Semiconductor wafer defect detection method and device

Abstract

The invention provides a semiconductor wafer defect detection method and device, in the application, after an infrared heating tube is used to irradiate a semiconductor wafer, the semiconductor wafer is heated, and when defects such as scratches, pits and cracks exist on the surface of the semiconductor wafer, the defects can hinder heat energy propagation, so that the defects can be prevented from being damaged. According to the principle, when defects exist on the surface of the semiconductor wafer, infrared imaging is carried out after the semiconductor wafer is heated, and due to the fact that pixel values corresponding to different temperatures in an infrared image are different, the defects of the surface of the semiconductor wafer cannot be detected, and the defects of the surface of the semiconductor wafer cannot be detected. Therefore, after the infrared image of the heated semiconductor wafer is obtained, whether the semiconductor wafer has defects or not can be obtained by analyzing the pixel values of the pixel points, the accuracy of the surface defects of the semiconductor wafer can be improved in the mode, and the yield of the semiconductor wafer can be improved.

Description

Technical Field

[0001] The present application relates to the field of semiconductor detection technology, and in particular, to a method and device for detecting semiconductor wafer defects. Background Art

[0002] Before the semiconductor wafer is etched, it is necessary to detect whether the semiconductor wafer has defects (such as scratches, pits, cracks, etc.). If the semiconductor wafer is not inspected for defects, the yield of the semiconductor wafer will be reduced. At present, a camera is usually used to scan the semiconductor wafer, that is, to inspect it by imaging. However, since the surface of the semiconductor wafer has a metallic luster and good reflective properties, it will have a certain impact on the imaging, thereby affecting the detection results and causing inaccurate detection results. Summary of the invention

[0003] In view of this, an embodiment of the present application provides a method and device for detecting semiconductor wafer defects to improve the accuracy of semiconductor wafer defect detection.

[0004] In a first aspect, an embodiment of the present application provides a method for detecting defects in semiconductor wafers, the detection method being applied to a front-end module EFEM device, the EFEM device being provided with a dark box, the dark box being provided with an infrared heating tube, and an infrared camera for collecting infrared images, after an overhead crane system places a wafer box in a material preparation area of ​​the EFEM device, before the EFEM device transfers the semiconductor wafer in the wafer box to an etching process device for etching by a wafer manipulator, the detection method comprises: Controlling the wafer robot to transfer the semiconductor wafer to a designated position of the dark box, and controlling the infrared camera to take a picture of the semiconductor wafer to obtain a first infrared image; Controlling the infrared heating tube to irradiate the semiconductor wafer, and controlling the infrared camera to take a picture of the semiconductor wafer after a preset time to obtain a second infrared image; After aligning the first infrared image and the second infrared image, comparing the pixels at the same position of the first infrared image and the second infrared image, and determining a target area consisting of pixels whose pixel value variation is greater than a preset threshold from the second infrared image; Deduplicating pixel values ​​of pixel points in the target area, and sorting the deduplicated pixel values ​​in ascending order to obtain a pixel value sequence; The sorting position in the pixel value sequence is determined using the following formula: ; Wherein, N is the total number of pixel values ​​included in the pixel value sequence, and K is 1 and 3 respectively; The pixel value threshold range is calculated using the following formula: R1=Q1-1.5M; R2=Q3+1.5M; Wherein, Q1 is the first pixel value corresponding to the sorting position I obtained by rounding when the value of K1 is 1, Q3 is the second pixel value corresponding to the sorting position I obtained by rounding when the value of K1 is 3, and M is the difference between the second pixel value and the first pixel value; Determine whether there is a target pixel value in the pixel value sequence that does not fall within the pixel value threshold range; If the target pixel value exists, it is determined that the semiconductor wafer has a defect.

[0005] Optionally, the method further comprises: Determine a target pixel point in the target area having a value equal to the target pixel value; The distance between any two target pixels is calculated using the following formula: ; in, and is the horizontal and vertical coordinates of a target pixel point, and is the horizontal and vertical coordinates of another target pixel; After obtaining the distance between any two target pixels, calculate the standard deviation of all distances; When the standard deviation is greater than or equal to a preset standard deviation, the wafer robot is controlled to transfer the semiconductor wafer to an etching process equipment for etching process.

