Method and apparatus for counting defects on silicon carbide wafers
By dividing the silicon carbide wafer image into cells, the area of the corrosion pits and the eccentricity are identified, which solves the problems of low efficiency and poor accuracy of defect density statistics in silicon carbide wafers in the prior art, and realizes more refined defect density statistics.
Patent Information
- Application Number
- CN202111648036.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2021-12-30
- Publication Date
- 2025-12-19
- Estimated Expiration
- 2041-12-30
AI Technical Summary
Existing technologies have low efficiency in statistical analysis of silicon carbide wafer defect density and poor accuracy in identification, making it impossible to accurately obtain wafer defect density.
A statistical method for defect density in silicon carbide wafers is adopted. By acquiring images of the wafer after etching, dividing them into multiple cells, identifying the area and eccentricity of the etching pits in each cell, determining the types of defects and calculating their number, and using the ratio of defect type to cell area to determine the defect density.
It improves the accuracy and efficiency of defect identification and enables precise statistical analysis of the defect density distribution of silicon carbide wafers.
Smart Images
Figure CN116413270B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of semiconductor materials, in particular to a silicon carbide wafer defect density statistical method and a silicon carbide wafer defect density statistical device. BACKGROUND
[0002] Silicon carbide is a typical third-generation wide-bandgap semiconductor, which is widely studied and applied in high-temperature, high-frequency, high-power and radiation-resistant fields due to its high saturation electron drift rate, high breakdown field strength and high thermal conductivity. The most mature technology for commercial preparation of silicon carbide substrate is the physical vapor transport (PVT) method. The difficulty of growing silicon carbide single crystal by the PVT method lies in the control of crystal quality, and only when the defect is below a certain level can it be used for subsequent device preparation. The density statistics of defects in silicon carbide crystals not only can evaluate the crystal quality, but also can provide important basis for studying the growth mechanism and analyzing the failure of devices, which has very important significance for basic research and industrial production.
[0003] A commonly used crystal quality characterization method is chemical etching method, that is, using molten alkali, such as KOH, to etch the silicon carbide wafer. The stress is larger in the place with defects, so the etching speed is faster than that in the place without defects, so etching pits are formed. Different defects will form etching pits of different shapes and sizes, and then the etching pits are counted by an optical microscope. However, in the prior art, the pictures of different positions of the wafer are taken by a microscope for manual counting, which is low in efficiency, and only a small range of defect density on the wafer can be obtained, and the accuracy of manual identification of defects is poor for etching pits with similar sizes, so that accurate wafer defect density cannot be obtained. SUMMARY
[0004] In view of the technical problems in the prior art that manual counting is low in efficiency and poor in accuracy of identifying defects, and accurate wafer defect density cannot be obtained, the present application provides a silicon carbide wafer defect density statistical method and a silicon carbide wafer defect density statistical device, which can improve the accuracy and efficiency of defect identification by using the method for more accurate counting.
[0005] To achieve the above object, the first aspect of the present application provides a method for counting defect density of a silicon carbide wafer, comprising the following steps: obtaining a wafer image of a silicon carbide wafer after etching treatment; dividing the wafer image into a plurality of unit cells; identifying defect density of each unit cell, comprising: determining etching pit area and etching pit eccentricity of each etching pit in the unit cell; determining defect type according to the etching pit area and the etching pit eccentricity, and counting number of each defect type, wherein the defect type comprises: screw dislocation defect, edge dislocation defect and basal plane dislocation defect; determining defect density of each defect type in the unit cell according to ratio of the number of each defect type to area of the unit cell; and determining defect density distribution of the silicon carbide wafer according to defect density of each defect type in the corresponding unit cell.
[0006] Further, the determining defect type according to the etching pit area and the etching pit eccentricity, and the counting number of each defect type, comprises: identifying defect type of etching pit with etching pit area between a first area range as screw dislocation defect; and counting number of the screw dislocation defect.
