A wafer surface defect detection method, device, equipment and medium

Abstract

The present disclosure provides a wafer surface defect detection method, device, equipment and medium; the detection method can comprise: obtaining the original height distribution information of the wafer surface; obtaining the topography fluctuation data of the target wavelength range based on the original height distribution information; in the multiple wave peak intervals of the topography fluctuation data, the position of the wafer surface defect is determined according to the highest peak value and the width of the wave peak interval.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of semiconductor manufacturing, and particularly relates to a wafer surface defect detection method, device, equipment and medium. BACKGROUND

[0002] In a wafer manufacturing process, after a single crystal silicon rod is prepared by a Czochralski method, the single crystal silicon rod is sequentially subjected to wire cutting, grinding, etching, grinding and chemical mechanical polishing (CMP) and other processing procedures, and finally a single crystal silicon wafer is obtained. For a single crystal silicon wafer, the number of defects on its surface is a key parameter for measuring its quality.

[0003] Currently, the related scheme usually identifies defects and confirms the occurrence position of surface defects by analyzing the image after detecting the nanotopography of the wafer, but for more slight surface defects, it is often impossible to identify and confirm them through the image. With the continuous reduction of the line width of semiconductor devices and the continuous development of device structures to multi-layer stacked structures, more stringent requirements are put forward for the slighter defects on the wafer surface. Therefore, it is necessary to propose a wafer surface defect detection method based on more stringent defect detection requirements. SUMMARY

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

[0005] The technical solution of the present disclosure is implemented as follows:

[0006] In a first aspect, the present disclosure provides a wafer surface defect detection method, comprising:

[0007] obtaining original height distribution information of a wafer surface;

[0008] obtaining topography fluctuation data of a target wavelength range based on the original height distribution information;

[0009] In a plurality of peak interval of the topography 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.

[0010] In a second aspect, the present disclosure provides a wafer surface defect detection device, comprising a first obtaining unit, a second obtaining unit and a determining unit; wherein,

[0011] The first obtaining unit is configured to obtain original height distribution information of a wafer surface;

[0012] The second acquisition unit is configured to acquire topography fluctuation data of a target wavelength range based on the original height distribution information.

[0013] The determination unit is configured to determine the position of the wafer surface defect according to the highest peak value and the width of the peak interval in the plurality of peak intervals of the topography fluctuation data; wherein the peak interval is an interval of fluctuation data between two adjacent inflection points, and the width of the peak interval is the distance between the corresponding two inflection points.

[0014] In a third aspect, the present disclosure provides a computing device, comprising: a processor and a memory; the processor is configured to execute instructions stored in the memory to implement the wafer surface defect detection method according to the first aspect.

[0015] In a fourth aspect, the present disclosure provides a computer readable storage medium, which stores at least one instruction for being executed by a processor to implement the wafer surface defect detection method according to the first aspect.

[0016] The present disclosure provides a wafer surface defect detection method, device, equipment and medium; after obtaining the original height distribution of the wafer surface, the original height distribution is filtered by using the target wavelength range corresponding to the size of the defect to be detected, to obtain the topography fluctuation data corresponding to the target wavelength range, and then the highest peak value of the peak interval and the width of the peak interval in the topography fluctuation data are used to determine the position of the defect, so that the slight defect on the wafer surface can be detected more accurately. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A wafer surface defect detection method flowchart is provided for the present disclosure.

[0018] Figure 2 An exemplary architecture schematic diagram of a double-sided Fizeau interferometer system is provided for the present disclosure.

[0019] Figure 3 A schematic diagram of obtaining the height distribution of the wafer by wavelength phase shift is provided for the present disclosure.

[0020] Figure 4 A topography fluctuation data curve schematic diagram is provided for the present disclosure.

[0021] Figure 5 A flowchart for determining the position of the wafer surface defect is provided for the present disclosure.

[0022] Figure 6 A flowchart for determining the selected peak interval is provided for the present disclosure.

