Nondestructive characterization method for penetrability blade type dislocation of silicon carbide material

Abstract

The invention discloses a nondestructive characterization method for penetrating blade type dislocation of a silicon carbide material, and belongs to the technical field of dislocation defect characterization of semiconductor materials. The nondestructive characterization method provided by the invention comprises the following steps: S1, selecting a sample as a diffraction vector, and calculating test parameters based on an X-ray morphology detection system based on a Bragg diffraction principle; and S2, carrying out initial setting according to the calculated test parameters, carrying out dynamic calibration on the test parameters through multi-dimensional loop optimization, and carrying out imaging on the penetrating blade profile dislocation of the silicon carbide material based on the calibrated test parameters. According to the characterization method disclosed by the invention, the destructiveness of KOH corrosion is avoided, the limitation of TEM small-area detection is overcome, and clear imaging and accurate identification of TED are realized while the integrity of the sample is ensured to support subsequent processing; and the equipment is simple, low in cost and high in overall detection efficiency.

Description

Technical Field

[0001] This invention relates to the field of semiconductor material dislocation defect characterization technology, and in particular to a non-destructive characterization method for penetrating edge dislocations in silicon carbide materials. Background Technology

[0002] The information disclosed in the background section of this invention is intended only to enhance the understanding of the overall background of the invention and is not necessarily to be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art.

[0003] In the field of semiconductor materials, silicon carbide (SiC) has become a key material for high-power, high-frequency, and high-temperature applications due to its excellent thermal conductivity, electrical conductivity, and radiation resistance. In recent years, with technological advancements, its application scope has continued to expand, demonstrating significant value in power electronic devices, optoelectronic devices, radar technology, and many other fields. However, despite the significant advantages of silicon carbide in these areas, its internal penetrating edge dislocations (TEDs), a common defect type, still have a significant negative impact on material performance—especially during epitaxial growth and device fabrication. These dislocations can lead to a decline in the material's mechanical and electrical properties, thereby affecting device stability and long-term reliability.

[0004] The formation of penetrating edge dislocations (TEDs) is closely related to the non-uniform distribution of lattice stress during epitaxial growth. Because they are difficult to avoid during crystal growth, TEDs not only compromise crystal integrity but can also trigger the aggregation of other defects, further reducing the overall structural quality of the material. These defects pose a serious challenge to the electrical and thermal properties of silicon carbide: in device manufacturing, the presence of TEDs leads to reduced current transport efficiency, poor heat dissipation, and even premature device failure, thus limiting the widespread application of silicon carbide in high-performance devices. To optimize material performance and improve device reliability, it is essential to accurately understand the distribution characteristics and evolution of TEDs. Therefore, efficient characterization of TEDs is a crucial prerequisite for overcoming this technological bottleneck.

[0005] Currently, traditional characterization methods for silicon carbide dislocations mainly include KOH etching, transmission electron microscopy (TEM), and synchrotron X-ray morphology system (SXRT). While KOH etching can reveal dislocation distribution, it damages the sample, making it unsuitable for subsequent epitaxial growth or device fabrication. TEM provides high-resolution dislocation images but has limitations, including stringent sample preparation requirements and the ability to detect only small areas. SXRT, while possessing some non-destructive characterization capabilities, is constrained in large-scale material characterization due to its expensive equipment, demanding light source requirements, and limited imaging area.

[0006] It is worth noting that the unique structure of TED further exacerbates the difficulty of representation: its Burgers vector is relatively small, being... (where a is the lattice constant of silicon carbide), which is only on the order of 1 / 3 to 1 / 5 of that of a screw dislocation (TSD), resulting in a low order of lattice distortion caused by it; at the same time, the dislocation lines of TED are along <0001> Extending in the c-axis direction, consistent with the TSD dislocation line, its lattice distortion is mainly concentrated within the basal plane {0001} perpendicular to the dislocation line. For X-ray topography (XRT / SXRT), its imaging relies on the modulation effect of local lattice distortion caused by dislocations on X-ray diffraction; the core condition is the diffraction vector. Burgers vector of dislocation satisfy (i.e., the "contrast condition"). However, the diffraction vectors commonly used in traditional XRT... (like 0008) and others are along the c-axis direction, and dot product Approaching zero, the contrast condition cannot be met; the superposition of weak diffraction signals from TED and the limitations of the X-ray scattering path in the SXRT reflection mode (such as shallow depth of action and background noise interference) ultimately makes it difficult for SXRT to capture weak signals, and it is prone to missed detections or artifacts.

