A method, system, device and medium for predicting surface roughness of a sapphire substrate cmp

Abstract

The present application relates to the field of integrated circuit manufacturing, and discloses a sapphire substrate CMP surface roughness prediction method, system, device and medium, comprising: establishing a first parameter relationship between abrasive particles, polishing cloth and sapphire substrate in the CMP process based on the ductile regime removal mechanism; based on the abrasive particle mechanics model, a second parameter relationship between the normal load transmitted by the polishing cloth and the surface roughness of the sapphire substrate is established; based on the cantilever beam theory model combined with the CMP process, an initial prediction model is constructed; the initial prediction model is solved through the first parameter relationship and the second parameter relationship, and the final prediction model about the surface roughness of the sapphire substrate is obtained. The prediction method of the present application is simple and efficient, and the prediction of the sapphire substrate CMP surface roughness can be completed by only substituting the process parameters into the final prediction model, the error between the actual result and the prediction result of the sapphire substrate CMP surface roughness is low, and the research and development cost is reduced.

Description

Technical Field

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

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

[0003] Before a sapphire wafer becomes a qualified substrate, it needs to undergo a series of processing steps, including cutting, grinding, and polishing. Chemical mechanical polishing (CMP) is a key process for improving surface quality. Its core function is to eliminate subsurface damage layers, microcracks, and roughness on the substrate surface caused by previous processes such as cutting and grinding through the synergistic effect of chemical etching and mechanical polishing, ultimately obtaining an atomically smooth surface (surface roughness Ra < 0.2 nm) and an ultra-high surface finish. However, obtaining an ultra-high surface finish presents various challenges. The CMP process involves multi-parameter coupling (such as polishing pressure, rotation speed, polishing slurry pH, abrasive particle size / concentration, temperature, etc.), and traditional trial-and-error methods are inefficient and costly. The high hardness of sapphire makes it easy for mechanical processing to generate subsurface microcracks and lattice distortions, affecting the performance of the epitaxial layer. CMP is a synergistic effect of chemical etching and mechanical polishing, but the contribution ratio between the two is difficult to measure directly. The surface roughness of the sapphire substrate directly affects the dislocation density and crystal quality of the GaN epitaxial layer, but epitaxial defects can only be evaluated after device fabrication. Summary of the Invention

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

[0005] This invention is achieved through the following technical solution:

[0006] A method for predicting the surface roughness of sapphire substrate CMP includes:

[0007] Establish the first parameter relationship between abrasive grains, polishing cloth, and sapphire substrate in CMP process based on ductile domain removal mechanism;

[0008] A second parameter relationship between the normal load transmitted by the polishing cloth and the surface roughness of the sapphire substrate is established based on the abrasive mechanics model.

[0009] An initial prediction model was constructed based on the cantilever beam theoretical model and the CMP process.

[0010] The initial prediction model is solved by the first parameter relationship and the second parameter relationship to obtain the final prediction model for the surface roughness of the sapphire substrate.

[0011] As an optimization, the initial prediction model is specifically as follows:

[0012] ;

[0013] in, This represents the normal load transmitted by the polishing cloth. A Indicates the effective polishing area. N This indicates the number of abrasive grains in dynamic contact. This indicates the elastic modulus of the polishing cloth. Indicates the spacing between adjacent abrasive grains. Indicates the equivalent beam length. Indicates the characteristic thickness. Indicates pressure from CMP process The transformed line load, Indicates the characteristic particle size of abrasive grains. The first parameter relationship, representing CMP operating pressure as an optimization parameter, is as follows:

[0014] ;

[0015] in, Indicates the characteristic particle size of the abrasive grains; This indicates the depth to which the abrasive particles are pressed into the sapphire substrate; The depth to which the abrasive grains are embedded in the polishing cloth.

[0016] As an optimization, the specific formula for the number of abrasive grains in dynamic contact is:

[0017] ;

[0018] in, Indicates the effective polishing area. This refers to the mass concentration of abrasive particles in the polishing slurry. The density of the polishing slurry, This refers to the density of the abrasive particles.

[0019] As an optimization, the relationship of the second parameter is specifically as follows:

[0020] ;

[0021] in, This indicates the surface hardness of the sapphire substrate. Indicates the characteristic particle size of the abrasive grains; This represents the normal load transmitted by the polishing cloth. This indicates the reaction force of the abrasive grains when the polishing cloth polishes the sapphire substrate. This indicates the surface roughness of the sapphire substrate.