[0023] Figure 7 A flowchart for determining whether a candidate peak interval has a defect is provided for the present disclosure.

[0024] Figure 8 Another profile fluctuation data curve diagram is provided for the present disclosure.

[0025] Figure 9 A defect detection result diagram is provided for the present disclosure.

[0026] Figure 10 A composition diagram of a wafer surface defect detection device is provided for the present disclosure.

[0027] Figure 11 A structure block diagram of a computing device is provided for the present disclosure. DETAILED DESCRIPTION

[0028] The technical solutions in the present disclosure will be described in detail below with reference to the drawings in the present disclosure.

[0029] The wafer surface profile presents a fluctuation phenomenon as a whole. This fluctuation phenomenon can be considered as a superposition of wave signals of different wavelengths, and the smaller the wavelength of the wave signal, the smaller the size of the region representing the fluctuation phenomenon; the larger the wavelength of the wave signal, the larger the size of the region representing the fluctuation phenomenon. When a defect occurs on the wafer surface, the defect will affect the above-mentioned fluctuation phenomenon, and when the size of the defect is smaller (i.e. the defect is more slight), it will not affect the wave signal with a larger wavelength in the above-mentioned fluctuation phenomenon, but will have a more significant effect on the wave signal with a smaller wavelength in the above-mentioned fluctuation phenomenon.

[0030] Based on this understanding, the present disclosure obtains wave signal data in a corresponding wavelength range after detecting a slight defect, and determines whether there is a slight defect based on the wave signal data.

[0031] Based on this, referring to FIG. 1, a wafer surface defect detection method is provided for the present disclosure, and the detection method includes steps S101-S103. Figure 1

[0032] In step S101, the original height distribution information of the wafer surface is obtained.

[0033] ​In some examples, the wafer can be placed vertically in a two-sided Fizeau interferometer, and the original height distribution information of the wafer surface can be obtained by wavelength phase shifting. This original height distribution information includes the height value of each sampling point on the front side of the wafer relative to a reference plane and the height value of each sampling point on the back side of the wafer relative to the reference plane. In this disclosure, the wafer surface includes the front and back sides of the wafer. The front side of the wafer is defined as the surface on which the wafer is patterned during semiconductor device manufacturing, and the surface opposite to the front side is the back side.

[0034] Specifically, an exemplary architecture of a two-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 within the interferometer system 200, thereby enabling the measurement of the morphology of both the front and back surfaces of the wafer 202. Furthermore, 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, 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. Laser beams 224-1 and 224-2 are generated by a laser 220 and provided to the beam splitter 212 of the corresponding interferometers 206-1 and 206-2 via corresponding optical fibers 222-1 and 222-2. In some examples, each Fizeau interferometer also includes a digital camera 216 for receiving a corresponding laser beam 224-1 or 224-2 reflected by a lens 214; and a computer 218 for processing data from the digital camera 216.

[0035] 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. By analyzing this 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 and back sides) of the wafer 202 are measured, and thus the original topography is measured.

[0036] like Figure 3 As shown, since the surface of wafer 202 is not perfectly flat, the distance between different locations on the wafer surface and the reference plane 208 is different. Therefore, the optical path difference varies depending on the location of the light rays traveling from the reference plane 208 to the surface of wafer 202 (e.g., the front side), being reflected by the surface of wafer 202, and then reaching the reference plane 208 again. Based on the optical path difference at different locations, the original height distribution of the wafer 202 surface can be obtained. Understandably, Figure 3 The surface of the wafer 202 shown can be either the front or the back of the wafer 202.

[0037] More specifically, a sampling point can be selected every 10 to 200 micrometers on both the front and back surfaces of wafer 202 and... Figure 2 The interferometer system 200 shown is used to perform measurements and calculate the optical path difference between each sampling point and the reference plane 208. After conversion, the original height distribution information of the front and back sides of the wafer 202 is obtained respectively.

[0038] In this disclosure, the technical solution is described using the front side of wafer 202 as an example. It can be understood that the technical solution of this disclosure can also be implemented to detect surface defects on the back side of wafer 202, which will not be elaborated here.