[0006] Optionally, the method further comprises: When the standard deviation is less than the preset standard deviation, the wafer robot is controlled to place the semiconductor wafer into a wafer box to be processed.

[0007] Optionally, the method further comprises: When the number of semiconductor wafers continuously placed in the wafer box to be processed exceeds a preset number, an alarm signal is issued.

[0008] Optionally, before deduplication of pixel values ​​of pixels in the target area, the method further includes: For each column of pixels in the target area, calculating the mean pixel value of the column of pixels; After obtaining the mean pixel value of each column of pixels, the mean pixel value of each column of pixels is averaged to obtain a global mean value; For each pixel value mean, calculate the difference between the pixel value mean and the global mean, and use the difference as a correction value; For each pixel point in the target area, the difference between the pixel value of the pixel point and the correction value is calculated to obtain a corrected image, so as to use the pixel values ​​of the pixels in the corrected image for deduplication.

[0009] In a second aspect, an embodiment of the present application provides a semiconductor wafer defect detection device, wherein the detection device is arranged in a front-end module EFEM device, wherein a dark box is arranged on the EFEM device, wherein an infrared heating tube and an infrared camera for collecting infrared images are arranged in the dark box, and the detection device comprises: A first control unit is used for, after the overhead crane system places the wafer box in the material preparation area of ​​the EFEM device, and before the EFEM device transfers the semiconductor wafers in the wafer box to the etching process device through the wafer robot for etching, controlling the wafer robot to transfer the semiconductor wafers to a designated position of the dark box, and controlling the infrared camera to take a picture of the semiconductor wafer to obtain a first infrared image; A second control unit is used to control the infrared heating tube to irradiate the semiconductor wafer, and control the infrared camera to take a picture of the semiconductor wafer after a preset time to obtain a second infrared image; A comparison unit, configured to compare the pixels at the same position of the first infrared image and the second infrared image after aligning the first infrared image and the second infrared image, and determine a target area consisting of pixels whose pixel value variation is greater than a preset threshold from the second infrared image; A sorting unit, used to remove duplicate pixel values ​​of the pixel points in the target area, and sort the removed duplicate pixel values ​​in ascending order to obtain a pixel value sequence; The first calculation unit is used to determine the sorting position in the pixel value sequence using the following formula: ; Wherein, N is the total number of pixel values ​​included in the pixel value sequence, and K is 1 and 3 respectively; The second calculation unit is used to calculate the pixel value threshold range using the following formula: R1=Q1-1.5M; R2=Q3+1.5M; Wherein, Q1 is the first pixel value corresponding to the sorting position I obtained by rounding when the value of K1 is 1, Q3 is the second pixel value corresponding to the sorting position I obtained by rounding when the value of K1 is 3, and M is the difference between the second pixel value and the first pixel value; The judging unit is used to judge whether there is a target pixel value that does not fall within the pixel value threshold range in the pixel value sequence; if the target pixel value exists, it is determined that the semiconductor wafer has a defect.

[0010] Optionally, the detection device further includes: A determination unit, configured to determine a target pixel point in the target area having a value equal to that of the target pixel; The third calculation unit is used to calculate the distance between any two target pixels using the following formula: ; in, and is the horizontal and vertical coordinates of a target pixel point, and is the horizontal and vertical coordinates of another target pixel; A fourth calculation unit, configured to calculate the standard deviation of all distances after obtaining the distance between any two target pixel points; The third control unit is used to control the wafer robot to transfer the semiconductor wafer to the etching process equipment for etching process when the standard deviation is greater than or equal to the preset standard deviation.

[0011] Optionally, the third control unit is further used to: When the standard deviation is less than the preset standard deviation, the wafer robot is controlled to place the semiconductor wafer into a wafer box to be processed.

[0012] Optionally, the detection device further includes: The alarm unit is used to send out an alarm signal when the number of semiconductor wafers continuously placed in the wafer box to be processed exceeds a preset number.

