Wafer surface defect detection method and device
Patent Information
- Application Number
- CN202610807347.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-05
- Publication Date
- 2026-08-18
AI Technical Summary
若通孔类型缺陷未被及时检测并拦截，将导致后续产品出现漏液、刻蚀失败等严重问题
[0034]本实施例中，首先对待测晶圆进行预设缺陷检测，以将Cloud缺陷统一识别为Particle缺陷，减少了大尺寸通孔类型缺陷未达Cloud NG判定标准而漏检的情况；在确定待测晶圆存在Particle缺陷的情况下，进一步基于Particle缺陷的目标尺寸确定待测晶圆的缺陷类型，实现了对常规Particle与通孔类型Particle的区分，减少直接提高Particle检测标准导致大量过检及晶圆良率损失的情况。相较于Pinhole设备单一的检测方式，本发明依托EBFIS可以实现自动化的判定，提升了对晶圆通孔类型缺陷检测的准确性以及检测效率。
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Figure CN122602836A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of semiconductor manufacturing technology, and in particular to a method and apparatus for detecting defects on the surface of a wafer. Background Technology
[0002] The quality of wafers directly affects chip yield, electrical performance, and reliability. During the wafer fabrication process, due to process fluctuations, material contamination, or equipment malfunctions, through-hole defects, such as through holes or dimples, can easily appear on the wafer surface. If these through-hole defects are not detected and intercepted in time, they will lead to serious problems such as liquid leakage and etching failure in subsequent products.
[0003] Currently, the detection of via defects on wafer surfaces mainly relies on pinhole equipment. However, pinhole equipment has a limited detection method and insufficient sensitivity for via defects of certain sizes, easily leading to missed detections. Furthermore, when there is a large amount of noise on the wafer surface, pinhole detection time is too long, and the equipment's detection capacity is reduced. It is evident that existing wafer surface defect detection methods suffer from low efficiency for via defects. Summary of the Invention
[0004] To address the aforementioned technical problems, this invention provides a method and apparatus for detecting defects on the wafer surface, which can improve the detection efficiency of via-type defects on the wafer surface.
[0005] To achieve the above objectives, the technical solution adopted in the embodiments of the present invention is as follows:
[0006] This invention provides a method for detecting defects on a wafer surface, comprising:
[0007] A preset defect detection is performed on the wafer under test to determine the detection result of the wafer under test. The preset defect detection is used to detect whether the wafer under test has particle defects.
[0008] If the detection result indicates that the wafer under test has the particle defect, the defect type of the wafer under test is determined based on the target size of the particle defect;
[0009] Wherein, if the target size of the particle defect is greater than or equal to the preset size, the defect type of the wafer under test is a through-hole type defect.
[0010] In some embodiments, determining the defect type of the wafer under test based on the target size of the particle defect includes:
[0011] The wafer under test is subjected to defect feature parameter detection to obtain the target size of the particle defect. The target size includes a first size and a second size. The first size is the size of the particle defect in a first direction, and the second size is the size of the particle defect in a second direction. The first direction and the second direction intersect.
[0012] The defect type of the wafer under test is determined based on the first size, the second size, the first preset size corresponding to the first size, and the second preset size corresponding to the second size. The preset size includes the first preset size and the second preset size.
[0013] In some embodiments, determining the defect type of the wafer under test based on the first size, the second size, a first preset size corresponding to the first size, and a second preset size corresponding to the second size includes:
[0014] If the first dimension is greater than or equal to the first preset dimension, the defect type of the wafer under test is determined to be a through-hole type defect;
[0015] And / or,
[0016] If the second dimension is greater than or equal to the second preset dimension, the defect type of the wafer under test is determined to be a through-hole type defect.
[0017] In some embodiments,
[0018] The process of performing a preset defect detection on the wafer under test and determining the detection result of the wafer under test includes:
[0019] Obtain preset parameter values, which include the brightness parameter value of the detection light source and the cloud area recognition threshold. The preset parameter values are used to adjust the parameters of the Edge Surface Feature Detection System (EBFIS) so that EBFIS can identify cloud defects as particle defects.
