Wafer surface defect detection method, device, equipment and medium

Abstract

The invention provides a wafer surface defect detection method and device, equipment and a medium. The detection method comprises the following steps: acquiring original height distribution information of a wafer surface; obtaining morphology fluctuation data of the target wavelength range based on the original height distribution information; and in a plurality of wave crest intervals of the morphology fluctuation data, determining the position of the wafer surface defect according to the highest peak value and the width of the wave crest intervals.

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 detecting surface defects of a wafer. Background Art

[0002] In the wafer manufacturing process, after the single crystal silicon rod is prepared by the Czochralski method, the single crystal silicon rod is processed in sequence by wire cutting, grinding, etching, grinding and chemical mechanical polishing (CMP) to finally obtain a single crystal silicon wafer. For a single crystal silicon wafer, the number of defects on its surface is a key parameter to measure its quality.

[0003] Currently, related solutions usually identify defects and confirm the location of surface defects by analyzing images after obtaining the nano-topography of the wafer. However, for more minor surface defects, it is often impossible to identify and confirm them through images. As the line width of semiconductor devices continues to shrink and the device structure continues to develop towards a multi-layer stacked structure, strict requirements are also placed on more minor defects on the wafer surface. Therefore, it is necessary to propose a method for detecting wafer surface defects based on more stringent defect detection requirements. Summary of the invention

[0004] The present disclosure provides a method, device, equipment and medium for detecting wafer surface defects, which can more accurately detect slight defects on the wafer surface.

[0005] The technical solution of the present disclosure is achieved as follows: In a first aspect, the present disclosure provides a method for detecting wafer surface defects, comprising: Obtaining original height distribution information of the wafer surface; Acquire the morphology fluctuation data of the target wavelength range based on the original height distribution information; In the multiple peak intervals of the morphology fluctuation data, the position of the wafer surface defect is determined according to the highest peak value and the width of the peak interval; wherein the peak interval is the fluctuation data interval between two adjacent inflection points, and the width of the peak interval is the distance between the corresponding two inflection points.

[0006] In a second aspect, the present disclosure provides a device for detecting wafer surface defects, the device comprising: a first acquisition unit, a second acquisition unit and a determination unit; wherein: The first acquisition unit is configured to acquire original height distribution information of the wafer surface; The second acquisition unit is configured to acquire the topography fluctuation data of the target wavelength range based on the original height distribution information; The determination unit is configured to determine the location of the wafer surface defect according to the highest peak value and the width of the peak interval in multiple peak intervals of the morphology fluctuation data; wherein the peak interval is the fluctuation data interval between two adjacent inflection points, and the width of the peak interval is the distance between the corresponding two inflection points.

[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 wafer surface defect detection method 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 wafer surface defect detection method as described in the first aspect.

[0009] The present disclosure provides a method, device, equipment and medium for detecting wafer surface defects. After obtaining the original height distribution of the wafer surface, the original height distribution is filtered using a target wavelength range corresponding to the defect size to be detected, and morphology fluctuation data corresponding to the target wavelength range is obtained. The highest peak value of the peak interval and the width of the peak interval in the morphology fluctuation data are then used to determine the location of the defect, thereby enabling more accurate detection of slight defects on the wafer surface. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic flow chart of a method for detecting wafer surface defects provided in the present invention.

[0011] Figure 2 An exemplary architecture diagram of a double-sided Fizeau interferometer system provided for the present disclosure.

[0012] Figure 3 A schematic diagram of obtaining the height distribution of a wafer by using wavelength phase shift provided in the present disclosure.

[0013] Figure 4 A schematic diagram of a morphology fluctuation data curve provided in the present disclosure.

[0014] Figure 5 A schematic diagram of a process for determining locations of wafer surface defects provided by the present disclosure.

[0015] Figure 6 A schematic diagram of a flow chart for determining a peak interval to be selected provided in the present disclosure.

[0016] Figure 7 A schematic diagram of a flow chart for determining whether a candidate peak interval has defects provided by the present disclosure.

[0017] Figure 8 Another schematic diagram of a morphology fluctuation data curve provided in the present disclosure.

[0018] Fig. 9 A schematic diagram of a defect detection result provided by the present invention.