[0013] Optionally, the sorting unit is further used to: Before deduplication of pixel values ​​of pixels in the target area, for each column of pixels in the target area, a mean pixel value of the column of pixels is calculated; After obtaining the mean pixel value of each column of pixels, the mean pixel value of each column of pixels is averaged to obtain a global mean value; For each pixel value mean, calculate the difference between the pixel value mean and the global mean, and use the difference as a correction value; For each pixel point in the target area, the difference between the pixel value of the pixel point and the correction value is calculated to obtain a corrected image, so as to use the pixel values ​​of the pixels in the corrected image for deduplication.

[0014] The technical solution provided by the embodiments of the present application may have the following beneficial effects: In the present application, after the semiconductor wafer is irradiated with an infrared heating tube, the semiconductor wafer will be heated. When there are defects such as scratches, pits and cracks on the surface of the semiconductor wafer, these defects will hinder the propagation of heat energy, causing local heat accumulation or loss, thereby resulting in a difference in temperature between the defective part and the temperature without defects. Based on this principle, when there are defects on the surface of the semiconductor wafer, the semiconductor wafer is heated and then infrared imaging is performed. Since different temperatures correspond to different pixel values ​​in the infrared image, after obtaining the infrared image of the heated semiconductor wafer, the pixel values ​​of the pixel points can be analyzed to determine whether the semiconductor wafer has defects. The above method can improve the accuracy of surface defects of semiconductor wafers, which is beneficial to improve the yield of semiconductor wafers.

[0015] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A schematic diagram of a process flow of a semiconductor wafer defect detection method provided in an embodiment of the present application; Figure 2 A schematic flow chart of another semiconductor wafer defect detection method provided in an embodiment of the present application; Figure 3 A schematic flow chart of another semiconductor wafer defect detection method provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of a semiconductor wafer defect detection device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0019] Figure 1 A flow chart of a semiconductor wafer defect detection method provided in an embodiment of the present application, wherein the detection method is applied to an EFEM (Equipment Front-End Module) device, wherein a dark box is provided on the EFEM device, wherein an infrared heating tube and an infrared camera for collecting infrared images are provided in the dark box, and after the overhead crane system places a wafer box in a material preparation area of ​​the EFEM device, before the EFEM device transfers the semiconductor wafer in the wafer box to an etching process device for etching by a wafer robot, such as Figure 1 As shown, the detection method comprises the following steps: Step 101: Control the wafer robot to transfer the semiconductor wafer to a designated position of the dark box, and control the infrared camera to take a picture of the semiconductor wafer to obtain a first infrared image.

[0020] Step 102: Control the infrared heating tube to irradiate the semiconductor wafer, and control the infrared camera to take a picture of the semiconductor wafer after a preset time to obtain a second infrared image.

[0021] Step 103: After aligning the first infrared image and the second infrared image, compare the pixels at the same position of the first infrared image and the second infrared image, and determine from the second infrared image a target area consisting of pixels whose pixel value changes are greater than a preset threshold.

[0022] Step 104: deduplicate pixel values ​​of the pixels in the target area, and sort the deduplicated pixel values ​​in ascending order to obtain a pixel value sequence.

[0023] Step 105: Determine the sorting position in the pixel value sequence using the following formula 1: ; (Formula 1) Wherein, N is the total number of pixel values ​​included in the pixel value sequence, and K is taken as 1 and 3 respectively.

[0024] Step 106: Calculate the pixel value threshold range using the following formula 2: R1=Q1-1.5M; R2=Q3+1.5M; (Formula 2) Among them, when Q1 is 1, the first pixel value corresponding to the sorting position I obtained by rounding is used, and when Q3 is 3, the second pixel value corresponding to the sorting position I obtained by rounding is used, and M is the difference between the second pixel value and the first pixel value.

[0025] Step 107 , determining whether there is a target pixel value in the pixel value sequence that does not fall within the pixel value threshold range; if there is the target pixel value, determining that the semiconductor wafer has a defect.

