Sapphire substrate CMP surface roughness prediction method, system, equipment and medium

Abstract

The invention relates to the field of integrated circuit manufacturing, and discloses a sapphire substrate CMP surface roughness prediction method, system and device and a medium, and the method comprises the steps: building a first parameter relation among abrasive particles, polishing cloth and a sapphire substrate in a CMP technology based on a ductile domain removal mechanism; establishing a second parameter relationship between the normal load transmitted by the polishing cloth and the surface roughness of the sapphire substrate based on the abrasive particle mechanical model; constructing an initial prediction model based on the cantilever beam theoretical model in combination with a CMP process; and solving the initial prediction model through the first parameter relationship and the second parameter relationship to obtain a final prediction model about the surface roughness of the sapphire substrate. The prediction method is simple and efficient, the CMP surface roughness of the sapphire substrate can be predicted only by substituting the technological parameters into the final prediction model, and the error between the actual result and the prediction result of the CMP surface roughness of the manufactured sapphire substrate is low; the research and development cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of integrated circuit manufacturing, and particularly to a method, system, device and medium for predicting the surface roughness of sapphire substrate CMP. Background Art

[0002] As an important wide-bandgap semiconductor material, sapphire (α-Al2O3 single crystal) substrate has been widely used in the optoelectronic field since the 1960s due to its excellent physical and chemical properties (such as high hardness, high temperature resistance, corrosion resistance, good chemical stability) and optical properties (high transmittance in the ultraviolet to infrared band). Its hexagonal lattice structure has a high lattice matching degree with group III nitrides (such as GaN) (the mismatch degree is about 16%). Coupled with excellent insulation and high thermal conductivity, it has become the preferred substrate material for preparing GaN-based LEDs, lasers and high-frequency power devices.

[0003] Before the sapphire wafer becomes a qualified substrate, a series of processes need to be carried out on the sapphire wafer, including a series of processes such as cutting, grinding and polishing; chemical mechanical polishing (CMP) is the key process to improve the surface quality. Its core role is to eliminate the subsurface damage layer, microcracks and roughness generated by the previous processes such as cutting and grinding on the substrate surface through the synergistic effect of chemical corrosion and mechanical grinding, and finally obtain an atomic-level flatness (surface roughness Ra < 0.2 nm) and a surface with ultra-high smoothness. However, in order to obtain a surface with ultra-high smoothness, there are various problems. The CMP process involves multi-parameter coupling (such as polishing pressure, rotation speed, pH value of polishing solution, abrasive particle size / concentration, temperature, etc.). The traditional trial-and-error method has low efficiency and high cost; the high hardness of sapphire leads to easy generation of subsurface microcracks and lattice distortion during mechanical processing, which affects the performance of the epitaxial layer; CMP is the synergistic effect of chemical corrosion and mechanical grinding, but the contribution ratio of the two is difficult to directly measure; the surface roughness of the sapphire substrate directly affects the dislocation density and crystal quality of the GaN epitaxial layer, but the epitaxial defects can only be evaluated after the device is fabricated. Summary of the Invention

[0004] In order to accurately predict the surface roughness of sapphire substrate CMP and optimize the CMP process parameters, based on the cantilever beam theory model and the ductile removal mechanism, the present invention provides a method, system, device and medium for predicting the surface roughness of sapphire substrate CMP to solve the above problems.

[0005] The present invention is realized through the following technical solutions: A method for predicting the surface roughness of sapphire substrate CMP includes: Establishing a first parameter relationship among abrasive grains, polishing cloth and sapphire substrate in the CMP process based on the ductile regime removal mechanism; Establish the second parameter relationship between the normal load transferred by the polishing cloth and the surface roughness of the sapphire substrate based on the abrasive mechanics model; Construct an initial prediction model based on the cantilever beam theory model combined with the CMP process; Solve the initial prediction model through the first parameter relationship and the second parameter relationship to obtain the final prediction model for the surface roughness of the sapphire substrate.

[0006] As an optimization, the initial prediction model is specifically: ; Wherein, represents the normal load transferred by the polishing cloth, A represents the effective polishing area, N represents the number of dynamically contacting abrasive grains, represents the elastic modulus of the polishing cloth, represents the spacing between adjacent abrasive grains, represents the equivalent beam length, represents the characteristic thickness, represents the linear load formed by converting the CMP process pressure ; represents the characteristic particle size of the abrasive grains, As an optimization, the first parameter relationship is specifically: ; Wherein, represents the characteristic particle size of the abrasive grains; represents the depth of penetration of the abrasive grains into the sapphire substrate; is the depth of embedding of the abrasive grains in the polishing cloth.