[0020] Based on the preset parameter values, EBFIS is used to perform preset defect detection on the wafer under test to determine whether the wafer under test has the particle defect.
[0021] In some embodiments, after determining the defect type of the wafer under test based on the target size of the particle defect, the method further includes:
[0022] The wafer under test is controlled based on the defect type of the wafer under test.
[0023] This invention also provides a wafer surface defect detection device, comprising:
[0024] The first determining module is used to perform preset defect detection on the wafer under test and determine the detection result of the wafer under test. The preset defect detection is used to detect whether the wafer under test has particle defects.
[0025] The second determining module is used to determine the defect type of the wafer under test based on the target size of the particle defect when the detection result indicates that the wafer under test has the particle defect.
[0026] Wherein, if the target size of the particle defect is greater than or equal to the preset size, the defect type of the wafer under test is a through-hole type defect.
[0027] In some embodiments, the second determining module is specifically used for:
[0028] The wafer under test is subjected to defect feature parameter detection to obtain the target size of the particle defect. The target size includes a first size and a second size. The first size is the size of the particle defect in a first direction, and the second size is the size of the particle defect in a second direction. The first direction and the second direction intersect.
[0029] The defect type of the wafer under test is determined based on the first size, the second size, the first preset size corresponding to the first size, and the second preset size corresponding to the second size. The preset size includes the first preset size and the second preset size.
[0030] This invention also provides an electronic device, including: a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the above method.
[0031] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method.
[0032] This invention also provides a computer program product, including computer instructions that, when executed by a processor, implement the steps of the above-described method.
[0033] The beneficial effects of this invention are:
[0034] In this embodiment, a preset defect detection is first performed on the wafer under test to uniformly identify cloud defects as particle defects, reducing the chance of missing large-size via defects that do not meet the cloud NG (Not In Seek Narrowing) criteria. If particle defects are confirmed in the wafer under test, the defect type is further determined based on the target size of the particle defect, thus distinguishing between regular particles and via-type particles. This reduces the likelihood of excessive over-testing and wafer yield loss due to directly raising the particle detection standard. Compared to the single detection method of pinhole equipment, this invention, relying on EBFIS (Electronic Embedded Filtration System), enables automated judgment, improving the accuracy and efficiency of detecting via-type defects in wafers. Attached Figure Description
[0035] Figure 1 This is a schematic flowchart illustrating one of the wafer surface defect detection methods in an embodiment of the present invention;
[0036] Figure 2 This represents an EBFIS detection image of a large-size through-hole type defect in an embodiment of the present invention.
[0037] Figure 3 This represents an EBFIS detection image of a small-size through-hole type defect in an embodiment of the present invention.
[0038] Figure 4 This is a schematic diagram illustrating how a large-size through-hole defect in this embodiment of the invention was identified as a particle defect after debugging.
[0039] Figure 5 The image shown represents a conventional particle detection example in an embodiment of the present invention.
[0040] Figure 6 This is the second schematic flowchart illustrating the wafer surface defect detection method in an embodiment of the present invention.
[0041] Figure 7 This represents a pixel grid diagram of a conventional Particle in an embodiment of the present invention;
[0042] Figure 8 This represents a pixel grid diagram of a through-hole type particle in an embodiment of the present invention;
[0043] Figure 9 This diagram illustrates a comparison of EBFIS, Wafer Sight, and Pinhole in an embodiment of the present invention.
[0044] Figure 10 This is a schematic diagram of the wafer surface defect detection device in an embodiment of the present invention;
[0045] Figure 11 This is a schematic diagram of the structure of the electronic device in an embodiment of the present invention. Detailed Implementation
[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.
[0047] To address the issues of limited detection methods in pinhole equipment, which can easily lead to missed detections and low detection efficiency when detecting through-hole defects on wafers, this invention provides a wafer surface defect detection method and apparatus that can improve the detection efficiency of through-hole defects on wafer surfaces and reduce the occurrence of missed detections.
