Method, device and equipment for identifying line defect of wafer and medium

Abstract

The invention provides a method, a device and equipment for identifying a line defect of a wafer and a medium. The method comprises the following steps: collecting stress fluctuation data of each sampling point on a pre-processed wafer to be detected; selecting candidate sampling points from all the sampling points according to the stress fluctuation data of each sampling point; constructing a linear connected region based on the candidate sampling points; and according to the stress fluctuation data of all the candidate sampling points in the linear connected region, judging whether the linear connected region has a linear defect or not.

Description

Technical Field

[0001] The present disclosure relates to the field of semiconductor manufacturing technology, and in particular to a method, device, equipment and medium for identifying linear defects of a wafer. Background Art

[0002] The manufacturing process of silicon wafers includes processes such as crystal pulling, cutting, grinding, etching, and polishing. In the process of pulling single crystal silicon rods by the Czochralski method (Cz), the silicon atoms slip during the arrangement process, resulting in dislocations in the arrangement of silicon atoms, which in turn form line defects (also called native line defects). If wafers with line defects are used to manufacture semiconductor devices, the leakage current of the semiconductor devices will increase, which will have a very large impact on the quality of the semiconductor devices and may even cause the semiconductor devices to fail.

[0003] In view of this, it is very important to detect line defects in the crystal rod. Summary of the invention

[0004] The present disclosure provides a method, device, equipment and medium for identifying linear defects of a wafer; the method can accurately identify native linear defects formed during the crystal pulling process of the wafer.

[0005] The technical solution of the present disclosure is achieved as follows: In a first aspect, the present disclosure provides a method for identifying line defects of a wafer, the method comprising: Collect stress fluctuation data of each sampling point on the pre-processed wafer to be tested; Selecting candidate sampling points from all sampling points according to the stress fluctuation data of each sampling point; Constructing a linear connected region based on the candidate sampling points; Whether the linear connected region is a line defect is determined according to the stress fluctuation data of all candidate sampling points in the linear connected region.

[0006] In a second aspect, the present disclosure provides a device for identifying line defects of a wafer, the device comprising: a collection unit, a selection unit, a construction unit and a determination unit; wherein, The collecting unit is configured to collect stress fluctuation data of each sampling point on the pre-processed wafer to be tested; The selection unit is configured to select candidate sampling points from all sampling points according to the stress fluctuation data of each sampling point; The construction unit is configured to construct a linear connected area based on the candidate sampling points; The determination unit is configured to determine whether the linear connected region is a line defect based on stress fluctuation data of all candidate sampling points in the linear connected region.

[0007] In a third aspect, the present disclosure provides a computing device, comprising a processor and a memory; the processor is used to execute instructions stored in the memory to implement the method for identifying linear defects of a wafer as described in the first aspect.

[0008] In a fourth aspect, the present disclosure provides a computer-readable storage medium storing at least one instruction, wherein the at least one instruction is used to be executed by a processor to implement the method for identifying linear defects of a wafer as described in the first aspect.

[0009] The present disclosure provides a method, device, equipment and medium for identifying linear defects of wafers; candidate sampling points are selected according to stress fluctuation data of the sampling points, and after a linear connected area is constructed from the candidate sampling points, whether it is a linear defect is determined according to the average stress fluctuation data of the linear connected area, so that native linear defects formed in the wafer during the crystal pulling process can be accurately identified. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flow chart of a method for identifying line defects of a wafer provided in the present disclosure.

[0011] Figure 2 (A) is a schematic diagram of the line defect direction of the notch with a crystal orientation of

[110] provided in the present invention.

[0012] Figure 2 (B) is a schematic diagram of the line defect direction of the notch with a crystal orientation of

[100] provided in the present invention.

[0013] Figure 3 A schematic diagram of a stress fluctuation curve provided by the present invention.

[0014] Figure 4 (A) is a schematic diagram of SIRD detection data of data collection points on a wafer provided by the present invention.

[0015] Figure 4 (B) is a schematic diagram of SIRD data of sampling points on the wafer provided by the present invention.

[0016] Figure 5 A schematic diagram of the pre-processing process provided in the present disclosure.

