Silicon carbide material validation method and apparatus

Abstract

The present application provides a silicon carbide material verification method and device. The method includes: obtaining a silicon carbide material; determining N first reference points on a first surface of the silicon carbide material, and obtaining a plurality of first X-ray diffraction results of the first reference points; determining N second reference points on a second surface of the silicon carbide material, and obtaining a plurality of second X-ray diffraction results of the second reference points; obtaining N diffraction result differences based on each first X-ray diffraction result and the corresponding second X-ray diffraction result; obtaining a reference difference upper limit, and finding a plurality of specific differences in the diffraction result differences based on the reference difference upper limit; and estimating a bending degree variation value of the silicon carbide material based on the reference difference upper limit and the specific differences.

Description

Technical Field

[0001] This invention relates to a material verification method and apparatus, and more particularly to a silicon carbide material verification method and apparatus. Background Technology

[0002] In the prior art, during the processing of silicon carbide materials (such as crystal balls, wafers, ingots, etc.), the characteristics of silicon carbide materials (such as the stress remaining on the silicon carbide materials) will have a decisive influence on the geometric quality of subsequent processing.

[0003] Therefore, for those skilled in the art, if the characteristics of silicon carbide materials can be known in advance, appropriate processing procedures can be adopted, thereby avoiding material waste. Summary of the Invention

[0004] In view of this, the present invention provides a method and apparatus for verifying silicon carbide materials, which can be used to solve the above-mentioned technical problems.

[0005] This invention provides a method for verifying silicon carbide materials, comprising: obtaining a silicon carbide material, wherein the silicon carbide material includes a first surface and a second surface; determining N first reference points on the first surface and obtaining multiple first X-ray diffraction results for the multiple first reference points, wherein N is a positive integer; determining N second reference points corresponding to the multiple first reference points on the second surface and obtaining multiple second X-ray diffraction results for the multiple second reference points; obtaining N diffraction result differences based on each first X-ray diffraction result and each corresponding second X-ray diffraction result; obtaining a reference difference upper limit and identifying multiple specific differences among the multiple diffraction result differences based on the reference difference upper limit; and estimating a curvature variation value of the silicon carbide material based on the reference difference upper limit and the multiple specific differences.

[0006] This invention provides a silicon carbide material verification device, including a storage circuit and a processor. The storage circuit stores program code. The processor is coupled to the storage circuit and accesses the program code to execute: obtaining a silicon carbide material, wherein the silicon carbide material includes a first surface and a second surface; determining N first reference points on the first surface and obtaining multiple first X-ray diffraction results for the multiple first reference points, where N is a positive integer; determining N second reference points corresponding to the multiple first reference points on the second surface and obtaining multiple second X-ray diffraction results for the multiple second reference points; obtaining N diffraction result differences based on each first X-ray diffraction result and each corresponding second X-ray diffraction result; obtaining a reference difference upper limit and identifying multiple specific differences among the multiple diffraction result differences based on the reference difference upper limit; estimating a curvature variation value of the silicon carbide material based on the reference difference upper limit and the multiple specific differences. Attached Figure Description

[0007] The accompanying drawings are included to further illustrate the invention, and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0008] Figure 1 This is a schematic diagram of a silicon carbide material verification device according to an embodiment of the present invention;

[0009] Figure 2 This is a flowchart illustrating a silicon carbide material verification method according to an embodiment of the present invention;

[0010] Figure 3A This is a schematic diagram of silicon carbide material according to an embodiment of the present invention;

[0011] Figure 3B It is based on Figure 3A A schematic diagram of the first surface is shown;

[0012] Figure 3C It is based on Figure 3B A schematic diagram of the second surface is shown;

[0013] Figure 4 Based on the multiple ΔP values ​​shown in Tables 1 and 2 i Schematic diagram;

[0014] Figure 5 It is based on Figure 4 The ΔP values ​​shown in Table 1 are as follows. i A schematic diagram showing the relative relationship between the upper limit of the reference difference and ΔAvg;

[0015] Figure 6 This is a schematic diagram illustrating the acquisition of the regression formula according to an embodiment of the present invention. Detailed Implementation

[0016] Reference will now be made in detail to exemplary embodiments of the invention, examples of which are illustrated in the accompanying drawings. Wherever possible, the same component reference numerals are used in the drawings and description to denote the same or similar parts.