[0022] As an optimization, the specific process of solving the initial prediction model using 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:

[0023] The intermediate prediction model is obtained by solving the initial prediction model using the first parameter relationship and the second parameter relationship. The intermediate prediction model is expressed as follows:

[0024] ;

[0025] in, This indicates the surface roughness of the sapphire substrate. Indicates work pressure. This indicates the elastic modulus of the polishing cloth. This indicates the surface hardness of the sapphire substrate. The mass concentration of an abrasive composed of several abrasive grains. The density of the polishing slurry, The density of the abrasive grains, Indicates the characteristic particle size of abrasive grains. Indicates the characteristic thickness;

[0026] Establish the characteristic particle size of abrasive grains Elastic modulus of polishing cloth Work pressure Feature thickness The third parameter relationship:

[0027] ;

[0028] in, , ;

[0029] The intermediate prediction model is combined with the third parameter relationship to obtain the final prediction model:

[0030] .

[0031] This invention also discloses a surface roughness prediction system for sapphire substrate CMP, used to perform the aforementioned surface roughness prediction method for sapphire substrate CMP, comprising:

[0032] The initial prediction model construction module is used to construct an initial prediction model based on the cantilever beam theoretical model combined with CMP process.

[0033] The first parameter relationship construction module is used to establish the first parameter relationship between abrasive grains, polishing cloth, and sapphire substrate in CMP process based on ductile domain removal mechanism;

[0034] The second parameter relationship construction module 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 mechanics model.

[0035] The solution module is used to 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.

[0036] As an optimization, the solution module performs the solution process as follows:

[0037] The intermediate prediction model is obtained by solving the initial prediction model using the first parameter relationship and the second parameter relationship. The intermediate prediction model is expressed as follows:

[0038] ;

[0039] in, This indicates the surface roughness of the sapphire substrate. Indicates work pressure. This indicates the elastic modulus of the polishing cloth. This indicates the surface hardness of the sapphire substrate. The mass concentration of an abrasive composed of several abrasive grains. The density of the polishing slurry, The density of the abrasive grains, Indicates the characteristic particle size of abrasive grains. Indicates the characteristic thickness;

[0040] Establish the characteristic particle size of abrasive grains Elastic modulus of polishing cloth Work pressure Feature thickness The third parameter relationship:

[0041] ;

[0042] in, , ;

[0043] The intermediate prediction model is combined with the third parameter relationship to obtain the final prediction model:

[0044] .

[0045] 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, the instructions being executed by the at least one processor to enable the at least one processor to perform a surface roughness prediction method for sapphire substrate CMP as described above.

[0046] The present invention also discloses a storage medium storing a computer program, which, when executed by a processor, implements the aforementioned method for predicting the surface roughness of a sapphire substrate CMP.

[0047] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0048] The prediction method of this invention is simple and efficient. It only requires substituting the process parameters into the final prediction model to complete the prediction of the CMP surface roughness of the sapphire substrate. The actual result of the CMP surface roughness of the sapphire substrate has a low error compared with the prediction result. The process parameters can be optimized and the process parameters can be adjusted through the final prediction model to produce a sapphire substrate with the required surface roughness, thereby reducing the R&D cost. Attached Figure Description

[0049] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0050] Figure 1 This invention relates to a three-body (abrasive grains, polishing cloth, and sapphire substrate) contact system.

[0051] Figure 2 This is the theoretical model of the cantilever beam of the present invention;

[0052] Figure 3 This is a schematic diagram of chemical mechanical polishing (CMP) in an embodiment of the present invention;

[0053] Figure 4 This is a bar chart of the actual CMP surface roughness data of the sapphire substrate in this embodiment of the invention;

[0054] Figure 5 These are four-level AFM images detected in this embodiment of the invention;

[0055] Figure 6 This is a graph showing the error between predicted and actual data in an embodiment of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0057] The following specific examples illustrate the implementation of the present invention. 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, and various details in this specification can 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 illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0058] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0059] In the accompanying 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 terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0060] The present invention will be further described below with reference to the accompanying drawings and embodiments, but this should not be construed as limiting the present invention.

[0061] This embodiment 1 provides a method for predicting the surface roughness of a sapphire substrate CMP, including: as follows Figure 1 , 3 As shown,

[0062] S1. Establish the first parameter relationship between abrasive grains, polishing cloth, and sapphire substrate in the CMP process based on the ductile domain removal mechanism.