[0048] This invention provides a method for detecting defects on the surface of a wafer, such as... Figure 1 As shown, it includes the following steps:
[0049] Step 101: Perform a preset defect detection on the wafer under test and determine the detection result of the wafer under test. The preset defect detection is used to detect whether there are particle defects on the wafer under test.
[0050] In this step, for defect detection of via types in the wafer, such as through holes or dimples, a pre-set defect detection is performed on the wafer under test using an Edge Bevel Feature Inspection System (EBFIS) with adjusted parameters to determine the inspection result. If the wafer under test has no surface defects, it is deemed qualified and can be transferred to the next production process. Simultaneously, the inspection data is recorded for subsequent process traceability.
[0051] If the wafer under test has surface defects, the type of surface defect is initially determined. In one example, when the detection result indicates that the wafer under test has defects other than particle defects, such as scratches or stains, i.e., no particle defects, the corresponding defect information can be recorded, and the corresponding processing mechanism can be initiated according to the defect type to prevent unqualified wafers from flowing into the next process. In another example, when the detection result indicates that the wafer under test has particle defects, considering that the particle defects may be regular particles or via-type particles, the defect type of the wafer under test can be further determined through subsequent step 102, i.e., whether the particle defect of the wafer under test is a via-type defect.
[0052] In some embodiments, step 101, performing a preset defect detection on the wafer under test and determining the detection result of the wafer under test, includes:
[0053] Obtain preset parameter values, which include the brightness parameter value of the detection light source and the cloud area recognition threshold. The preset parameter values are used to adjust the parameters of the Edge Surface Feature Detection System (EBFIS) so that EBFIS can identify cloud defects as particle defects.
[0054] Based on the preset parameter values, EBFIS is used to perform preset defect detection on the wafer under test to determine whether the wafer under test has the particle defect.
[0055] In this embodiment, considering that during wafer defect detection, large-size via defects are typically identified as cloud defects by EBFIS, such as... Figure 2 As shown; small-sized through-hole type defects are typically identified as particle defects by EBFIS, such as... Figure 3 As shown. Large-size via defects can be compared to wafer sight inspection results. For this type of defect, the wafer NT 10^10 size > 400nm. When the cloud size does not meet the NG standard, this type of defect is prone to being missed. For small-size via defects, the wafer NT 10^10 size ≤ 400nm. Optimization for detecting large-size via defects is mainly based on the following description:
[0056] For example, when detecting and identifying large-size through-hole type defects, the main influencing parameters identified through analysis include the target parameters of the detection unit in EBFIS, namely the detection light source brightness (High Lamp Power) and the cloud area recognition threshold (CLW Size Threshold).
[0057] The initial setting for High Lamp Power can be A1, and the initial setting for CLW Size Threshold can be B1. Testing shows that when High Lamp Power is increased to A2 and CLW Size Threshold is increased to B2, the number of via-type defects identified as clouds can be effectively reduced, assuming no cloud defects are missed. Specifically, B1 can be 5000, B2 can be 10000, and High Lamp Power can be adjusted accordingly based on the CLW Size Threshold.
[0058] Based on this, when performing pre-defined defect detection on the wafer under test using EBFIS, the pre-defined parameter values (i.e., A2 and B2) are first obtained. Then, the target parameters (i.e., High Lamp Power and CLWSize Threshold) of the detection unit in EBFIS are adjusted according to these pre-defined parameter values, so that EBFIS applying the pre-defined parameter values can identify cloud defects as particle defects. In this way, EBFIS based on the pre-defined parameter values performs defect detection on the wafer under test, uniformly identifying large-size via defects that would otherwise be easily identified as cloud defects as particle defects. Figure 4 As shown, this method can determine whether the wafer under test has particle defects, effectively reducing the chance of missing via defects. Furthermore, it reduces reliance on pinhole equipment, enabling accurate and efficient wafer surface defect detection based on EBFIS.