[0007] Therefore, developing a non-destructive, high-precision characterization method for penetrating edge dislocations in silicon carbide materials is an urgent problem to be solved. Summary of the Invention

[0008] In view of this, the present invention provides a non-destructive characterization method for penetrating edge dislocations in silicon carbide materials. The present invention can perform high-precision characterization of penetrating edge dislocations in large-size silicon carbide materials without damaging the sample, while having low requirements for equipment.

[0009] This invention provides a non-destructive characterization method for penetrating edge dislocations in silicon carbide materials, comprising the following steps: S1, Select = As the diffraction vector, the test parameters based on the Bragg diffraction principle are calculated for the X-ray topography detection system. S2. Initial settings are made based on the calculated test parameters. The test parameters are dynamically calibrated through multi-dimensional iterative optimization. Based on the calibrated test parameters, the penetrating edge dislocations of silicon carbide material are imaged.

[0010] Preferably, the diffraction vector is = , = or = .

[0011] Preferably, before characterization, the surface of the silicon carbide material is mechanically polished, and the surface scratch depth does not exceed 0.8 μm, and the number of scratches with a length exceeding 10 mm does not exceed 10.

[0012] Preferably, the test parameters of the X-ray topography detection system include the X-ray incident angle (theta_s), the X-ray exit angle (theta_d), and the sample stage rotation angle.

[0013] Furthermore, the X-ray incident angle (theta_s) is -90° to 70°, the X-ray exit angle (theta_d) is 20° to 150°, and the sample stage rotation angle is -180° to 180°.

[0014] Furthermore, when the X-ray incident angle is less than 0°, after the imaging step in step S2, a specific location is selected, a scanning range is set along the x-axis, and an exposure time is set to perform tomographic imaging on the penetrating edge dislocation at the selected specific location.

[0015] Furthermore, the scanning range of the x-axis is ±1~±5mm, and the exposure time is 10~120s.

[0016] Preferably, the multi-dimensional iterative optimization includes: performing 3 to 7 alternating iterations of omega optimization and phi optimization on the X-ray incident angle and the sample stage rotation angle, with the alternating iteration optimization process starting and ending with omega optimization.

[0017] Furthermore, after the alternating iterative optimization steps, a small-area snap defect morphology imaging is performed, and the current test parameters are evaluated based on the imaging quality to determine whether further optimization is needed.

[0018] Preferably, the imaging of penetrating edge dislocations in silicon carbide material includes a curvature correction scan.

[0019] Preferably, in the step of imaging penetrating edge dislocations in silicon carbide material, the scanning speed is preferably 10~100 mm / min.

[0020] Preferably, after step S2, the method further includes setting a threshold based on defect density recognition software, identifying and statistically analyzing the imaged penetrating edge dislocations to obtain density distribution data, and processing the density distribution data to obtain a density distribution map of penetrating edge dislocations.

[0021] Compared with the prior art, the present invention has achieved the following beneficial effects: (1) Based on the diffraction vector design of the X-ray morphology system, this invention combines the Bragg diffraction principle and the dislocation extinction principle to solve the problem of weak signal imaging caused by the direction of the small Burgers vector and the c-axis dislocation line in TED. It avoids the destructiveness of KOH corrosion and overcomes the limitations of TEM small area detection. While ensuring the integrity of the sample to support subsequent processing, it achieves clear imaging and accurate identification of TED.

[0022] (2) The present invention can perform full-coverage imaging of large-size silicon carbide samples in one go, avoiding the omission of dislocation information caused by the limited detection area of ​​traditional methods, and significantly improving the comprehensiveness and representativeness of dislocation distribution data.

[0023] (3) Compared with traditional synchrotron radiation X-ray morphology system (SXRT) or transmission electron microscope (TEM), the X-ray morphology system (XRT) of the present invention is simpler, has a significantly lower cost, does not require complex sample preparation, does not require the construction of a large light source station, can perform dislocation characterization in a laboratory environment, and has a simplified operation process.