[0063] In chemical mechanical polishing (CMP) based on the ductile domain removal mechanism, material removal is achieved 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 interaction interface. When the chip is subjected to pressure, the micro-protrusions on the surface of the polishing cloth create an encapsulation effect on the abrasive grains, establishing the following geometric parameter relationship, i.e., the first parameter relationship:

[0064]

[0065] Among them, the characteristic particle size of the abrasive particles is The abrasive particles were pressed into the sapphire substrate to a depth of [depth missing]. The embedding depth of the polishing cloth is .

[0066] S2. Based on the abrasive mechanics model, establish a second parameter relationship between the normal load transmitted by the polishing cloth and the surface roughness of the sapphire substrate.

[0067] According to the theory of brittle material removal, surface morphology characteristics can be decomposed into two dimensions: transverse crack depth and radial crack depth. Experimental studies show that under ductile-domain processing conditions, the transverse crack depth is related to the surface roughness parameter. There is a significant correlation, while radial crack formation is negligible. Therefore, CMP on sapphire substrates can be performed... The value is equivalent to: Parameters (i.e.) This is because CMP is a ductile removal method that produces almost no radial cracks. The transverse crack depth is significantly correlated with (approximately equal to) the surface roughness. In the ductile processing model of this invention, the transverse crack depth is the depth caused by the abrasive grains pressing into the workpiece, which can realize the equivalent characterization relationship in this invention.

[0068] When analyzing the abrasive mechanics model, consider the following force balance relationship: the normal load transmitted by the polishing pad. reaction force with abrasive particles It constitutes the main force system. Since the gravity of the micron-sized abrasive grains (on the order of approximately 10⁻⁶ N) is three orders of magnitude lower than the mechanical force (typically 10⁻³ N), the influence of the gravity term can be ignored in mechanical analysis.

[0069]

[0070]

[0071] in, The surface hardness of the sapphire substrate.

[0072] S3. An initial prediction model is constructed based on the cantilever beam theoretical model combined with CMP process, such as... Figure 2 As shown.

[0073] In some embodiments, the initial prediction model is specifically:

[0074]

[0075] in, This represents the normal load transmitted by the polishing cloth. A Indicates the effective polishing area. N This indicates the number of abrasive grains in dynamic contact. This indicates the elastic modulus of the polishing cloth. Indicates the spacing between adjacent abrasive grains (see details) Figure 2 (marks in) Indicates the equivalent beam length. Indicates the characteristic thickness. Indicates pressure from CMP process The transformed line load, Indicates the characteristic particle size of abrasive grains. This indicates the CMP operating pressure.

[0076] The specific formula for the number of abrasive grains in dynamic contact is as follows:

[0077]

[0078] in, Indicates the effective polishing area. This refers to the mass concentration of abrasive in the polishing slurry (e.g., the mass concentration of SiO2 in a silicon-based polishing slurry). The density of the polishing slurry, This refers to the density of the abrasive particles.

[0079] The number of abrasive grains in dynamic contact refers to the number of abrasive grains that are in dynamic contact with the workpiece during a dynamic machining process such as CMP.

[0080] S4. Solve the initial prediction model using the first parameter relationship and the second parameter relationship to obtain the final prediction model for the surface roughness of the sapphire substrate.

[0081] In some embodiments, the specific process of S4 is as follows:

[0082] S4.1. Solve the initial prediction model using the first parameter relationship and the second parameter relationship to obtain the intermediate prediction model, which is expressed as:

[0083]

[0084] in, This indicates the surface roughness of the sapphire substrate. Indicates work pressure. This indicates the elastic modulus of the polishing cloth. This indicates the surface hardness of the sapphire substrate. The mass concentration of an abrasive composed of several abrasive grains. The density of the polishing slurry, The density of the abrasive grains, Indicates the characteristic particle size of abrasive grains. Indicates the characteristic thickness.

[0085] S4.2, due to feature thickness It is influenced by multiple factors, including the characteristic particle size D of the abrasive particles and the elastic modulus of the polishing cloth. And the working pressure P, therefore, the characteristic particle size of the abrasive grains is established. Elastic modulus of polishing cloth Work pressure Feature thickness The third parameter relationship:

[0086]

[0087]

[0088] Under standard chemical mechanical polishing (CMP) conditions, when the process pressure P is within a typical range, the equivalent contact stress at the interface between the roughness peak of the polishing cloth surface and the wafer is... This can be considered a stable parameter. Based on this assumption, the normal load transmitted by the polishing pad... Maintaining a constant depth results in a stable indentation depth of the abrasive grains into the sapphire substrate. From this, a dimensionless coefficient can be obtained. = (The value of this parameter is derived through analysis. Since the pressure parameter is considered a stable parameter based on this assumption, the stable parameter can be eliminated, resulting in c1.) The coefficient... As a process-independent calibration constant, it can be calculated through a specific CMP process (let... Substituting the other parameters from Table 2, we can obtain... (value), therefore .