[0007] Further, the determining defect type according to the etching pit area and the etching pit eccentricity, and the counting number of each defect type, comprises: identifying etching pit with etching pit area greater than maximum value of the first area range as superimposed etching pit, and identifying etching pit with etching pit area between a second area range and etching pit eccentricity less than a set eccentricity as independent etching pit; identifying defect type of the superimposed etching pit and the independent etching pit as edge dislocation defect; counting first number of the edge dislocation defect in the superimposed etching pit, and counting second number of the independent etching pit, wherein maximum value of the second area range is less than or equal to minimum value of the first area range; and taking sum of the first number and the second number as number of the edge dislocation defect of the unit cell.
[0008] Further, the counting first number of the edge dislocation defect in the superimposed etching pit comprises:
[0009]
[0010] wherein m is the first number of the edge dislocation defect in the superimposed etching pit, q is number of the superimposed etching pit, S1 and S2 are boundary values of the second area range, p is a constant, S i is area of the superimposed etching pit.
[0011] Further, the second determining unit is specifically configured to: identify the defect type of the etch pit with the etch pit area between the first area range as the screw dislocation defect; and calculate the number of the screw dislocation defect.
[0012] The second aspect of the present application provides a silicon carbide wafer defect density statistical device, which comprises: an acquisition unit configured to acquire a wafer image of a silicon carbide wafer after etching treatment; a division unit configured to divide the wafer image into a plurality of unit cells; an identification unit configured to identify the defect density of each unit cell, comprising: a first determining unit configured to determine the etch pit area and the etch pit centrifugal rate of each etch pit in the unit cell; a second determining unit configured to determine the defect type according to the etch pit area and the etch pit centrifugal rate, and calculate the number of each defect type, wherein the defect type comprises: a screw dislocation defect, a screw edge dislocation defect and a basal plane dislocation defect; and a third determining unit configured to determine the defect density of each defect type in the unit cell according to the ratio of the number of each defect type to the area of the unit cell; and a defect density distribution determining unit configured to determine the defect density distribution of the silicon carbide wafer according to the defect density of each defect type in the corresponding unit cell.
[0013] Further, the second determining unit is specifically configured to: identify the defect type of the etch pit with the etch pit area between the first area range as the screw dislocation defect; and calculate the number of the screw dislocation defect.
[0014] Further, the second determining unit is specifically configured to: identify the etch pit with the etch pit area greater than the maximum value of the first area range as a superimposed etch pit, and identify the etch pit with the etch pit area between the second area range and the etch pit centrifugal rate less than a set centrifugal rate as an independent etch pit, and identify the defect type of the superimposed etch pit and the independent etch pit as the screw edge dislocation defect; calculate the first number of the screw edge dislocation defect in the superimposed etch pit, and calculate the second number of the independent etch pit, wherein the maximum value of the second area range is less than or equal to the minimum value of the first area range; and take the sum of the first number and the second number as the number of the screw edge dislocation defect of the unit cell.
[0015] Further, the second determining unit is specifically configured to calculate the first number of the screw edge dislocation defect in the superimposed etch pit as follows:
[0016]
[0017] wherein m is a first number of the screw dislocation defects in the superimposed etch pits, q is a number of the superimposed etch pits, S1 and S2 are boundary values of a second area range, p is a constant, S i is an area of the superimposed etch pits.
[0018] Further, the second determining unit is specifically configured to: identify that a defect type of the etch pit with an etch pit area between a third area range and an etch pit eccentricity greater than or equal to the set eccentricity is a basal plane dislocation defect, wherein a minimum value of the second area range is greater than or equal to a minimum value of the third area range, and a maximum value of the second area range and a maximum value of the third area range are the same; and calculate a number of the basal plane dislocation defects.
[0019] By the technical solution provided by the present application, the present application has at least the following technical effects:
[0020] The silicon carbide wafer defect density statistical method provided by the present application can identify defects more finely, and improve the accuracy and efficiency of defect identification.
