Wafer defect classification method, wafer defect classification system and electronic equipment

Abstract

The invention provides a wafer defect classification method, a wafer defect classification system and electronic equipment, which can directly extract a plurality of defect characteristics from scanning data after scanning wafer defects, find corresponding common defect types from a defect type database by using the plurality of defect characteristics, realize classification of each defect scanned on a wafer, and improve the classification efficiency. Therefore, the defect classification result of the whole wafer is obtained, an electron microscope device does not need to be used for photographing the defects scanned on the wafer, the electron microscope device and the photographing step are omitted, the time and the device cost are greatly saved, the wafer defect classification rate is improved, sample analysis through sampling is not needed, and the accuracy can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wafer defect classification, and in particular to a wafer defect classification method, a wafer defect classification system, and electronic equipment. Background Art

[0002] During the manufacturing process, wafers may develop defects due to various factors such as lack of machine maintenance and contamination of manufacturing materials, which in turn leads to a decrease in wafer yield. Therefore, there will be many intermediate links in the manufacturing process that require wafer defect detection and classification to determine the degree of harm of each defect and thus decide how to deal with the wafers later.

[0003] Please refer to Figure 1 The current wafer defect classification scheme is mainly as follows: after scanning the wafer defects by the detection equipment (i.e., step S10), some defects (i.e., samples) on the wafer are photographed with the help of an electron microscope (i.e., step S11) to obtain defect photos, and then the engineer manually or automatically uses the defect classification system software on the market to analyze the defect types based on the defect photos (i.e., step S12), and then manually or automatically calculates the defect type distribution of the entire wafer by the above-mentioned defect classification system software (i.e., step S13), and then determines the degree of harm of each defect, thereby deciding how to deal with the wafer in the future.

[0004] The above-mentioned wafer defect classification scheme, whether it is implemented manually or through the defect classification system software on the market, requires the use of an electron microscope to obtain defect photos first. Since the number of defects on the wafer may be large (possibly reaching hundreds of thousands or more), the electron microscope cannot take photos of all defects. It can only implement sampling analysis and classification (that is, it can only identify the defect types that have been photographed) and calculate the defect type distribution of the entire wafer based on the sampling classification results. Therefore, it will be limited by the ability of the electron microscope. The more deviations in the number of defects in the defect photo (that is, the sample), the greater the deviations in the calculated defect type distribution of the entire wafer, resulting in insufficient accuracy and timeliness of wafer defect classification, which may further lead to major errors in the subsequent disposal of the wafer.

[0005] Therefore, there is an urgent need for a wafer defect classification method and a wafer defect classification system that can break away from the dilemma of existing defect classification technology that requires obtaining defect photos before classification, and can achieve overall classification of defects on the wafer and improve the defect classification rate and accuracy. Summary of the invention

[0006] The purpose of the present invention is to provide a wafer defect classification method, a wafer defect classification system, and an electronic device, which can get rid of the dilemma of the existing defect classification technology that requires obtaining defect photos through an electron microscope before classification, realize the defect classification of the entire wafer, and improve the wafer defect classification speed and accuracy.

[0007] To achieve the above object, the present invention provides a wafer defect classification method. After scanning wafer defects, the following steps are performed for each defect scanned:

[0008] directly acquiring a plurality of defect features of the defect from the scan data of the defect;

[0009] Retrieving target defect types respectively associated with each of the defect features of the defect from a pre-established defect type database;

[0010] Common defect types associated with multiple or all of the defect features of the defect are screened out from the search results, and the defect is classified into the common defect type with the highest matching degree.

[0011] Optionally, the plurality of defect characteristics of the defect include at least two of a signal characteristic, a process characteristic, a distribution characteristic and a size characteristic; wherein,

[0012] The signal feature is a corresponding signal value obtained when scanning the defect, and the signal value is obtained by receiving optical signals from the surface of the wafer from different angles through multiple signal receivers when scanning the defect, and further performing comprehensive calculations on the optical signals received by the multiple signal receivers;

[0013] The process feature is process information that produces the defect;

[0014] The distribution feature is the distribution position information of the defect on the wafer;

[0015] The dimensional characteristic is the size of the defect.

[0016] Optionally, each defect type in the defect type database is associated with at least one corresponding defect feature, and each defect feature in the defect type database is associated with at least one defect type.

[0017] Optionally, each defect feature directly obtained from the defect belongs to the top-layer feature of the wafer; and / or, the defect types in the defect type database include tiny particle defects, metal grain defects, pit defects, crystallization defects, repeatability defects, chromatic aberration defects, metal surface roughness defects, corrosion defects, crack defects, void defects, buried defects, metal residue defects, defocus defects, bubble defects, silicon damage defects, surface defects, arc defects, photoresist residue defects, metal damage defects, silicon residue defects, large particle defects, spherical defects, film peeling defects, scratch defects and fragmentation defects.