[0007] As an optimization, the specific formula for the number of dynamically contacting abrasive grains is: ; Wherein, represents the effective polishing area, is the mass concentration of the abrasive in the polishing liquid, is the density of the polishing liquid, is the density of the abrasive grains.

[0008] As an optimization, the second parameter relationship is specifically: ; Wherein, represents the surface hardness of the sapphire substrate, represents the characteristic particle size of the abrasive grains; represents the normal load transferred by the polishing cloth, represents the reaction force of the abrasive grains when the polishing cloth polishes the sapphire substrate through the abrasive grains, Represents the surface roughness of the sapphire substrate.

[0009] As an optimization, the specific process of solving the initial prediction model through the first parameter relationship and the second parameter relationship to obtain the final prediction model for the surface roughness of the sapphire substrate is as follows: Solving the initial prediction model through the first parameter relationship and the second parameter relationship to obtain an intermediate prediction model, which is expressed as: ; Wherein, Represents the surface roughness of the sapphire substrate, Represents the working pressure, Represents the elastic modulus of the polishing cloth, Represents the surface hardness of the sapphire substrate, Is the mass concentration of the abrasive composed of several abrasive grains, Is the density of the polishing liquid, Is the density of the abrasive grains, Represents the characteristic particle size of the abrasive grains, Represents the characteristic thickness; Establish the third parameter relationship of the characteristic particle size Of the abrasive grains, the elastic modulus Of the polishing cloth, the working pressure And the characteristic thickness : ; Wherein, , ; Combining the intermediate prediction model with the third parameter relationship to obtain the final prediction model: .

[0010] The present invention also discloses a surface roughness prediction system for sapphire substrate CMP, which is used to execute the foregoing surface roughness prediction method for sapphire substrate CMP, and includes: An initial prediction model construction module, which is used to construct an initial prediction model based on the cantilever beam theory model in combination with the CMP process; A first parameter relationship construction module, which is used to establish the first parameter relationship among the abrasive grains, the polishing cloth, and the sapphire substrate in the CMP process based on the ductile regime removal mechanism; A second parameter relationship construction module, which is used to establish the second parameter relationship between the normal load transmitted by the polishing cloth and the surface roughness of the sapphire substrate based on the abrasive grain mechanical model; A solving module, configured to solve the initial prediction model through the first parameter relationship and the second parameter relationship, so as to obtain a final prediction model for the surface roughness of the sapphire substrate.

[0011] As an optimization, the process of the solving module for solving is specifically as follows: Solve the initial prediction model through the first parameter relationship and the second parameter relationship to obtain an intermediate prediction model, and the intermediate prediction model is expressed as: ; Wherein, represents the surface roughness of the sapphire substrate, represents the working pressure, represents the elastic modulus of the polishing cloth, represents the surface hardness of the sapphire substrate, is the mass concentration of the abrasive composed of several abrasive grains, is the density of the polishing liquid, is the density of the abrasive grains, represents the characteristic particle size of the abrasive grains, represents the characteristic thickness; Establish a third parameter relationship of the characteristic particle size of the abrasive grains, the elastic modulus of the polishing cloth, the working pressure and the characteristic thickness : ; Wherein, , ; Combine the intermediate prediction model with the third parameter relationship to obtain a final prediction model: .

[0012] The present invention also discloses an electronic device, including at least one processor, and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute a method for predicting the surface roughness of CMP of a sapphire substrate as described above.

[0013] The present invention also discloses a storage medium, storing a computer program, and when the computer program is executed by a processor, the method for predicting the surface roughness of CMP of a sapphire substrate as described above is implemented.