[0059] The acquisition of preset parameter values can be a type of preset parameter value (e.g., A2 and B2) read by EBFIS from a pre-configured parameter library when a preset defect detection is determined. It should be understood that after completing the preset defect detection, when routine defect detection of the wafer is required using EBFIS, EBFIS can read another type of preset parameter value (e.g., A1 and B1) from the pre-configured parameter library. EBFIS can thus achieve defect detection at different imaging qualities.
[0060] Step 102: If the detection result indicates that the wafer under test has the particle defect, determine the defect type of the wafer under test based on the target size of the particle defect. Wherein, if the target size of the particle defect is greater than or equal to a preset size, the defect type of the wafer under test is a through-hole type defect.
[0061] In this step, EBFIS surface inspection is performed on the wafer under test to determine the inspection results. When the inspection results indicate the presence of particle defects on the wafer under test, these particle defects could be ordinary particles, such as… Figure 5 As shown; it could also be a via-type particle. Directly adding NG (Not From Good) standards to particle defects would result in a large number of over-inspections and yield losses. Therefore, to ensure accurate identification of via-type defects, this embodiment determines the defect type of the wafer under test based on the target size of the particle defect, thereby distinguishing between conventional particles and via-type particles, as detailed below:
[0062] In some embodiments, step 102, determining the defect type of the wafer under test based on the target size of the particle defect, includes:
[0063] The wafer under test is subjected to defect feature parameter detection to obtain the target size of the particle defect. The target size includes a first size and a second size. The first size is the size of the particle defect in a first direction, and the second size is the size of the particle defect in a second direction. The first direction and the second direction intersect.
[0064] The defect type of the wafer under test is determined based on the first size, the second size, the first preset size corresponding to the first size, and the second preset size corresponding to the second size. The preset size includes the first preset size and the second preset size.
[0065] In this embodiment, as Figure 6As shown, the process begins by initiating a preset defect detection for the wafer under test. Images of the wafer surface are acquired using the EBFIS inspection camera to obtain raw image data. The acquired raw image data undergoes preprocessing such as noise reduction, contrast enhancement, and distortion correction to optimize image quality. Then, a raw map calculation is performed: pixel-level processing is applied to the preprocessed image to complete coordinate mapping, defect signal extraction, and basic feature calculation, generating Raw Map data containing raw information such as defect location, grayscale, and size. Defects are classified based on the Raw Map data to determine the detection result of the wafer under test.
[0066] When the test results indicate that the wafer under test has defects other than particle defects, such as scratch or stain defects (i.e., no particle defects exist), the corresponding defect information can be recorded, and the corresponding processing mechanism can be initiated according to the defect type to prevent unqualified wafers from flowing into the next process. When the test results indicate that the wafer under test has particle defects, defect feature parameter detection can be performed on the wafer under test, and the first and second dimensions of the particle defects can be obtained from the RawMap data.
[0067] The first dimension is the size of the particle defect in the first direction, and the second dimension is the size of the particle defect in the second direction. The first direction and the second direction intersect. In other words, the direction in which the first dimension is located and the direction in which the second dimension is located are set at an angle, such as 10°, 20°, 30°, 45°, 60° or 90°.
[0068] For ease of calculation, 90° is preferred, and the following explanation uses 90° as an example. It should be understood that there can be other angles between the first and second directions. Based on RawMap data and trigonometric relationships, the first and second dimensions of the particle defect can be obtained.
[0069] For example, the first dimension can be the dimension along the X-axis in a Cartesian coordinate system, denoted as X Size; the second dimension can be the dimension along the Y-axis in a Cartesian coordinate system, denoted as Y Size. By detecting the size and shape of regular particles determined from multiple examples (e.g., 100 examples), it was found that regular particles are mostly single or two regularly arranged pixels with small areas, such as... Figure 7 As shown in Table 1 below, the X-Size and Y-Size detection values of conventional particles are usually small.