[0019] Fig.10 A schematic diagram of the composition of a wafer surface defect detection device provided by the present invention.

[0020] Fig.11 A structural block diagram of a computing device provided by the present disclosure. DETAILED DESCRIPTION

[0021] The technical solutions in the present disclosure will be described clearly and completely below in conjunction with the accompanying drawings in the present disclosure.

[0022] The surface morphology of the wafer as a whole presents a fluctuation phenomenon, which can be considered to be caused by the superposition of wave signals of different wavelengths. The smaller the wavelength of the wave signal, the smaller the size of the region representing the morphological fluctuation phenomenon; the larger the wavelength of the wave signal, the larger the size of the region representing the morphological fluctuation phenomenon. When a defect appears on the surface of the wafer, the defect will affect the above-mentioned fluctuation phenomenon. When the size of the defect is smaller (that is, the defect is milder), it usually will not affect the wave signal with a larger wavelength in the above-mentioned fluctuation phenomenon, but will have a more significant impact on the wave signal with a smaller wavelength in the above-mentioned fluctuation phenomenon.

[0023] Based on such understanding, the present disclosure determines whether a slight defect exists based on the wave signal data after obtaining the wave signal data within the corresponding wavelength range for the slight defect that is expected to be detected.

[0024] Based on this, see Figure 1 , which shows a method for detecting wafer surface defects provided by the present disclosure, and the detection method includes steps S101 to S103.

[0025] In step S101, original height distribution information of the wafer surface is obtained.

[0026] In some examples, the wafer can be placed vertically in a double-sided Fizeau interferometer, and the original height distribution information of the wafer surface can be obtained by using wavelength phase shift, and the original height distribution information of the wafer surface includes the height value of each sampling point on the front side of the wafer relative to the reference plane and the height value of each sampling point on the back side of the wafer relative to the reference plane. In the present disclosure, the wafer surface includes the front side and the back side of the wafer, wherein the front side of the wafer is defined as the surface on which the wafer is patterned during the manufacturing process of the semiconductor device, and the surface opposite to the front side of the wafer is the back side.

[0027] Specifically, an exemplary architecture of a double-sided Fizeau interferometer system is as follows Figure 2 As shown, the interferometer system 200 includes a tray 204 for holding a wafer 202. In some examples, the tray 204 holds the wafer 202 vertically in the interferometer system 200, so that the topography of both the front and back surfaces of the wafer 202 can be measured. In addition, the interferometer system 200 includes a first interferometer 206-1 for measuring a first side (e.g., the front side) of the wafer 202, and a second interferometer 206-2 for measuring a second side (e.g., the back side) of the wafer 202. The first side is opposite to the second side. In some embodiments, the interferometers 206-1 and 206-2 are Fizeau interferometers: the first interferometer 206-1 is a first Fizeau interferometer, and the second interferometer 206-2 is a second Fizeau interferometer. Each Fizeau interferometer includes: a reference plane 208; a lens 210 for focusing a corresponding laser beam 224-1 or 224-2 onto the reference plane 208 and the wafer 202; a beam splitter 212 for guiding the corresponding laser beam 224-1 or 224-2 to the lens 210 and transmitting the corresponding laser beam 224-1 or 224-2 reflected by the reference plane 208 and the wafer 202; and a lens 214 for focusing the reflected corresponding laser beam 224-1 or 224-2. The laser beams 224-1 and 224-2 are generated by the laser 220 and provided to the beam splitters 212 of the corresponding interferometers 206-1 and 206-2 through the corresponding optical fibers 222-1 and 222-2. In some examples, each Fizeau interferometer further includes a digital camera 216 for receiving the reflected corresponding laser beam 224 - 1 or 224 - 2 focused by the lens 214 , and a computer 218 for processing data from the digital camera 216 .

[0028] In each interferometer 206-1 and 206-2, a portion of the corresponding laser beam 224-1 or 224-2 reflected by the reference plane 208 interferes with a portion of the corresponding laser beam 224-1 or 224-2 reflected by the wafer 202, and by analyzing the interference, the distance between a point on the wafer 202 and the reference plane 208 can be measured, thereby measuring the original height of the point. The measured original height of the point indicates the surface topography of the wafer 202. By using two interferometers 206-1 and 206-2, the heights of both sides (i.e., the front side and the back side) of the wafer 202 are measured, and thus the original topography is measured.