[0026] Specifically, in order to avoid the non-semiconductor wafer area in the infrared image from adversely affecting the detection results, when using the infrared image to detect defects of the semiconductor wafer, it is necessary to remove the non-semiconductor wafer area in the infrared image as much as possible. In order to achieve the above purpose, firstly, a first infrared image is taken before the semiconductor wafer is heated, and then the infrared heating tube is controlled to work and irradiate the semiconductor wafer so as to heat the semiconductor wafer. After the preset heating time, the irradiation of the semiconductor wafer is stopped. Since the infrared heating tube also heats the surrounding environment in the process of heating the semiconductor wafer, and since the heat dissipation rate of the surrounding environment is higher than the heat dissipation rate of the semiconductor wafer, it is possible to wait for the preset time. After a preset time, the semiconductor wafer is photographed to obtain a second infrared image. Since there is a large difference in temperature between the surrounding environment and the semiconductor wafer after the preset time, the second infrared image can reflect which area is the semiconductor wafer area and which area is the non-semiconductor wafer area. In order to distinguish the area where the semiconductor wafer is located through the infrared image, the first infrared image and the second infrared image can be aligned, and the pixels at the same position of the first infrared image and the second infrared image are compared. The pixel value of the pixel point in the area where the semiconductor wafer is located in the second infrared image changes more greatly than that of the pixel point in the non-semiconductor wafer area. Therefore, the target area, that is, the area where the semiconductor wafer is located, can be determined by setting a threshold.

[0027] After determining the target area (i.e., the area where the semiconductor wafer is located in the second infrared image), the pixel values ​​of the pixels in the target area are deduplicated. For example, there are 5 pixels in the target area, and the pixel values ​​are 10, 20, 30, 30, and 40, respectively. Then, the deduplicated data set is 10, 20, 30, and 40. Then, the deduplicated pixel values ​​are sorted in ascending order to obtain a pixel value sequence. Then, formula 1 is used to determine the sorting position and obtain the pixel value corresponding to the position. Assume that the pixel value sequence is: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, the sorting positions obtained by formula 1 include: 2.75 and 8.25, and then the sorting positions 3 and 8 are obtained by rounding, and the pixel values ​​corresponding to the sorting positions are 3 and 8 respectively. At this time, it can be determined that Q1 is 3, Q2 is 8, and M is 5, so R1 can be obtained as -4.5 and R2 is 15.5. Therefore, the pixel value threshold range calculated by formula 2 is (-4.5, 15.5), and then the pixel value sequence is screened using this pixel value threshold range to determine which pixel values ​​in the pixel value sequence are not within this range. In the above pixel value sequence, only 100 is not within this range. If there is such a pixel value, it is determined that the semiconductor wafer is defective.

[0028] It should be noted that the presence of defects does not mean that the semiconductor wafer is completely unusable.

[0029] In one possible embodiment, Figure 2 A flow chart of another semiconductor wafer defect detection method provided in an embodiment of the present application is provided. After determining that a semiconductor wafer has a defect, in order to determine whether the defect affects the yield, such as Figure 2 As shown, the method further comprises the following steps: Step 201: Determine a target pixel point in the target area that has the same value as the target pixel.

[0030] Step 202: Calculate the distance between any two target pixels using the following formula: ; in, and is the horizontal and vertical coordinates of a target pixel point, and is the horizontal and vertical coordinates of another target pixel.

[0031] Step 203: After obtaining the distance between any two target pixels, calculate the standard deviation of all distances.

[0032] Step 204: When the standard deviation is greater than or equal to a preset standard deviation, control the wafer robot to transfer the semiconductor wafer to an etching process equipment for etching process.

[0033] Specifically, after determining the target pixel value that does not fall within the pixel value threshold range, it can be determined that the target pixel point is the pixel point corresponding to the defective area. Therefore, the target pixel point where the target pixel value is located can be found in the target area, and the distance between any two target pixel points can be calculated using formula three. All the distances obtained above are used to calculate the standard deviation. Since the standard deviation can reflect the discreteness of these target pixel points, the larger the standard deviation, the more dispersed these target pixel points are, that is: the smaller the defect problem of the semiconductor wafer, the smaller the standard deviation, the more concentrated these target pixel points are, which will cause the defect problem of the semiconductor wafer to be larger. Therefore, when the standard deviation is greater than or equal to the preset standard deviation, it means that the defect problem of the semiconductor wafer is smaller. Therefore, the wafer robot can be controlled to move the semiconductor wafer to the etching process equipment for etching process.

[0034] In a feasible implementation manner, when the standard deviation is less than the preset standard deviation, the wafer robot is controlled to place the semiconductor wafer into a wafer box to be processed.