[0017] Please refer to Figure 1 This is a schematic diagram of a silicon carbide material verification device according to an embodiment of the present invention. Figure 1 In this context, the silicon carbide material verification device 100 may include various computer devices and / or intelligent devices, but may not be limited to these.

[0018] like Figure 1As shown, the silicon carbide material verification device 100 may include a storage circuit 102 and a processor 104. The storage circuit 102 may be, for example, any type of fixed or removable random access memory (RAM), read-only memory (ROM), flash memory, hard disk, or other similar device or combination of these devices, and may be used to record multiple program codes or modules.

[0019] Processor 104 is coupled to storage circuit 102 and may be a general purpose processor, special purpose processor, conventional processor, digital signal processor, multiple microprocessors, one or more microprocessors incorporating a digital signal processor core, controller, microcontroller, application specific integrated circuit (ASIC), field programmable gate array (FPGA), any other type of integrated circuit, state machine, processor based on advanced RISC machine (ARM), and the like.

[0020] In an embodiment of the present invention, the processor 104 can access the modules and program code recorded in the storage circuit 102 to implement the silicon carbide material verification method proposed in the present invention, the details of which are described below.

[0021] Please refer to Figure 2 This is a flowchart illustrating a silicon carbide material verification method according to an embodiment of the present invention. The method of this embodiment can be derived from... Figure 1 The silicon carbide material verification device 100 is executed, and the following is the configuration. Figure 1 Component description shown Figure 2 Details of each step.

[0022] First, in step S210, the processor 104 obtains silicon carbide material, which may include a first surface and a second surface. In different embodiments, the silicon carbide material may include silicon carbide ingots, silicon carbide wafers, or other similar materials, but is not limited thereto. For ease of explanation, the silicon carbide material under consideration is assumed below to be... Figure 3A The silicon carbide wafer shown is merely an example and is not intended to limit the possible implementations of the invention.

[0023] Please refer to Figure 3A This is a schematic diagram of silicon carbide material according to an embodiment of the present invention. Figure 3AIn this embodiment, silicon carbide material 300 is, for example, a silicon carbide wafer, which may include a first surface 310 and a second surface 320. In different embodiments, silicon carbide material 300 may include a carbon surface and a silicon surface, wherein the first surface 310 is, for example, one of the carbon surface and the silicon surface, and the second surface 320 is, for example, the other of the carbon surface and the silicon surface.

[0024] Subsequently, in step S220, the processor 104 may determine N first reference points on the first surface 310 and obtain multiple first X-ray diffraction results for the plurality of first reference points. In embodiments of the present invention, N may be a positive integer. In a preferred embodiment, N may be a positive integer greater than or equal to 25. For ease of explanation, it is assumed below that N is 25, but it is not limited thereto.

[0025] Please refer to Figure 3B It is based on Figure 3A A schematic diagram of the first surface is shown. Figure 3B In this embodiment, the processor 104 can determine 25 (i.e., N) first reference points P1 to P25 on the first surface 310, wherein the first reference points P1 to P25 can be distributed on reference axes A1 to A4. In different embodiments, the processor 104 can arbitrarily select N points on the first surface 310 as the first reference points P1 to P25. In some embodiments, the first reference points P1 to P25 selected by the processor 104 can be uniformly distributed on the first surface 310. In this way, the first reference points P1 to P25 can better characterize the statistical properties of the first surface 310 in terms of quality, but it is not limited to this.

[0026] After determining the first reference points P1 to P25, the processor 104 can measure the full width at half maximum (FWHM) of the diffraction peaks of each of the first reference points P1 to P25 as the first X-ray diffraction result for each of the first reference points P1 to P25, but is not limited to this. In some embodiments, the X-ray diffraction result of the i-th (1≤i≤N) first reference point (which can be represented as Pi) among the first reference points P1 to P25 can be characterized as P 1,i In this case, the X-ray diffraction results of the first reference points P1 to P25 can be characterized as P 1,1 ~P 1,25 However, it is not limited to this.

[0027] Next, in step S230, the processor 104 may determine N second reference points corresponding to the plurality of first reference points on the second surface 320, and obtain a plurality of second X-ray diffraction results of the plurality of second reference points.