[0029] like Figure 3As shown, since the surface of the wafer 202 is not completely flat, the distances between various positions on the wafer surface and the reference plane 208 are different, and the optical path differences of the light from the reference plane 208 to the surface (e.g., the front side) of the wafer 202 and then reflected by the surface of the wafer 202 and then reaching the reference plane 208 again are different. The original height distribution of the surface of the wafer 202 can be obtained according to the optical path differences at different positions. It can be understood that Figure 3 The surface of the wafer 202 shown in the figure may be either the front side of the wafer 202 or the back side of the wafer 202 .

[0030] More specifically, a sampling point may be selected every 10 to 200 microns on the surface of the front and back sides of the wafer 202 and the sampling point may be selected every 10 to 200 microns. Figure 2 The interferometer system 200 shown performs measurement, calculates the optical path difference between each sampling point and the reference plane 208, and obtains the original height distribution information of the front and back sides of the wafer 202 after conversion.

[0031] In the present disclosure, the technical solution is explained by taking the front side of the wafer 202 as an example. It can be understood that the technical solution of the present disclosure can also be implemented to detect surface defects on the back side of the wafer 202, which will not be repeated here.

[0032] In step S102, the topography fluctuation data in the target wavelength range is acquired based on the original height distribution information.

[0033] In the present disclosure, after the original height data of each sampling point is obtained by measuring S101, the original height distribution information of the wafer surface can be formed. In some examples, a Cartesian coordinate system is set with the center of the wafer surface as the origin and the height direction of the wafer as the Z axis. Then for the height distribution information, the fluctuation phenomenon of the measurement data of each sampling point will be presented as a whole, that is, a three-dimensional fluctuating surface will be presented in the coordinate system. The present disclosure regards this fluctuation phenomenon from the perspective of waves as caused by the superposition of wave signals of different wavelengths. Different wavelengths can correspond to the size of the area where the waveform phenomenon occurs, that is, the smaller the wavelength of the wave signal, the smaller the size of the area where the morphological fluctuation phenomenon occurs; the larger the wavelength of the wave signal, the larger the size of the area where the morphological fluctuation phenomenon occurs. When a defect occurs on the surface of the wafer, the defect will affect the different wavelength signals in the above-mentioned fluctuation phenomenon. Specifically, when the size of the defect is smaller (that is, the defect is milder), it will not affect the wave signal with a larger wavelength in the above-mentioned fluctuation phenomenon, but will have a more significant impact on the wave signal with a smaller wavelength in the above-mentioned fluctuation phenomenon. Therefore, the impact of slight defects on the waveform phenomenon presented on the wafer surface is usually captured in the fluctuation signal with a smaller wavelength. Based on this understanding, in the present disclosure, the target wavelength range corresponds to the defect size expected to be detected. For example, for some slight defects, the corresponding target wavelength range can be 0.2 millimeters (mm) to 20 millimeters (mm) in wavelength. For even milder defects, the corresponding target wavelength range can be 1.8 to 22 microns (μm) in wavelength, or even 0 to 1.8 microns in wavelength.

[0034] In the present disclosure, since the original height distribution information is presented as a superposition of signals of different wavelengths, and the size of the defect corresponds to the target wavelength range, in order to be able to obtain the ability to capture such slight defects, the wave signal of the target wavelength range can be obtained by filtering based on the original height distribution information, that is, the morphology fluctuation data of the target wavelength range. In some examples, the morphology fluctuation data of the target wavelength range is obtained based on the original height distribution information, including: After filtering the original height distribution information of the wafer surface using the filter corresponding to the target wavelength range, the filtering value of each sampling point is obtained; The topography fluctuation data are formed according to the filtered values ​​of all sampling points.

