A method for detecting and processing small faces of SiC crystal ingot

Abstract

The present invention relates to a small face detection method and a processing method for SiC crystal ingots. The small face detection method for SiC crystal ingots includes establishing a Δω-n linear model; determining the doping concentration n by the Δω-n linear model; ij , these doping concentrations n ij , the detection image, and the corresponding XY coordinate position are associated to construct a doping concentration distribution map of the workpiece surface layer; the processing method of the SiC crystal ingot includes, based on the doping concentration distribution map and the preset n-P curve model, ij , the detected image, and the corresponding XY coordinate positions to construct a power distribution map of the workpiece's modified layer. The processing laser beam is then focused at a predetermined depth within the workpiece to perform internal modification, resulting in a silicon carbide workpiece with a modified layer. This method improves the accuracy of ingot doping concentration detection, minimizes the impact of outliers, enables non-destructive and precise small-face detection, improves processing consistency, and reduces material loss in the modified layer.

Description

Technical Field

[0001] The present invention relates to the technical field of semiconductor device processing, and in particular to a small face detection method and a processing method for a SiC crystal ingot. Background Art

[0002] Wide-bandgap semiconductor materials, such as SiC, offer excellent properties such as high thermal conductivity, strong voltage breakdown resistance, high saturated electron drift velocity, strong chemical stability, radiation resistance, and high-temperature resistance, making them highly sought after in high-frequency, high-temperature, radiation-resistant, high-power, and high-density integrated electronic devices. Among the multiple steps involved in producing SiC ingots into qualified substrate wafers, the ingot slicing process is the primary cause of SiC material loss. Currently, ingot slicing techniques primarily include wire sawing and laser lift-off (LAS). Compared to traditional wire sawing, laser lift-off (LAS) offers significant development potential due to its high production efficiency and low material consumption. Its core principle is to focus the laser at a specific depth within the ingot, creating a large modified region for subsequent wafer removal. However, during ingot growth, varying doping concentrations (carrier concentrations) create faceted and non-faceted regions with varying resistivity, refractive index, and energy absorption. When the modified laser passes through these faceted and non-faceted regions, the depth of the focus varies, resulting in poor flatness in the resulting wafers and significant losses during subsequent polishing. Furthermore, the non-uniform resistivity will lead to a large mismatch between the SiC substrate and the epitaxial layer grown on it. Therefore, doping concentration is an important basic parameter for characterizing the properties of semiconductor materials, and accurately measuring the carrier concentration of semiconductor materials has extremely important practical value.

[0003] Chinese patent application number CN110911268B discloses a method for producing a SiC wafer from a SiC ingot. The method comprises the following steps: a flat surface forming step; a coordinate setting step, wherein after the flat surface forming step is performed, a facet region is detected from the top surface of the SiC ingot, and the X- and Y-coordinates of the boundary between the facet region and the non-facet region are set using the following two directions as the X-axis and the Y-axis: the c-plane tilt deviation angle relative to the top surface of the SiC ingot, the X-axis being perpendicular to the direction forming the deviation angle between the c-plane and the flat surface, and the Y-axis being perpendicular to the X-axis; a processing feed step; an indexing feed step; and a peeling step. The above method uses a camera to capture the top surface of the ingot and uses an image processing unit to binarize the captured image to distinguish between facet regions and non-facet regions. However, due to the relatively thick ingot, distinguishing between facet regions and non-facet regions by direct imaging is not easy, and errors are easily generated during implementation.

[0004] Chinese Patent Publication No. CN115472515A discloses a method for processing an ingot, comprising the following steps: a fluorescence detection step, wherein the ingot is irradiated with excitation light of a specified wavelength from above the ingot and the number of fluorescence photons generated from the ingot's upper surface is detected; a storage step, wherein the distribution of the fluorescence photons detected on the ingot's upper surface, as detected by the fluorescence detection step, is associated with the XY coordinate position on an XY plane perpendicular to the ingot's height direction and stored as two-dimensional data, and the height position of the ingot obtained from the two-dimensional data, i.e., the Z coordinate position, is associated with the two-dimensional data; a laser beam irradiation step; and a wafer generation step. The above method utilizes laser light of a specific wavelength to irradiate the ingot and detects the intensity of the fluorescence light generated on the ingot's upper surface to determine the facet area. However, the laser power density used in the fluorescence detection step is relatively high, which can easily damage the ingot surface.