[0021] Other features and advantages of the present application will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF DRAWINGS
[0022] The accompanying drawings are included to provide a further understanding of the embodiments of the present application, and constitute a part of the specification, and are used together with the following specific embodiments to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the drawings:
[0023] Figure 1 A flowchart of the silicon carbide wafer defect density statistical method provided by the embodiments of the present application;
[0024] Figure 2 A schematic diagram of the defect density distribution of the screw dislocation defects in the silicon carbide wafer defect density statistical method provided by the embodiments of the present application;
[0025] Figure 3A schematic diagram of a defect density distribution of a screw dislocation defect in a silicon carbide wafer defect density statistical method provided by the embodiment of the present application;
[0026] Figure 4 A schematic diagram of a defect density distribution of a base vector surface dislocation defect in a silicon carbide wafer defect density statistical method provided by the embodiment of the present application;
[0027] Figure 5 A schematic diagram of a silicon carbide wafer defect density statistical device provided by the embodiment of the present application. DETAILED DESCRIPTION
[0028] The specific embodiments of the embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely intended to illustrate and explain the embodiments of the present application, and are not intended to limit the embodiments of the present application.
[0029] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.
[0030] In the present application, the orientation words such as "upper", "lower", "top", "bottom" are generally used for the directions shown in the drawings or the positional relationship between the components in the vertical, perpendicular or gravity direction, unless otherwise stated.
[0031] The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.
[0032] Please refer to Figure 1 The embodiment of the present application provides a silicon carbide wafer defect density statistical method, which comprises the following steps: S101: obtaining a wafer image of a silicon carbide wafer after etching treatment; S102: dividing the wafer image into a plurality of unit cells; S103: identifying the defect density of each unit cell, comprising: determining the etching pit area and the etching pit eccentricity of each etching pit in the unit cell; determining the defect type according to the etching pit area and the etching pit eccentricity, and calculating the number of each defect type, wherein the defect type comprises a screw dislocation defect, a screw edge dislocation defect and a base vector surface dislocation defect; determining the defect density of each defect type in the unit cell according to the ratio of the number of each defect type to the area of the unit cell; S104: determining the defect density distribution of the silicon carbide wafer according to the defect density of each defect type in the corresponding unit cell.
[0033] Specifically, in the embodiment of the present application, after etching the silicon carbide wafer, a microscope is used to select an appropriate magnification (for example, 100 times or more) to ensure that the etching pits are clearly visible, and then scanning is performed at a certain magnification to obtain multiple images, which are combined to obtain a complete image of the etched wafer. The wafer image is divided into multiple cells (for example, 3cm*3cm), and defect identification is performed according to the cells. In each cell identification, the etching pit area and the etching pit eccentricity of each etching pit in the cell are determined, and the types of defects include threading screw dislocation defects (TSD), threading edge dislocation defects (TED) and basal plane dislocation defects (BPD). The areas of threading edge dislocation defects and basal plane dislocation defects are relatively small, and it is not easy to determine the defect type only by the area range, so the etching pit eccentricity is introduced, and the threading screw dislocation defects, the threading edge dislocation defects and the basal plane dislocation defects are identified by combining the etching pit area and the etching pit eccentricity. After determining the corresponding defect type of each etching pit, the number of each defect type in the cell is counted, and the defect density of each defect type in the cell is determined according to the ratio of the number of each defect type to the area of the cell. In turn, according to the above method, the defect densities of all cells are counted, and the defect density distributions of the three defects on the silicon carbide wafer are obtained. Please refer to Figures 2-4 , Figures 2-4 The density distributions of the threading screw dislocation defects, the threading edge dislocation defects and the basal plane dislocation defects on the silicon carbide wafer are shown in Figures 1, 2 and 3, respectively.
[0034] According to the silicon carbide wafer defect density counting method provided by the present application, the defect identification is more accurate, and the accuracy and efficiency of defect identification are improved.
[0035] Further, the defect type is determined according to the etching pit area and the etching pit eccentricity, and the number of each defect type is calculated, including: identifying the defect type of the etching pit with an etching pit area in a first area range as a threading screw dislocation defect; and calculating the number of the threading screw dislocation defects.
[0036] Specifically, in the embodiment of the present application, the etching pit area of the threading screw dislocation defect is the largest, so the first area range can be determined, for example, the first area range is 1962.5-3846.5μm 2 The defect type of the etching pit with an etching pit area in the first area range is determined as a threading screw dislocation defect, and the number of the threading screw dislocation defects is counted.