[0028] Please refer to Figure 3C It is based on Figure 3B A schematic diagram of the second surface is shown. Figure 3C In this embodiment, the processor 104 can determine 25 (i.e., N) second reference points P1' to P25' on the second surface 320, wherein the second reference points P1' to P25' can be distributed on reference axes A1 to A4. In this embodiment, the processor 104 can select N points on the second surface 320 corresponding to the first reference points P1 to P25 as the second reference points P1' to P25'.

[0029] In one embodiment, the distribution positions of the first reference points P1 to P25 on the first surface 310 can correspond one-to-one with the distribution positions of the second reference points P1' to P25' on the second surface 320, but this is not limited to this. For example, suppose there is an imaginary reference line RL perpendicular to the first surface 310 and the second surface 320 that passes through the first reference point P1 located on the first surface 310, then this imaginary reference line RL will also correspondingly pass through the second reference point P1' located on the second surface 320. In this way, the second reference points P1' to P25' can be used as a preferred comparison reference for the first reference points P1 to P25.

[0030] After determining the second reference points P1' to P25', the processor 104 can measure the full width at half maximum (FWHM) of the diffraction peaks of each of the second reference points P1' to P25' as the individual second X-ray diffraction results for each of the second reference points P1' to P25', but is not limited to this. In some embodiments, the X-ray diffraction result of the i-th second reference point (which may be represented as Pi') among the second reference points P1' to P25' can be characterized as P 2,i In this case, the X-ray diffraction results of the second reference points P1' to P25' can be characterized as P 2,1 ~P 2,25 However, it is not limited to this.

[0031] Subsequently, in step S240, the processor 104 can obtain N diffraction result differences based on each first X-ray diffraction result and each corresponding second X-ray diffraction result.

[0032] In some embodiments, the i-th diffraction result difference among the N diffraction result differences can be characterized as ΔP. i In some embodiments, the processor 104 can P 1,i Subtract P 2,i To obtain ΔP i (i.e., ΔP) i =P 1,i -P 2,i In some embodiments, the processor 104 may also P 2,i Subtract P 1,i To obtain ΔP i (i.e., ΔP) i =P 2,i -P1,i ).

[0033] In an embodiment of the present invention, it is assumed that each P of the silicon carbide material 300 1,i P 2,i and ΔP i With the following

[0034] The embodiments illustrated in Table 1 are not limited to these.

[0035]

[0036] Table 1

[0037] The differences in the various diffraction results obtained for silicon carbide material 300 (i.e., ΔP1 to ΔP in Table 1) 25 After that, in step S250, the processor 104 can obtain the reference difference upper limit U1 and find multiple specific differences among the multiple diffraction result differences based on the reference difference upper limit U1.

[0038] In one embodiment, in determining the upper limit of the reference difference U1, the processor 104 may be configured to: obtain a reference silicon carbide material, wherein the reference silicon carbide material may include a third surface and a fourth surface; determine N third reference points on the third surface and obtain multiple third X-ray diffraction results for the multiple third reference points; determine N fourth reference points corresponding to the multiple third reference points on the fourth surface and obtain multiple fourth X-ray diffraction results for the multiple fourth reference points; obtain N reference diffraction result differences based on each third X-ray diffraction result and each corresponding fourth X-ray diffraction result; obtain the average value and standard deviation of the multiple reference diffraction result differences, and determine the upper limit of the reference difference U1 accordingly.

[0039] In one embodiment, the aforementioned reference silicon carbide material is, for example, another silicon carbide material with better quality. For instance, the aforementioned reference silicon carbide material is, for example, a silicon carbide material with a bending variation below a preset threshold (e.g., 20 μm), but it is not limited to this.

[0040] In an embodiment of the present invention, the processor 104 may be based on obtaining each P of the silicon carbide material 300. 1,i P 2,i and ΔP i The method of obtaining various P of the reference silicon carbide material 1,i P 2,i and ΔP i In one embodiment, it is assumed that each P of the reference silicon carbide material... 1,i P 2,i and ΔP i The embodiments illustrated in Table 2 below are available, but are not limited thereto.