[0039] In step S102, topographic fluctuation data of the target wavelength range is obtained based on the original height distribution information.

[0040] In this disclosure, after obtaining the original height data of each sampling point through S101 measurement, 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. Therefore, this height distribution information will exhibit fluctuations in the measurement data of each sampling point as a whole, i.e., a three-dimensional undulating surface in the coordinate system. This disclosure considers this fluctuation phenomenon as being caused by the superposition of wave signals of different wavelengths. Different wavelengths correspond to the size of the region where the waveform phenomenon occurs; that is, the smaller the wavelength of the wave signal, the smaller the region representing the topographic fluctuation phenomenon; the larger the wavelength of the wave signal, the larger the region representing the topographic fluctuation phenomenon. When defects appear on the wafer surface, these defects will affect the different wavelength signals in the above-mentioned fluctuation phenomenon. Specifically, smaller defects (i.e., less severe defects) do not significantly affect the longer wavelength waves in the aforementioned fluctuation phenomenon, but they do have a more significant impact on the shorter wavelength waves. Therefore, the influence of minor defects on the waveform phenomena exhibited on the wafer surface is typically captured in the smaller wavelength fluctuation signals. Based on this understanding, in this disclosure, the target wavelength range corresponds to the desired defect size. For example, for some minor defects, the corresponding target wavelength range could be 0.2 mm to 20 mm. For even more severe defects, the corresponding target wavelength range could be 1.8 to 22 μm, or even 0 to 1.8 μm.

[0041] In this disclosure, since the original height distribution information is a superposition of signals of different wavelengths, and the size of the defect corresponds to the target wavelength range, in order to obtain a signal within the target wavelength range that can capture such a slight defect, the original height distribution information can be filtered to obtain the wave signal within the target wavelength range, i.e., the topographic fluctuation data within the target wavelength range. In some examples, obtaining the topographic fluctuation data within the target wavelength range based on the original height distribution information includes:

[0042] After filtering the original height distribution information of the wafer surface using a filter corresponding to the target wavelength range, the filtered value of each sampling point is obtained.

[0043] Topographic fluctuation data are generated based on the filtered values ​​of all sampling points.

[0044] In the above example, specifically, the filter corresponding to the target wavelength range can be, for example, a circular filter, and consists of a double Gaussian low-pass filter function. More specifically, the first Gaussian low-pass filter function G covers the upper limit of the target wavelength range. LP1A second Gaussian low-pass filter function G whose low-pass filtering range covers 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 :

[0045] G DHP = G LP1 (1-G) LP2 ).

[0046] For example, the relationship between the cutoff wavelength and the standard deviation is defined as follows: The first and second Gaussian low-pass filter functions are both ,in, The parameter of the function is the wavelength. It should be noted that the standard deviations of the first and second Gaussian low-pass filter functions are... They are different, thus enabling the preservation of signals in different wavelength ranges.

[0047] For example, this 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 in the implementation of the technical solution of this disclosure, the target wavelength range can also be reduced to 1.8 μm to 22 μm wavelength, or even 0 to 1.8 μm wavelength.

[0048] In step S103, the location of the wafer surface defect is determined based on the highest peak value and the width of the peak interval in the multiple peak intervals of the topographic fluctuation data.

[0049] In this 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 graph, the horizontal axis represents the distance from the center of the wafer surface, in millimeters (mm), and the vertical axis represents the morphology fluctuation data at that distance, in nanometers (nm). Figure 4 As can be seen, four distinct peak intervals appear in the morphological fluctuation data of this diameter, labeled as follows: Each peak interval is divided by two adjacent inflection points, and the width of the peak interval is the distance between the two inflection points. Each peak interval also has a highest peak value. When a defect of the size corresponding to the target wavelength range exists, it will affect the morphological fluctuation data corresponding to the target wavelength range at its location. In this disclosure, the highest peak value and width of the peak intervals are measured, and the measurement data is used to determine whether an impact occurs. If an impact occurs, the location of the defect of the size corresponding to the target wavelength range on this diameter can be obtained based on the measurement data.