[0018] Optionally, the matching degree of each of the common defect types screened out is determined according to the number of defect features associated with each of the common defect types screened out, and the more defect features associated with the common defect types, the higher the matching degree.

[0019] Based on the same inventive concept, the present invention also provides a wafer defect classification system, which includes:

[0020] A defect type database, each defect type in the defect type database is associated with at least one corresponding defect feature, and each defect feature in the defect type database is associated with at least one defect type;

[0021] The defect classification module is used to perform the following steps for each defect scanned after scanning the wafer defects:

[0022] directly acquiring a plurality of defect features of the defect from the scan data of the defect;

[0023] Retrieving target defect types respectively associated with each of the defect features of the defect from the defect type database;

[0024] Common defect types associated with multiple or all of the defect features of the defect are screened out from the search results, and the defect is classified into the common defect type with the highest matching degree.

[0025] Optionally, the defect classification module includes:

[0026] A data receiving unit, used for receiving scanning data of each defect obtained by a wafer defect scanning device scanning the wafer;

[0027] A feature extraction unit, used for directly acquiring a plurality of defect features of the defect from the scanning data of each defect;

[0028] A retrieval unit, configured to retrieve target defect types respectively associated with each of the defect features of the defect from the defect type database;

[0029] a screening unit, configured to screen out common defect types associated with multiple or all of the defect features of the defect from the search results of the search unit, and determine a matching degree of each of the common defect types screened out by the screening unit for the defect;

[0030] The classification output unit is used to classify the defects into the common defect type with the highest matching degree determined by the screening unit, and output the defect classification result of the entire wafer after the defect classification module completes the classification of all scanned defects on the wafer.

[0031] Optionally, the defect classification module directly obtains multiple defect features of the defect including at least two of signal features, process features, distribution features and size features; wherein,

[0032] The signal feature is a corresponding signal value obtained when scanning the defect, and the signal value is obtained by receiving optical signals from the surface of the wafer from different angles through multiple signal receivers when scanning the defect, and further performing comprehensive calculations on the optical signals received by the multiple signal receivers;

[0033] The process feature is process information that produces the defect;

[0034] The distribution feature is the distribution position information of the defect on the wafer;

[0035] The dimensional characteristic is the size of the defect.

[0036] Optionally, the defect classification module determines the matching degree of each of the screened common defect types according to the number of defect features associated with each of the screened common defect types, and the more defect features associated with the common defect types, the higher the matching degree.

[0037] Based on the same inventive concept, the present invention further provides an electronic device, the electronic device comprising at least one processor and at least one memory in communication with the at least one processor, the at least one memory storing a corresponding program, and when the program is executed by the at least one processor, the at least one processor implements the wafer defect classification method according to the present invention;

[0038] Alternatively, the electronic device has a wafer defect classification system as described in the present invention.

[0039] Compared with the prior art, the technical solution of the present invention has at least one of the following effects:

[0040] 1. After scanning the wafer defects, multiple defect features can be directly extracted from the scanning data, and the corresponding common defect types can be found from the defect type database using the multiple defect features to classify the defects scanned on the wafer, thereby obtaining the defect classification result of the entire wafer. That is, the solution of the present invention directly classifies all defects scanned on the wafer after scanning the wafer defects, which greatly saves time and improves the wafer defect classification rate.

[0041] 2. Since all defects scanned on the wafer can be directly classified without sampling for sample analysis, the accuracy of the defect classification results of the entire wafer can be improved.

[0042] 3. There is no need to use electron microscope equipment to take pictures of defects scanned on the wafer, which saves electron microscope equipment and photographing steps and saves costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flowchart of the existing wafer defect classification method.

[0044] Figure 2 It is a flow chart of a wafer defect classification method according to a specific embodiment of the present invention.

[0045] Figure 3 A defect schematic diagram of a wafer defect classification method in a specific embodiment of the present invention

[0046] Figure 4 It is a schematic diagram of the generation principle of signal features in the wafer defect classification method according to a specific embodiment of the present invention.

[0047] Figure 5 It is a defect map corresponding to different process features in the wafer defect classification method of a specific embodiment of the present invention.

[0048] Figure 6 and Figure 7 These are the different distribution characteristics of different defects on a wafer map scanned in a wafer defect classification method according to a specific embodiment of the present invention.

[0049] Figure 8 It is a size feature of a defect on a wafer map scanned in the wafer defect classification method of a specific embodiment of the present invention.

[0050] Fig. 9 It is a schematic diagram of the correlation mapping relationship between defect features and defect types in a defect type database of a wafer defect classification method according to a specific embodiment of the present invention.

[0051] Fig.10 yes Figure 2 Schematic diagram of the specific implementation process of step S20 in FIG.

[0052] Fig.11 It is a schematic diagram of the search results in the defect type database when a certain defect is classified using the wafer defect classification method of the present invention.