[0037] Further, the defect types are determined based on the corrosion pit area and the corrosion pit eccentricity, and the number of each defect type is calculated, including: identifying corrosion pits with an area greater than the maximum value of the first area range as superimposed corrosion pits, and identifying corrosion pits with an area between the second area range and an eccentricity less than a set eccentricity as independent corrosion pits; identifying the defect types of the superimposed corrosion pits and the independent corrosion pits as spiral edge dislocation defects; calculating a first number of spiral edge dislocation defects in the superimposed corrosion pits, and calculating a second number of independent corrosion pits, wherein the maximum value of the second area range is less than or equal to the minimum value of the first area range; and using the sum of the first number and the second number as the number of spiral edge dislocation defects in that cell.
[0038] Specifically, in this embodiment of the invention, multiple spiral edge dislocation defects may overlap. It is necessary to identify both individual spiral edge dislocation defects and the overlapped spiral edge dislocation defects separately, and then count the total number of spiral edge dislocation defects. For a single spiral edge dislocation defect, the area of the spiral edge dislocation defect overlaps with the area of the basis plane dislocation defect. Therefore, spiral edge dislocation defects and basis plane dislocation defects can be identified by combining the area of the corrosion pit with the eccentricity of the corrosion pit. A second area range is determined, for example, the second area range is 706.5-1962.5 μm. 2 Corrosion pits whose area is within the second area range and whose eccentricity is less than a set eccentricity are identified as independent corrosion pits corresponding to a single spiral edge dislocation defect. In this embodiment of the invention, the set eccentricity is 0.5.
[0039] For superimposed spiral edge dislocation defects, the area of the superimposed corrosion pit is relatively large. The corrosion pit of multiple superimposed spiral edge dislocation defects can be identified by the area of the corrosion pit. The corrosion pit with the largest area, that is, the corrosion pit that is greater than the maximum value of the first area range, is identified as the superimposed corrosion pit. The defect type of superimposed corrosion pit and independent corrosion pit is identified as spiral edge dislocation defect.
[0040] After identifying the spiral edge dislocation defects, calculate the first number of spiral edge dislocation defects in the superimposed corrosion pits and the second number of independent corrosion pits. Then, add the first and second numbers together to obtain the number of spiral edge dislocation defects in that cell.
[0041] Furthermore, the first number of spiral edge dislocation defects within the superimposed corrosion pits is calculated in the following manner:
[0042]
[0043] wherein m is a first number of the spiral edge dislocation defects in the superimposed etch pits, q is a number of the superimposed etch pits, S1 and S2 are boundary values of a second area range, p is a constant, preferably p is between 0.6 and 1, further preferably p = 0.8, S i is an area of the superimposed etch pits.
[0044] Further, the second determining unit is specifically configured to: identify the defect type of the etch pit with the etch pit area in the first area range as the spiral screw dislocation defect; and calculate the number of the spiral screw dislocation defects.
[0045] Specifically, in the embodiment of the present application, the shape of the basal plane dislocation defect is relatively round compared with the shape of the spiral edge dislocation defect, and the defect type of the etch pit with the etch pit area in the third area range and the etch pit centrifugal rate greater than or equal to the set centrifugal rate is the basal plane dislocation defect, and in the embodiment of the present application, the third area range is 314-1962.5 μm 2 .
[0046] Please refer to Figure 5 The second aspect of the present application provides a silicon carbide wafer defect density statistical device, the silicon carbide defect density statistical device comprising: an acquisition unit configured to acquire a wafer image of a silicon carbide wafer after etching treatment; a division unit configured to divide the wafer image into a plurality of unit cells; an identification unit configured to identify the defect density of each unit cell, comprising: a first determining unit configured to determine the etch pit area and the etch pit centrifugal rate of each etch pit in the unit cell; a second determining unit configured to determine the defect type according to the etch pit area and the etch pit centrifugal rate, and calculate the number of each defect type, wherein the defect type comprises: a spiral screw dislocation defect, a spiral edge dislocation defect and a basal plane dislocation defect; a third determining unit configured to determine the defect density of each defect type in the unit cell according to the ratio of the number of each defect type to the area of the unit cell; and a defect density distribution determining unit configured to determine the defect density distribution of the silicon carbide wafer according to the defect density of each defect type in the corresponding unit cell.