[0024] (4) The X-ray morphology system of the present invention is not sensitive to the conductivity type of silicon carbide samples and can accurately characterize them independently of the conductivity properties of the samples, thus ensuring accurate analysis of different types of silicon carbide materials.

[0025] (5) This invention significantly improves the detection speed of dislocations through efficient image processing and automated analysis, thereby enhancing the overall detection efficiency. Simultaneously, it allows for intuitive observation of the density distribution of dislocations in the sample and their affected areas, facilitating further analysis and optimization of material design and device performance. This efficient distribution identification and statistical analysis method provides strong data support for the production and processing of silicon carbide materials.

[0026] (6) The tomographic imaging function of the present invention can directly observe the generation, annihilation, dislocation reaction and other evolution of penetrating edge dislocations, providing more accurate data support for dislocation analysis of silicon carbide materials. Attached Figure Description

[0027] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention and do not constitute an undue limitation thereof. Obviously, those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0028] Figure 1 These are the TED morphology diagram (a) and TED distribution diagram (b) of the entire sample in Embodiment 1 of the present invention; Figure 2These are the TED morphology diagrams (a) of the entire sample in Example 2 of the present invention and the TED distribution diagrams (b~d) of different small regions. Figure 3 These are TED topography images (b~d) of the entire sample (a) and a specific region at a specific depth in Embodiment 3 of the present invention. Figure 4 These are the TED distribution diagrams (a) and (b) measured in Example 4 of the present invention and Comparative Example 1, respectively. Figure 5 The diagram shows the distribution of TED measured in Example 5 of the present invention (a) and the distribution of TED measured in Comparative Example 2 (b). Detailed Implementation

[0029] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0030] This invention provides a non-destructive characterization method for penetrating edge dislocations in silicon carbide materials, comprising the following steps: S1, Select = As the diffraction vector, the test parameters based on the Bragg diffraction principle are calculated for the X-ray topography detection system. S2. Dynamically calibrate the test parameters through multi-dimensional cyclic optimization, and image the penetrating edge dislocations of silicon carbide material based on the calibrated test parameters.

[0031] In this invention, Bragg diffraction (reflection mode) is achieved through the interaction of incident X-rays with the Bragg condition (2) on the crystal surface. d ·sin θ = λ) enables imaging of surface or near-surface defects. Only perfectly defect-free areas that satisfy Bragg diffraction can be incident and emitted by X-rays according to the theoretical light path. Defective areas experience lattice distortion, leading to uneven X-ray distribution at that location, forming a black area, which is then analyzed as a defect. This method is suitable for detecting TED (Differential Thermogravimetric Analysis) on the sample surface. The rocking curve (RC) is the sample's refraction around the diffraction angle. θ The curve showing the diffraction intensity as a function of angle during small-range oscillation; its full width at half maximum (FWHM), peak intensity, and other characteristic parameters directly reflect the uniformity of lattice distortion and the strength of the diffraction signal. This invention optimizes the test parameters through the oscillation curve, which can precisely adjust the X-rays to be on the same plane as the diffraction vector, enabling the sample's defect morphology to be imaged with maximum clarity.

[0032] Traditional XRT commonly uses diffraction vectors (such as...) =0008) and others along the c-axis direction, and The dot product of the given information approaches zero, failing to satisfy the contrast condition. This invention, through extensive experimental screening, has determined that... = , = or = As a diffraction vector, ensure This effectively enhances the diffraction contrast of TED, solving the imaging problem caused by insufficient contrast in traditional techniques.

[0033] This invention does not impose special restrictions on the shape of the selected silicon carbide material, but preferably it is a regular shape such as a circle or square. In this invention, the diameter of the silicon carbide material is no greater than 300 mm and the thickness is no more than 2.2 mm; more preferably, its size is 2 to 12 inches and its thickness is no more than 1.8 mm.

[0034] This invention does not impose special restrictions on the crystal form of the selected silicon carbide material, including but not limited to hexagonal SiC materials such as 4H-SiC and 6H-SiC. This invention also does not impose special restrictions on the conductivity type and doping type of the silicon carbide material, which can be unintentionally doped, semi-insulating, N-type SiC substrates, or P-type SiC substrates, etc.