[0035] For the above example, specifically, the filter corresponding to the target wavelength range can be exemplarily a circular filter, and is composed of a double Gaussian low-pass filter function. Specifically, for the first Gaussian low-pass filter function G whose low-pass filter range covers the upper limit of the target wavelength range, LP1 A second Gaussian low-pass filter function G with a low-pass filter range covering to the lower limit of the target wavelength range LP2, the function G used to describe the filter corresponding to the target wavelength range is obtained according to the following formula: DHP : G DHP = G LP1 (1-G LP2 ).

[0036] For example, the relationship between the cutoff wavelength and the standard deviation is defined as , the first Gaussian low-pass filter function and the second Gaussian low-pass filter function are ,in, The parameter of the function is the wavelength. It should be noted that for the first Gaussian low-pass filter function and the second Gaussian low-pass filter function, the standard deviation of the two is are different, so that signals in different wavelength ranges can be retained.

[0037] Exemplarily, the present disclosure uses a target wavelength range of 0.2 mm to 20 mm as an example to illustrate the solution. It can be understood that during the implementation of the technical solution of the present disclosure, the target wavelength range can also be narrowed to 1.8 μm to 22 μm, or even 0 to 1.8 μm.

[0038] In step S103 , the position of the wafer surface defect is determined according to the highest peak value and the width of the peak interval in the multiple peak intervals of the profile fluctuation data.

[0039] In the present disclosure, taking the cross-sectional view of the diameter corresponding to the Y-axis of the wafer surface in the Cartesian coordinate system as an example, the morphology fluctuation data curve corresponding to the target wavelength is as follows: Figure 4 As shown, in Figure 4 In the figure, the horizontal axis represents the distance from the center of the wafer surface in millimeters (mm), and the vertical axis represents the corresponding morphological fluctuation data at this distance in nanometers (nm). Figure 4 It can be seen that in the morphological fluctuation data of the diameter, four relatively obvious peak intervals appear, which are marked as , wherein each peak interval is divided by two adjacent inflection points, the width of the peak interval is the distance between the two inflection points, and each peak interval has a highest peak value. When there is a defect of a size corresponding to the target wavelength range, it will affect the morphological fluctuation data corresponding to the target wavelength range at its location. In the present disclosure, the highest peak value and width of the peak interval are measured, and the measurement data is used to determine whether an impact occurs. If an impact occurs, the position of the defect of a size corresponding to the target wavelength range on the diameter can be obtained based on the measurement data.

[0040] pass Figure 1The technical solution shown, after obtaining the original height distribution of the wafer surface, uses the target wavelength range corresponding to the defect size to be detected to filter the original height distribution, and obtains morphology fluctuation data corresponding to the target wavelength range, and then uses the highest peak value and the peak interval width in the morphology fluctuation data to determine the location of the defect, so as to more accurately detect slight defects on the wafer surface.

[0041] for Figure 1 The technical solution shown in Figure 5 In some possible implementations, in multiple peak intervals of morphology fluctuation data, the location of the wafer surface defect is determined according to the highest peak value and the width of the peak interval, including steps S501 to S503.

[0042] In step S501, for each peak interval in the morphology fluctuation data, whether the peak interval is a to-be-selected peak interval is determined according to the highest peak value in the peak interval and the lowest value of the fluctuation values ​​corresponding to the two inflection points.

[0043] In the present disclosure, defects (such as protrusions or depressions on the surface) will increase the Peak to Valley (PV) value of the morphological fluctuation data at the location where the defect is located. Based on this, the present disclosure uses the PV value of each peak interval to perform a preliminary screening of the peak intervals, and selects candidate peak intervals where defects may occur from all the peak intervals based on the PV value.

[0044] Specifically, if Figure 6 As shown, the process of screening each peak interval may include: S61: Obtain the PV value of the peak interval according to the difference between the highest peak value in the peak interval and the lowest value of the fluctuation values ​​corresponding to the two inflection points.

[0045] S62: Compare the PV value of the peak interval with the set first evaluation threshold to determine whether the PV value of the peak interval is greater than or equal to the first evaluation threshold: If the PV value of the peak interval is greater than or equal to the first evaluation threshold, execute S63: determine that the peak interval is a candidate peak interval. If the PV value of the peak interval is less than the first evaluation threshold, execute S64: determine that there is no defect in the peak interval.

[0046] In some examples, the first evaluation threshold may be 10 nm.