[0035] In a feasible implementation manner, when the number of semiconductor wafers continuously placed into the wafer box to be processed exceeds a preset number, an alarm signal is issued.

[0036] Specifically, when multiple semiconductor wafers are placed successively into a wafer box to be processed, it indicates that there may be a batch problem at this time, so an alarm message may be issued to remind the staff to conduct an inspection to avoid batch incidents.

[0037] In one possible embodiment, Figure 3 A schematic flow chart of another semiconductor wafer defect detection method provided in an embodiment of the present application, before deduplication of pixel values ​​of pixels in the target area, such as Figure 3 As shown, the following steps are also included: Step 301: For each column of pixels in the target area, calculate the mean pixel value of the column of pixels.

[0038] Step 302: After obtaining the mean pixel value of each column of pixels, average the mean pixel value of each column of pixels to obtain a global mean.

[0039] Step 303: For each pixel value mean, calculate the difference between the pixel value mean and the global mean, and use the difference as a correction value.

[0040] Step 304: For each pixel point in the target area, calculate the difference between the pixel value of the pixel point and the correction value to obtain a corrected image, so as to use the pixel values ​​of the pixels in the corrected image for deduplication.

[0041] Specifically, the output responses of the detector units on the infrared focal plane array in the infrared camera are not completely consistent, which will lead to the existence of fixed stripe pattern noise in the obtained infrared image. This stripe pattern usually appears as alternating light and dark in the column direction. Figure 3 The method shown can eliminate this column-wise brightness deviation, thereby removing the stripe noise and providing accurate data for subsequent deduplication.

[0042] Figure 4 The present invention provides a schematic diagram of the structure of a semiconductor wafer defect detection device provided in an embodiment of the present invention. The detection device is arranged in a front-end module EFEM device. The EFEM device is provided with a dark box, an infrared heating tube, and an infrared camera for collecting infrared images. Figure 4 As shown, the detection device comprises: The first control unit 41 is used for, after the overhead crane system places the wafer box in the material preparation area of ​​the EFEM device, and before the EFEM device transfers the semiconductor wafers in the wafer box to the etching process device through the wafer robot for etching, controlling the wafer robot to transfer the semiconductor wafers to the designated position of the dark box, and controlling the infrared camera to take a picture of the semiconductor wafer to obtain a first infrared image; A second control unit 42 is used to control the infrared heating tube to irradiate the semiconductor wafer, and control the infrared camera to take a picture of the semiconductor wafer after a preset time to obtain a second infrared image; A comparison unit 43 is used to compare the pixels at the same position of the first infrared image and the second infrared image after aligning the first infrared image and the second infrared image, and determine a target area composed of pixels whose pixel value variation is greater than a preset threshold from the second infrared image; A sorting unit 44 is used to remove duplicate pixel values ​​of the pixel points in the target area, and sort the removed duplicate pixel values ​​in ascending order to obtain a pixel value sequence; The first calculation unit 45 is used to determine the sorting position in the pixel value sequence using the following formula: ; Wherein, N is the total number of pixel values ​​included in the pixel value sequence, and K is 1 and 3 respectively; The second calculation unit 46 is used to calculate the pixel value threshold range using the following formula: R1=Q1-1.5M; R2=Q3+1.5M; Wherein, Q1 is the first pixel value corresponding to the sorting position I obtained by rounding when the value of K1 is 1, Q3 is the second pixel value corresponding to the sorting position I obtained by rounding when the value of K1 is 3, and M is the difference between the second pixel value and the first pixel value; The judging unit 47 is used to judge whether there is a target pixel value that does not fall within the pixel value threshold range in the pixel value sequence; if the target pixel value exists, it is determined that the semiconductor wafer has a defect.

[0043] In a feasible embodiment, the detection device further comprises: A determination unit, configured to determine a target pixel point in the target area having a value equal to that of the target pixel; The third calculation unit is used to calculate the distance between any two target pixels using the following formula: ; in, and is the horizontal and vertical coordinates of a target pixel point, and is the horizontal and vertical coordinates of another target pixel; A fourth calculation unit, configured to calculate the standard deviation of all distances after obtaining the distance between any two target pixel points; The third control unit is used to control the wafer robot to transfer the semiconductor wafer to the etching process equipment for etching process when the standard deviation is greater than or equal to the preset standard deviation.