[0070] Table 1
[0071]
[0072] It can be seen that the X size of a regular particle is usually less than or equal to 3, and the Y size of a regular particle is usually less than or equal to 3.
[0073] For example, by detecting the size and morphology of via-type particles identified in multiple cases (e.g., 100 cases), it was found that via-type particles are mostly multiple irregularly arranged pixels with a large area, such as... Figure 8 As shown in Table 2 below, the X-Size and Y-Size of through-hole type particles are usually large.
[0074] Table 2
[0075]
[0076] It can be seen that the X size of a through-hole type particle is usually greater than 3, or the Y size of a through-hole type particle is usually greater than 3.
[0077] Based on this, a first preset size corresponding to the first size can be set to 4, and a second preset size corresponding to the second size can be set to 4. In this way, after performing defect feature parameter detection on the wafer under test and obtaining the first and second sizes of the particle defects, the defect type of the wafer under test can be determined based on the first size, the second size, the first preset size, and the second preset size.
[0078] In some embodiments, determining the defect type of the wafer under test based on the first size, the second size, a first preset size corresponding to the first size, and a second preset size corresponding to the second size includes:
[0079] If the first dimension is greater than or equal to the first preset dimension, the defect type of the wafer under test is determined to be a through-hole type defect;
[0080] And / or,
[0081] If the second dimension is greater than or equal to the second preset dimension, the defect type of the wafer under test is determined to be a through-hole type defect.
[0082] Specifically, if the X size of a particle defect is ≥4 and / or the Y size of a particle defect is ≥4, then the particle defect is determined to be a through-hole type particle defect; if the X size of a particle defect is <4 and the Y size of a particle defect is <4, then the particle defect is determined to be a regular particle defect. This determination method accurately distinguishes between regular particle defects and through-hole type particle defects, reducing over-inspection and yield loss caused by misjudging regular particle defects. It effectively identifies and intercepts through-hole type particle defects, ensuring the accuracy of the inspection. Furthermore, all dimensional parameters can be acquired and output by the EBFIS device, reducing reliance on pinhole equipment and improving inspection efficiency.
[0083] In some embodiments, after determining the defect type of the wafer under test based on the target size of the particle defect, the method further includes:
[0084] The wafer under test is controlled based on the defect type of the wafer under test.
[0085] In this embodiment, targeted control based on the defect type of the wafer under test effectively distinguishes between conventional particle defects and via-type particle defects. This reduces the large number of over-inspection issues and wafer yield losses caused by directly raising particle inspection standards. Simultaneously, it enables precise interception of via-type defects, reducing the flow of defective products into subsequent processes. Furthermore, the control method can rely on EBFIS for automated judgment, improving inspection efficiency and adapting to via-type defects of different sizes, providing reliable data support for quality control and process optimization in wafer manufacturing.
[0086] To further verify the beneficial technical effects of the wafer surface defect detection method provided in this embodiment of the invention, detection data can be collected from EBFIS using this method. Detection data from the most recent 3 days of EBFIS were collected, totaling 56,986 wafers. See the following description for details:
[0087] Under the testing conditions of X Size ≥ 4 or Y Size ≥ 4, a total of 234 pieces were rejected (NG), with an NG rate of 0.41%. This means that when using this method to intercept through-hole type defects, a total of 234 products with through-hole type defects were intercepted within the testing period.
[0088] Specifically, the NG (Not Found) cases for X Size and / or Y Size were statistically analyzed separately. NG data was identified as including three types: X Size NG, Y Size NG, and X Size / Y Size NG. The number of X Size NG cases was 68pcs(X, X+Y), with an NG rate of 0.12%, as shown in Table 3 below; the number of Y Size NG cases was 224pcs(Y, X+Y), with an NG rate of 0.40%, as shown in Table 3 below.
[0089] Table 3
[0090]
[0091] Table 4
[0092]
[0093] It can be seen that the detection conditions of X Size≥4 or Y Size≥4 can be used to intercept via-hole type particles, and most of the intercepted via-hole type particles are of Y Size NG type.