[0017] Figure 6 A schematic diagram of fluctuation curves of multiple sampling points provided in the present disclosure.

[0018] Figure 7 A schematic diagram of the average fluctuation curve provided for the present disclosure.

[0019] Figure 8(A) Schematic diagram of the peak range of the highest peak provided by the present disclosure.

[0020] Figure 8 (B) Schematic diagram of the FWHM range provided for the present disclosure.

[0021] Fig. 9 Schematic diagram of the identified line defects provided for the present disclosure.

[0022] Fig.10 A schematic diagram of the composition of a device for identifying linear defects of a wafer provided by the present invention.

[0023] Fig.11 A schematic diagram of the structure of a computing device provided by the present disclosure. DETAILED DESCRIPTION

[0024] The technical solutions in the present disclosure will be described clearly and completely below in conjunction with the accompanying drawings in the present disclosure.

[0025] In the present disclosure, the linear defects formed by dislocations will cause local stress field fluctuations in the silicon wafer. The amplitude of the fluctuations is related to the defect size, and the observable size of the defect is determined by the thickness and depth of the stacking fault atomic plane. Generally speaking, the size of linear defects is small, resulting in a weak amplitude of stress field fluctuations, which is easily masked by other noises on the wafer surface. Based on this, linear defects are usually detected after the polishing process to reduce missed detection and improve detection accuracy.

[0026] In order to accurately detect line defects as early as possible, for example, accurately detect line defects on a wafer after dicing but before grinding, the present disclosure provides a solution for identifying line defects on a wafer. Figure 1 , which shows a method flow for identifying linear defects of a wafer provided by the present disclosure, the method comprising steps S101 to S104.

[0027] In step S101 , stress fluctuation data of each sampling point on the pre-processed wafer to be tested is collected.

[0028] In the present disclosure, the wafer to be tested can be obtained by wire cutting of a pulled single crystal silicon crystal, or can be obtained by grinding or polishing, which is not described in detail in the present disclosure. For the wafers obtained by wire cutting, or the wafers obtained by grinding or polishing, pretreatment is required before the technical solution of the present disclosure is implemented.

[0029] In some examples, the preprocessing process may include: providing a wafer; rotating the wafer according to the notch direction of the wafer so that the crystal direction

[110] of the wafer is horizontal or vertical; and performing edge removal processing on the rotated wafer according to a set edge exclusion (EE) value to obtain a wafer to be tested.

[0030] In the above example, specifically, the wafer can be obtained by wire cutting of a pulled single crystal silicon crystal, or by grinding or polishing. The crystal orientation of the line defect direction formed by the dislocation is {110}, and the crystal orientation of the notch of the wafer is usually

[110] or

[100] . Figure 2 (A) and Figure 2 (B) shows the wafer surface. Figure 2 In (A), the crystal orientation of the notch is

[110] . Figure 2 On the wafer surface shown in (A), the possible direction of the line defect is shown as the dotted line in Figure 2 (A), which is horizontal or vertical. Figure 2 In (B), the crystal orientation of the notch is

[100] , so Figure 2 On the wafer surface shown in (B), the possible direction of the line defect is shown by the dotted line in Figure 2 (B), which is a direction 45 degrees to the horizontal or vertical direction. In the process of online defect detection, the horizontal direction or the vertical direction is easier to detect than the oblique direction. Therefore, when the wafer to be tested is performing stress fluctuation data detection, it is necessary to keep

[110] in the horizontal or vertical direction. In the specific implementation process, when the notch crystal direction is

[110] , the wafer is not rotated. When the notch crystal direction is

[100] , the wafer is rotated 45 degrees to ensure that

[110] is in the horizontal or vertical direction. Because there are usually defects on the edge of the wafer that cause abnormal stress fluctuation data detection, and usually the wafer will be tested only after the edge EE is removed. In some examples, the EE value is usually 3mm, 5mm. After the above preprocessing, a wafer to be tested that can implement the technical solution of the present disclosure is obtained.