[0041]

[0042] Table 2

[0043] In this case, the aforementioned third reference point can be understood as corresponding to multiple first reference points in Table 2, and the third X-ray diffraction result of each third reference point is, for example, P in Table 2. 1,1 ~P 1,25 Furthermore, the aforementioned fourth reference point can be understood as corresponding to multiple second reference points in Table 2, and the fourth X-ray diffraction result for each fourth reference point is, for example, P in Table 2. 2,1 ~P 2,25 Based on this, the differences between the N reference diffraction results obtained by the processor 104 are, for example, ΔP1 to ΔP in Table 2. 25 .

[0044] Then, the processor 104 can, based on the differences in the N reference diffraction results (i.e., ΔP1 to ΔP in Table 2), 25 The average value and standard deviation of the differences of the N reference diffraction results are used to determine the upper limit of the reference difference U1. In one embodiment, assuming that the average value and standard deviation of the differences of the N reference diffraction results are ΔAvg and σ respectively, the upper limit of the reference difference U1 can be characterized as ΔAvg+3σ, but it is not limited to this.

[0045] After determining the upper limit of the reference difference U1, the processor 104 can, for example, use the differences in the plurality of diffraction results (i.e., ΔP1 to ΔP of the silicon carbide material 300 in Table 1) to determine the upper limit of the reference difference U1. 25 The processor 104 identifies multiple specific differences from the diffraction results. In one embodiment, the processor 104 may identify one or more of the diffraction result differences that are greater than the reference difference upper limit U1 as the multiple specific differences. That is, each specific difference is greater than the reference difference upper limit U1.

[0046] Please refer to Figure 4 It is based on the multiple ΔP values ​​shown in Tables 1 and 2. i Schematic diagram. Figure 4 In this context, curve 410 corresponds, for example, to the multiple ΔP values ​​shown in Table 1. i Curve 420, for example, corresponds to multiple ΔP values ​​shown in Table 2. i The dashed line, for example, corresponds to the upper limit of the reference difference U1.

[0047] exist Figure 4 In this scenario, the processor 104 identifies several specific differences that exceed the reference difference upper limit U1, such as the 12 selected diffraction result differences, which are, for example, ΔP6 and ΔP in Table 1. 10 ΔP 11 ΔP 12 ΔP 16 ΔP 17ΔP 20 ΔP 21 ΔP 22 ΔP 23 ΔP 24 ΔP 25 .

[0048] Please refer to again Figure 5 It is based on Figure 4 The ΔP values ​​shown in Table 1 are as follows. i A schematic diagram illustrating the relative relationship between the upper limit of the reference difference and ΔAvg. Figure 5 In the middle, the ΔP of each of the first reference points P1 to P25 of silicon carbide material 300 i Individually, these are shown as corresponding point ranges, and within each of the first reference points P1 to P25, the point ranges corresponding to the upper limit of the reference difference U1 and ΔAvg can be shown. Thus, each ΔP can be observed. i The relative relationship with the upper limit of the reference difference and ΔAvg. For example, ΔP of the first reference point P25. i (i.e., ΔP) 25 This can be represented as the range R25, and the relative size between the range R25 and the corresponding upper limit of the reference difference U1 shows that ΔP 25 The system is greater than the upper limit of the reference difference U1, therefore ΔP 25 This will be taken as one of the specific differences mentioned above.

[0049] Based on the principle of similarity, due to ΔP6, ΔP 10 ΔP 11 ΔP 12 ΔP 16 ΔP 17 ΔP 20 ΔP 21 ΔP 22 ΔP 23 ΔP 24 Each corresponding point range is larger than the corresponding upper limit of the reference difference U1, therefore ΔP6 and ΔP 10 ΔP 11 ΔP 12 ΔP 16 ΔP 17 ΔP 20 ΔP 21 ΔP 22 ΔP 23 ΔP 24 All will be taken as the specific differences mentioned above.

[0050] Subsequently, in step S260, the processor 104 can estimate the curvature variation value of the silicon carbide material 300 based on the reference difference upper limit U1 and the plurality of specific differences. In one embodiment, the processor 104 can obtain a first difference between each specific difference and the reference difference upper limit U1. For example, assuming the reference difference upper limit U1 is characterized as ΔUL, the processor 104 can obtain ΔP6-ΔUL, ΔP 10 -ΔUL、ΔP 11 -ΔUL、ΔP 12 -ΔUL、ΔP 16 -ΔUL、ΔP 17 -ΔUL、ΔP 20 -ΔUL、ΔP 21 -ΔUL、ΔP 22 -ΔUL、ΔP 23 -ΔUL、ΔP 24 -ΔUL、ΔP 25 -ΔUL can be considered as the first difference corresponding to the specific differences mentioned above, but it is not limited to this.