[0047] Further, the second determining unit is specifically configured to: identify the defect type of the etch pit with the etch pit area in the first area range as the spiral screw dislocation defect; and calculate the number of the spiral screw dislocation defects.
[0048] Further, the second determining unit is specifically configured to: identify the corrosion pits with an area greater than a maximum value of the first area range as superimposed corrosion pits, and identify the corrosion pits with an area between a second area range and a centrifugal rate less than a set centrifugal rate as independent corrosion pits, and identify the defect type of the superimposed corrosion pits and the independent corrosion pits as screw dislocation defects; calculate a first number of the screw dislocation defects in the superimposed corrosion pits, and calculate a second number of the independent corrosion pits, wherein the maximum value of the second area range is less than or equal to a minimum value of the first area range; and take the sum of the first number and the second number as the number of the screw dislocation defects of the unit cell.
[0049] Further, the second determining unit is specifically configured to calculate the first number of the screw dislocation defects in the superimposed corrosion pits:
[0050]
[0051] wherein m is the first number of the screw dislocation defects in the superimposed corrosion pits, q is the number of the superimposed corrosion pits, S1 and S2 are boundary values of the second area range, p is a constant, S i is the area of the superimposed corrosion pits.
[0052] Further, the second determining unit is specifically configured to: identify the corrosion pits with an area between a third area range and a centrifugal rate greater than or equal to the set centrifugal rate as basal plane dislocation defects, wherein the minimum value of the second area range is greater than or equal to the minimum value of the third area range, and the maximum value of the second area range is the same as the maximum value of the third area range; and calculate the number of the basal plane dislocation defects.
[0053] The preferred embodiments of the present application are described in detail above with reference to the drawings, but the present application is not limited to the specific details in the above-described embodiments, and various simple modifications can be made to the technical solutions of the present application within the technical concept of the present application, and these simple modifications all belong to the protection scope of the present application.
[0054] In addition, it should be noted that each specific technical feature described in the above-described specific embodiments can be combined in any appropriate manner without contradiction, and in order to avoid unnecessary repetition, the present application will not describe various possible combinations again.
[0055] In addition, various different embodiments of the present application can also be combined in any appropriate manner, as long as it does not deviate from the technical concept of the present application, and it should be considered as disclosed by the present application.
Claims
1. A method of statistical analysis of defects in a silicon carbide wafer, comprising: The silicon carbide wafer defect density statistical method comprises: obtaining a wafer image of a silicon carbide wafer after etching treatment; dividing the wafer image into a plurality of cells; identifying the defect density of each cell, comprising: determining the etching pit area and the etching pit eccentricity of each etching pit in the cell; According to the corrosion pit area and the corrosion pit eccentricity, a defect type is determined, and the number of each defect type is calculated, wherein the defect type includes a screw screw dislocation defect, a screw edge dislocation defect, and a base vector surface dislocation defect; a corrosion pit with an area greater than a maximum value of a first area range is identified as a superimposed corrosion pit, and a corrosion pit with an area between a second area range and an eccentricity less than a set eccentricity is identified as an independent corrosion pit, and the defect type of the superimposed corrosion pit and the independent corrosion pit is a screw edge dislocation defect; wherein the set eccentricity is 0.5; a first number of screw edge dislocation defects in the superimposed corrosion pit is calculated, and a second number of the independent corrosion pit is calculated, wherein the maximum value of the second area range is less than or equal to the minimum value of the first area range; the first number is calculated by the following formula: ; wherein m is the first number of screw edge dislocation defects in the superimposed corrosion pit, q is the number of superimposed corrosion pits, S1 and S2 are boundary values of the second area range, p is a constant, S i is the area of the superimposed corrosion pit; and the sum of the first number and the second number is taken as the number of screw edge dislocation defects of the unit cell. determining the defect density of each defect type in the cell according to the ratio of the number of each defect type to the area of the cell; determining the defect density distribution of the silicon carbide wafer according to the defect density of each defect type in the corresponding cell.
