Monocrystalline silicon crystal defect distribution detection method

Abstract

The invention provides a monocrystalline silicon crystal defect distribution detection method which comprises the following steps: cutting a monocrystalline silicon raw material to obtain a crude sample, and pretreating the crude sample to obtain a pretreated sample; sequentially carrying out primary corrosion, thermal oxidation treatment, secondary corrosion and defect corrosion on the pretreated sample to obtain a sample to be detected; acquiring a plurality of image data of the surface of the to-be-tested sample by using an optical microscope, respectively identifying the crystal defect density in each image data, and recording a position density associated data set according to the test position of the acquired image data; and based on the position density associated data group, drawing a defect radial distribution diagram taking the test position as an abscissa and the defect density as an ordinate, and confirming the distribution mode of the monocrystalline silicon crystal defects. The detection method disclosed by the invention is simple to operate, pollution is prevented from being introduced, the distribution condition of crystal defects can be rapidly and accurately detected, and guidance is provided for adjustment of a crystal growth process.

Description

Technical Field

[0001] This invention belongs to the field of semiconductor material testing technology, and relates to a method for detecting the defect distribution in single-crystal silicon crystals. Background Technology

[0002] Currently, the growth processes for single-crystal silicon include the Czochralski method and the zone melting method. The Czochralski method uses a seed crystal as a template, allowing molten silicon to grow epitaxially according to the crystal structure of the seed crystal, enabling the growth of large-diameter silicon ingots at a relatively low cost. The zone melting method utilizes a moving, narrow molten zone to locally melt and sequentially recrystallize a polycrystalline silicon rod, achieving extremely high-purity single crystals. In single-crystal growth processes, surface defects are unavoidable, and different process methods can generate different primary crystal defects, such as interstitial defects or vacancy defects.

[0003] When the process parameters are optimally adjusted, a perfect crystal will exist, free of both vacancy and interstitial defects. However, even with a perfect crystal, it's necessary to consider whether slight fluctuations in the process will cause defects to form closer to vacancy or interstitial defects. This necessitates a method to determine which region the distribution of the crystal's native defects is closer to.

[0004] Therefore, a method for evaluating the defect distribution of monocrystalline silicon is needed to guide the process and adjust to achieve a stable monocrystalline silicon growth process. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a method for detecting the distribution of defects in monocrystalline silicon crystals. This method is simple to operate, pollution-free, and can quickly evaluate the distribution of crystal defects, providing guidance for rapidly adjusting the monocrystalline silicon growth process to produce high-quality monocrystalline silicon crystals.

[0006] To achieve this objective, the present invention adopts the following technical solution:

[0007] This invention provides a method for detecting the distribution of defects in monocrystalline silicon. The method includes: cutting monocrystalline silicon raw material to obtain a rough sample; pre-treating the rough sample to obtain a pre-treated sample; sequentially performing primary etching, thermal oxidation, secondary etching, and defect etching on the pre-treated sample to obtain a test sample; acquiring several image data of the surface of the test sample using an optical microscope, and identifying the crystal defect density in each image data; recording a position-density correlation data set based on the test position of the acquired image data; and based on the position-density correlation data set, plotting a radial distribution map of defects with the test position as the abscissa and the defect density as the ordinate, and confirming the distribution pattern of defects in the monocrystalline silicon crystal.

[0008] As a preferred embodiment of the present invention, the curvature of the rough sample obtained after cutting is ≤50μm.

[0009] As a preferred embodiment of the present invention, the warpage of the coarse sample is ≤50μm.

[0010] It should be noted that the curvature is used to measure the degree of uniform, spherical curvature of the cut coarse sample; the warp is used to measure the degree of non-uniform, complex torsional deformation of the cut coarse sample.

[0011] As a preferred embodiment of the present invention, the pretreatment includes sequential surface grinding, cleaning and drying.

[0012] As one embodiment of the present invention, after the surface grinding is completed, the TTV (Total Thickness Variation) of the rough sample surface is ≤20μm.