[0051] The processor 104 can then sum the first differences corresponding to each specific difference to obtain a reference score for the silicon carbide material 300. In one embodiment, the reference score RC of the silicon carbide material 300 can be calculated, for example, as:

[0052] ΔP6+ΔP 10 +ΔP 11 +ΔP 12 +ΔP 16 +ΔP 17 +ΔP 20 +ΔP 21 +ΔP 22 +ΔP 23 +ΔP 24 +ΔP 25 -ΔUL×12

[0053] However, it is not limited to this.

[0054] Next, the processor 104 can apply the reference fraction RC to a regression formula to obtain a specific curvature variation value corresponding to the reference fraction RC, and use this specific curvature variation value as the curvature variation value of the silicon carbide material 300.

[0055] In one embodiment, the regression formula described above can be derived in advance based on multiple scores corresponding to multiple candidate silicon carbide materials and the curvature variation value corresponding to each candidate silicon carbide material.

[0056] Please refer to Figure 6This is a schematic diagram illustrating the acquisition of a regression formula according to an embodiment of the present invention. In this embodiment, for multiple candidate silicon carbide materials with known curvature variation values, the processor 104 can estimate the score of each candidate silicon carbide material based on the estimated reference score RC of the silicon carbide material 300, and display the score of each candidate silicon carbide material and its corresponding curvature variation value as shown below. Figure 6 The trend chart shown.

[0057] exist Figure 6 In the diagram, the five reference points RP1 to RP5 can, for example, characterize the individual fractions of the five candidate silicon carbide materials and their corresponding curvature variation values. Based on Figure 6 Using reference points RP1 to RP5, processor 104 can derive the corresponding regression formulas. Figure 6 In this scenario, the regression formula derived by processor 104 is, for example, y = 2.0863x - 28.717, but it is not limited to this.

[0058] Therefore, the processor 104 can apply the reference fraction RC to the above regression formula to obtain a specific curvature variation value corresponding to the reference fraction RC. In one embodiment, the processor 104 can substitute the reference fraction RC as x into the above regression formula and calculate the corresponding y value as the specific curvature variation value corresponding to the reference fraction RC. Then, the processor 104 can further use the specific curvature variation value of the reference fraction RC as the curvature variation value of the silicon carbide material 300.

[0059] This allows the estimated curvature variation of the silicon carbide material 300 to be known in advance before processing it. In this case, the processor 104 can appropriately determine the processing procedure for the silicon carbide material 300 based on the curvature variation value, thereby avoiding material waste caused by selecting an inappropriate processing procedure.

[0060] In summary, the embodiments of the present invention can identify multiple corresponding first and second reference points on two surfaces of a silicon carbide material, and obtain multiple diffraction result differences based on the X-ray diffraction results of each first and second reference point. Then, one or more differences exceeding the upper limit of the reference difference can be identified from these diffraction result differences as specific differences, and the curvature variation value of the silicon carbide material can be estimated accordingly. Therefore, the estimated curvature variation value of the silicon carbide material can be known in advance before processing, allowing for the selection of an appropriate processing procedure and avoiding material waste.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for verifying silicon carbide materials, characterized in that, include: Obtain a silicon carbide material, wherein the silicon carbide material includes a first surface and a second surface; N first reference points are determined on the first surface, and the first X-ray diffraction result of each first reference point is obtained, where N is a positive integer; On the second surface, N second reference points corresponding to the N first reference points are determined, and the second X-ray diffraction result of each second reference point is obtained; Based on the first X-ray diffraction results of each first reference point and the corresponding second X-ray diffraction results of each second reference point, N diffraction result differences are obtained; Obtain a reference difference upper limit, and based on the reference difference upper limit, identify multiple specific differences among the N diffraction result differences; Estimating the curvature variation of the silicon carbide material based on the reference difference upper limit and the plurality of specific differences includes: Obtain the first difference between each of the specific differences and the upper limit of the reference difference; The first difference corresponding to each of the specific differences is summed to obtain the reference score of the silicon carbide material; The reference score is applied to the regression formula to obtain a specific curvature variation value corresponding to the reference score, and the specific curvature variation value is used as the curvature variation value of the silicon carbide material.