[0035] In this invention, before characterization, the surface of the silicon carbide material is mechanically polished to remove the surface rough layer, thereby controlling the surface roughness to the submicron level. Furthermore, the surface scratch depth does not exceed 0.8 μm, and the number of scratches longer than 10 mm does not exceed 10. More preferably, the surface scratch depth does not exceed 0.3 μm, and the number of scratches longer than 10 mm does not exceed 4. This is because scratches on the surface of the silicon carbide material cause diffuse scattering of X-rays, generating additional background noise that masks the weak diffraction signal of TED (Diffraction Theorem). Simultaneously, the scratches themselves may induce local lattice distortion, forming artifacts that interfere with TED identification.

[0036] In this invention, the test parameters of the X-ray topography detection system include the X-ray incident angle (theta_s), the X-ray exit angle (theta_d), and the sample stage rotation angle. The incident angle determines the angle of incidence between the X-rays and the sample surface, affecting the matching of Bragg diffraction conditions; the exit angle controls the collection direction of the diffraction signal, ensuring that the detector can effectively receive the diffraction signal of the TED; the sample stage rotation angle adjusts the sample orientation, so that the dislocation lines of the TED form the optimal angle with the X-ray beam, maximizing the contrast difference. The three parameters work together to achieve accurate imaging of the TED.

[0037] In this invention, the X-ray incident angle (theta_s) is -90° to 70°, the X-ray exit angle (theta_d) is 20° to 150°, and the sample stage rotation angle is -180° to 180°.

[0038] Furthermore, when the X-ray incident angle is less than 0°, i.e., in transmission mode, after the imaging step in step S2, a specific location is selected, the scanning range is set along the x-axis, and the exposure time is set to perform tomographic imaging on the penetrating edge dislocation at the selected specific location. Based on the already measured imaging image, a specific location can be selected according to the research intention, especially to study the interaction between TED and other defects (such as BPD). Tomographic imaging can acquire two-dimensional slice images at different x-positions; combined with software reconstruction of three-dimensional tomographic images, the spatial distribution of TED on the surface (such as depth and lateral position) can be accurately located. It can clarify how TED at a specific location undergoes dislocation evolution along the z-direction, such as annihilation, merging, multiplication, and interaction with other dislocations, avoiding misjudgment of surface interference signals. Based on the Bragg diffraction and extinction principles, this invention selects the diffraction vectors that can be clearly imaged by TED, and combines sample section characterization methods to fundamentally solve the problem of weak signal imaging of TED caused by the small Burgers vector and the c-axis dislocation line direction, achieving non-destructive and high-precision characterization of TED in silicon carbide materials.

[0039] In this invention, the scanning range of the x-axis is ±1 to ±5 mm. It should be noted that the scanning range of ±1 to ±5 mm refers to setting any real value between -5 and -1 mm at the starting position and any real value between 1 and 5 mm at the ending position, with the selected specific location as the origin (point O). For example, the starting position can be set to -1 mm and the ending position to 1 mm; or, the starting position can be set to -5 mm and the ending position to 5 mm; or, the starting position can be set to -1 mm and the ending position to 5 mm. The exposure time is 10 to 120 seconds. Too short an exposure time will result in insufficient signal and blurred images; too long an exposure time will increase the risk of radiation damage.

[0040] In this invention, the multi-dimensional iterative optimization includes performing 3 to 7 alternating iterations of omega(ω) optimization and phi(φ) optimization on the X-ray incident angle and the sample stage rotation angle, with the alternating iteration optimization process starting and ending with omega optimization. Specifically, if the alternating iteration optimization process is performed 3 times, the optimization process is omega optimization + phi optimization + omega optimization; if it is performed 5 times, the optimization process is omega optimization + phi optimization + omega optimization + phi optimization + omega optimization; if it is performed 7 times, the optimization process is omega optimization + phi optimization + omega optimization + phi optimization + omega optimization + phi optimization + omega optimization. During the omega optimization process, the peak position is determined by the rocking curve, at which point the diffraction signal is strongest (highest peak intensity, smallest half-width at half-maximum). Since the diffraction vector determines the angle between the incident angle and the exit angle, there is a binding relationship between the X-ray incident angle and the X-ray exit angle; optimizing the X-ray incident angle is equivalent to optimizing the X-ray exit angle.