[0031] In the present disclosure, the stress fluctuation data on the wafer surface can represent the fluctuation degree of the local stress field of the wafer, which can be detected by scanning infrared depolarization (SIRD) technology. The specific detection process of SIRD technology is not described in detail in the present disclosure. Figure 3As shown, in the present disclosure, stress fluctuation data can be displayed as a stress fluctuation curve, the horizontal axis of which represents the angle of a circle, i.e., from 0 to 360 degrees, and the vertical axis represents the signal intensity, in units of Du. The fluctuation curve has multiple peaks, and the peak value of each peak and the full width at half maximum (FWHM) of the highest peak can characterize the stress fluctuation intensity at that point.

[0032] In step S102, candidate sampling points are selected from all sampling points according to the stress fluctuation data of each sampling point.

[0033] In the present disclosure, since line defects will cause fluctuations in the local stress field of the wafer, the fluctuations can be determined through stress fluctuation data, that is, stress fluctuation curves. Therefore, when the stress fluctuation data at a sampling point can determine that a stress field fluctuation has occurred at the sampling point, it means that the sampling point may be a sampling point in the line defect. In the present disclosure, these sampling points are used as candidate sampling points for subsequent detection and identification of line defects.

[0034] Specifically, when the stress fluctuation data is a stress fluctuation curve, the fluctuation degree of the stress field can be characterized by the peak value and FWHM of the highest peak of the stress fluctuation curve.

[0035] In step S103, a linear connected region is constructed based on the candidate sampling points.

[0036] In the present disclosure, a line defect is usually composed of multiple points, and combined with the aforementioned preprocessing process, the line defect will appear as a horizontal or vertical line. Based on this, the coordinates of the candidate sampling points can be used to construct a linear connected area, that is, the candidate sampling points in the connected area can be considered as candidate sampling points connected in a straight line.

[0037] In some examples, constructing a linear connected area based on the candidate sampling points includes: Using a depth-first search algorithm, searching for a candidate sampling point group that can form a straight line in the horizontal or vertical direction from all candidate sampling points; Each candidate sampling point group is formed into a corresponding linear connected area.

[0038] Specifically, first, a depth-first search (DFS) algorithm is used to search for multiple candidate sampling points that can form a horizontal straight line in the horizontal direction. Each horizontal straight line corresponds to a group of candidate sampling points. Then, each group of candidate sampling points is formed into a horizontal line-shaped connected area.

[0039] Similar to the above, a depth-first search algorithm can also be used to search for multiple candidate sampling points that can form a vertical line in the vertical direction among the candidate sampling points, each vertical line corresponds to a group of candidate sampling points. Then, each group of candidate sampling points is formed into a vertical line-shaped connected area.

[0040] In the specific implementation of the DFS algorithm, the interval threshold is set to 3, that is, the maximum length of the continuous line allowed to be interrupted by noise is 3. The linear threshold is set to 2, that is, the threshold for line defects to deviate from linearity due to noise is 2. The straight line length threshold is set to 5, that is, at least 5 consecutive candidate sampling points are required to form a straight line in the horizontal or vertical direction.

[0041] In step S104 , whether the linear connected region is a line defect is determined based on the stress fluctuation data of all candidate sampling points in the linear connected region.

[0042] In the present disclosure, for a linear connected region, it is still necessary to accurately determine whether it is a line defect. Based on this, for each linear connected region, the average stress fluctuation data is used to characterize the overall stress field fluctuation of the connected region. After obtaining the overall stress field fluctuation of each linear connected region, it is possible to further determine whether each connected region is a line defect.

[0043] In some examples, determining whether the linear connected region is a line defect according to the stress fluctuation data of all candidate sampling points in the linear connected region includes: Obtaining average stress fluctuation data of the linear connected area according to the stress fluctuation data of all candidate sampling points in the linear connected area; Whether the linear connected region is a line defect is determined based on average stress fluctuation data of the linear connected region.

[0044] Specifically, the average stress fluctuation data can characterize the stress field fluctuation of the entire connected region. Figure 3 In the case of the stress fluctuation curve shown, for each linear connected area, the stress fluctuation curves of all candidate sampling points in the connected area can be aligned, and then the signal values ​​corresponding to all the aligned stress fluctuation curves for each horizontal coordinate value are averaged to obtain the average stress fluctuation curve of the linear connected area, that is, the average stress fluctuation data of the linear connected area.