[0005] Chinese patent publication number CN117316791A discloses a facet detection method, comprising: irradiating a first surface of a crystal ingot with a laser beam to produce a peeling layer on the crystal ingot; applying ultrasonic waves to the formed crystal ingot using an ultrasonic vibration component to obtain a wafer corresponding to the peeling layer; emitting a detection beam at the wafer using a light source of a specific wavelength and recording the light source emission intensity at the wafer receiving point; comparing the wafer's transmission intensity with the light source emission intensity to obtain the wafer's transmittance; locating a facet region in the wafer based on a preset transmittance threshold and the wafer's transmittance, and determining position information corresponding to the facet region; updating the laser processing conditions of the next wafer adjacent to the wafer using the position information corresponding to the facet region as guidance information, and peeling the next wafer based on the laser processing conditions. The above method detects the first peeled wafer by comparing the wafer's transmission intensity with the light source emission intensity, and then optimizes the laser processing conditions of the next wafer based on the position information corresponding to the facet region of the previous wafer. However, laser energy fluctuations or other types of damage on the wafer surface can affect the measurement results, and the detection accuracy is also insufficient.

[0006] Currently, electrochemical capacitance-voltage (CV) longitudinal carrier concentration profilers are commonly used to measure the longitudinal carrier concentration profile. Four-probe and Hall effect methods are used to directly measure the average carrier concentration of a material. Far-infrared reflectance spectra can also be used to determine the crystal's phonon vibrational parameters, plasma oscillation frequency, and damping constant, and thus the carrier concentration. CV results are affected by factors such as the depletion layer assumption, junction fabrication technique, and leakage. Both four-probe and Hall effect methods require ohmic contact between the electrodes and the standard being measured, and factors such as the standard's size, shape, test current, and probe pressure can introduce significant errors. Wang Guanghong et al. studied the carrier concentration of n-type 4H- and 6H-SiC crystals using Raman spectroscopy. Raman scattering is a non-destructive method. Their results further confirmed that for n-type 4H- and 6H-SiC crystals, analyzing the line shape of the LOPC mode can accurately determine the carrier concentration of the relevant material. However, these methods directly use the plasma frequency and carrier concentration to establish curve-fitting equations, resulting in complex models that hinder rapid analysis and decision-making. Summary of the Invention

[0007] In view of the shortcomings of the existing technology, the first object of the present invention is to provide a small face detection method for SiC crystal ingots, which has the advantages of improving the detection accuracy of the crystal ingot doping concentration, being less affected by abnormal values, and realizing non-destructive and accurate small face detection.

[0008] The second object of the present invention is to provide a method for processing SiC ingots, which avoids the phenomenon of inconsistent depth and completion of the ingot modified layer caused by uneven doping concentration, and has the advantages of improving processing consistency and reducing the loss of modified layer material.

[0009] To achieve the above first object, the present invention provides the following technical solutions:

[0010] A method for detecting a small facet of a SiC ingot comprises the following steps:

[0011] S11 provides multiple silicon carbide standards with different doping concentrations n, and obtains the Raman characteristic peak frequency ω of these standards. n , taking the Raman characteristic peak frequency ω of one of the standards n The difference calculation is performed based on the reference, and the relative Raman frequency shift Δω is determined, and then a Δω-n linear model of the relative Raman frequency shift Δω and the doping concentration n is established based on the least squares method;

[0012] S12 provides a silicon carbide workpiece and obtains the Raman characteristic peak frequency ω at different detection points on the surface layer of the workpiece based on a preset Raman detection scanning path. i , detection image, and XY coordinate position on the XY plane perpendicular to the height direction of the workpiece (X i, Y j ), and then determine the doping concentration n by the Δω-n linear model ij , and these doping concentrations n ij , the detection image, and the corresponding XY coordinate positions to construct a doping concentration distribution map of the surface layer of the workpiece.

[0013] Among them, the Raman characteristic peak frequency ω n Where n≥0, which corresponds to the doping concentration n; i and j are integers ≥0, and their maximum values correspond to the number of detection points in the X and Y directions, respectively.

[0014] Furthermore, in said S11, the Raman characteristic peak frequency ω n The Raman detection was performed in the longitudinal optical phonon-plasmon coupling (LOPC) mode.