[0014] Compared with the prior art, the present invention has the following advantages and beneficial effects: The prediction method of the present invention is simple and efficient. Only by substituting process parameters into the final prediction model can the prediction of the surface roughness of the sapphire substrate during CMP be completed, and the error between the actual result and the prediction result of the surface roughness of the manufactured sapphire substrate during CMP is low. It can optimize process parameters, adjust process parameters through the final prediction model to manufacture sapphire substrates with the required surface roughness, and reduce R & D costs. Description of the Drawings

[0015] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not limit the embodiments of the present invention. In the drawings: Figure 1 is the three-body (abrasive grain, polishing cloth, sapphire substrate) contact system of the present invention; Figure 2 is the cantilever beam theoretical model of the present invention; Figure 3 is the schematic diagram of chemical mechanical polishing (CMP) in the embodiments of the present invention; Figure 4 is the bar graph of the surface roughness data of the sapphire substrate during CMP actually detected in the embodiments of the present invention; Figure 5 is the four-level detection AFM image in the embodiments of the present invention; Figure 6 is the error line graph of the predicted data and the actual data in the embodiments of the present invention. Detailed Embodiments

[0016] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in combination with embodiments and drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and do not limit the present invention.

[0017] The following illustrates the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the drawings provided in the following embodiments only illustrate the basic concept of the present invention schematically. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0018] Among them, the attached drawings are only for illustrative purposes, showing only schematic diagrams rather than physical diagrams, and should not be construed as limiting the present invention; for better illustration of the embodiments of the present invention, some components in the attached drawings may be omitted, enlarged or reduced, which do not represent the dimensions of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the attached drawings may be omitted.

[0019] In the attached drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "rear", etc. indicating the orientation or positional relationship, they are based on the orientation or positional relationship shown in the attached drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the positional relationship in the attached drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0020] The present invention will be further described below in conjunction with the attached drawings and embodiments, but it shall not be used as a basis for limiting the present invention.

[0021] Embodiment 1 of the present invention provides a method for predicting the surface roughness of a sapphire substrate CMP, including: as Figure 1 、 3 shown, S1. Establish a first parameter relationship among abrasive grains, polishing cloth, and sapphire substrate in the CMP process based on the ductile regime removal mechanism.

[0022] In the chemical mechanical polishing process based on the ductile regime removal mechanism, the material is removed through plastic deformation. In this three-body (abrasive grains, polishing cloth, sapphire substrate) contact system, the abrasive grains are embedded between the soft polishing cloth and the sapphire substrate to form a composite action interface. When the chip is pressurized, the micro-protrusions on the surface of the polishing cloth form a wrapping effect on the abrasive grains, and the following geometric parameter relationship, that is, the first parameter relationship, can be established: Among them, the characteristic particle size of the abrasive grains is , the depth of the abrasive grains pressed into the sapphire substrate is , and the embedding depth of the polishing cloth is .

[0023] S2. Establish a second parameter relationship between the normal load transmitted by the polishing cloth and the surface roughness of the sapphire substrate based on the abrasive grain mechanical model.

[0024] According to the brittle material removal theory, the surface topography features can be decomposed into two dimensions: the depth of transverse cracks and the depth of radial cracks. Experimental studies have shown that under the processing conditions in the ductile regime, the depth of transverse cracks has a significant correlation with the surface roughness parameter , while the generation of radial cracks can be neglected. Therefore, the value of CMP of sapphire substrate can be equivalently characterized as parameter (i.e., ). This is because CMP is a ductile removal method, almost no radial cracks are generated, and there is a significant correlation (approximate equality) between the depth of transverse cracks and the surface roughness. In the ductile processing model of the present invention, the depth of transverse cracks is the depth caused by the abrasive grains pressing into the workpiece, so the equivalent characterization relationship in the present invention can be realized.

[0025] When analyzing the mechanical model of abrasive grains, consider the following force balance relationship: the normal load transmitted by the polishing pad and the reaction force of the abrasive grains form the main force system. Since the self-gravity of micron-sized abrasive grains (order of magnitude about 10-6 N) is three orders of magnitude lower than the mechanical force (typical value 10-3 N), the influence of the gravity term can be neglected in the mechanical analysis.

[0026] Among them, is the surface hardness of the sapphire substrate.

[0027] S3. Based on the cantilever beam theory model and combined with the CMP process, an initial prediction model is constructed, as shown in Figure 2 .

[0028] In some embodiments, the initial prediction model is specifically: Among them, represents the normal load transmitted by the polishing cloth, A represents the effective polishing area, N represents the number of abrasive grains in dynamic contact, represents the elastic modulus of the polishing cloth, represents the spacing between adjacent abrasive grains (see the markings in Figure 2 ), represents the equivalent beam length, represents the characteristic thickness, represents the line load formed by converting the CMP process pressure , represents the characteristic particle size of the abrasive grains, represents the CMP working pressure.