[0045] It should be noted that, for each connected area, in addition to the aforementioned exemplary method of obtaining the average stress fluctuation curve by averaging the signal values, the average stress fluctuation curve can also be obtained by other means based on the stress fluctuation curves of all candidate sampling points in the connected area. For example, the stress fluctuation curves of all candidate sampling points can be fitted by data fitting to obtain a line that can characterize the stress field fluctuation of the entire connected area. The specific data fitting method may include but is not limited to polynomial fitting, linear fitting, nonlinear fitting, spline function fitting, etc., which will not be described in detail in this disclosure. In addition, the stress fluctuation curves of all candidate sampling points in each connected area can also be input into a trained machine learning model, and the machine learning model can output the average stress fluctuation curve that can characterize the stress field fluctuation of the entire connected area. For example, a random forest regression model, a long short-term memory network (LSTM) model, etc.

[0046] In the present disclosure, after obtaining the average stress fluctuation data corresponding to each linear connected area, the fluctuation of the stress field of the connected area as a whole can be determined in turn, and whether the connected area is a line defect can be judged based on the stress field fluctuation.

[0047] Specifically, the average stress fluctuation data of the linear connected region, that is, the peak value and FWHM of the highest peak of the average waveform curve, can represent the fluctuation degree of the stress field of the entire connected region.

[0048] For the above technical solution, candidate sampling points are selected based on the stress fluctuation data of the sampling points. After the candidate sampling points are constructed into a linear connected area, it is determined whether it is a line defect based on the average stress fluctuation data of the linear connected area. This can accurately identify the native line defects formed on the wafer during the crystal pulling process.

[0049] for Figure 1 In some possible implementations of the technical solution shown, the collecting of the stress fluctuation data of each sampling point on the pre-processed wafer to be tested includes: Selecting data collection points on the surface of the wafer to be tested in polar coordinates; Use SIRD to measure stress fluctuation data at each data collection point; Convert each data collection point into a rectangular coordinate system; Interpolating the surface of the wafer to be tested according to the set rectangle size and the stress fluctuation data of the data collection points in the rectangular coordinate system to obtain interpolated data points and stress fluctuation data of each interpolated data point; wherein the outer contour of the wafer to be tested is the inscribed circle of the rectangle; The data acquisition points and the interpolation data points constitute the sampling points, and the stress fluctuation data of each sampling point is obtained.

[0050] For the above implementation, specifically, the present disclosure uses SIRD to detect stress fluctuation data, and the detected data is usually presented in the form of polar coordinates, such as Figure 4 (A) is shown. Data collection points are selected on the surface of the wafer to be tested, and SIRD detection is performed on each data collection point to obtain stress fluctuation data at each data collection point. In order to facilitate subsequent data processing, the present disclosure converts polar coordinates into rectangular coordinates, and performs linear interpolation in a rectangular coordinate system based on the data collection points where stress fluctuation data has been detected, thereby obtaining interpolation points on the surface of the wafer to be tested and stress fluctuation data values ​​corresponding to the interpolation points. The data collection points and interpolation points on the surface to be tested are arranged in a circle.

[0051] In addition, based on the circular layout, the present disclosure expands it into a rectangular layout, and the inscribed circle of the rectangle is the outer contour of the wafer to be tested. In the rectangle, the part outside the wafer to be tested is interpolated, and each interpolation point is assigned a value of 0. Through the above rectangular expansion and interpolation, the above data collection points and interpolation points can be integrated to form a set of sampling points, such as Figure 4 As shown in (B), the set can present relevant data in the form of a matrix, which is conducive to subsequent data processing.

[0052] for Figure 1 In the technical solution shown, the stress fluctuation data, i.e., the peak value and FWHM of the highest peak of the waveform curve, can characterize the degree of fluctuation of the stress field, and can then be used as an evaluation indicator to determine whether it is a line defect. Based on this, the present disclosure can obtain the evaluation indicator range corresponding to the line defect in advance. Specifically, Figure 5 As shown, it may include steps S51 to S56.