[0013] In one embodiment of the present invention, the cleaning process is carried out using a cleaning agent.

[0014] In one embodiment of the present invention, the cleaning agent includes a neutral cleaning agent.

[0015] As one embodiment of the present invention, the cleaning process takes 40 to 60 minutes, for example, 40 minutes, 45 minutes, 50 minutes, 55 minutes or 60 minutes, but is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0016] As one embodiment of the present invention, the drying temperature is 40~60℃, for example, it can be 40℃, 45℃, 50℃, 55℃ or 60℃, but it is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0017] As a preferred embodiment of the present invention, the corrosion rate of the first corrosion is >1μm / s, and the fluctuation range is ±0.2μm / s.

[0018] As one embodiment of the present invention, the removal amount of the first corrosion is 50~110μm, for example, it can be 50μm, 60μm, 70μm, 80μm, 90μm, 100μm or 110μm, but it is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0019] In one embodiment of the present invention, after the first corrosion is completed, the heavy metal content in the pretreated sample is <1×10⁻⁶. 10 atoms / cm 2 .

[0020] As a preferred technical solution of the present invention, the thermal oxidation treatment includes: placing the pretreated sample in a heat treatment device at a set temperature for a first heating, and after reaching the first temperature, performing a first stage of isothermal oxidation, followed by a second heating to reach the second temperature, and then performing a second stage of isothermal oxidation.

[0021] As one embodiment of the present invention, the set temperature is 750~820℃, for example, it can be 750℃, 760℃, 770℃, 780℃, 785℃, 790℃, 800℃, 805℃, 810℃, 815℃ or 820℃, but it is not limited to the listed values, and other unlisted values ​​within this range are also applicable.

[0022] As one embodiment of the present invention, the heating rate of the single heating is 4~8℃ / min, for example, it can be 4.0℃ / min, 4.5℃ / min, 5.0℃ / min, 5.5℃ / min, 6.0℃ / min, 6.5℃ / min, 7.0℃ / min, 7.5℃ / min or 8.0℃ / min, but is not limited to the listed values, other unlisted values ​​within this range are also applicable.

[0023] As one embodiment of the present invention, the first temperature is 850~900℃, for example, it can be 850℃, 855℃, 860℃, 865℃, 870℃, 875℃, 880℃, 885℃, 890℃, 895℃ or 900℃, but is not limited to the listed values, other unlisted values ​​within this range are also applicable.

[0024] As one embodiment of the present invention, the isothermal oxidation period is 10 to 20 hours, for example, it can be 10 hours, 11 hours, 12 hours, 13 hours, 14 hours, 15 hours, 16 hours, 17 hours, 18 hours, 19 hours or 20 hours, but it is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0025] As one embodiment of the present invention, the heating rate of the secondary heating is 4~8℃ / min, for example, it can be 4.0℃ / min, 4.5℃ / min, 5.0℃ / min, 5.5℃ / min, 6.0℃ / min, 6.5℃ / min, 7.0℃ / min, 7.5℃ / min or 8.0℃ / min, but is not limited to the listed values, other unlisted values ​​within this range are also applicable.

[0026] As one embodiment of the present invention, the second temperature is 1000~1100℃, for example, it can be 1000℃, 1010℃, 1020℃, 1030℃, 1040℃, 1050℃, 1060℃, 1080℃, 1080℃ or 1100℃, but is not limited to the listed values, other unlisted values ​​within this range are also applicable.

[0027] As one embodiment of the present invention, the time for the two-stage isothermal oxidation is 1 to 4 hours, for example, it can be 1.0h, 1.5h, 2.0h, 2.5h, 3.0h, 3.5h or 4.0h, but it is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0028] As one embodiment of the present invention, after the two-stage isothermal oxidation is completed, the temperature inside the heat treatment device is reduced to 750-820°C at a cooling rate of 3-5°C / min, and then the pretreated sample is taken out.