[0029] Among them, the specific formula for the number of dynamically contacting abrasive grains is as follows: Among them, represents the effective polishing area, is the mass concentration of abrasives in the polishing liquid (for example, the mass concentration of SiO2 in the silicon-based polishing liquid), is the density of the polishing liquid, is the density of the abrasive grains.

[0030] The number of dynamically contacting abrasive grains refers to the number of abrasive grains that are in dynamic contact with the workpiece during a dynamic processing process such as the CMP process.

[0031] S4. Solve the initial prediction model through the first parameter relationship and the second parameter relationship to obtain the final prediction model for the surface roughness of the sapphire substrate.

[0032] In some embodiments, the specific process of S4 is as follows: S4.1. Solve the initial prediction model through the first parameter relationship and the second parameter relationship to obtain an intermediate prediction model, and the intermediate prediction model is expressed as: Among them, represents the surface roughness of the sapphire substrate, represents the working pressure, represents the elastic modulus of the polishing cloth, represents the surface hardness of the sapphire substrate, is the mass concentration of the abrasive composed of several abrasive grains, is the density of the polishing liquid, is the density of the abrasive grains, represents the characteristic particle size of the abrasive grains, represents the characteristic thickness.

[0033] S4.2. Since the characteristic thickness is regulated by multiple factors, including the characteristic particle size D of the abrasive grains, the elastic modulus of the polishing cloth, and the working pressure P, therefore, establish a third parameter relationship among the characteristic particle size of the abrasive grains, the elastic modulus of the polishing cloth, the working pressure , and the characteristic thickness : Under the standard chemical mechanical polishing process, when the process pressure P is in a typical range, the equivalent contact stress at the contact interface between the rough peaks on the surface of the polishing cloth and the wafer can be regarded as a stable parameter. Based on this assumption, the normal load transmitted by the polishing pad remains constant, resulting in a stable state of the indentation depth of the abrasive grains into the sapphire substrate. Thus, the dimensionless coefficient = , (the value of this parameter is obtained through analysis. Since the pressure parameter is regarded as a stable parameter through analysis, based on this assumption, the stable parameter can be eliminated to obtain c1), and the coefficient as a calibration constant independent of the process can be calculated through a specific CMP process (let , substitute the other parameters in Table 2, and the value of can be obtained), so .

[0034] S4.3. Combine the intermediate prediction model with the third parameter relationship to obtain the final prediction model: .

[0035] Next, verify the prediction method of the present invention through a specific experimental case.

[0036] The experiment in this embodiment uses nano-silica slurry (produced by Jinwei Group, product model SS-100-05, SiO2 mass fraction 40%, abrasive particle size 98nm - 103nm, pH 10.5, viscosity 2.74, specific gravity 1.302, milky white appearance characteristics). Mix the polishing stock solution and deionized water in different volume ratios to prepare the polishing liquid. The workpiece is made of a C-plane sapphire wafer with a diameter of 6 inches and a thickness of 1.3 mm.

[0037] A polishing test was carried out on an XDSF-CP-008 high-precision vertical single-sided grinding and polishing machine. The polishing disc is produced by Yunnan Jingmo Technology Co., Ltd., product model JM-TD001-SP004PDBM007A0. The outer diameter of the polishing disc is 1450 ± 2 mm, the groove width is 20 ± 0.1 mm, the groove spacing is 2 ± 0.1 mm, the groove depth is 0.8 ± 0.1 mm, and the thickness (including glue and release paper) is 1.6 ± 0.05 mm. A wax-free pad produced by Yunnan Jingmo Technology Co., Ltd., product model JM-TD001-022TP005A0, is used. The outer diameter of the wax-free pad is 515 ± 0.5 mm, the inner hole diameter is 150.2 ± 0.10 mm, the inner hole depth is 0.7 ± 0.05 mm, and the total thickness is 1.5 ± 0.05 mm. The single-sided chemical mechanical polishing process diagram is as shown in Figure 3 .

[0038] Use the standard orthogonal table L of 5 factors and 4 levels 16 (4 5)A single-sided CMP experiment was conducted on sapphire wafers. The design of the experimental scheme is shown in Table 2. The experimental environment was a thousand-class ultra-clean laboratory. During the experiment, the temperature of the ultra-clean environment in the laboratory was 20.8 °C and the humidity was 55.9%. The experimental scheme is shown in Table 1: Table 1 Embodiment test scheme Before and after polishing the sapphire wafers, they were cleaned using a stainless-steel ultrasonic cleaner (SR-5A). The surface roughness of the wafers was experimentally measured using an atomic force microscope (AFM) (Bruker AFM Dimension Icon).