[0041] After alternating iterative optimization, this invention can also perform small-area snap defect morphology imaging, and determine whether to continue optimizing the current test parameters based on the imaging quality. If the snap image is still blurry or the noise is too high, repeat the process of "adjusting omega optimization / phi optimization → rescanning → evaluating imaging quality" until the best effect is achieved.

[0042] In this invention, curvature correction scanning is included before imaging penetrating edge dislocations in silicon carbide material. Silicon carbide wafers may have basal plane curvature, resulting in inconsistent crystal plane tilt angles in different regions. Without correction, the incident angle of X-rays at the wafer edge differs from that at the center, potentially deviating from the Bragg condition and leading to local TED signal loss or uneven contrast. Curvature correction scanning measures the height distribution on the wafer surface (e.g., using laser ranging) to calculate the tilt angle of each region. During imaging, the sample stage angle or X-ray incident angle is dynamically adjusted to compensate for diffraction angle deviations caused by curvature, ensuring consistent imaging conditions across the entire sample area and avoiding "edge blurring" or "center overexposure" problems due to curvature. Specifically, feature points are selected on the silicon carbide material surface at 5-20 mm intervals, and omega scanning is performed on each point, recording the optimal X-ray incident angle. During formal imaging, the system automatically retrieves and dynamically adjusts the optimal X-ray incident angle for the corresponding feature points based on the current imaging area, thereby achieving high imaging quality.

[0043] In this invention, the scanning speed in the step of imaging penetrating edge dislocations of silicon carbide material is preferably 10~100 mm / min. This invention does not impose any special limitations on the imaging size during the imaging process; preferably, x = ±(30~120) mm and y = ±(30~120) mm.

[0044] In this invention, after step S2, a threshold setting is performed based on defect density recognition software to identify and statistically analyze the imaged penetrating edge dislocations, obtaining density distribution data. This density distribution data is then processed to obtain a density distribution map of the penetrating edge dislocations. First, the dislocation morphology image of the entire large sample is cut into small regions, and the BPD densities identified in all small regions are merged. This step can be done using XRT Micron software. Next, the density of BPDs is identified from defects in the small regions. This step can be done using topography software or toolbox software, etc. Batch processing of the obtained density distribution data yields the density distribution map. In X-ray morphology images, TEDs appear as contrast spots of a specific shape (such as lines or dots), but background noise (such as sample surface contamination or equipment thermal noise) can interfere with identification. When using defect density recognition software, setting a specific threshold helps filter background noise. After filtering, the software automatically identifies the location of TEDs and counts their number, calculating the density (the dimension of density distribution is counts / cm²) based on the area of ​​the imaging region. 2 In this invention, the preferred threshold setting is as follows: the image background parameter is 0.8 to 2.0. This parameter distinguishes defects from background noise. A smaller background parameter makes it easier to identify TED dislocations, but if it is too small, background noise may be mistakenly identified as TED. The TED feature parameter is 20 to 80. This parameter sets the threshold for TED identification; a smaller value makes it easier to identify TED.

[0045] The technical solution of the present invention will be further described below with reference to specific embodiments.

[0046] Example 1 This embodiment provides a non-destructive characterization method for penetrating edge dislocations in silicon carbide materials.

[0047] (1) TED dislocation non-destructive characterization was performed using 6-inch N-type conductive 4H-SiC material with a thickness of 330μm.

[0048] (2) Select diffraction vector = Based on the Bragg diffraction principle, the X-ray incident angle (theta_s=-58.48°), X-ray exit angle (theta_d=113.522°), and sample stage rotation angle (phi=0°) corresponding to the X-ray topography detection system (XRT) were calculated.

[0049] (3) Testing and Imaging: Based on the X-ray Topography System (XRT), the X-ray generator, X-ray detector, and sample stage are adjusted and moved according to the test parameters calculated in step (2). Omega optimization, phi optimization, and omega optimization are performed on the X-ray incident angle and the sample stage rotation angle. After three alternating iterations, the calibrated test parameters are obtained. During omega optimization, the peak position is determined using a rocking curve. With the sample center as the origin, 10 points are selected along the x-axis and y-axis respectively for curvature correction. The imaging size is set to x=±78mm and y=±78mm, and the scanning speed is 20mm / min. Based on the calibrated test parameters, the penetrating edge dislocations of silicon carbide are imaged. The imaging results are as follows: Figure 1 As shown in 'a'.