[0050] passFigure 1 The technical solution shown in the figure, after obtaining the original height distribution of the wafer surface, filters the original height distribution using the target wavelength range corresponding to the expected defect size, and obtains the topographic fluctuation data corresponding to the target wavelength range. Then, the highest peak value and the width of the peak interval in the topographic fluctuation data are used to determine the location of the defect, so as to detect minor defects on the wafer surface more accurately.

[0051] for Figure 1 The technical solution shown is described in the following reference. Figure 5 In some possible implementations, the location of wafer surface defects is determined based on the highest peak value and the width of the peak interval in multiple peak intervals of the topographic fluctuation data, including steps S501 to S503.

[0052] In step S501, for each peak interval in the morphological fluctuation data, it is determined whether the peak interval is a candidate peak interval based on the highest peak value in the peak interval and the lowest value among the fluctuation values ​​corresponding to the two inflection points.

[0053] In this disclosure, defects (such as protrusions or depressions on the surface) increase the peak-to-valley (PV) value of the topographic fluctuation data at the location of the defect. Based on this, this disclosure uses the PV value of each peak interval to perform preliminary screening of peak intervals, and selects candidate peak intervals that may have defects from all peak intervals based on the PV value.

[0054] Specifically, such as Figure 6 As shown, the process for filtering each peak interval may include:

[0055] S61: The 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 value corresponding to the two inflection points.

[0056] 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, then proceed to S63: Determine the peak interval as a candidate peak interval. If the PV value of the peak interval is less than the first evaluation threshold, then proceed to S64: Determine that there are no defects in the peak interval.

[0057] In some examples, the first evaluation threshold can be 10 nm.

[0058] In step S502, for each candidate peak interval, it is determined whether there is a defect in the candidate peak interval based on the highest peak value in the candidate peak interval, the lowest value of the fluctuation values ​​corresponding to the two inflection points, and the width of the candidate peak interval.

[0059] In this disclosure, within a defective peak region, if the increase in the PV value between the highest peak and the lowest value of the region is due to the defect, then the width of the peak region will not change compared to the normal, defect-free condition. Based on this, for the candidate peak regions selected in step S501, the ratio of the PV value to the peak region width can be used to characterize whether the increase in the PV value is due to a defect. Specifically, as... Figure 7 As shown, the process for determining whether a candidate peak region has defects may include:

[0060] S71: Calculate the ratio between the PV value of the candidate peak interval and the width of the candidate peak interval.

[0061] In this disclosure, as previously stated, the PV value of the candidate peak interval is the difference between the highest peak value within the candidate peak interval and the lowest value among the fluctuation values ​​corresponding to the two inflection points.

[0062] S72: Compare the ratio with the set second evaluation threshold to determine if 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, proceed to step S73: Determine if there is a defect in the candidate peak interval. If the ratio is less than the second evaluation threshold, proceed to step S74: Determine if there is no defect in the candidate peak interval.

[0063] In some examples, the second evaluation threshold can be 0.05.

[0064] In step S503, for each candidate peak interval with a defect, the position of the highest peak value within the candidate peak interval is determined as the position of the defect on the wafer surface.

[0065] for Figure 5 The implementation shown is as follows: Figure 4 The topographic fluctuation data curve shown is an example of a cross-sectional view with the diameter corresponding to the Y-axis. This topographic fluctuation data curve includes four peak intervals. See [link / reference]. Figure 8 Within each peak interval, the Peak value represents the highest peak value, i.e., the highest value of the fluctuation data in that interval, while the Valley value represents the lowest value of the fluctuation data in that interval. It is typically the lower value of the fluctuation data between the two inflection points that define the peak interval. For example, in... Figure 8 The second peak interval from left to right in the middle, where 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.