[0029] The cooling rate can be 3.0℃ / min, 3.2℃ / min, 3.5℃ / min, 3.8℃ / min, 4.0℃ / min, 4.2℃ / min, 4.5℃ / min, 4.8℃ / min or 5.0℃ / min, but is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0030] The temperature reduction achieved in the heat treatment apparatus can be 750°C, 760°C, 770°C, 780°C, 790°C, 800°C, 810°C, or 820°C, but is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0031] As a preferred embodiment of the present invention, the corrosion rate of the secondary corrosion is >1μm / s, and the fluctuation range is ±0.2μm / s.

[0032] As one embodiment of the present invention, the removal amount of the secondary corrosion is 150~250μm, for example, it can be 150μm, 160μm, 170μm, 180μm, 190μm, 200μm, 210μm, 220μm, 230μm, 240μm or 250μm, but is not limited to the listed values, and other unlisted values ​​within this range are also applicable.

[0033] As one embodiment of the present invention, after the secondary corrosion is completed, the TTV of the pretreated sample surface is <20μm.

[0034] As a preferred embodiment of the present invention, the corrosion rate of the defect corrosion is ≤1μm / s.

[0035] As one embodiment of the present invention, the removal amount of the defect corrosion is 4~8μm, for example, it can be 4μm, 4.5μm, 5μm, 5.5μm, 6μm, 6.5μm, 7μm, 7.5μm or 8μm, but it is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0036] As one embodiment of the present invention, after the defect corrosion is completed, the TTV of the sample surface to be tested is <30μm.

[0037] As a preferred embodiment of the present invention, the method for identifying crystal defect density includes: counting the number of all crystal defects on a single image data, and obtaining the crystal defect density based on the field of view area of ​​the corresponding image data.

[0038] In one embodiment of the present invention, the optical microscope is configured to continuously scan and capture images of the surface of the sample to be tested, thereby obtaining the plurality of image data.

[0039] As a preferred embodiment of the present invention, the method for confirming the distribution pattern of defects in a single-crystal silicon crystal includes: identifying a continuous region where the number of defects is 0 based on a defect radial distribution map as a target region; automatically dividing the defect radial distribution map into three sub-regions based on the target region; the sub-region where the target region is located is a single-crystal silicon crystal without defect distribution; identifying the position of the point with the highest number of defects, and determining that the defect type distributed in the sub-region where the point is located is a hole defect; the defect type distributed in the remaining sub-regions is an interstitial defect.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] This invention provides a method for detecting defect distribution in monocrystalline silicon wafers. By subjecting the monocrystalline silicon wafer to multiple etching and thermal oxidation processes, defect growth within the wafer is fully induced. Detection is then performed using an optical microscope, providing a direct and comprehensive evaluation of the single-crystal defect situation. This detection method is simple to operate, pollution-free, and fast. Combined with precise adjustments to the crystal growth process, it can stably produce high-quality monocrystalline silicon crystals. Attached Figure Description

[0042] Figure 1 This is a flowchart of a method for detecting defect distribution in a single-crystal silicon crystal according to Embodiment 1 of the present invention.

[0043] Figure 2 This is a radial distribution diagram of crystal defect density in Embodiment 1 of the present invention. Detailed Implementation

[0044] It should be understood that in the description of this invention, the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number or frequency of the indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality" or "several" means two or more.

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

[0046] In one specific embodiment, the present invention provides a method for detecting the defect distribution in a single-crystal silicon crystal, the method comprising the following steps.

[0047] Step 1: Cut the single-crystal silicon raw material to obtain a rough sample, and perform pretreatment on the rough sample to obtain a pretreated sample.

[0048] The monocrystalline silicon raw materials mentioned in this invention include, but are not limited to, monocrystalline silicon rods, monocrystalline silicon ingots, or monocrystalline silicon wafers, etc.

[0049] This invention employs a cutting machine to complete the cutting and sampling process, with the cutting and sampling locations being the beginning and end of the crystal segment. The curvature and warpage of the coarse sample after cutting are ≤50μm. This invention strictly controls the microstructure of the sample after cutting to avoid introducing optical distortion during subsequent optical inspection, which would reduce the fidelity of the image data and affect the accuracy of crystal defect analysis.