[0094] In addition, confirm all NG detection images, such as Figure 9 As shown, the number of via-hole type defects detected by the pinhole device was only 28 pieces, indicating that there were obviously missed detections. Through verification, the wafer surface defect detection method provided in this embodiment of the invention can identify via-hole type defects as particles, and then detect and intercept these defects. Furthermore, via-hole type defects of different sizes can be identified.
[0095] In this embodiment of the invention, a preset defect detection is first performed on the wafer under test to uniformly identify cloud defects as particle defects, reducing the possibility of missed detections due to large-size via defects not meeting the cloud NG (Not In Seek Narrowing) criteria. If particle defects are confirmed in the wafer under test, the defect type is further determined based on the target size of the particle defects, thus distinguishing between conventional particles and via-type particles. This reduces the likelihood of excessive over-detection and wafer yield loss due to directly raising the particle detection standard. Compared to the single detection method of pinhole equipment, this invention, relying on EBFIS (Electronic Embedded Filtration System), enables automated judgment, improving the accuracy and efficiency of detecting via-type defects in wafers.
[0096] This invention also provides a wafer surface defect detection device, such as... Figure 10 As shown, it includes:
[0097] The first determining module 11 is used to perform preset defect detection on the wafer under test and determine the detection result of the wafer under test. The preset defect detection is used to detect whether the wafer under test has particle defects.
[0098] The second determining module 12 is used to determine the defect type of the wafer under test based on the target size of the particle defect when the detection result indicates that the wafer under test has the particle defect.
[0099] Wherein, if the target size of the particle defect is greater than or equal to the preset size, the defect type of the wafer under test is a through-hole type defect.
[0100] In some embodiments, the second determining module 12 is specifically used for:
[0101] The wafer under test is subjected to defect feature parameter detection to obtain the target size of the particle defect. The target size includes a first size and a second size. The first size is the size of the particle defect in a first direction, and the second size is the size of the particle defect in a second direction. The first direction and the second direction intersect.
[0102] The defect type of the wafer under test is determined based on the first size, the second size, the first preset size corresponding to the first size, and the second preset size corresponding to the second size. The preset size includes the first preset size and the second preset size.
[0103] In some embodiments, determining the defect type of the wafer under test based on the first size, the second size, a first preset size corresponding to the first size, and a second preset size corresponding to the second size includes:
[0104] If the first dimension is greater than or equal to the first preset dimension, the defect type of the wafer under test is determined to be a through-hole type defect;
[0105] And / or,
[0106] If the second dimension is greater than or equal to the second preset dimension, the defect type of the wafer under test is determined to be a through-hole type defect.
[0107] In some embodiments, the first determining module 11 is specifically used for:
[0108] Obtain preset parameter values, which include the brightness parameter value of the detection light source and the cloud area recognition threshold. The preset parameter values are used to adjust the parameters of the Edge Surface Feature Detection System (EBFIS) so that EBFIS can identify cloud defects as particle defects.
[0109] Based on the preset parameter values, EBFIS is used to perform preset defect detection on the wafer under test to determine whether the wafer under test has the particle defect.
[0110] In some embodiments, the apparatus further includes:
[0111] The control module is used to control the wafer under test based on the defect type of the wafer under test.
[0112] The wafer surface defect detection device is designed to implement each process of the above-described method, with each technical feature corresponding to the other, and can achieve the same technical effect. To avoid repetition, it will not be described again here.
[0113] Please refer to Figure 11 The present invention also provides an electronic device 20, including a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the computer program is executed by the processor 21, it implements the various processes of the above-described wafer surface defect detection method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.
[0114] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described wafer surface defect detection method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.
[0115] The computer-readable storage medium, as defined herein, includes both permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage, or any other non-transfer medium that can be used to store information accessible to the terminal device under test. As defined herein, computer-readable storage media does not include transient media, such as modulated data signals and carrier waves.
[0116] This invention also provides a computer program product, including computer instructions that, when executed by a processor, implement the above-described... Figure 1 The various processes of the method embodiments shown can achieve the same technical effect, and will not be described again here to avoid repetition.