[0044] In a feasible embodiment, the third control unit is further used for: When the standard deviation is less than the preset standard deviation, the wafer robot is controlled to place the semiconductor wafer into a wafer box to be processed.

[0045] In a feasible embodiment, the detection device further includes: The alarm unit is used to send out an alarm signal when the number of semiconductor wafers continuously placed in the wafer box to be processed exceeds a preset number.

[0046] In a feasible implementation scheme, the sorting unit is further used for: Before deduplication of pixel values ​​of pixels in the target area, for each column of pixels in the target area, a mean pixel value of the column of pixels is calculated; After obtaining the mean pixel value of each column of pixels, the mean pixel value of each column of pixels is averaged to obtain a global mean value; For each pixel value mean, calculate the difference between the pixel value mean and the global mean, and use the difference as a correction value; For each pixel point in the target area, the difference between the pixel value of the pixel point and the correction value is calculated to obtain a corrected image, so as to use the pixel values ​​of the pixels in the corrected image for deduplication.

[0047] about Figure 4 The principles of related content can be found in Figure 1-Figure 3 The explanation of the contents shown will not be repeated in detail here.

[0048] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0049] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0050] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0051] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0052] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0053] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application. They should all be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for detecting semiconductor wafer defects, characterized in that: The detection method is applied to a front-end module EFEM device, wherein a dark box is provided on the EFEM device, an infrared heating tube and an infrared camera for collecting infrared images are provided in the dark box, and after the overhead crane system places a wafer box in a material preparation area of ​​the EFEM device, before the EFEM device transfers the semiconductor wafer in the wafer box to an etching process device for etching by a wafer robot, the detection method comprises: Controlling the wafer robot to transfer the semiconductor wafer to a designated position of the dark box, and controlling the infrared camera to take a picture of the semiconductor wafer to obtain a first infrared image; Controlling the infrared heating tube to irradiate the semiconductor wafer, and controlling the infrared camera to take a picture of the semiconductor wafer after a preset time to obtain a second infrared image; After aligning the first infrared image and the second infrared image, comparing the pixels at the same position of the first infrared image and the second infrared image, and determining a target area consisting of pixels whose pixel value variation is greater than a preset threshold from the second infrared image; Deduplicating pixel values ​​of pixel points in the target area, and sorting the deduplicated pixel values ​​in ascending order to obtain a pixel value sequence; The sorting position in the pixel value sequence is determined using the following formula: ； Wherein, N is the total number of pixel values ​​included in the pixel value sequence, and K is 1 and 3 respectively; The pixel value threshold range is calculated using the following formula: R1=Q1-1.5M; R2=Q3+1.5M; Wherein, Q1 is the first pixel value corresponding to the sorting position I obtained by rounding when the value of K1 is 1, Q3 is the second pixel value corresponding to the sorting position I obtained by rounding when the value of K1 is 3, and M is the difference between the second pixel value and the first pixel value; Determine whether there is a target pixel value in the pixel value sequence that does not fall within the pixel value threshold range; If the target pixel value exists, it is determined that the semiconductor wafer has a defect.

2. The detection method according to claim 1, characterized in that The method further comprises: Determine a target pixel point in the target area having a value equal to the target pixel value; The distance between any two target pixels is calculated using the following formula: ； in, and is the horizontal and vertical coordinates of a target pixel point, and is the horizontal and vertical coordinates of another target pixel; After obtaining the distance between any two target pixels, calculate the standard deviation of all distances; When the standard deviation is greater than or equal to a preset standard deviation, the wafer robot is controlled to transfer the semiconductor wafer to an etching process equipment for etching process.

3. The detection method according to claim 2, characterized in that The method further comprises: When the standard deviation is less than the preset standard deviation, the wafer robot is controlled to place the semiconductor wafer into a wafer box to be processed.

4. The detection method according to claim 3, characterized in that The method further comprises: When the number of semiconductor wafers continuously placed in the wafer box to be processed exceeds a preset number, an alarm signal is issued.

