into which the surface nodes included in the group fall, and determine the surface nodes falling in the same sub-region as a point set.

[0084] For any sub-region, the distance interval between the surface nodes and the central node falling into the sub-region is determined. Moreover, the distance interval corresponding to each sub-region is determined by the boundary of the sub-region, and the radius length corresponding to the circular boundary of the sub-region is used as the endpoint value of the distance interval corresponding to the sub-region. For example, the distance interval corresponding to the circular sub-region Q1 is (0, r1], the distance interval corresponding to the annular sub-region Q2 is (r1, r2], and the distance interval corresponding to the annular sub-region Q3 is (r2, r3].

[0085] After determining the distance interval corresponding to each sub-region, the sub-region that the surface node falls into can be determined based on the distance between the surface node and the center node. Then, the surface nodes that fall into the same sub-region are determined as a point set.

[0086] Furthermore, the difference between the inner and outer radii of each annular subregion can be the same, that is, each annular subregion has the same annular width, and the radius of the circular subregion is equal to the annular width of the annular subregion, to facilitate subsequent analysis of the differences in predicted deposition rates at different radial locations on the target surface. The annular width can be set according to actual needs and, for example, can be set to 4 mm.

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

[0088] Figure 5 FIG. 1 is a flow chart of a step of determining 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 according to an embodiment of the present disclosure. Figure 5 As shown, the following steps are performed for multiple point sets respectively.

[0089] S131 , 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.

[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 the point set.

[0091] S132 : Determine an 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.

[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, thereby determining the average deposition rate corresponding to each of the multiple point sets.

[0093] In the disclosed embodiments, the average deposition rate of each point in the point set is calculated to obtain the average deposition rate of sub-areas with different radii. 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 can improve the consistency of the steady-state algorithm with the rotation process, while also achieving high computational efficiency.

[0094] After obtaining the average deposition rate for each sub-region, the simulation results corresponding to the target device can be further obtained. For example, the simulation results can be displayed in the form of a bar graph. The following is a detailed description.

[0095] Figure 6 FIG. 1 is a flow chart of steps for obtaining simulation results of a chemical vapor deposition process of a target device based on the average deposition rate corresponding to each of a plurality of point sets and the distance interval corresponding to each of a plurality of point sets, provided by an embodiment of the present disclosure. Figure 6 As shown, the steps of obtaining the simulation results of the chemical vapor deposition process of the target device based on the average deposition rates corresponding to the multiple point sets and the distance intervals corresponding to the multiple point sets include the following steps.

[0096] S141, establishing a target coordinate system.

[0097] The target coordinate system includes a first coordinate axis and a second coordinate axis that are orthogonal to each other.

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

[0099] The second coordinate axis may 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 the multiple point sets, determine the coordinate intervals of the first coordinate axis corresponding to the multiple point sets, and use the average deposition rates corresponding to the multiple point sets as the coordinate values ​​of the second coordinate axis to draw the simulation results expressed based on 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 column in the histogram. Based on the endpoint values ​​of the distance interval, the coordinate interval of the first coordinate axis corresponding to the point set is determined and used as the coordinate value of the column on the first coordinate axis. The average deposition rate corresponding to the point set is used as the coordinate value of the column on the second coordinate axis.

[0103] You can plot simulation results for each target surface separately. Alternatively, you can combine simulation results for multiple target surfaces of the same device into a single histogram. For example, for a graphite susceptor, you can plot simulation results for both the top and bottom surfaces on a single histogram to more intuitively visualize the differences in deposition rates between the top and bottom surfaces within different radii.

[0104] Figure 7FIG. 1 is a diagram showing simulation results provided by an embodiment of the present disclosure. Figure 7 Graphite susceptor upper and lower surfaces are predicted to have deposition rates based on the method of the embodiment of the present disclosure. Figure 8 The figure shows the simulation results obtained by using the transient CFD simulation process in the related technology. Figure 8 The predicted deposition rates on the upper and lower surfaces of the graphite susceptor are drawn based on the transient prediction data.

[0105] contrast Figure 7 、 Figure 8 It can be found that in the simulation results obtained by different methods, the changing trends of the deposition rates corresponding to different radii are consistent. It can be seen that the simulation results of the embodiment of the present disclosure can better capture the dynamic effects caused by the rotation process of the target device, providing a valuable reference for further analysis and optimization of process parameters, and the calculation amount and time are much less than the transient CFD simulation process.

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

[0107] The following briefly introduces the specific implementation method of using the steady-state CFD simulation algorithm to predict the CVD process of the target device.