[0089] S4.3. Combine the intermediate prediction model with the third parameter relationship to obtain the final prediction model:

[0090] .

[0091] Next, the prediction method of the present invention will be verified through specific experimental cases.

[0092] 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, with a milky white appearance). Polishing fluid was prepared by mixing the polishing stock solution with deionized water at different volume ratios. The workpiece was made of a 6-inch diameter, 1.3 mm thick C-side sapphire wafer.

[0093] Polishing tests were conducted on an XDSF-CP-008 high-precision vertical single-sided polishing machine. A polishing disc manufactured by Yunnan Jingmo Technology Co., Ltd., model number JM-TD001-SP004PDBM007A0, was used. The disc had an outer diameter of 1450±2 mm, a groove width of 20±0.1 mm, a groove spacing of 2±0.1 mm, a groove depth of 0.8±0.1 mm, and a thickness (including adhesive and release paper) of 1.6±0.05 mm. A wax-free pad manufactured by Yunnan Jingmo Technology Co., Ltd., model number JM-TD001-022TP005A0, was also used. The pad had an outer diameter of 515±0.5 mm, an inner diameter of 150.2±0.10 mm, an inner depth of 0.7±0.05 mm, and a total thickness of 1.5±0.05 mm. The single-sided chemical mechanical polishing process diagram is shown below. Figure 3 As shown.

[0094] Using a 5-factor, 4-level standard orthogonal array L 16 (4 5 A single-sided CMP experiment was performed on a sapphire wafer. The experimental design is shown in Table 2. The experimental environment was a Class 1000 cleanroom. During the experiment, the cleanroom environment temperature was 20.8 °C and the humidity was 55.9%. The experimental scheme is shown in Table 1.

[0095] Table 1. Test Scheme for Examples

[0096]

[0097] The sapphire wafers were cleaned using a stainless steel ultrasonic cleaner (SR-5A) before and after polishing, and the surface roughness of the wafers was experimentally measured using an atomic force microscope (AFM) (Bruker AFM Dimension Icon).

[0098] Table 2 Experimental parameters of the examples

[0099]

[0100] The experiment in this embodiment used the same polishing cloth, the same substrate material, and the same type of polishing liquid. Therefore, the parameters uniformly set in this experiment were substituted into the calculation formula (9) to obtain:

[0101]

[0102] Substituting the other parameters set in the experiment of this embodiment into the calculation formula of the prediction method of the present invention, the predicted range of CMP surface roughness of sapphire substrate at different levels is calculated to be 0.20741~0.21799nm, 0.1991~0.20926nm, 0.19128~0.20104nm, and 0.18459~0.194nm.

[0103] The actual values ​​of CMP surface roughness of the sapphire substrate detected in this embodiment are shown in [the relevant data]. Figure 4 As shown, Figure 5 The actual test results for (a), (b), (c), and (d) are 0.218 nm, 0.204 nm, 0.194 nm, and 0.183 nm, respectively; they pass the error bar. Figure 6 It can be seen that the prediction method of the present invention provides relatively accurate prediction results.

[0104] Example 2 also discloses a surface roughness prediction system for sapphire substrate CMP, used to execute the surface roughness prediction method for sapphire substrate CMP described in Example 1, including:

[0105] The initial prediction model construction module is used to construct an initial prediction model based on the cantilever beam theoretical model combined with CMP process.

[0106] The first parameter relationship construction module is used to establish the first parameter relationship between abrasive grains, polishing cloth, and sapphire substrate in CMP process based on ductile domain removal mechanism;

[0107] The second parameter relationship construction module 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 mechanics model.

[0108] The solution module is used to 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.

[0109] In some embodiments, the solution module performs the solution process as follows:

[0110] The intermediate prediction model is obtained by solving the initial prediction model using the first parameter relationship and the second parameter relationship. The intermediate prediction model is expressed as follows:

[0111] ;

[0112] in, This indicates the surface roughness of the sapphire substrate. Indicates work pressure. This indicates the elastic modulus of the polishing cloth. This indicates the surface hardness of the sapphire substrate. The mass concentration of an abrasive composed of several abrasive grains. The density of the polishing slurry, The density of the abrasive grains, Indicates the characteristic particle size of abrasive grains. Indicates the characteristic thickness;

[0113] Establish the characteristic particle size of abrasive grains Elastic modulus of polishing cloth Work pressure Feature thickness The third parameter relationship:

[0114] ;

[0115] in, , ;

[0116] The intermediate prediction model is combined with the third parameter relationship to obtain the final prediction model:

[0117] .