[0039] Table 2 Embodiment experimental parameters In this embodiment of the experiment, the same polishing cloth, the same type of processing substrate material, and the same type of polishing liquid were used. Therefore, the parameters uniformly set in this experiment were substituted into the calculation formula of Equation (9) to obtain: Substituting the other parameters set in this embodiment of the experiment into the calculation formula of the prediction method of the present invention, the predicted ranges of the CMP surface roughness of the sapphire substrate corresponding to different levels were calculated to be 0.20741~0.21799 nm, 0.1991~0.20926 nm, 0.19128~0.20104 nm, and 0.18459~0.194 nm.

[0040] The actual values of the CMP surface roughness of the sapphire substrate detected in this embodiment of the experiment are shown in Figure 4 as follows, Figure 5 in which the actual test results of (a), (b), (c), and (d) are 0.218 nm, 0.204 nm, 0.194 nm, and 0.183 nm respectively; through the error bars Figure 6 it can be seen that the prediction results of the prediction method of the present invention are relatively accurate.

[0041] Embodiment 2 also discloses a surface roughness prediction system for CMP of a sapphire substrate, which is used to execute the surface roughness prediction method for CMP of a sapphire substrate described in Embodiment 1, including: An initial prediction model construction module, which is used to construct an initial prediction model based on the cantilever beam theory model combined with the CMP process; A first parameter relationship construction module, which is used to establish a first parameter relationship among abrasive grains, polishing cloth, and sapphire substrate in the CMP process based on the ductile regime removal mechanism; A second parameter relationship construction module, which is used to establish a second parameter relationship between the normal load transmitted by the polishing cloth and the surface roughness of the sapphire substrate based on the abrasive grain mechanical model; A solving module, configured to solve the initial prediction model through the first parameter relationship and the second parameter relationship, so as to obtain a final prediction model for the surface roughness of the sapphire substrate.

[0042] In some embodiments, the process of the solving module for solving is specifically as follows: Solve the initial prediction model through the first parameter relationship and the second parameter relationship to obtain an intermediate prediction model, and the intermediate prediction model is expressed as: ; Wherein, represents the surface roughness of the sapphire substrate, represents the working pressure, represents the elastic modulus of the polishing cloth, represents the surface hardness of the sapphire substrate, is the mass concentration of the abrasive composed of several abrasive grains, is the density of the polishing liquid, is the density of the abrasive grains, represents the characteristic particle size of the abrasive grains, represents the characteristic thickness; Establish a third parameter relationship of the characteristic particle size of the abrasive grains, the elastic modulus of the polishing cloth, the working pressure and the characteristic thickness : ; Wherein, , ; Combine the intermediate prediction model with the third parameter relationship to obtain a final prediction model: .

[0043] Embodiment 3 also discloses an electronic device, including at least one processor and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for predicting the surface roughness of a sapphire substrate CMP as described in Embodiment 1.

[0044] Embodiment 4 also discloses a storage medium storing a computer program, and when the computer program is executed by a processor, it implements a method for predicting the surface roughness of a sapphire substrate CMP as described in Embodiment 1.

[0045] The specific embodiments described above further elaborate on the object, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only for the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for predicting the surface roughness of sapphire substrate CMP, characterized in that, Including: Establish a first parameter relationship among abrasive grains, polishing cloth, and sapphire substrate in the CMP process based on the ductile regime removal mechanism; Based on the abrasive grain mechanical model, establish a second parameter relationship between the normal load transmitted by the polishing cloth and the surface roughness of the sapphire substrate; Construct an initial prediction model based on the cantilever beam theory model combined with the CMP process; Solve the initial prediction model through the first parameter relationship and the second parameter relationship to obtain a final prediction model for the surface roughness of the sapphire substrate.

2. The surface roughness prediction method of sapphire substrate CMP according to claim 1, wherein The specific form of the initial prediction model is: ； Among them, represents the normal load transmitted by the polishing cloth, A represents the effective polishing area, N represents the number of abrasive grains in dynamic contact, represents the elastic modulus of the polishing cloth and the spacing between adjacent abrasive grains, represents the equivalent beam length, represents the characteristic thickness, represents the linear load formed by converting the CMP process pressure into, represents the characteristic particle size of the abrasive grains, represents the CMP working pressure.