[0053] S51: preparing a plurality of wafers having line defects.

[0054] For each wafer, the following steps S52 to S55 are performed: S52: After etching the wafer, the range where the line defects exist is observed through a microscope.

[0055] Specifically, the HF solution may be used to etch the surface of the wafer so that the line defects are more clearly exposed, facilitating visual observation through a microscope.

[0056] S53: Detecting the stress fluctuation curve of each sampling point within the range by SIRD.

[0057] Specifically, the stress fluctuation data detected by SIRD is usually presented in the form of polar coordinates. Similar to the above-mentioned implementation, the polar coordinates can be converted to a rectangular coordinate system and expanded into a rectangle, and linear interpolation can be performed within the expanded rectangular range. For sampling points within the range where line defects exist, the line defects can be rotated so that they appear in a horizontal or vertical direction.

[0058] S54: Calculate the average fluctuation curve of the stress fluctuation curves of all sampling points.

[0059] Specifically, if Figure 6 As shown in Figure 2, there are 15 sampling points within the range of line defects, and the stress fluctuation curve corresponding to each sampling point is Figure 6 They are marked as Waveform N, where N represents the sequence number of the sampling point.

[0060] After obtaining the stress fluctuation curves of these sampling points, these stress fluctuation curves are aligned along the horizontal axis coordinates, and the signal values ​​corresponding to each horizontal coordinate value in all fluctuation curves are averaged to obtain the average waveform curve, such as Figure 7 shown.

[0061] S55: Obtain the peak value and FWHM of the highest peak of the average fluctuation curve.

[0062] against Figure 7 Take the average waveform curve shown and obtain the peak value and FWHM of its highest peak.

[0063] S56: Summarize the peak values ​​and FWHM of the highest peaks of the average fluctuation curves of all wafers within the range of line defects to obtain the peak value range and FWHM range of the highest peak for evaluating line defects.

[0064] Specifically, the peak value and FWHM of the highest peak of the average fluctuation curve within the online defect range of all wafers are summarized respectively to obtain the following Figure 8 (A) and Figure 8 (B) shows the statistical results, marked as C2 and C3. Figure 8 (A) It can be seen that the peak value range of the highest peak used in the present disclosure to evaluate line defects is [20, 40], which can also be referred to as the first determination interval in the present disclosure. Figure 8 (B) It can be seen that the FWHM range used in the present disclosure for evaluating line defects is [2.2, 2.7], which may also be referred to as the second determination interval in the present disclosure.

[0065] Based on the first determination interval and the second determination interval, in some examples, selecting a candidate sampling point from all sampling points according to the stress fluctuation data of each sampling point includes: Obtaining the peak absolute value and half-peak width FWHM of the highest peak corresponding to each sampling point according to the stress fluctuation data of each sampling point; Compare the peak absolute value and half-peak width FWHM of the highest peak corresponding to each sampling point with the first determination interval and the second determination interval respectively; When the peak absolute value and the half-maximum width (FWHM) of the highest peak corresponding to each sampling point are respectively within the first determination interval and the second determination interval, the sampling point is determined to be a candidate sampling point.

[0066] For the above example, it should be noted that the stress fluctuation data of each sampling point is presented as a stress fluctuation curve, and the peak absolute value and FWHM of the highest peak in the stress fluctuation curve can characterize the stress fluctuation situation. Based on the first determination interval and the second determination interval obtained in the above implementation, it is determined whether the peak absolute value and FWHM of the highest peak corresponding to each sampling point are respectively within the first determination interval and the second determination interval. If both are within, the sampling point is a candidate sampling point. Otherwise, the sampling point is not determined as a candidate sampling point.

[0067] Based on the first determination interval and the second determination interval, in some examples, determining whether the linear connected area is a line defect according to the average stress fluctuation data of the linear connected area includes: According to the average stress fluctuation data of each linear connected area, the peak absolute value and the half-peak width FWHM of the highest peak corresponding to each linear connected area are obtained; Compare the peak absolute value and half-maximum width FWHM of the highest peak corresponding to each linear connected area with the first determination interval and the second determination interval respectively; A linear connected region in which both the peak absolute value and the half maximum width FWHM of the highest peak are respectively within the first determination interval and the second determination interval is determined as a line defect.