2. The method of claim 1, wherein: According to the etching pit area and the etching pit eccentricity, the defect type is determined, and the number of each defect type is calculated, comprising: identifying the defect type of the etching pit with the etching pit area in the first area range as a screw dislocation defect; calculating the number of the screw dislocation defects.
3. The method of claim 1, wherein: According to the etching pit area and the etching pit eccentricity, the defect type is determined, and the number of each defect type is calculated, comprising: identifying the defect type of the etching pit with the etching pit area in the third area range and the etching pit eccentricity greater than or equal to the set eccentricity as a basal plane dislocation defect, wherein the minimum value of the second area range is greater than or equal to the minimum value of the third area range, and the maximum value of the second area range and the maximum value of the third area range are the same; calculating the number of the basal plane dislocation defects.
4. A silicon carbide wafer defect density statistical device, comprising: The silicon carbide defect density statistical device comprises: an obtaining unit configured to obtain a wafer image of a silicon carbide wafer after etching treatment; a dividing unit configured to divide the wafer image into a plurality of cells; The recognition unit is used for recognizing the defect density of each cell, comprising: a first determining unit used for determining the etch pit area and the etch pit eccentricity of each etch pit in the cell; a second determining unit used for determining the defect type according to the etch pit area and the etch pit eccentricity and calculating the number of each defect type, wherein the defect type comprises a screw dislocation defect, a spiral edge dislocation defect and a basal plane dislocation defect; the etch pit with an etch pit area greater than the maximum value of a first area range is recognized as a superimposed etch pit, and the etch pit with an etch pit area between a second area range and an etch pit eccentricity less than a set eccentricity is recognized as an independent etch pit, and the defect type of the superimposed etch pit and the independent etch pit is the spiral edge dislocation defect; wherein the set eccentricity is 0.5; a first number of the spiral edge dislocation defects in the superimposed etch pit is calculated, and a second number of the independent etch pit is calculated, wherein the maximum value of the second area range is less than or equal to the minimum value of the first area range; the first number is calculated by the following formula: ; wherein m is the first number of the spiral edge dislocation defects in the superimposed etch pit, q is the number of the superimposed etch pit, S1 and S2 are the boundary values of the second area range, p is a constant, S i is the area of the superimposed etch pit; and the sum of the first number and the second number is taken as the number of the spiral edge dislocation defects of the cell. a third determining unit configured to determine the defect density of each defect type in the cell according to the ratio of the number of each defect type to the area of the cell; a defect density distribution determining unit configured to determine the defect density distribution of the silicon carbide wafer according to the defect density of each defect type in the corresponding cell.
5. The silicon carbide wafer defect density statistical device of claim 4, wherein, The second determining unit is specifically configured to: identify the defect type of the etching pit with the etching pit area in the first area range as a screw dislocation defect; calculate the number of the screw dislocation defects.
6. The silicon carbide wafer defect density statistical device of claim 4, wherein, The second determining unit is specifically configured to: identify the defect type of the etching pit with the etching pit area in the third area range and the etching pit eccentricity greater than or equal to the set eccentricity as a basal plane dislocation defect, wherein the minimum value of the second area range is greater than or equal to the minimum value of the third area range, and the maximum value of the second area range and the maximum value of the third area range are the same; calculate the number of the basal plane dislocation defects. The second determining unit is specifically configured to: identify the defect type of the etching pit with the etching pit area in the first area range as a screw dislocation defect; calculate the number of the screw dislocation defects. The second determining unit is specifically configured to: identify the defect type of the etching pit with the etching pit area in the third area range and the etching pit eccentricity greater than or equal to the set eccentricity as a basal plane dislocation defect, wherein the minimum value of the second area range is greater than or equal to the minimum value of the third area range, and the maximum value of the second area range and the maximum value of the third area range are the same; calculate the number of the basal plane dislocation defects.