[0050] In some embodiments, the pretreatment includes sequential surface grinding, cleaning, and drying.

[0051] The surface grinding process employs commonly used grinding pads or discs to polish the sample surface, removing the surface damage layer, correcting geometric parameters, and avoiding the introduction of other contaminants. After the surface grinding is completed, the TTV of the rough sample surface is ≤20μm, resulting in a sample with uniform thickness and a smooth surface.

[0052] The cleaning process involves using a cleaning agent for 40-60 minutes to thoroughly remove residual impurities, ensuring the sample surface is free of particles and crystallized. This prevents the formation of new defects during subsequent thermal oxidation, which could reduce detection accuracy. The cleaning agent includes a neutral cleaning agent, specifically a water-based one.

[0053] The drying process uses nitrogen or argon as the working gas to purge and clean the sample, and is also assisted by infrared heating. Under the condition of 40~60℃, the residual liquid on the sample surface is removed to obtain a dry and water-mark-free pretreated sample.

[0054] Step 2: The pretreated sample is subjected to a first corrosion, a thermal oxidation treatment, a second corrosion, and a defect corrosion treatment in sequence to obtain the sample to be tested.

[0055] The primary etching process removes the damaged or oxide layer from the sample surface, resulting in a clean and contamination-free pretreated sample surface. The etching rate is >1 μm / s, with a fluctuation range of ±0.2 μm / s. The amount of material removed in the primary etching is 50–110 μm. After the primary etching is completed, the heavy metal content in the pretreated sample is <1 × 10⁻⁶. 10 atoms / cm 2 The primary corrosion process can be either wet corrosion or dry corrosion.

[0056] The thermal oxidation treatment method includes: placing the pretreated sample in a heat treatment device at a set temperature for a first heating, and then performing a first stage of isothermal oxidation after reaching the first temperature, followed by a second heating to reach the second temperature, and then performing a second stage of isothermal oxidation.

[0057] The set temperature is 750~820℃; the heating rate of the first heating is 4~8℃ / min; the first temperature is 850~900℃; the first stage of isothermal oxidation lasts 10~20h; the second heating rate is 4~8℃ / min; the second temperature is 1000~1100℃; and the second stage of isothermal oxidation lasts 1~4h. The crystals within the pretreated sample undergo a first stage of isothermal oxidation, resulting in the growth of small defects and their increasing size. Then, a second stage of isothermal oxidation causes these defects to grow into stacking fault defects at high temperatures.

[0058] After the two-stage isothermal oxidation is completed, the temperature inside the heat treatment device is reduced to 750-820°C at a cooling rate of 3-5°C / min, and then the pretreated sample is taken out.

[0059] The secondary etching further removes the corrosion pits formed on the surface of the pretreated sample, enhancing surface smoothness and cleanliness. The etching rate of the secondary etching is >1 μm / s, with a fluctuation range of ±0.2 μm / s. The removal amount by the secondary etching is 150~250 μm, and after the secondary etching is completed, the TTV of the pretreated sample surface is <20 μm. The primary etching process is either wet etching or dry etching.

[0060] The defect etching rate is ≤1 μm / min, used to expose defects on the sample surface. The amount of defect removed by the etching is 4~8 μm. After the defect etching is completed, the TTV of the sample surface is <30 μm.

[0061] Step 3: Use an optical microscope to acquire several image data of the surface of the sample to be tested, and identify the crystal defect density in each image data. Record the location density correlation data group according to the test location of the acquired image data.

[0062] The optical microscope described in this invention is configured to continuously scan and capture images of the surface of the sample to be tested, obtaining a plurality of image data, and can also realize automated image analysis. Based on the TTV state of the oxygen plate to be tested, a laser focusing function can be equipped on the optical microscope.

[0063] The method for identifying crystal defect density includes: counting the number of all crystal defects on a single image data, and converting the number into crystal defect density based on the field of view area of ​​the corresponding image data.