[0066] Based on 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 ​​for each peak interval are labeled PV1, PV2, PV3, and PV4, respectively. The width of the peak interval is the distance between the two inflection points that define the peak interval. Specifically, it is the distance between the two points where the tangent lines intersect the peak base.

[0067] for Figure 8 The four peak intervals shown have PV values ​​of 7nm, 13nm, 7nm, and 8nm respectively. Each PV value is compared to a first evaluation threshold of 10nm. It can be seen that PV2 is greater than 10nm; therefore, this peak interval can be selected as a candidate peak interval. Next, the ratio of PV2 to the width D2 of this peak interval is calculated. Figure 8 The data shown shows a ratio of 0.289, which is also greater than the second evaluation threshold, for example, 0.05. Therefore, it can be finally confirmed that a defect exists in this peak range. If the position corresponding to the highest peak of this peak range is reflected in the Cartesian coordinate system described in the aforementioned scheme, then the coordinates of the defect position on the wafer surface are (-40, 0).

[0068] Through the above technical solutions, such as Figure 9 In the nanotopography (NT) diagram shown, the locations indicated by the arrows clearly indicate the presence of defects. Using the technical solution of this disclosure, the PV value to width ratios calculated at the corresponding locations are 0.135, 0.168, and 0.130, all greater than 0.05. This means that the technical solution of this disclosure can accurately detect minute-sized defects on the wafer surface corresponding to the wavelength range of the NT value. Understandably, as the target wavelength range of this disclosure shrinks to 1.8 μm to 22 μm, or even 0 to 1.8 μm, the technical solution of this disclosure can still detect the corresponding minute-sized defects.

[0069] Based on the same inventive concept as the aforementioned technical solution, see [link to inventive concept]. Figure 10 This disclosure illustrates the composition of a wafer surface defect detection device 100, which includes: a first acquisition unit 1001, a second acquisition unit 1002, and a determination unit 1003; wherein,

[0070] The first acquisition unit 1001 is configured to acquire the original height distribution information of the wafer surface;

[0071] The second acquisition unit 1002 is configured to acquire topographic fluctuation data of the target wavelength range based on the original height distribution information;

[0072] The determining unit 1003 is configured to determine the location of a wafer surface defect based on the highest peak value and the width of the peak interval in multiple peak intervals of topographic 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.

[0073] In some examples, the determining unit 1003 is configured to:

[0074] For each peak interval in the morphological fluctuation data, determine whether the peak interval is a candidate peak interval based on the highest peak value in the peak interval and the lowest value of the fluctuation value corresponding to the two inflection points.

[0075] For each candidate peak interval, determine whether the candidate peak interval has defects based on the highest peak value within the candidate peak interval, the lowest value among the fluctuation values ​​corresponding to the two inflection points, and the width of the candidate peak interval.

[0076] For each candidate peak interval with defects, the location of the highest peak value within the candidate peak interval is determined as the location of the defect on the wafer surface.

[0077] In some examples, the determining unit 1003 is configured to:

[0078] The peak-to-valley PV value of the peak interval is obtained by the difference between the highest peak value within the peak interval and the lowest value of the fluctuation value corresponding to the two inflection points.

[0079] Compare the PV value in the peak region with the set first evaluation threshold:

[0080] If the PV value of the peak interval is greater than or equal to the first evaluation threshold, then the peak interval is determined as a candidate peak interval.

[0081] If the PV value of the peak interval is less than the first evaluation threshold, then it is determined that there are no defects in the peak interval.

[0082] In some examples, the determining unit 1003 is configured to:

[0083] Calculate the ratio between the PV value of the candidate peak interval and the width of the candidate peak interval; where the PV value of the candidate peak interval is the difference between the highest peak value and the lowest fluctuation value between the two inflection points within the candidate peak interval.

[0084] Compare the ratio to the set second evaluation threshold:

[0085] If the ratio is greater than or equal to the second evaluation threshold, it is determined that there is a defect in the candidate peak interval;

[0086] If the ratio is less than the second evaluation threshold, then it is determined that there are no defects in the candidate peak interval.