[0108] Figure 9 FIG. 1 is a flow chart of the steps for determining the prediction data of the chemical vapor deposition process of the target device according to an embodiment of the present disclosure. Figure 9 As shown, the step of determining prediction data of the chemical vapor deposition process of the target device includes the following steps.

[0109] S111, determining a three-dimensional geometric model of a target device and a chemical vapor deposition reactor body.

[0110] The 3D geometric models of the various parts of the CVD reactor body and the target device are constructed using 3D drawing modeling software, and the various parts of the reactor body and the target device are assembled together.

[0111] During the modeling process, the dimensions of the reactor components and the target device must match those of the actual components used. During assembly, gaps between interconnected components due to improper assembly and fit must be avoided.

[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, small steps, and grooves that will not have a significant impact on the flow field, in order to improve the simulation calculation efficiency and the stability of the results.

[0113] Characteristic structures such as air inlets, air outlets, and heating sources are set at corresponding positions of 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 models of the reactor body and target device for redundant split edges, extra edges, extra faces, small faces, inaccurate edges, and missing faces. If any, use 3D drawing modeling software to process and repair them.

[0115] S112, dividing the three-dimensional geometric model into a plurality of solid grid units to obtain a gridded three-dimensional geometric model.

[0116] The fluid domain is created using the volume extraction method. The fluid domain is divided into multiple solid grid cells to obtain a meshed three-dimensional geometric model.

[0117] S113, importing the meshed three-dimensional geometric model into a CFD solver to establish a CFD simulation model.

[0118] Import the gridded three-dimensional geometric model into the CFD solver, set parameters such as working conditions, turbulence model, chemical reaction equation, and set regional conditions and boundary conditions, and finally obtain the CFD simulation model.

[0119] It is understandable that the parameters listed above can be adjusted by those skilled in the art according to actual conditions and should not be construed as limiting the present disclosure.

[0120] S114, performing steady-state calculations using the CFD simulation model to obtain predicted data.

[0121] Initialize the flow field equation matrix and set the convergence criteria. Use an iterative method to calculate and solve the flow field result matrix. Once the residual reaches the convergence criteria, the calculation is considered converged. Next, process the calculated data and export the 3D coordinates of all target device surface nodes and predicted deposition rates as a data file to obtain the predicted data.

[0122] Combined with the above Figures 1 to 9 The method embodiment of the present disclosure is described in detail. Figure 10 The device embodiment of the present disclosure is described in detail. It should be understood that the description of the method embodiment corresponds to the description of the device embodiment, so for parts not described in detail, reference can be made to the previous method embodiment.

[0123] Figure 10 FIG. 1 is a schematic diagram of the structure of a simulation device provided by an embodiment of the present disclosure. Figure 10As shown, the simulation device 1000 of the embodiment of the present disclosure 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, the predicted data includes the predicted deposition rate of each of the multiple surface nodes in the target surface, and the predicted data is obtained through a steady-state CFD simulation process. The division module 1020 is configured to divide the multiple surface nodes into multiple point sets based on the distance between each of the multiple surface nodes and the central node of the target surface, and the distance intervals corresponding to the multiple point sets do not overlap with each other. 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 rate of the surface nodes included in each of the multiple point sets. The simulation module 1040 is configured to obtain the simulation result 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.

[0125] In some embodiments, the division module 1020 is further configured to determine the height coordinate values ​​of each of the multiple surface nodes; divide 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 central node corresponding to each group based on the surface nodes included in the group, and determine the point set corresponding to the group based on the distance between the surface nodes included in the group and the central node corresponding to the group.

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

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

[0128] In some embodiments, the determination 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 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; 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 draw the simulation results expressed based on 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 grid units to obtain a gridded three-dimensional geometric model; import the gridded three-dimensional geometric model into the CFD solver to establish a CFD simulation model; and perform steady-state calculations using the CFD simulation model to obtain predicted data.

[0131] Below, reference Figure 11 An electronic device according to an embodiment of the present disclosure is described. Figure 11 FIG. 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. Figure 11 As shown, 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 forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 1100 to perform desired functions.

[0133] 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), a hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and processor 1110 may execute the program instructions to implement the simulation methods of the various embodiments of the present 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 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

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

[0136] Of course, to simplify, Figure 11 Only some of the components related to the present disclosure in the electronic device 1100 are shown, and components such as buses, input / output interfaces, etc. are omitted. In addition, according to specific application scenarios, the electronic device 1100 may further include any other appropriate components.

[0137] In addition to the above methods and devices, an embodiment of the present disclosure may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps in the simulation method according to various embodiments of the present disclosure described above in this specification.