3. A method for predicting the surface roughness of sapphire substrate CMP according to claim 1, wherein, The specific form of the first parameter relationship is: ； Among them, represents the characteristic particle size of the abrasive grains; represents the depth of the abrasive grains pressed into the sapphire substrate; The depth of the abrasive grains embedded in the polishing cloth.

4. A method for predicting the surface roughness of a sapphire substrate CMP according to claim 3, wherein, The specific formula for the number of dynamically contacting abrasive grains is: ； Among them, represents the effective polishing area, is the mass concentration of abrasives in the polishing liquid, is the density of the polishing liquid, is the density of abrasive grains.

5. A method for predicting the surface roughness of a sapphire substrate CMP according to claim 1, characterized in that, The specific form of the second parameter relationship is: ； Among them, represents the surface hardness of the sapphire substrate, represents the characteristic particle size of the abrasive grains; represents the normal load transmitted by the polishing cloth, represents the reaction force of the abrasive grains when the polishing cloth polishes the sapphire substrate through the abrasive grains, represents the surface roughness of the sapphire substrate.

6. The surface roughness prediction method of sapphire substrate CMP according to claim 1, wherein The specific process of solving the initial prediction model through the first parameter relationship and the second parameter relationship to obtain a final prediction model for the surface roughness of the sapphire substrate is: Solve the initial prediction model through the first parameter relationship and the second parameter relationship to obtain an intermediate prediction model, and the intermediate prediction model is expressed as: ； Among them, represents the surface roughness of the sapphire substrate and represents the working pressure, represents the elastic modulus of the polishing cloth, represents the surface hardness of the sapphire substrate, is the mass concentration of the abrasive composed of several abrasive grains, is the density of the polishing liquid, is the density of the abrasive grains, represents the characteristic particle size of the abrasive grains, represents the characteristic thickness; Establish the characteristic particle size of abrasive grains , the elastic modulus of the polishing cloth , the working pressure , the characteristic thickness The third parameter relationship of: ； Among them, , ; Combine the intermediate prediction model with the third parameter relationship to obtain the final prediction model: 。 7. A surface roughness prediction system for sapphire substrate CMP, which is used to execute the surface roughness prediction method for sapphire substrate CMP according to any one of claims 1-6, characterized in that, Including: An initial prediction model construction module for constructing an initial prediction model based on the cantilever beam theory model combined with the CMP process; A first parameter relationship construction module for establishing a first parameter relationship among abrasive grains, polishing cloth, and sapphire substrate in the CMP process based on the ductile regime removal mechanism; A second parameter relationship construction module for establishing a second parameter relationship between the normal load transmitted by the polishing cloth and the surface roughness of the sapphire substrate based on the abrasive grain mechanical model; A solving module for solving the initial prediction model through the first parameter relationship and the second parameter relationship to obtain a final prediction model for the surface roughness of the sapphire substrate.

8. A surface roughness prediction system for sapphire substrate CMP according to claim 7, wherein, The specific process of the solving module for solving is: Solve the initial prediction model through the first parameter relationship and the second parameter relationship to obtain an intermediate prediction model, and the intermediate prediction model is expressed as: ； Among them, represents the surface roughness of the sapphire substrate, represents the working pressure, represents the elastic modulus of the polishing cloth, represents the surface hardness of the sapphire substrate, is the mass concentration of the abrasive composed of several abrasive grains, is the density of the polishing liquid, is the density of the abrasive grains, represents the characteristic particle size of the abrasive grains, represents the characteristic thickness; Establish the characteristic particle size of abrasive grains , the elastic modulus of the polishing cloth , the working pressure , the characteristic thickness The third parameter relationship of: ； Among them, , ; Combine the intermediate prediction model with the third parameter relationship to obtain the final prediction model: 。 9. An electronic device, characterized in that, Including at least one processor and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute a method for predicting the surface roughness of a sapphire substrate CMP according to any one of claims 1 to 6.

10. A storage medium stores a computer program, characterized in that, When the computer program is executed by a processor, it implements a method for predicting the surface roughness of a sapphire substrate CMP according to any one of claims 1 to 6.

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