[0087] In some examples, the first acquisition unit 1001 is configured to:

[0088] The wafer is placed vertically in a double-sided Fizeau interferometer, and the original height distribution information of the wafer surface is obtained by wavelength phase shifting. 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.

[0089] In some examples, the second acquisition unit 1002 is configured to:

[0090] After filtering the original height distribution information of the wafer surface using a filter corresponding to the target wavelength range, the filtered value of each sampling point is obtained.

[0091] Topographic fluctuation data are generated based on the filtered values ​​of all sampling points.

[0092] In some examples, the target wavelength range corresponds to the size of the defect that is expected to be detected.

[0093] Please refer to Figure 11 This diagram illustrates a structural block diagram of a computing device provided in an exemplary embodiment of this disclosure. In some examples, the computing device 110 may be at least one of devices such as a smartphone, smartwatch, desktop computer, laptop, virtual reality terminal, augmented reality terminal, wireless terminal, and laptop computer. The computing device 110 has communication capabilities and can access wired or wireless networks. The computing device 110 may refer to one of a plurality of terminals, and those skilled in the art will understand that the number of such terminals may be more or less. In some examples, the computing device 110 may receive data based on the accessed wired or wireless network. It is understood that the computing device 110 undertakes the computation and processing work of the technical solution of this disclosure, and this disclosure does not limit it in this respect.

[0094] like Figure 11 As shown, the computing device in this disclosure may include one or more of the following components: processor 1110 and memory 1120.

[0095] Optionally, the processor 1110 connects various parts within the computing device using various interfaces and lines. It executes various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1120, and by calling data stored in the memory 1120. Optionally, the processor 1110 can be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 1110 can integrate one or more of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), Neural-network Processing Unit (NPU), and baseband chip. Specifically, the CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display on the touch screen; the NPU is used to implement Artificial Intelligence (AI) functions; and the baseband chip is used for wireless communication. It is understandable that the aforementioned baseband chip may not be integrated into the processor 1110, but may be implemented using a separate chip.

[0096] The memory 1120 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 1120 may include a non-transitory computer-readable storage medium. The memory 1120 may be used to store instructions, programs, code, 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 touch function, sound playback function, image playback function, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data created according to the use of the computing device, etc.

[0097] In addition, those skilled in the art will understand that the structure of the computing device shown in the above figures does not constitute a limitation on the computing device. The computing device may include more or fewer components than shown, or combine certain components, or have different component arrangements. For example, the computing device may also include a display screen, camera assembly, microphone, speaker, radio frequency circuit, input unit, sensors (such as accelerometer, angular velocity sensor, light sensor, etc.), audio circuit, WiFi module, power supply, Bluetooth module, etc., which will not be described in detail here.

[0098] This disclosure also provides a computer-readable storage medium storing at least one instruction that is executed by a processor to implement the wafer surface defect detection method as described in the various embodiments above.

[0099] This disclosure also provides a computer program product including 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 executes the computer instructions, causing the computing device to perform the wafer surface defect detection method described in the above embodiments.

[0100] Those skilled in the art will recognize that the functions described in this disclosure in one or more of the examples above can be implemented using hardware, software, firmware, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transfer of a computer program from one place to another. Storage media can be any available medium accessible to a general-purpose or special-purpose computer.

[0101] It should be noted that the technical solutions described in this disclosure can be combined arbitrarily as long as they do not conflict.