[0047] In step S502, for each peak interval to be selected, it is determined whether there is a defect in the peak interval to be selected according to the highest peak value in the peak interval to be selected, the lowest value of the fluctuation values ​​corresponding to the two inflection points, and the width of the peak interval to be selected.

[0048] In the present disclosure, in a peak interval with defects, if the increase in the PV value between the highest peak value and the lowest value of the interval is caused by the defect, then the width of the peak interval will not change compared to the normal situation without defects. Based on this, for the selected peak interval selected in step S501 above, the ratio of the PV value to the peak interval width can be used to characterize whether the increase in the PV value is caused by a defect. Specifically, Figure 7 As shown, the process of determining whether there is a defect in the to-be-selected peak interval may include: S71: Calculate the ratio between the PV value of the peak interval to be selected and the width of the peak interval to be selected.

[0049] In the present disclosure, as mentioned above, the PV value of the peak interval to be selected is the difference between the highest peak value in the peak interval to be selected and the lowest value of the fluctuation values ​​corresponding to the two inflection points.

[0050] S72: Compare the ratio with the set second evaluation threshold to determine whether the ratio is greater than or equal to the second evaluation threshold: If the ratio is greater than or equal to the second evaluation threshold, execute step S73: Determine whether there is a defect in the selected peak interval. If the ratio is less than the second evaluation threshold, execute step S74: Determine whether there is no defect in the selected peak interval.

[0051] In some examples, the second evaluation threshold may be 0.05.

[0052] In step S503, for each defective candidate peak interval, the position of the highest peak in the candidate peak interval is determined as the position of the defect on the wafer surface.

[0053] for Figure 5 The implementation shown is Figure 4 The morphology fluctuation data curve of the cross-sectional view of the diameter corresponding to the Y axis is shown as an example, and the morphology fluctuation data curve includes 4 peak intervals. Figure 8 In each peak interval, the Peak value is the highest peak value, that is, the highest value of the fluctuation data in the interval, and the Valley value is the lowest value of the fluctuation data in the interval, usually the lower value of the fluctuation data between the two inflection points that define the peak interval, for example Figure 8 For the second peak interval from left to right, if the fluctuation data value of the left inflection point of the interval is greater than the fluctuation data value of the right inflection point, then the Valley value of the peak interval is the fluctuation data value of the right inflection point of the interval.

[0054] According to the difference between the Peak value and the Valley value, the PV value of the peak interval is obtained, such as Figure 6As shown, the PV values ​​of each peak interval are marked as PV1, PV2, PV3 and PV4. The width of the peak interval is the distance between the two inflection points that define the peak interval. Specifically, the distance between the two points where the tangents are drawn at the two inflection points and intersect the peak bottom.

[0055] for Figure 8 The PV values ​​of the four peak intervals shown in the figure are: PV1 = 7nm; PV2 = 13nm; PV3 = 7nm; PV4 = 8nm. Each of these PV values ​​is compared with the first evaluation threshold, and the first evaluation threshold is set to 10nm. It can be seen that PV2 is greater than 10nm, so this peak interval can be used as a candidate peak interval. Next, the ratio of PV2 to the width D2 of the peak interval is calculated, and the value is calculated by Figure 8 The data shown in the figure shows that the ratio is 0.289, which is also greater than the second evaluation threshold, such as 0.05, so it can be finally confirmed that there is a defect in the peak interval. The position corresponding to the highest peak value of the peak interval is reflected in the Cartesian coordinate system described in the above scheme, and the coordinates of the defect position on the wafer surface are (-40, 0).

[0056] Through the above technical solution, such as Fig. 9 In the nanotopography (NT) diagram shown, the position indicated by the arrow can be visually distinguished to have defects at that position. Through the technical solution of the present disclosure, the ratios of PV value to width calculated at the corresponding positions are 0.135, 0.168 and 0.130, respectively, all greater than 0.05, that is, the technical solution of the present disclosure can accurately detect the tiny size defects on the surface of the wafer corresponding to the wavelength range of the NT value. It can be understood that when the target wavelength range of the present technical solution is reduced to 1.8μm to 22μm wavelength, or even 0 to 1.8μm wavelength, the technical solution of the present disclosure can also detect the corresponding tiny size defects.