[0015] Furthermore, in the step S11, an undoped silicon carbide standard and a plurality of silicon carbide standards with other doping concentrations n' are provided, and the Raman characteristic peak frequencies ω0 and ω n' , with Raman characteristic peak frequencies ω0 and ω n' The difference calculation is performed to determine the relative Raman frequency shift Δω, and then a Δω-n linear model of the relative Raman frequency shift Δω and the doping concentration n is established based on the least squares method.

[0016] Furthermore, in said S11, the detection laser of the Raman detection laser is a continuous laser with a laser wavelength of 450-1000 nm, a laser detection power adjustable from 0 to 500 mW, and a beam quality factor M 2 <1.2, wavelength drift <10pm.

[0017] Furthermore, in S11, the fitting function of the Δω-n linear model is n=k1Δω+b1, k1=1.00×10 17 ~1.50×10 17 , b1=0~1.

[0018] Furthermore, in S11, the fitting function of the Δω-n linear model is n=1.27×10 17 Δω(R=99%).

[0019] Furthermore, in said S12, the doping concentration n is determined by said Δω-n linear model. ij Then, firstly, the doping concentration n ij , and the corresponding XY coordinate positions are associated into two-dimensional data (X i , Y j , n ij ), and then the two-dimensional data (Xi , Y j , n ij ) is associated with the detection image to form image data, and a doping concentration distribution map of the surface layer of the workpiece is constructed.

[0020] Furthermore, in said S12, the Raman characteristic peak frequency ω ij The Raman detection was performed in the longitudinal optical phonon-plasmon coupling (LOPC) mode.

[0021] Furthermore, in said S12, the detection laser of the Raman detection laser is a continuous laser with a laser wavelength of 450-1000 nm, a laser detection power adjustable in the range of 0-500 mW, and a beam quality factor M 2 <1.2, wavelength drift <10pm.

[0022] Furthermore, in said S12, the Z-axis coordinate positions (Z ij ), these Z-axis coordinate positions and the corresponding XY coordinate positions are associated to construct a morphology distribution map of the surface layer of the workpiece.

[0023] To achieve the above second purpose, the present invention provides the following technical solutions:

[0024] A method for processing a SiC ingot comprises the following steps:

[0025] S1 provides a silicon carbide workpiece, and constructs a doping concentration distribution map of the surface layer of the workpiece according to the above-mentioned small facet detection method;

[0026] S2 determines the laser processing power P at different detection points on the surface layer of the workpiece based on the doping concentration distribution diagram obtained in S1 and the nP curve model between the preset doping concentration n and the laser processing power P. ij , and these laser processing powers P ij , the detection image, and the corresponding XY coordinate positions to construct a power distribution map of the modified layer of the workpiece;

[0027] S3 focuses the machining laser beam on a predetermined depth of the workpiece based on the power distribution diagram obtained in S2 to perform internal modification, and moves the workpiece and the focus point relatively approximately in the XY directions to obtain a silicon carbide workpiece with a modified layer.

[0028] Furthermore, in S2, the processing laser beam of the processing laser is a pulsed laser with a laser pulse width of 200 fs to 10 ns, a laser wavelength of 400 to 1100 nm, and a laser energy of 0.01 to 1500 mJ.

[0029] Furthermore, in S2, the fitting function of the nP curve model is P=k2e [n / (1.18×10^18)] +b2, k2=0.10~1.00, b2=0~1.

[0030] Furthermore, in S2, the fitting function of the nP curve model is P=0.57e [n / (1.18×10^18)] (R=99%). The fitting function of the nP curve model can also be expressed as P=0.57exp[n / (1.18×10 18 )].

[0031] Furthermore, in S3, the relative movement speed of the workpiece and the focal point is 5-5000 mm / s.

[0032] Furthermore, in said S3, the Z-axis coordinate positions (Z ij ), these Z-axis coordinate positions and the corresponding XY coordinate positions are associated to construct a topography distribution map of the workpiece surface layer, and then the Z-axis coordinate positions of the focusing objective lens of the processing laser beam corresponding to different detection points on the workpiece surface layer are determined, so that the workpiece and the focusing point can be moved relative to each other approximately in the XY directions.

[0033] In summary, the beneficial technical effects of the present invention are:

[0034] 1. The facet detection method of the present invention uses a low-power laser with photon energy far below the bandgap energy of silicon carbide to generate a Raman signal. A relationship model is established between doping concentration (carrier concentration) and the frequency shift of the Raman characteristic peak (Raman frequency shift). The Raman frequency shift depends on changes in molecular vibrational energy levels and is highly characteristic. Due to this characteristic, the Raman frequency shift is unaffected by laser wavelength, energy fluctuations, or background noise. Therefore, by detecting the Raman frequency shift, the doping concentration of the ingot can be accurately determined while avoiding interference from background noise such as laser scattered light and fluorescence signals on the Raman characteristic peak, achieving non-destructive and precise measurement.