[0064] Step 4: Based on the location density correlation data set, draw a radial distribution map of defects with the test location as the horizontal axis and the defect density as the vertical axis, and confirm the distribution mode of defects in the single crystal silicon.

[0065] Specifically, based on the defect radial distribution map, the continuous area where the number of defects is 0 is identified as the target area; using the target area as a reference, the defect radial distribution map is automatically divided into three sub-regions; the sub-region where the target area is located is a single-crystal silicon crystal with no defect distribution; the location of the point with the highest number of defects is identified, and the defect type distributed in the sub-region where it is located is a hole defect; the defect type distributed in the remaining sub-regions is an interstitial defect, thus obtaining the distribution of the single-crystal silicon crystal.

[0066] The interstitial defects refer to the formation of silicon atoms squeezing into the interstices of the crystal lattice due to thermal vibration, high-energy particle bombardment, or surface implantation. Their aggregation causes stacking faults in the crystal, reducing carrier lifetime in monocrystalline silicon products and leading to degraded product performance. The vacancy defects refer to the formation of missing atoms in the crystal lattice due to thermal vibration or high-energy particle bombardment. Their aggregation within the crystal forms voids, which can easily cause leakage problems during the use of monocrystalline silicon products, reducing product reliability. This invention analyzes the distribution of different defects in monocrystalline silicon crystals, which can guide continuous improvement and upgrading of crystal processing to achieve the goal of producing high-quality monocrystalline silicon.

[0067] Example 1

[0068] This embodiment provides a method for detecting defect distribution in single-crystal silicon crystals, such as... Figure 1 As shown, the specific steps include the following.

[0069] (1) Use a cutting machine to cut single crystal silicon raw materials to obtain a rough sample, ensuring that the curvature of the rough sample is ≤50μm and the warp is ≤50μm.

[0070] (2) The surface of the rough sample is ground to make the TTV of its surface ≤ 20 μm.

[0071] (3) The crude sample was cleaned with a water-based cleaning agent for 50 minutes, and then dried at a temperature of 50°C to obtain a dry and water-free pretreated sample.

[0072] (4) The pretreated sample was subjected to one etching process, with the etching rate controlled at 2 μm / s and a fluctuation range of ±0.2 μm / s, and the removal amount being 90 μm, in order to remove the damaged layer on the surface and ensure that the heavy metal content in the pretreated sample is <1×10 after the first etching process. 10 atoms / cm 2 .

[0073] (5) Place the pretreated sample in a heat treatment device at 800℃ and heat it to 850℃ at a rate of 6℃ / min. Then, perform a first-stage isothermal oxidation for 15 hours. Then, heat it to 1000℃ at a rate of 4℃ / min and perform a second-stage isothermal oxidation for 3 hours. After the second-stage isothermal oxidation is completed, cool the temperature in the heat treatment device down to 800℃ at a rate of 4℃ / min and then take out the pretreated sample.

[0074] (6) The pretreated sample is subjected to secondary corrosion, with the corrosion rate controlled at 1.5 μm / s and the fluctuation range being ±0.2 μm / s. The amount removed is 150~250 μm to remove corrosion pits on the sample surface and ensure that the TTV of the pretreated sample surface is <30 μm after the secondary corrosion is completed.

[0075] (7) Defect corrosion is performed on the pretreated sample, and the corrosion rate is controlled at 1 μm / s and the removal amount is 4-8 μm. This is used to expose the defects on the sample surface and ensure that the TTV of the sample surface is <30 μm after the defect corrosion is completed, so as to obtain a dry and clean sample.

[0076] (8) Use an optical microscope to continuously scan the sample to obtain multiple image data of the sample surface. Then count the number of all crystal defects in a single image data and convert it into crystal defect density based on the field of view area of ​​the corresponding image data. That is, obtain the crystal defect density in each image data. Record the location density correlation data group according to the test location of the acquired image data.