[0057] Based on the same inventive concept as the above technical solution, see Fig.10 , which shows the composition of a wafer surface defect detection device 100 provided by the present disclosure, the detection device 100 includes: a first acquisition unit 1001, a second acquisition unit 1002 and a determination unit 1003; wherein, The first acquisition unit 1001 is configured to acquire original height distribution information of the wafer surface; The second acquisition unit 1002 is configured to acquire the topography fluctuation data of the target wavelength range based on the original height distribution information; The determination unit 1003 is configured to determine the location of the wafer surface defect according to the highest peak value and the width of the peak interval in multiple peak intervals of the morphology fluctuation data; wherein the peak interval is the fluctuation data interval between two adjacent inflection points, and the width of the peak interval is the distance between the corresponding two inflection points.

[0058] In some examples, the determining unit 1003 is configured to: For each peak interval in the morphology fluctuation data, determining whether the peak interval is a candidate peak interval according to the highest peak value in the peak interval and the lowest value of the fluctuation values ​​corresponding to the two inflection points; For each peak interval to be selected, determine whether there is a defect in the peak interval to be selected according to the highest peak value in the peak interval to be selected, the lowest value of the fluctuation values ​​corresponding to the two inflection points, and the width of the peak interval to be selected; For each defective candidate peak interval, the position of the highest peak in the candidate peak interval is determined as the position of the defect on the wafer surface.

[0059] In some examples, the determining unit 1003 is configured to: The peak-to-valley PV value of the peak interval is obtained by the difference between the highest peak value in the peak interval and the lowest value of the fluctuation values ​​corresponding to the two inflection points; Compare the PV value of the peak interval with the set first evaluation threshold: If the PV value of the peak interval is greater than or equal to the first evaluation threshold, the peak interval is determined to be the candidate peak interval; If the PV value of the peak interval is less than the first evaluation threshold, it is determined that there is no defect in the peak interval.

[0060] In some examples, the determining unit 1003 is configured to: Calculate the ratio between the PV value of the peak interval to be selected and the width of the peak interval to be selected; wherein the PV value of the peak interval to be selected is the difference between the highest peak value in the peak interval to be selected and the lowest value of the fluctuation values ​​corresponding to the two inflection points; Compare the ratio with the set second evaluation threshold: If the ratio is greater than or equal to the second evaluation threshold, it is determined that there is a defect in the to-be-selected peak interval; If the ratio is less than the second evaluation threshold, it is determined that there is no defect in the to-be-selected peak interval.

[0061] In some examples, the first acquisition unit 1001 is configured to: The wafer is placed vertically in a double-sided Fizeau interferometer, and the original height distribution information of the wafer surface is obtained using wavelength phase shift. The original height distribution information of the wafer surface includes the height value of each sampling point on the front side of the wafer relative to the reference plane and the height value of each sampling point on the back side of the wafer relative to the reference plane.

[0062] In some examples, the second acquisition unit 1002 is configured to: After filtering the original height distribution information of the wafer surface using the filter corresponding to the target wavelength range, the filtering value of each sampling point is obtained; The topography fluctuation data are formed according to the filtered values ​​of all sampling points.

[0063] In some examples, the target wavelength range corresponds to a defect size that is desired to be detected.

[0064] 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.

[0065] 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 .

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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 wafer surface defect detection method as described in the above embodiments.

[0070] The present disclosure also provides a computer program product, which includes computer instructions, which are 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 wafer surface defect detection method described in the above-mentioned embodiments.

[0071] 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.

[0072] It should be noted that the technical solutions described in the present disclosure can be combined arbitrarily without conflict.

[0073] The above description is only a specific implementation mode of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present disclosure, which should be included in the protection scope of the present disclosure.

Claims

1. A method for detecting wafer surface defects, characterized in that: include: Obtaining original height distribution information of the wafer surface; Acquire the morphology fluctuation data of the target wavelength range based on the original height distribution information; In the multiple peak intervals of the morphology fluctuation data, the position of the wafer surface defect is determined according to the highest peak value and the width of the peak interval; wherein the peak interval is the fluctuation data interval between two adjacent inflection points, and the width of the peak interval is the distance between the corresponding two inflection points.