[0068] For the above example, it should be noted that the average stress fluctuation data of each linear connected area is presented as an average waveform curve, and the peak absolute value and FWHM of the highest peak in the average stress fluctuation curve can characterize the stress fluctuation of each linear connected area. Based on the first determination interval and the second determination interval obtained in the above implementation, it is determined whether the peak absolute value and FWHM of the highest peak corresponding to each linear connected area are respectively within the first determination interval and the second determination interval. If they are, the linear connected area is a line defect, and the length of the linear connected area is the line defect length. Otherwise, the linear connected area is not determined to be a line defect.

[0069] Through the above technical solution, taking the wafer obtained after wire cutting as an example, the exemplary line defects identified are as follows: Fig. 9 As shown, from Fig. 9 It can be seen from the figure that the technical solution disclosed in the present invention can accurately identify line defects.

[0070] Based on the same concept as the above technical solution, see Fig.10 , which shows a device 100 for identifying line defects of a wafer provided by the present disclosure, comprising: a collection unit 1001, a selection unit 1002, a construction unit 1003 and a determination unit 1004; wherein, The collecting unit 1001 is configured to collect stress fluctuation data of each sampling point on the pre-processed wafer to be tested; The selection unit 1002 is configured to select candidate sampling points from all sampling points according to the stress fluctuation data of each sampling point; The construction unit 1003 is configured to construct a linear connected area based on the candidate sampling points; The determination unit 1004 is configured to determine whether the linear connected region is a line defect based on the stress fluctuation data of all candidate sampling points in the linear connected region.

[0071] In some examples, the selection unit 1002 is configured to: Obtaining the peak absolute value and half-peak width FWHM of the highest peak corresponding to each sampling point according to the stress fluctuation data of each sampling point; Compare the peak absolute value and half-peak width FWHM of the highest peak corresponding to each sampling point with the first determination interval and the second determination interval respectively; When the peak absolute value and the half-maximum width (FWHM) of the highest peak corresponding to each sampling point are respectively within the first determination interval and the second determination interval, the sampling point is determined to be a candidate sampling point.

[0072] In some examples, the determination unit 1004 is configured to: Obtaining average stress fluctuation data of the linear connected area according to the stress fluctuation data of all candidate sampling points in the linear connected area; Whether the linear connected region is a line defect is determined based on average stress fluctuation data of the linear connected region.

[0073] In some examples, the determination unit 1004 is configured to: According to the average stress fluctuation data of each linear connected area, the peak absolute value and the half-peak width FWHM of the highest peak corresponding to each linear connected area are obtained; Compare the peak absolute value and half-maximum width FWHM of the highest peak corresponding to each linear connected area with the first determination interval and the second determination interval respectively; A linear connected region in which both the peak absolute value and the half maximum width FWHM of the highest peak are respectively within the first determination interval and the second determination interval is determined as a line defect.

[0074] In some examples, the first decision interval is [20-40], and the second decision interval is [2.2-2.7].

[0075] In some examples, the determination unit 1004 is configured to: When the stress fluctuation data is a stress fluctuation curve, aligning the stress fluctuation curves of all candidate sampling points in each linear connected area according to the horizontal coordinate; The signal values ​​corresponding to all the aligned stress fluctuation curves at each abscissa value are averaged to obtain the average stress fluctuation curve of each linear connected area.

[0076] In some examples, the construction unit 1003 is configured to: Using a depth-first search algorithm, searching for a candidate sampling point group that can form a straight line in the horizontal or vertical direction from all candidate sampling points; Each candidate sampling point group is formed into a corresponding linear connected area.

[0077] In some examples, the determination unit 1004 is configured to: The stress fluctuation data of all candidate sampling points in each linear connected area are averaged to obtain the average stress fluctuation data of each linear connected area.