5. The detection method according to claim 1, characterized in that Before deduplication of pixel values ​​of pixels in the target area, the method further includes: For each column of pixels in the target area, calculating the mean pixel value of the column of pixels; After obtaining the mean pixel value of each column of pixels, the mean pixel value of each column of pixels is averaged to obtain a global mean value; For each pixel value mean, calculate the difference between the pixel value mean and the global mean, and use the difference as a correction value; For each pixel point in the target area, the difference between the pixel value of the pixel point and the correction value is calculated to obtain a corrected image, so as to use the pixel values ​​of the pixels in the corrected image for deduplication.

6. A semiconductor wafer defect detection device, characterized in that: The detection device is arranged in the front-end module EFEM device, the EFEM device is provided with a dark box, the dark box is provided with an infrared heating tube, and an infrared camera for collecting infrared images, the detection device comprises: A first control unit is used for, after the overhead crane system places the wafer box in the material preparation area of ​​the EFEM device, and before the EFEM device transfers the semiconductor wafers in the wafer box to the etching process device through the wafer robot for etching, controlling the wafer robot to transfer the semiconductor wafers to a designated position of the dark box, and controlling the infrared camera to take a picture of the semiconductor wafer to obtain a first infrared image; A second control unit is used to control the infrared heating tube to irradiate the semiconductor wafer, and control the infrared camera to take a picture of the semiconductor wafer after a preset time to obtain a second infrared image; A comparison unit, configured to compare the pixels at the same position of the first infrared image and the second infrared image after aligning the first infrared image and the second infrared image, and determine a target area consisting of pixels whose pixel value variation is greater than a preset threshold from the second infrared image; A sorting unit, used to remove duplicate pixel values ​​of the pixel points in the target area, and sort the removed duplicate pixel values ​​in ascending order to obtain a pixel value sequence; The first calculation unit is used to determine the sorting position in the pixel value sequence using the following formula: ； Wherein, N is the total number of pixel values ​​included in the pixel value sequence, and K is 1 and 3 respectively; The second calculation unit is used to calculate the pixel value threshold range using the following formula: R1=Q1-1.5M; R2=Q3+1.5M; Wherein, Q1 is the first pixel value corresponding to the sorting position I obtained by rounding when the value of K1 is 1, Q3 is the second pixel value corresponding to the sorting position I obtained by rounding when the value of K1 is 3, and M is the difference between the second pixel value and the first pixel value; The judging unit is used to judge whether there is a target pixel value that does not fall within the pixel value threshold range in the pixel value sequence; if the target pixel value exists, it is determined that the semiconductor wafer has a defect.

7. The detection device according to claim 6, characterized in that: The detection device also includes: A determination unit, configured to determine a target pixel point in the target area having a value equal to that of the target pixel; The third calculation unit is used to calculate the distance between any two target pixels using the following formula: ； in, and is the horizontal and vertical coordinates of a target pixel point, and is the horizontal and vertical coordinates of another target pixel; A fourth calculation unit, configured to calculate the standard deviation of all distances after obtaining the distance between any two target pixel points; The third control unit is used to control the wafer robot to transfer the semiconductor wafer to the etching process equipment for etching process when the standard deviation is greater than or equal to the preset standard deviation.

8. The detection device according to claim 7, characterized in that: The third control unit is further used for: When the standard deviation is less than the preset standard deviation, the wafer robot is controlled to place the semiconductor wafer into a wafer box to be processed.

9. The detection device according to claim 8, characterized in that: The detection device further comprises: The alarm unit is used to send out an alarm signal when the number of semiconductor wafers continuously placed in the wafer box to be processed exceeds a preset number.

10. The detection device according to claim 6, characterized in that: The sorting unit is further used for: Before deduplication of pixel values ​​of pixels in the target area, for each column of pixels in the target area, a mean pixel value of the column of pixels is calculated; After obtaining the mean pixel value of each column of pixels, the mean pixel value of each column of pixels is averaged to obtain a global mean value; For each pixel value mean, calculate the difference between the pixel value mean and the global mean, and use the difference as a correction value; For each pixel point in the target area, the difference between the pixel value of the pixel point and the correction value is calculated to obtain a corrected image, so as to use the pixel values ​​of the pixels in the corrected image for deduplication.

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