2. The method according to claim 1, wherein the first X-ray diffraction result of each first reference point is the half-width at half maximum (WHM) of the diffraction peak of each first reference point, and the second X-ray diffraction result of each second reference point is the half-width at half maximum (WHM) of the diffraction peak of each second reference point.

3. The method according to claim 1, wherein the first X-ray diffraction result of the i-th first reference point among the N first reference points is characterized as follows: The second X-ray diffraction result of the i-th second reference point among the N second reference points is characterized as follows: The difference in the i-th diffraction result among the N diffraction result differences is characterized as The step of obtaining the differences of the N diffraction results based on the first X-ray diffraction results of each first reference point and the corresponding second X-ray diffraction results of each second reference point includes: To Subtract To obtain where .

4. The method of claim 1, wherein the step of obtaining the upper limit of the reference difference comprises: Obtain a reference silicon carbide material, wherein the reference silicon carbide material includes a third surface and a fourth surface; N third reference points are determined on the third surface, and the third X-ray diffraction results of each of the third reference points are obtained; On the fourth surface, N fourth reference points corresponding to the N third reference points are determined, and the fourth X-ray diffraction results of each of the fourth reference points are obtained; Based on the third X-ray diffraction results of each of the third reference points and the corresponding fourth X-ray diffraction results of each of the fourth reference points, N reference diffraction result differences are obtained; The average value and standard deviation of the differences among the N reference diffraction results are obtained, and the upper limit of the reference difference is determined accordingly.

5. The method of claim 4, wherein the mean and the standard deviation of the N reference diffraction result differences are respectively characterized as and and the reference difference upper limit is .

6. The method according to claim 4, wherein the curvature variation value of the reference silicon carbide material is lower than a preset threshold value.

7. The method of claim 1, wherein each of the specific differences is greater than the upper limit of the reference difference.

8. The method according to claim 1, wherein the regression formula is derived based on multiple scores corresponding to multiple candidate silicon carbide materials and the curvature variation value corresponding to each candidate silicon carbide material.

9. The method of claim 1, wherein after estimating the curvature variation value of the silicon carbide material, the method further comprises: The processing procedure for the silicon carbide material is determined based on the curvature variation value of the silicon carbide material.

10. The method according to claim 1, wherein the silicon carbide material comprises a carbon surface and a silicon surface, the first surface being one of the carbon surface and the silicon surface, and the second surface being the other of the carbon surface and the silicon surface.

11. The method of claim 1, wherein N is greater than or equal to 25.

12. The method of claim 1, wherein the positions of the N first reference points on the first surface correspond to the positions of the N second reference points on the second surface.

13. The method according to claim 1, wherein the N first reference points are uniformly distributed on the first surface, and the N second reference points are uniformly distributed on the second surface.

14. A silicon carbide material verification apparatus, comprising: include: Storage circuitry stores program code; as well as The processor is coupled to the storage circuitry and accesses the program code for execution: Obtain a silicon carbide material, wherein the silicon carbide material includes a first surface and a second surface; N first reference points are determined on the first surface, and the first X-ray diffraction result of each first reference point is obtained, where N is a positive integer; On the second surface, N second reference points corresponding to the N first reference points are determined, and the second X-ray diffraction result of each second reference point is obtained; Based on the first X-ray diffraction results of each first reference point and the corresponding second X-ray diffraction results of each second reference point, N diffraction result differences are obtained; Obtain a reference difference upper limit, and based on the reference difference upper limit, identify multiple specific differences among the N diffraction result differences; Estimating the curvature variation of the silicon carbide material based on the reference difference upper limit and the plurality of specific differences includes: Obtain the first difference between each of the specific differences and the upper limit of the reference difference; The first difference corresponding to each of the specific differences is summed to obtain the reference score of the silicon carbide material; The reference score is applied to the regression formula to obtain a specific curvature variation value corresponding to the reference score, and the specific curvature variation value is used as the curvature variation value of the silicon carbide material.

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