[0077] (9) Based on the location density correlation data set, draw a radial distribution map of defects with the test location as the abscissa and the defect density as the ordinate, such as Figure 2 As shown.

[0078] (10) Based on the radial distribution map of defects, the continuous area where the number of defects is 0 is identified as the target area; the radial distribution map of defects is automatically divided into three sub-regions based on the target area; the sub-region where the target area is located is a single-crystal silicon crystal without defect distribution; the position of the point with the highest number of defects is identified, and the defect type distributed in the sub-region where it is located is a hole defect; the defect type distributed in the remaining sub-regions is a gap defect, thus obtaining the defect distribution of the single-crystal silicon crystal.

[0079] Depend on Figure 2 It is easy to see that the number of defects in the continuous region of the sample greater than 50 cm and less than 110 cm is 0, meaning that this region is a defect-free perfect crystal. The highest point of the number of defects in the sample is in the sub-region of 110~150 cm, meaning that the defect type distributed in this sub-region is vacancy defects, while the main defect type distributed in the remaining sub-region of 0~50 cm is interstitial defects.

[0080] Example 2

[0081] This embodiment provides a method for detecting defect distribution in single-crystal silicon crystals, which specifically includes the following steps.

[0082] (1) Use a cutting machine to cut single crystal silicon raw materials to obtain a rough sample, ensuring that the curvature of the rough sample is ≤50μm and the warp is ≤50μm.

[0083] (2) The surface of the rough sample is ground to make the TTV of its surface ≤ 20 μm.

[0084] (3) The crude sample was cleaned with a neutral cleaning agent for 40 minutes and then dried at a temperature of 40°C to obtain a dry and water-free pretreated sample.

[0085] (4) The pretreated sample was subjected to one etching process, with the etching rate controlled at 3 μm / s and a fluctuation range of ±0.2 μm / s, and the removal amount being 80 μm, in order to remove the damaged layer on the surface and ensure that the heavy metal content in the pretreated sample is <1×10 after the first etching process. 10 atoms / cm 2 .

[0086] (5) Place the pretreated sample in a heat treatment device at 750℃ and heat it once to 900℃ at a rate of 8℃ / min. Then, perform a first-stage isothermal oxidation for 20 hours. Then, heat it a second time to 1100℃ at a rate of 3℃ / min and perform a second-stage isothermal oxidation for 1 hour. After the second-stage isothermal oxidation is completed, cool the temperature in the heat treatment device down to 750℃ at a rate of 5℃ / min and then take out the pretreated sample.

[0087] (6) The pretreated sample is subjected to secondary corrosion, with the corrosion rate controlled at 2 μm / s and the fluctuation range being ±0.2 μm / s. The amount removed is 150~250 μm to remove corrosion pits on the sample surface and ensure that the TTV of the pretreated sample surface is 16 μm after the secondary corrosion is completed.

[0088] (7) Defect corrosion is performed on the pretreated sample, and the corrosion rate is controlled at 0.8 μm / s and the removal amount is 7 μm. This is used to expose the defects on the sample surface and ensure that the TTV of the sample surface is 25 μm after the defect corrosion is completed, so as to obtain a dry and clean sample.

[0089] (8) Use an optical microscope to continuously scan the sample to obtain multiple image data of the sample surface. Then count the number of all crystal defects in a single image data and convert it into crystal defect density based on the field of view area of ​​the corresponding image data. That is, obtain the crystal defect density in each image data. Record the location density correlation data group according to the test location of the acquired image data.

[0090] (9) Based on the location density correlation data set, draw a radial distribution map of defects with the test location as the horizontal axis and the defect density as the vertical axis, and confirm the distribution of different types of defects in single-crystal silicon crystals.

[0091] (10) Based on the radial distribution map of defects, the continuous area where the number of defects is 0 is identified as the target area; the radial distribution map of defects is automatically divided into three sub-regions based on the target area; the sub-region where the target area is located is a single-crystal silicon crystal without defect distribution; the position of the point with the highest number of defects is identified, and the defect type distributed in the sub-region where it is located is a hole defect; the defect type distributed in the remaining sub-regions is a gap defect, thus obtaining the defect distribution of the single-crystal silicon crystal.