[0035] 2. The processing method of the present invention simultaneously optimizes the processing laser parameters according to the doping concentration at the ingot detection point, avoiding the phenomenon of inconsistent depth and completion of the ingot modified layer caused by uneven doping concentration, improving the wafer stripping quality, reducing material loss, and thus improving overall production efficiency and product quality;

[0036] 3. The present invention also utilizes the three-dimensional coordinate position of the detection point to construct the morphological information of the ingot surface, thereby adjusting the processing depth in real time according to the morphological fluctuations of the ingot surface layer during the laser processing process, that is, adjusting the Z-axis coordinate position of the focusing objective lens of the processing laser beam so that the workpiece and the focusing point move approximately relative to each other in the XY direction, and finally achieving the consistency of the processing depth of the wafer. This method can significantly improve the processing accuracy and quality of the wafer, and further improve production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Schematic diagram of the structure of the silicon carbide workpiece of Example 1 of the present invention.

[0038] Figure 2 1 is a Raman spectrum diagram of two silicon carbide standards with different doping concentrations according to Example 2 of the present invention.

[0039] Figure 3 4 is a fitting function diagram between the relative Raman frequency shift Δω and the doping concentration n in Example 2 of the present invention.

[0040] Figure 4 Schematic diagram of the Raman detection scanning path of the surface layer of the workpiece in Example 3 of the present invention.

[0041] Figure 5 It is a fitting function diagram between the doping concentration n and the laser processing power P of Example 5 of the present invention. DETAILED DESCRIPTION

[0042] In order to make the technical means, creative features, objectives and functions achieved by the present invention clearer and easier to understand, the present invention is further explained below with reference to the accompanying drawings and specific implementation methods.

[0043] Example 1: Reference Figure 1 , a method for detecting a small facet of a SiC crystal ingot disclosed in the present invention, comprising the following steps:

[0044] S11 provides multiple silicon carbide standards with different doping concentrations n, and obtains the Raman characteristic peak frequency ω of these standards. n , taking the Raman characteristic peak frequency ω of one of the standards n The difference calculation is performed based on the reference, and the relative Raman frequency shift Δω is determined, and then a Δω-n linear model of the relative Raman frequency shift Δω and the doping concentration n is established based on the least squares method;

[0045] S12 provides a silicon carbide workpiece and obtains the Raman characteristic peak frequency ω at different detection points on the workpiece surface layer based on the preset Raman detection scanning path. i , detection image, and XY coordinate position on the XY plane perpendicular to the height direction of the workpiece (X i , Y j), and then determine the doping concentration n through the Δω-n linear model ij , and these doping concentrations n ij , the detection image, and the corresponding XY coordinate positions to construct a doping concentration distribution map of the workpiece surface layer.

[0046] The present invention also discloses a method for processing SiC ingots, comprising the following steps:

[0047] S1 provides a silicon carbide workpiece and constructs a doping concentration distribution map of the workpiece surface layer according to the above-mentioned small facet detection method;

[0048] S2 determines the laser processing power P at different detection points on the workpiece surface layer based on the doping concentration distribution map obtained in S1 and the preset nP curve model between the doping concentration n and the laser processing power P. ij , and these laser processing powers P ij , the detection image, and the corresponding XY coordinate position are associated to construct a power distribution map of the workpiece modified layer;

[0049] S3 focuses the processing laser beam on a predetermined depth of the workpiece based on the power distribution diagram obtained in S2 to perform internal modification, and moves the workpiece and the focus point approximately relative to each other in the XY direction to obtain a silicon carbide workpiece with a modified layer.