[0050] (4) Statistical analysis of penetrating edge dislocation density: Based on XRT micron and topography software, the thresholds were set as follows: image background parameter = 1.8, TED feature parameter = 50, and statistical analysis was performed on the imaged TED dislocations. The distribution of penetrating edge dislocations in the silicon carbide sample is as follows. Figure 1 As shown in b in the figure.

[0051] Example 2 This embodiment provides a non-destructive characterization method for penetrating edge dislocations in silicon carbide materials.

[0052] (1) TED dislocation non-destructive characterization was performed using 8-inch semi-insulating 4H-SiC material with a thickness of 500μm.

[0053] (2) Select diffraction vector = Based on the Bragg diffraction principle, the X-ray incident angle (theta_s=-72.64°), X-ray exit angle (theta_d=99.36°), and sample stage rotation angle (phi=0°) corresponding to the X-ray topography detection system (XRT) were calculated.

[0054] (3) Test Imaging: Based on the X-ray Topography System (XRT), the X-ray generator, X-ray detector, and sample stage are adjusted and moved according to the test parameters calculated in step (2). Omega optimization, phi optimization, phi optimization, and omega optimization are performed on the X-ray incident angle and sample stage rotation angle. After five alternating iterations, the calibrated test parameters are obtained. During omega optimization, the peak position is determined using a rocking curve. Then, small-area snap imaging is performed. The current small-area snap imaging quality is good, and dislocation lines within the small area can be clearly distinguished. The imaging size is set to x=±105mm, y=±105mm, and the scanning speed is 10mm / min. Based on the calibrated test parameters, penetrating edge dislocations in silicon carbide are imaged. The imaging results are as follows: Figure 2 As shown in 'a', the TED distribution in small region 1 is as follows: Figure 2 As shown in b, the TED distribution in small region 2 is as follows: Figure 2 As shown in c, the TED distribution in small region 3 is as follows. Figure 2 As shown in d.

[0055] Example 3 This embodiment provides a non-destructive characterization method for penetrating edge dislocations in silicon carbide materials.

[0056] (1) TED dislocation non-destructive characterization was performed using 8-inch N-type conductive 4H-SiC material with a thickness of 550μm.

[0057] (2) Select diffraction vector = Based on the Bragg diffraction principle, the X-ray incident angle (theta_s=-42.12°), X-ray exit angle (theta_d=129.88°), and sample stage rotation angle (phi=0°) corresponding to the X-ray topography detection system (XRT) were calculated.

[0058] (3) Testing and Imaging: Based on the X-ray Topography System (XRT), the X-ray generator, X-ray detector, and sample stage were adjusted and moved according to the test parameters calculated in step (2). Omega optimization, phi optimization, omega optimization, phi optimization, omega optimization, phi optimization, and omega optimization were performed on the X-ray incident angle and sample stage rotation angle. After five alternating iterations, the calibrated test parameters were obtained. During the omega optimization process, the peak position was determined using a rocking curve. The imaging size was set to x = ±78 mm, y = ±78 mm, and the scanning speed was 100 mm / min. Based on the calibrated test parameters, the penetrating edge dislocations of silicon carbide were imaged. The imaging results are as follows: Figure 3 As shown in 'a'.

[0059] (4) Section imaging: In the penetrating edge dislocation image obtained in step (4), select the position corresponding to the red box, set the range along the x-axis to ±1mm, and the y-axis to a fixed 16mm (device limitation), set the exposure time to 30s, and perform tomographic imaging on the penetrating edge dislocation at this position. The imaging result is as follows: Figure 3 b~d in the middle, where Figure 3 In the figure, b represents the distribution of penetrating edge dislocations at a distance of 20 μm from the surface. Figure 3 In this context, 'c' represents the distribution of penetrating edge dislocations at a distance of 200 μm from the surface. Figure 3 In the figure, d represents the distribution of penetrating edge dislocations at a distance of 400 μm from the surface.

[0060] Example 4 The difference between this embodiment and Embodiment 1 is that the scanning speed during imaging in this embodiment is 30 mm / min. The resulting TED distribution is as follows. Figure 4 As shown in 'a', the distribution pattern is that there are more in the middle and fewer at the edges, with an average density of 1757 count / cm². 2 .