[0092] Example 3

[0093] This embodiment provides a method for detecting defect distribution in single-crystal silicon crystals, which specifically includes the following steps.

[0094] (1) Use a cutting machine to cut single crystal silicon raw materials to obtain a rough sample, ensuring that the curvature of the rough sample is ≤50μm and the warp is ≤50μm.

[0095] (2) The surface of the rough sample is ground to make the TTV of its surface ≤ 20 μm.

[0096] (3) The crude sample was cleaned with a water-based cleaning agent for 60 minutes and then dried at a temperature of 50°C to obtain a dry and water-free pretreated sample.

[0097] (4) The pretreated sample was subjected to one etching process, with the etching rate controlled at 2.5 μm / s and a fluctuation range of ±0.2 μm / s, and the removal amount was 50 μm, in order to remove the damaged layer on the surface and ensure that the heavy metal content in the pretreated sample was <1×10 after the first etching process. 10 atoms / cm 2 .

[0098] (5) The pretreated sample was placed in a heat treatment device at 820℃ and heated to 860℃ at a rate of 4℃ / min. Then, it was subjected to a first-stage isothermal oxidation for 10 hours. Then, it was heated to 1050℃ at a rate of 5℃ / min and subjected to a second-stage isothermal oxidation for 3 hours. After the second-stage isothermal oxidation was completed, the temperature in the heat treatment device was reduced to 820℃ at a cooling rate of 3℃ / min, and the pretreated sample was removed.

[0099] (6) The pretreated sample is subjected to secondary etching, with the etching rate controlled at 2 μm / s and the fluctuation range being ±0.2 μm / s. The amount removed is 150~250 μm to remove the corrosion pits on the sample surface and ensure that the TTV of the pretreated sample surface is 28 nm after the secondary etching is completed.

[0100] (7) Defect corrosion is performed on the pretreated sample, and the corrosion rate is controlled at 0.8 μm / s and the removal amount is 7 μm. This is used to expose the defects on the sample surface and ensure that the TTV of the sample surface is 28 μm after the defect corrosion is completed, so as to obtain a dry and clean sample.

[0101] (8) Use an optical microscope to continuously scan the sample to obtain multiple image data of the sample surface. Then count the number of all crystal defects in a single image data and convert it into crystal defect density based on the field of view area of ​​the corresponding image data. That is, obtain the crystal defect density in each image data. Record the location density correlation data group according to the test location of the acquired image data.

[0102] (9) Based on the location density correlation data set, draw a radial distribution map of defects with the test location as the horizontal axis and the defect density as the vertical axis, and confirm the distribution of different types of defects in single-crystal silicon crystals.

[0103] (10) Based on the radial distribution map of defects, the continuous area where the number of defects is 0 is identified as the target area; the radial distribution map of defects is automatically divided into three sub-regions based on the target area; the sub-region where the target area is located is a single-crystal silicon crystal without defect distribution; the position of the point with the highest number of defects is identified, and the defect type distributed in the sub-region where it is located is a hole defect; the defect type distributed in the remaining sub-regions is a gap defect, thus obtaining the defect distribution of the single-crystal silicon crystal.

[0104] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A method for detecting defect distribution in a single-crystal silicon crystal, characterized in that, The detection method includes: A rough sample is obtained by cutting single-crystal silicon raw material, and the rough sample is pretreated to obtain a pretreated sample. The pretreated sample was subjected to a first corrosion, a thermal oxidation, a second corrosion, and a defect corrosion in sequence to obtain the sample to be tested; Several image data of the surface of the sample under test are acquired using an optical microscope, and the crystal defect density in each image data is identified. Based on the test location of the acquired image data, the location density correlation data set is recorded. Based on the location density correlation data set, a radial distribution map of defects was plotted with the test location as the horizontal axis and the defect density as the vertical axis, and the distribution mode of single-crystal silicon crystal defects was confirmed.