[0102] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for detecting defects on the surface of a wafer, characterized in that, include: Obtain the original height distribution information of the wafer surface; Based on the original height distribution information, obtain the topographic fluctuation data within the target wavelength range; In the multiple peak intervals of the topographic fluctuation data, the location of the wafer surface defect is determined based on 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; Specifically, in the multiple peak intervals of the topographic fluctuation data, the location of wafer surface defects is determined based on the highest peak value and the width of the peak interval, including: For each peak interval in the morphology fluctuation data, whether the peak interval is a candidate peak interval is determined based on the highest peak value within the peak interval and the lowest fluctuation value among the two inflection points; for each candidate peak interval, whether the candidate peak interval has a defect is determined based on the highest peak value within the candidate peak interval, the lowest fluctuation value among the two inflection points, and the width of the candidate peak interval; for each candidate peak interval with a defect, the position of the highest peak value within the candidate peak interval is determined as the position of the wafer surface defect; The step of determining whether a peak interval is a candidate peak interval based on the highest peak value within the peak interval and the lowest value among the fluctuation values ​​corresponding to the two inflection points includes: The peak-to-valley 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 value corresponding to the two inflection points; the peak-to-valley value of the peak interval is compared with a set first evaluation threshold: if the peak-to-valley value of the peak interval is greater than or equal to the first evaluation threshold, the peak interval is determined as a candidate peak interval. The step of determining whether the candidate peak interval has defects based on the highest peak value within the candidate peak interval, the lowest value among the fluctuation values ​​corresponding to the two inflection points, and the width of the candidate peak interval includes: Calculate the ratio between the peak-to-valley value of the candidate peak interval and the width of the candidate peak interval; wherein the peak-to-valley value of the candidate peak interval is the difference between the highest peak value and the lowest fluctuation value among the two inflection points in the candidate peak interval; compare the ratio with a set second evaluation threshold: if the ratio is greater than or equal to the second evaluation threshold, it is determined that the candidate peak interval has a defect; if the ratio is less than the second evaluation threshold, it is determined that the candidate peak interval has no defect.

2. The detection method according to claim 1, characterized in that, The acquisition of the 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 wavelength phase shifting. 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.

3. The detection method according to claim 2, characterized in that, The acquisition of topographic fluctuation data within the target wavelength range based on the original height distribution information includes: After filtering the original height distribution information of the wafer surface using a filter corresponding to the target wavelength range, the filtered value of each sampling point is obtained; The morphological fluctuation data is generated based on the filtered values ​​of all sampling points.

4. The detection method according to claim 1, characterized in that, The target wavelength range corresponds to the size of the defect that is expected to be detected.

5. A device for detecting defects on the surface of a wafer, characterized in that, The detection device includes: a first acquisition unit, a second acquisition unit, and a determination unit; wherein... The first acquisition unit is configured to acquire the original height distribution information of the wafer surface; The second acquisition unit is configured to acquire topographic fluctuation data of the target wavelength range based on the original height distribution information; The determining unit is configured to determine the location of a wafer surface defect based on the highest peak value and the width of the peak interval in the multiple peak intervals of the topographic 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; The determining unit is configured to: For each peak interval in the morphology fluctuation data, whether the peak interval is a candidate peak interval is determined based on the highest peak value within the peak interval and the lowest fluctuation value among the two inflection points; for each candidate peak interval, whether the candidate peak interval has a defect is determined based on the highest peak value within the candidate peak interval, the lowest fluctuation value among the two inflection points, and the width of the candidate peak interval; for each candidate peak interval with a defect, the position of the highest peak value within the candidate peak interval is determined as the position of the wafer surface defect; The determining unit is configured to: The peak-to-valley 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 value corresponding to the two inflection points; the peak-to-valley value of the peak interval is compared with a set first evaluation threshold: if the peak-to-valley value of the peak interval is greater than or equal to the first evaluation threshold, the peak interval is determined as a candidate peak interval. Calculate the ratio between the peak-to-valley value of the candidate peak interval and the width of the candidate peak interval; wherein the peak-to-valley value of the candidate peak interval is the difference between the highest peak value and the lowest fluctuation value among the two inflection points in the candidate peak interval; compare the ratio with a set second evaluation threshold: if the ratio is greater than or equal to the second evaluation threshold, it is determined that the candidate peak interval has a defect; if the ratio is less than the second evaluation threshold, it is determined that the candidate peak interval has no defect.

6. 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 wafer surface defect detection method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which is executed by a processor to implement the wafer surface defect detection method as described in any one of claims 1 to 4.

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