[0118] Example 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, the instructions being executed by the at least one processor to enable the at least one processor to perform a surface roughness prediction method for sapphire substrate CMP as described in Example 1.

[0119] Example 4 also discloses a storage medium storing a computer program that, when executed by a processor, implements a surface roughness prediction method for sapphire substrate CMP as described in Example 1.

[0120] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting the surface roughness of sapphire substrate CMP, characterized in that, include: Establish the first parameter relationship between abrasive grains, polishing cloth, and sapphire substrate in CMP process based on ductile domain removal mechanism; A second parameter relationship between the normal load transmitted by the polishing cloth and the surface roughness of the sapphire substrate is established based on the abrasive mechanics model. An initial prediction model was constructed based on the cantilever beam theoretical model and the CMP process. The initial prediction model is solved by the first parameter relationship and the second parameter relationship to obtain the final prediction model for the surface roughness of the sapphire substrate; The specific process is as follows: The intermediate prediction model is obtained by solving the initial prediction model using the first parameter relationship and the second parameter relationship. The intermediate prediction model is expressed as follows: ； in, This indicates the surface roughness of the sapphire substrate. Indicates work pressure. This indicates the elastic modulus of the polishing cloth. This indicates the surface hardness of the sapphire substrate. The mass concentration of an abrasive composed of several abrasive grains. The density of the polishing slurry, The density of the abrasive grains, Indicates the characteristic particle size of abrasive grains. Indicates the characteristic thickness; Establish the characteristic particle size of abrasive grains Elastic modulus of polishing cloth Work pressure Feature thickness The third parameter relationship: ； in, , ; The intermediate prediction model is combined with the third parameter relationship to obtain the final prediction model: 。 2. The surface roughness prediction method for sapphire substrate CMP according to claim 1, characterized in that, The initial prediction model is specifically as follows: ； in, This represents the normal load transmitted by the polishing cloth, where A represents the effective polishing area and N represents the number of abrasive grains in dynamic contact. This represents the elastic modulus of the polishing cloth, and the distance between adjacent abrasive grains. Indicates the equivalent beam length. Indicates the characteristic thickness. Indicates work pressure The transformed line load, Indicates the characteristic particle size of abrasive grains. It indicates work pressure.

3. The surface roughness prediction method for sapphire substrate CMP according to claim 1, characterized in that, The first parameter relationship is as follows: ； in, Indicates the characteristic particle size of the abrasive grains; This indicates the depth to which the abrasive particles are pressed into the sapphire substrate; The depth to which the abrasive grains are embedded in the polishing cloth.

4. The surface roughness prediction method for sapphire substrate CMP according to claim 3, characterized in that, The specific formula for the number of abrasive grains in dynamic contact is: ； in, Indicates the effective polishing area. This refers to the mass concentration of abrasive particles in the polishing slurry. The density of the polishing slurry, This refers to the density of the abrasive particles.

5. The surface roughness prediction method for sapphire substrate CMP according to claim 1, characterized in that, The specific relationship of the second parameter is as follows: ； in, This indicates the surface hardness of the sapphire substrate. Indicates the characteristic particle size of the abrasive grains; This represents the normal load transmitted by the polishing cloth. This indicates the reaction force of the abrasive grains when the polishing cloth polishes the sapphire substrate. This indicates the surface roughness of the sapphire substrate.

6. A surface roughness prediction system for sapphire substrate CMP, used to execute the surface roughness prediction method for sapphire substrate CMP according to any one of claims 1-5, characterized in that, include: The initial prediction model construction module is used to construct an initial prediction model based on the cantilever beam theoretical model combined with CMP process. The first parameter relationship construction module is used to establish the first parameter relationship between abrasive grains, polishing cloth, and sapphire substrate in CMP process based on ductile domain removal mechanism; The second parameter relationship construction module 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 mechanics model. The solution module is used to 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.

7. An electronic device, characterized in that, The method includes 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, the instructions being executed by the at least one processor to enable the at least one processor to perform a surface roughness prediction method for CMP of a sapphire substrate as described in any one of claims 1 to 5.

8. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a surface roughness prediction method for sapphire substrate CMP according to any one of claims 1 to 5.

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