[0050] Example 2: Reference Figure 2 and Figure 3 , which is a method for detecting a small facet of a SiC crystal ingot disclosed in the present invention. The difference from Example 1 is that, in S11, an undoped silicon carbide standard and a plurality of silicon carbide standards with other doping concentrations n' are provided, and Raman detection is performed in the longitudinal optical phonon-plasmon coupling (LOPC) mode to obtain the Raman characteristic peak frequencies ω0 and ω n' , with Raman characteristic peak frequencies ω0 and ω n' The difference calculation was performed and the relative Raman frequency shift Δω was determined. Then, a Δω-n linear model of the relative Raman frequency shift Δω and the doping concentration n was established based on the least squares method. The fitting function was n=1.27×10 17 Δω(R=99%). Among them, the detection laser of the Raman detection laser is a continuous laser with a laser wavelength of 450~1000nm, the laser detection power is adjustable from 0~500mW, and the beam quality factor M 2 <1.2, wavelength drift <10pm.

[0051] Example 3: Reference Figure 4, which is a facet detection method for SiC ingots disclosed in the present invention, differs from Example 2 in that, in S12, a silicon carbide workpiece is provided, and based on a preset Raman detection scanning path Ⅰ→Ⅱ→Ⅲ→Ⅳ→Ⅴ→VI→VII→VIII→IX, the Raman characteristic peak frequencies ω at different detection points on the surface layer of the workpiece are obtained. i , detection image, and XY coordinate position on the XY plane perpendicular to the height direction of the workpiece (X i , Y j ), and then determine the doping concentration n through the Δω-n linear model ij Then, firstly, the doping concentration n ij , and the corresponding XY coordinate positions are associated into two-dimensional data (X i , Y j , n ij ), and then the two-dimensional data (X i , Y j , n ij ) is associated with the detection image to form image data, and the doping concentration distribution map of the workpiece surface layer is constructed. Among them, the detection laser of the Raman detection laser is a continuous laser with a laser wavelength of 450~1000nm, and the laser detection power is adjustable from 0~500mW. The beam quality factor M 2 <1.2, wavelength drift <10pm.

[0052] Example 4: A method for detecting a small facet of a SiC crystal ingot disclosed in the present invention. The difference from Example 3 is that, in S12, the Z-axis coordinate positions (Z ij ), these Z-axis coordinate positions and the corresponding XY coordinate positions are associated into three-dimensional data (X i , Y j , Z ij ), construct the morphology distribution map of the workpiece surface layer.

[0053] Example 5: Reference Figure 5 , which is the SiC ingot processing method disclosed in the present invention, is different from Example 4 in that, in S2, based on the doping concentration distribution diagram obtained in S1 and the nP curve model between the preset doping concentration n and the laser processing power P, its fitting function is P=0.57e [n / (1.18×10^18)] (R=99%), determine the laser processing power P at different detection points on the surface layer of the workpiece ij , and these laser processing powers P ij , the detection image, and the corresponding XY coordinate position to construct the power distribution map of the workpiece modified layer (X i , Y j , P ijThe processing laser beam of the processing laser is a pulsed laser with a laser pulse width of 200fs~10ns, a laser wavelength of 400~1100nm, and a laser energy of 0.01~1500mJ.

[0054] Example 6: The processing method of SiC ingot disclosed in the present invention is different from Example 5 in that, in S3, based on the topography distribution map obtained in S1 and the power distribution map obtained in S2, the processing laser beam is focused at a predetermined depth d of the workpiece to perform internal modification, not only guiding the processing laser beam to move along a fixed scanning path in the XY plane with optimal laser parameters, but also determining the Z-axis coordinate position of the focusing objective lens of the processing laser beam corresponding to different detection points on the surface layer of the workpiece for real-time adjustment (X i , Y j , Z ij , P ij ), so that the workpiece and the focal point move approximately relative to each other in the XY direction, that is, the processing laser beam is always focused at the depth d and forms the modified point along a fixed scanning path, and finally a silicon carbide workpiece with a modified layer is obtained.

[0055] Taking multiple detection points (from left to right) on a straight line passing through the small face area as an example, the two-dimensional data corresponding to the doping concentration distribution diagram obtained according to Example 3 and the power distribution diagram obtained according to Example 5 are shown in Table 1 below.