[0138] The computer program product may be written in any combination of one or more programming languages ​​to implement the operations of the disclosed embodiments, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0139] In addition, an embodiment of the present disclosure may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enables the processor to execute the steps of the simulation method according to various embodiments of the present disclosure described above in this specification.

[0140] Computer-readable storage media can take the form of any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0141] The basic principles of the present disclosure have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this disclosure are merely illustrative and not restrictive, and should not be construed as necessarily possessed by each embodiment of the present disclosure. Furthermore, the specific details disclosed above are provided for illustrative purposes and to facilitate understanding, rather than as limitations. These details do not limit the present disclosure to necessarily being implemented using these specific details.

[0142] The block diagrams of the devices, devices, equipment, and systems involved in this disclosure 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 will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0143] It should also be noted that in the systems, devices, and methods of the present disclosure, each component or each step can be decomposed and / or recombined, and such decompositions and / or recombinations should be regarded as equivalent solutions of the present disclosure.

[0144] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present 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 the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0145] The above description has been provided for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A simulation method, characterized in that: include: Determining predicted data for a chemical vapor deposition process of a target device, the target device including a target surface, the predicted data including predicted deposition rates for each of a plurality of 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 plurality of surface nodes and the central node of the target surface, the plurality of surface nodes are divided into a plurality of point sets, wherein the distance intervals corresponding to the plurality of point sets do not overlap with each other; Determining an average deposition rate corresponding to each of the plurality of point sets based on predicted deposition rates of surface nodes included in each of the plurality of point sets; A simulation result of the chemical vapor deposition process of the target device is obtained based on the average deposition rates corresponding to each of the plurality of point sets and the distance intervals corresponding to each of the plurality of point sets.

2. The simulation method according to claim 1, wherein: The step of dividing the plurality of surface nodes into a plurality of point sets based on the distance between each of the plurality of surface nodes and the central node of the target surface comprises: determining a height coordinate value of each of the plurality of surface nodes; Dividing the plurality of surface nodes based on respective height coordinate values ​​of the plurality of surface nodes to generate at least one group; The central node corresponding to each group is determined based on the surface nodes included in the 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 central node corresponding to the group.

3. The simulation method according to claim 2, characterized in that: The dividing the plurality of surface nodes based on the respective height coordinate values ​​of the plurality of surface nodes to generate at least one group includes: Counting the number of occurrences of the height coordinate values ​​of each of the plurality of surface nodes, and sorting them from high to low according to the number of occurrences to obtain a sorting result; Select the top N height values ​​of the height coordinate values ​​in the sorting result; For each height value in the top N height values, the surface nodes corresponding to the height value are divided into the same group.

4. The simulation method according to claim 2, wherein: The determining of the point set corresponding to the group based on the distance between the surface nodes included in the group and the central node corresponding to the group includes: Taking the central node corresponding to the group as the center, the target surface corresponding to the group is divided into a plurality of sub-areas along the radial direction of the target surface corresponding to the group, wherein the target surface corresponding to the group is circular and the sub-areas are annular or circular; Based on the distances between the surface nodes included in the group and the central nodes corresponding to the group, the sub-regions into which the surface nodes included in the group fall are determined, and the surface nodes falling into the same sub-region are determined as a point set.

5. The simulation method according to claim 1, wherein: The determining, based on the predicted deposition rates of the surface nodes respectively included in the plurality of point sets, an average deposition rate corresponding to each of the plurality of point sets, comprises: 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; An 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, wherein: The obtaining of 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 includes: Establishing a target coordinate system, wherein a first coordinate axis of the target coordinate system represents a distance between the surface node and the central node, and a second coordinate axis of the target coordinate system represents the predicted deposition rate; Based on the distance intervals corresponding to the multiple point sets, the coordinate intervals of the first coordinate axis corresponding to the multiple point sets are determined, and the average deposition rates corresponding to the multiple point sets are used as the coordinate values ​​of the second coordinate axis to plot the simulation results expressed based on a bar chart.

7. The simulation method according to claim 1, characterized in that: The prediction data of the chemical vapor deposition process of the target device is determined, including: Determining a three-dimensional geometric model of the target device and a chemical vapor deposition reactor; Dividing the three-dimensional geometric model into a plurality of solid grid units to obtain a gridded three-dimensional geometric model; Importing the gridded three-dimensional geometric model into a CFD solver to establish a CFD simulation model; The CFD simulation model is used to perform steady-state calculations to obtain the predicted data.

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

9. An electronic device, characterized in that: include: processor; as well as a memory for storing 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 a processor, the simulation method according to any one of claims 1 to 7 is implemented.

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