2. The detection method according to claim 1, characterized in that: In the plurality of peak intervals of the morphology fluctuation data, determining the position of the wafer surface defect according to the highest peak value and the width of the peak interval includes: For each peak interval in the morphology fluctuation data, determining whether the peak interval is a to-be-selected peak interval according to the highest peak value in the peak interval and the lowest value of the fluctuation values ​​corresponding to two inflection points; For each wave peak interval to be selected, determine whether there is a defect in the wave peak interval to be selected according to the highest peak value in the wave peak interval to be selected, the lowest value of the fluctuation values ​​corresponding to the two inflection points, and the width of the wave peak interval to be selected; For each defective candidate wave peak interval, the position of the highest peak in the candidate wave peak interval is determined as the position of the defect on the wafer surface.

3. The detection method according to claim 2, characterized in that: The determining whether the peak interval is a to-be-selected peak interval according to the highest peak value in the peak interval and the lowest value of the fluctuation values ​​corresponding to the two inflection points includes: Obtaining the peak-to-valley value of the peak interval according to the difference between the highest peak value in the peak interval and the lowest value of the fluctuation values ​​corresponding to the two inflection points; Compare the peak-to-valley value of the peak interval with the set first evaluation threshold: If the peak-to-valley value of the peak interval is greater than or equal to the first evaluation threshold, determining the peak interval as a candidate peak interval; If the peak-to-valley value of the peak interval is less than the first evaluation threshold, it is determined that there is no defect in the peak interval.

4. The detection method according to claim 2, characterized in that: The determining whether there is a defect in the to-be-selected peak interval according to the highest peak value in the to-be-selected peak interval, the lowest value of the fluctuation values ​​corresponding to the two inflection points, and the width of the to-be-selected peak interval includes: Calculate the ratio between the peak-to-valley value of the peak interval to be selected and the width of the peak interval to be selected; wherein the peak-to-valley value of the peak interval to be selected is the difference between the highest peak value in the peak interval to be selected and the lowest value of the fluctuation values ​​corresponding to the two inflection points; The ratio is compared with a set second evaluation threshold: If the ratio is greater than or equal to the second evaluation threshold, it is determined that there is a defect in the to-be-selected peak interval; If the ratio is less than the second evaluation threshold, it is determined that there is no defect in the to-be-selected peak interval.

5. The detection method according to claim 1, characterized in that: The obtaining of original height distribution information of the wafer surface includes: The wafer is placed vertically in a double-sided Fizeau interferometer, and the original height distribution information of the wafer surface is obtained by using wavelength phase shift. The original height distribution information of the wafer surface includes the height value of each sampling point on the front side of the wafer relative to the reference plane and the height value of each sampling point on the back side of the wafer relative to the reference plane.

6. The detection method according to claim 5, characterized in that: The step of acquiring the morphology fluctuation data in the target wavelength range based on the original height distribution information comprises: After filtering the original height distribution information of the wafer surface using a filter corresponding to the target wavelength range, a filtering value of each sampling point is obtained; The topography fluctuation data is formed according to the filtered values ​​of all sampling points.

7. The detection method according to claim 1, characterized in that: The target wavelength range corresponds to the size of defects that are expected to be detected.

8. A wafer surface defect detection device, characterized in that: The detection device comprises: a first acquisition unit, a second acquisition unit and a determination unit; wherein, The first acquisition unit is configured to acquire original height distribution information of the wafer surface; The second acquisition unit is configured to acquire the topography fluctuation data of the target wavelength range based on the original height distribution information; The determination unit is configured to determine the location of the wafer surface defect according to the highest peak value and the width of the peak interval in multiple peak intervals of the morphology fluctuation data; wherein the peak interval is the fluctuation data interval between two adjacent inflection points, and the width of the peak interval is the distance between the corresponding two inflection points.

9. A computing device, characterized in that The computing device comprises: a processor and a memory; the processor is used to execute instructions stored in the memory to implement the wafer surface defect detection method as described in any one of claims 1 to 7.

10. 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 wafer surface defect detection method as described in any one of claims 1 to 7.

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