[0061] Example 5 The difference between this embodiment and Embodiment 1 is that the sample in this embodiment is an 8-inch N-type conductive SiC material, and the scanning speed during imaging is 40 mm / min. The resulting TED distribution is as follows. Figure 5 As shown in 'a', the distribution pattern is that there are more in the middle and fewer at the edges, with an average density of 1983 count / cm². 2 .

[0062] Comparative Example 1 Compared with Example 4, this comparative example uses KOH wet etching to characterize TED dislocations in adjacent wafers of the same ingot. The resulting TED density distribution is as follows. Figure 4 As shown in b, the distribution pattern is that there are more in the middle and fewer at the edges, with an average density of 1860 counts / cm². 2 The test results were basically consistent with those of Example 4.

[0063] Comparative Example 2 Compared with Example 5, this comparative example uses KOH wet etching to characterize TED dislocations in adjacent wafers of the same ingot. The resulting TED density distribution is as follows. Figure 5 As shown in b, the distribution pattern is also more in the middle and less at the edges, with an average density of 1905 counts / cm². 2 The test results were basically consistent with those of Example 5.

[0064] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. 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 non-destructive characterization method for penetrating edge dislocations in silicon carbide materials, characterized in that, Includes the following steps: S1, Select = As the diffraction vector, the test parameters based on the Bragg diffraction principle are calculated for the X-ray topography detection system. S2. Initial settings are made based on the calculated test parameters. The test parameters are dynamically calibrated through multi-dimensional iterative optimization. Based on the calibrated test parameters, the penetrating edge dislocations of silicon carbide material are imaged.

2. The non-destructive characterization method for penetrating edge dislocations in silicon carbide materials as described in claim 1, characterized in that, The diffraction vector is = , = or = .

3. The non-destructive characterization method for penetrating edge dislocations in silicon carbide materials as described in claim 1, characterized in that, The test parameters of the X-ray morphology detection system include the X-ray incident angle, the X-ray exit angle, and the sample stage rotation angle; the X-ray incident angle is -90° to 70°, the X-ray exit angle is 20° to 150°, and the sample stage rotation angle is -180° to 180°.

4. The non-destructive characterization method for penetrating edge dislocations in silicon carbide materials as described in claim 3, characterized in that, When the X-ray incident angle is less than 0°, after the imaging step in step S2, the method further includes selecting a specific location, setting the scanning range along the x-axis, setting the exposure time, and performing tomographic imaging on the penetrating edge dislocation at the selected specific location.

5. The non-destructive characterization method for penetrating edge dislocations in silicon carbide materials as described in claim 4, characterized in that, The scanning range of the x-axis is ±1~±5mm, and the exposure time is 10~120s.

6. The non-destructive characterization method for penetrating edge dislocations in silicon carbide materials as described in claim 1, characterized in that, The multi-dimensional iterative optimization includes performing 3 to 7 alternating iterations of omega optimization and phi optimization on the X-ray incident angle and the sample stage rotation angle, with the alternating iteration optimization process starting and ending with omega optimization.

7. The non-destructive characterization method for penetrating edge dislocations in silicon carbide materials as described in claim 6, characterized in that, After the alternating iterative optimization steps, the process also includes small-area snap defect morphology imaging, and determining whether further optimization of the current test parameters is needed based on the imaging quality.

8. The non-destructive characterization method for penetrating edge dislocations in silicon carbide materials as described in claim 1, characterized in that, The imaging of penetrating edge dislocations in silicon carbide material includes curvature correction scanning before the imaging process.

9. The non-destructive characterization method for penetrating edge dislocations in silicon carbide materials as described in claim 1, characterized in that, In the step of imaging penetrating edge dislocations in silicon carbide material, the scanning speed is preferably 10~100mm / min.

10. The non-destructive characterization method for penetrating edge dislocations in silicon carbide materials as described in claim 1, characterized in that, After step S2, the method further includes setting a threshold based on defect density recognition software, identifying and statistically analyzing the imaged penetrating edge dislocations to obtain density distribution data, and processing the density distribution data to obtain a density distribution map of penetrating edge dislocations.

Images