[0078] In some examples, the collection unit 1001 is configured to: Selecting data collection points on the surface of the wafer to be tested in polar coordinates; Use SIRD to measure stress fluctuation data at each data collection point; Convert each data collection point into a rectangular coordinate system; Interpolating the surface of the wafer to be tested according to the set rectangle size and the stress fluctuation data of the data collection points in the rectangular coordinate system to obtain interpolated data points and stress fluctuation data of each interpolated data point; wherein the outer contour of the wafer to be tested is the inscribed circle of the rectangle; The data acquisition points and the interpolation data points constitute the sampling points, and the stress fluctuation data of each sampling point is obtained.

[0079] In some examples, the collection unit 1001 is further configured to: providing a wafer; Rotating the wafer according to the notch direction of the wafer so that the crystal direction

[110] of the wafer is horizontal or vertical; The rotated wafer is subjected to edge removal processing according to the set EE value to obtain the wafer to be tested.

[0080] Please refer to Fig.11 , which shows a structural block diagram of a computing device provided by an exemplary embodiment of the present disclosure. In some examples, the computing device 110 may be at least one of a smart phone, a smart watch, a desktop computer, a laptop, a virtual reality terminal, an augmented reality terminal, a wireless terminal, and a laptop portable computer. The computing device 110 has a communication function and can access a wired network or a wireless network. The computing device 110 may generally refer to one of a plurality of terminals, and those skilled in the art may know that the number of the above terminals may be more or less. In some examples, the computing device 110 may receive data based on the wired network or wireless network to which it is connected. It can be understood that the computing device 110 undertakes the calculation and processing work of the technical solution of the present disclosure, and the present disclosure does not limit this.

[0081] like Fig.11 As shown, the computing device in the present disclosure may include one or more of the following components: a processor 1110 and a memory 1120 .

[0082] Optionally, the processor 1110 uses various interfaces and lines to connect various parts of the entire computing device, and executes various functions of the computing device and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 1120, and calling data stored in the memory 1120. Optionally, the processor 1110 can be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 1110 can integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), a neural network processor (NPU), and a baseband chip. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content that needs to be displayed on the touch display; the NPU is used to implement artificial intelligence (AI) functions; and the baseband chip is used to process wireless communications. It is understandable that the above-mentioned baseband chip may not be integrated into the processor 1110, but may be implemented by a separate chip.

[0083] The memory 1120 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory 1120 includes a non-transitory computer-readable storage medium. The memory 1120 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 1120 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above various method embodiments, etc.; the data storage area may store data created according to the use of the computing device, etc.

[0084] In addition, those skilled in the art can understand that the structure of the computing device shown in the above drawings does not constitute a limitation on the computing device, and the computing device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. For example, the computing device also includes a display screen, a camera assembly, a microphone, a speaker, a radio frequency circuit, an input unit, a sensor (such as an acceleration sensor, an angular velocity sensor, a light sensor, etc.), an audio circuit, a WiFi module, a power supply, a Bluetooth module and other components, which will not be described in detail here.

[0085] The present disclosure also provides a computer-readable storage medium storing at least one instruction, wherein the at least one instruction is used to be executed by a processor to implement the method for identifying linear defects of a wafer as described in the above embodiments.

[0086] The present disclosure also provides a computer program product, which includes computer instructions stored in a computer-readable storage medium; a processor of a computing device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computing device executes to implement the method of identifying linear defects of a wafer as described in the above-mentioned various embodiments.

[0087] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the present disclosure can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any media that facilitates the transmission of computer programs from one place to another. Storage media can be any available media that can be accessed by a general or special-purpose computer.

[0088] It should be noted that the technical solutions described in the present disclosure can be combined arbitrarily without conflict.

[0089] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for identifying line defects of a wafer, characterized in that: The method comprises: Collect stress fluctuation data of each sampling point on the pre-processed wafer to be tested; Selecting candidate sampling points from all sampling points according to the stress fluctuation data of each sampling point; Constructing a linear connected region based on the candidate sampling points; Whether the linear connected region is a line defect is determined according to the stress fluctuation data of all candidate sampling points in the linear connected region.