[0117] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0118] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.
[0119] In the various method embodiments of this disclosure, the sequence numbers of each step are not intended to limit the order of the steps. For those skilled in the art, any changes in the order of the steps are within the scope of protection of this disclosure without any creative effort.
[0120] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, since the embodiments are basically similar to the product embodiments, the descriptions are relatively simple, and the relevant parts can be referred to the descriptions of the product embodiments.
[0121] The above description represents the preferred embodiments of this disclosure. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles described herein, and these improvements and modifications should also be considered within the scope of protection of this disclosure.
Claims
1. A method for detecting defects on a wafer surface, characterized in that, include: A preset defect detection is performed on the wafer under test to determine the detection result of the wafer under test. The preset defect detection is used to detect whether the wafer under test has particle defects. If the detection result indicates that the wafer under test has the particle defect, the defect type of the wafer under test is determined based on the target size of the particle defect; Wherein, if the target size of the particle defect is greater than or equal to the preset size, the defect type of the wafer under test is a through-hole type defect.
2. The method according to claim 1, characterized in that, Determining the defect type of the wafer under test based on the target size of the particle defect includes: The wafer under test is subjected to defect feature parameter detection to obtain the target size of the particle defect. The target size includes a first size and a second size. The first size is the size of the particle defect in a first direction, and the second size is the size of the particle defect in a second direction. The first direction and the second direction intersect. The defect type of the wafer under test is determined based on the first size, the second size, the first preset size corresponding to the first size, and the second preset size corresponding to the second size. The preset size includes the first preset size and the second preset size.
3. The method according to claim 2, characterized in that, Determining the defect type of the wafer under test based on the first size, the second size, the first preset size corresponding to the first size, and the second preset size corresponding to the second size includes: If the first dimension is greater than or equal to the first preset dimension, the defect type of the wafer under test is determined to be a through-hole type defect; And / or, If the second dimension is greater than or equal to the second preset dimension, the defect type of the wafer under test is determined to be a through-hole type defect.
4. The method according to any one of claims 1 to 3, characterized in that, The process of performing a preset defect detection on the wafer under test and determining the detection result of the wafer under test includes: Obtain preset parameter values, which include the brightness parameter value of the detection light source and the cloud area recognition threshold. The preset parameter values are used to adjust the parameters of the Edge Surface Feature Detection System (EBFIS) so that EBFIS can identify cloud defects as particle defects. Based on the preset parameter values, EBFIS is used to perform preset defect detection on the wafer under test to determine whether the wafer under test has the particle defect.
5. The method according to any one of claims 1 to 3, characterized in that, After determining the defect type of the wafer under test based on the target size of the particle defect, the method further includes: The wafer under test is controlled based on the defect type of the wafer under test.
6. A wafer surface defect detection device, characterized in that, include: The first determining module is used to perform preset defect detection on the wafer under test and determine the detection result of the wafer under test. The preset defect detection is used to detect whether the wafer under test has particle defects. The second determining module is used to determine the defect type of the wafer under test based on the target size of the particle defect when the detection result indicates that the wafer under test has the particle defect. Wherein, if the target size of the particle defect is greater than or equal to the preset size, the defect type of the wafer under test is a through-hole type defect.
7. The apparatus according to claim 6, characterized in that, The second determining module is specifically used for: The wafer under test is subjected to defect feature parameter detection to obtain the target size of the particle defect. The target size includes a first size and a second size. The first size is the size of the particle defect in a first direction, and the second size is the size of the particle defect in a second direction. The first direction and the second direction intersect. The defect type of the wafer under test is determined based on the first size, the second size, the first preset size corresponding to the first size, and the second preset size corresponding to the second size. The preset size includes the first preset size and the second preset size.
8. An electronic device, characterized in that, include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the method as described in any one of claims 1 to 5.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 5.
10. A computer program product, characterized in that, Includes computer instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 5.