[0056] Table 1

[0057] Detection location <![CDATA[XY coordinate position (X i , Y j )]]> <![CDATA[Raman characteristic peak frequency ω ij (cm -1 )]]> <![CDATA[Relative Raman frequency shift Δω (ω0 = 964 cm -1 ).]]> <![CDATA[Doping concentration n (×10 17 cm -3 )]]> Processing laser power (W) Non-facet area <![CDATA[X1,Y1]]> 980.79 16.69 21.20 3.51 Small face area <![CDATA[X2,Y1]]> 982.18 18.08 22.96 4.03 Small face area <![CDATA[X3,Y1]]> 984.07 19.97 25.36 4.89 Small face area <![CDATA[X4,Y1]]> 983.03 18.93 24.04 4.45 Non-facet area <![CDATA[X5,Y1]]> 981.32 17.22 21.87 3.71 Non-facet area <![CDATA[X6,Y1]]> 981.37 17.27 21.93 3.73

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for processing a SiC ingot, characterized in that: The following steps are included: S1 provides a silicon carbide workpiece and constructs a doping concentration distribution map of a surface layer of the workpiece according to a small facet detection method; S2 determines the laser processing power P at different detection points on the surface layer of the workpiece based on the doping concentration distribution diagram obtained in S1 and the nP curve model between the preset doping concentration n and the laser processing power P. ij , and these laser processing powers P ij , the detection image, and the corresponding XY coordinate positions to construct a power distribution map of the modified layer of the workpiece; S3 focuses the machining laser beam on a predetermined depth of the workpiece based on the power distribution diagram obtained in S2 to perform internal modification, and moves the workpiece and the focus point relative to each other approximately in the XY directions to obtain a silicon carbide workpiece with a modified layer; The facet detection method comprises the following steps: S11 provides multiple silicon carbide standards with different doping concentrations n, and obtains the Raman characteristic peak frequency ω of these standards. n , taking the Raman characteristic peak frequency ω of one of the standards n The difference calculation is performed based on the reference, and the relative Raman frequency shift Δω is determined, and then a Δω-n linear model of the relative Raman frequency shift Δω and the doping concentration n is established based on the least squares method; S12 provides a silicon carbide workpiece and obtains the Raman characteristic peak frequency ω at different detection points on the surface layer of the workpiece based on a preset Raman detection scanning path. i , detection image, and XY coordinate position on the XY plane perpendicular to the height direction of the workpiece (X i , Y j ), and then determine the doping concentration n by the Δω-n linear model ij , and these doping concentrations n ij , the detection image, and the corresponding XY coordinate positions to construct a doping concentration distribution map of the surface layer of the workpiece.

2. The method for processing a SiC ingot according to claim 1, wherein: In the S11, the Raman characteristic peak frequency ω n The Raman detection was performed in the longitudinal optical phonon-plasmon coupling mode.

3. The method for processing a SiC ingot according to claim 2, wherein: In the step S11, an undoped silicon carbide standard and a plurality of silicon carbide standards with other doping concentrations n' are provided, and the Raman characteristic peak frequencies ω0 and ω n' , with Raman characteristic peak frequencies ω0 and ω n' The difference calculation is performed to determine the relative Raman frequency shift Δω, and then a Δω-n linear model of the relative Raman frequency shift Δω and the doping concentration n is established based on the least squares method.

4. The method for processing a SiC ingot according to claim 2, wherein: In the above S11 and S12, the detection laser of the Raman detection laser is a continuous laser with a laser wavelength of 450-1000 nm, a laser detection power adjustable from 0 to 500 mW, and a beam quality factor M. 2 <1.2, wavelength drift <10pm.

5. The method for processing a SiC ingot according to claim 4, wherein: In S11, the fitting function of the Δω-n linear model is n=k1Δω+b1, k1=1.00×10 17 ~1.50×10 17 , b1=0~1.

6. The method for processing a SiC ingot according to claim 1, wherein: In the above S2, the processing laser beam of the processing laser is a pulsed laser with a laser pulse width of 200 fs to 10 ns, a laser wavelength of 400 to 1100 nm, and a laser energy of 0.01 to 1500 mJ.

7. The method for processing a SiC ingot according to claim 6, wherein: In S2, the fitting function of the nP curve model is P=k2e [n / (1.18×10^18）] +b2, k2=0.10~1.00, b2=0~1.

8. The method for processing a SiC ingot according to claim 1, wherein: In S3, the relative movement speed of the workpiece and the focus point is 5-5000 mm / s.

9. The method for processing a SiC ingot according to claim 1, wherein: In said S3, the Z-axis coordinate positions (Z ij ), these Z-axis coordinate positions and the corresponding XY coordinate positions are associated to construct a topography distribution map of the workpiece surface layer, and then the Z-axis coordinate positions of the focusing objective lens of the processing laser beam corresponding to different detection points on the workpiece surface layer are determined, so that the workpiece and the focusing point can be moved relative to each other approximately in the XY directions.

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