2. The method according to claim 1, characterized in that The step of selecting a candidate sampling point from all sampling points according to the stress fluctuation data of each sampling point comprises: Obtaining the peak absolute value and half-peak width FWHM of the highest peak corresponding to each sampling point according to the stress fluctuation data of each sampling point; Compare the peak absolute value and half-peak width FWHM of the highest peak corresponding to each sampling point with the first determination interval and the second determination interval respectively; When the peak absolute value and the half-maximum width (FWHM) of the highest peak corresponding to each sampling point are respectively within the first determination interval and the second determination interval, the sampling point is determined to be a candidate sampling point.

3. The method according to claim 1, characterized in that The step of determining whether the linear connected region is a line defect according to the stress fluctuation data of all candidate sampling points in the linear connected region comprises: Obtaining average stress fluctuation data of the linear connected area according to the stress fluctuation data of all candidate sampling points in the linear connected area; Whether the linear connected region is a line defect is determined based on average stress fluctuation data of the linear connected region.

4. The method according to claim 3, characterized in that The determining whether the linear connected region is a line defect according to the average stress fluctuation data of the linear connected region comprises: According to the average stress fluctuation data of each linear connected area, the peak absolute value and the half-peak width FWHM of the highest peak corresponding to each linear connected area are obtained; Compare the peak absolute value and half-maximum width FWHM of the highest peak corresponding to each linear connected area with the first determination interval and the second determination interval respectively; A linear connected region in which both the peak absolute value and the half maximum width FWHM of the highest peak are respectively within the first determination interval and the second determination interval is determined as a line defect.

5. The method according to claim 2 or 4, characterized in that: The first determination interval is [20-40], and the second determination interval is [2.2-2.7].

6. The method according to claim 3, characterized in that The step of obtaining average stress fluctuation data of the linear connected region according to the stress fluctuation data of all candidate sampling points in the linear connected region comprises: When the stress fluctuation data is a stress fluctuation curve, aligning the stress fluctuation curves of all candidate sampling points in each linear connected area according to the horizontal coordinate; The signal values ​​corresponding to all the aligned stress fluctuation curves at each abscissa value are averaged to obtain the average stress fluctuation curve of each linear connected area.

7. The method according to claim 1, characterized in that The step of constructing a linear connected region based on the candidate sampling points includes: Using a depth-first search algorithm, searching for a candidate sampling point group that can form a straight line in the horizontal or vertical direction from all candidate sampling points; Each candidate sampling point group is formed into a corresponding linear connected area.

8. The method according to claim 1, characterized in that The method further comprises: providing a wafer; Rotating the wafer according to the notch direction of the wafer so that the crystal direction [110] of the wafer is horizontal or vertical; The rotated wafer is subjected to edge removal processing according to the set EE value to obtain the wafer to be tested.

9. The method according to claim 1, characterized in that: The collecting of the stress fluctuation data of each sampling point on the pre-processed wafer to be tested comprises: Selecting data collection points on the surface of the wafer to be tested in polar coordinates; The stress fluctuation data of each data collection point is measured using the scanning infrared depolarization SIRD method; Convert each data collection point into a rectangular coordinate system; Interpolating the surface of the wafer to be tested according to the set rectangle size and the stress fluctuation data of the data collection points in the rectangular coordinate system to obtain interpolated data points and stress fluctuation data of each interpolated data point; wherein the outer contour of the wafer to be tested is the inscribed circle of the rectangle; The data acquisition points and the interpolation data points constitute the sampling points, and the stress fluctuation data of each sampling point is obtained.

10. A device for identifying line defects of a wafer, characterized in that: The device comprises: a collection unit, a selection unit, a construction unit and a determination unit; wherein, The collecting unit is configured to collect stress fluctuation data of each sampling point on the pre-processed wafer to be tested; The selection unit is configured to select candidate sampling points from all sampling points according to the stress fluctuation data of each sampling point; The construction unit is configured to construct a linear connected area based on the candidate sampling points; The determination unit is configured to determine whether the linear connected region is a line defect based on stress fluctuation data of all candidate sampling points in the linear connected region.

11. A computing device, characterized in that: The computing device includes a processor and a memory; the processor is used to execute instructions stored in the memory to implement the method for identifying linear defects of a wafer as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and the at least one instruction is used to be executed by a processor to implement the method for identifying line defects of a wafer as claimed in any one of claims 1 to 9.

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