2. The method for detecting defect distribution in a single-crystal silicon crystal according to claim 1, characterized in that, The curvature of the rough sample obtained after cutting is ≤50μm; Preferably, the warpage of the coarse sample is ≤50μm.

3. The method for detecting defect distribution in a single-crystal silicon crystal according to claim 1 or 2, characterized in that, The pretreatment includes sequential surface grinding, cleaning, and drying. Preferably, after the surface grinding is completed, the TTV of the rough sample surface is ≤20μm; Preferably, the cleaning process uses a cleaning agent. Preferably, the cleaning agent comprises a neutral cleaning agent; Preferably, the cleaning process takes 40-60 minutes; Preferably, the drying temperature is 40~60℃.

4. The method for detecting defect distribution in a single-crystal silicon crystal according to any one of claims 1-3, characterized in that, The corrosion rate of the first corrosion is >1 μm / s, and the fluctuation range is ±0.2 μm / s; Preferably, the amount of material removed by the primary corrosion is 50~110 μm; Preferably, after the first corrosion cycle, the heavy metal content in the pretreated sample is <1×10⁻⁶. 10 atoms / cm 2 .

5. The method for detecting defect distribution in a single-crystal silicon crystal according to any one of claims 1-4, characterized in that, The thermal oxidation process includes: placing the pretreated sample in a heat treatment device at a set temperature for a first heating, and then performing a first stage of isothermal oxidation after reaching the first temperature, followed by a second heating to reach the second temperature, and then performing a second stage of isothermal oxidation.

6. The method for detecting defect distribution in a single-crystal silicon crystal according to claim 5, characterized in that, The set temperature is 750~820℃; Preferably, the heating rate for the first heating step is 4~8℃ / min; Preferably, the first temperature is 850~900℃; Preferably, the isothermal oxidation period is 10-20 hours; Preferably, the heating rate of the secondary heating is 4~8℃ / min; Preferably, the second temperature is 1000~1100℃; Preferably, the time for the two-stage isothermal oxidation is 1-4 hours; Preferably, after the two-stage isothermal oxidation is completed, the temperature inside the heat treatment device is reduced to 750-820°C at a cooling rate of 3-5°C / min before the pretreated sample is taken out.

7. The method for detecting defect distribution in a single-crystal silicon crystal according to any one of claims 1-6, characterized in that, The corrosion rate of the secondary corrosion is >1 μm / s, and the fluctuation range is ±0.2 μm / s; Preferably, the amount of material removed by the secondary corrosion is 150~250 μm; Preferably, after the secondary etching is completed, the TTV of the pretreated sample surface is <20μm.

8. The method for detecting defect distribution in a single-crystal silicon crystal according to any one of claims 1-7, characterized in that, The corrosion rate of the defect corrosion is ≤1μm / min; Preferably, the amount of defect removal by the corrosion is 4~8 μm; Preferably, after the defect corrosion is completed, the TTV of the sample surface is <30μm.

9. The method for detecting defect distribution in a single-crystal silicon crystal according to any one of claims 1-8, characterized in that, The method for identifying crystal defect density includes: counting the number of all crystal defects on a single image data, and obtaining the crystal defect density based on the field of view area of ​​the corresponding image data; Preferably, the optical microscope is configured to continuously scan and capture images of the surface of the sample to be tested, thereby obtaining the plurality of image data.

10. The method for detecting defect distribution in a single-crystal silicon crystal according to any one of claims 1-9, characterized in that, The method for confirming the distribution pattern of defects in a single-crystal silicon crystal includes: Based on the radial distribution map of defects, the continuous area where the number of defects is shown as 0 is identified as the target area. Based on the target area, the radial distribution map of defects is automatically divided into three sub-regions; The sub-region where the target area is located is a single-crystal silicon crystal with no defect distribution; Identify the location of the point with the highest number of defects, and determine that the defect type distributed within the sub-region containing this point is cavitation defects; The defect type distributed in the remaining sub-region is gap defect.

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