[0053] Fig.12 Schematic diagram of the architecture of a wafer defect classification system according to a specific embodiment of the present invention. DETAILED DESCRIPTION

[0054] In the following description, a large number of specific details are given in order to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features known in the art are not described. It should be understood that the present invention can be implemented in different forms and should not be interpreted as being limited to the embodiments proposed here. On the contrary, providing these embodiments will make the disclosure thorough and complete, and the scope of the present invention will be fully conveyed to those skilled in the art. The same reference numerals represent the same elements from beginning to end. It should be understood that when an element is referred to as "connected to", "coupled" other elements, it can be directly connected to other elements, or there can be intervening elements. On the contrary, when an element is referred to as "directly connected to" other elements, there is no intervening element. When used here, the singular forms of "one", "an" and "said / the" are also intended to include plural forms, unless the context clearly indicates another way. It should also be understood that the term "comprising" is used to identify the presence of features, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, steps, operations, elements, components and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items. The term "plurality" refers to two or more, and other quantifiers are similar.

[0055] The technical solution proposed by the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The advantages and features of the present invention will become clearer according to the following description. It should be noted that the accompanying drawings are all in a very simplified form and are not in precise proportions, and are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0056] Please refer to Figure 2 , an embodiment of the present invention provides a wafer defect classification method, which can directly execute step S20 after scanning wafer defects (i.e., after completing step S10): classify all defects scanned on the wafer using the defect type database to obtain the defect classification result of the entire wafer. Figure 1 The wafer defect classification method of the present invention can save the electron microscope (or "electron microscope") shooting steps and equipment (that is, save the Figure 1 Step S11 in the above example) and classify all the defects scanned on the wafer (i.e., eliminating the need for Figure 1 Step S13 in the above method can get rid of the dilemma that the existing defect classification technology needs to obtain defect photos through an electron microscope before classification, thereby improving the wafer defect classification rate and accuracy.

[0057] Among them, in the defect type database based on the technical solution of the present invention, each defect type is associated with at least one corresponding defect feature, and each defect feature is associated with at least one defect type. When constructing the defect type database used in the technical solution of the present invention, each selected defect feature belongs to the top-level feature of the wafer. These defect features can be intuitively obtained after the wafer is scanned, and those features that need to be obtained through additional processes or equipment (such as electron microscopes, etc.) (such as appearance features that need to be obtained by taking pictures, etc.) are not suitable for the defect type database used in the technical solution of the present invention. All defect features in the defect type database can cover all defect types, thereby ensuring the comprehensiveness and reliability of the defect type database.

[0058] The defect features in the defect type database used in the technical solution of the present invention include:

[0059] 1). Signal characteristics. There are thousands of dies on a wafer. Figure 3 Figure 1 shows three dies Die1 to Die3. Under normal circumstances, each die is exactly the same. However, since wafer manufacturing requires hundreds of processes, defects may occur on the wafer surface during any process or during wafer handling. Figure 3 See Die2 in the figure. Figure 4 , wafer defect scanning is performed by using a high-precision optical instrument to scan each die (that is, the light source of the optical instrument emits light to the die). Through the reflection or scattering of light, signal receivers at different positions on the optical instrument (that is, multiple signal receivers) receive light signals from the surface of the wafer from different angles. The optical instrument will further perform comprehensive operations on the light signals received by these signal receivers to obtain a corresponding signal value. Exemplarily, the light signals received by these signal receivers are a multi-segment signal curve. The optical instrument will perform comprehensive operations on the information received by these signal receivers to calculate a signal value. If the signal value exceeds the reference value range, it is considered that a defect exists. When scanning wafer defects, each type of defect will obtain a corresponding signal value when it is scanned. These signal values ​​are called signal characteristics. Simply put, the signal characteristic is the corresponding signal value obtained when scanning defects on the wafer. Different signal values ​​can be further attributed to different signal value ranges (that is, different conditions of signal characteristics), such as Fig. 9 The “0-20”, “20-50”, “50-100”, “100-200” and “>200” in the signal will form different signal features, and each signal feature can be associated with at least one (for example, 5) defect type.

[0060] For example, please refer to Fig. 9 In the defect type database used in the present invention, the defect types associated with the "0-20" signal feature are "tiny", "metal grain", "pits", "crystal", and "repeating". The defect types associated with the "20-50" signal feature are "discolor", "metal roughness", "corrosion", "crack", and "voild". The defect types associated with the "50-100" signal feature are "burried", "metal residue", "defoucs", "bubble", and "Si damage". The defect types associated with the "100-200" signal feature are "surface", "arcing", "PR residue", "metal damage", and "Si residue". The defect types associated with the ">200" signal feature are "largeparticle", "Oil ball", "film peeling", "scratch" and "crush".

[0061] 2). Process characteristics. Wafer manufacturing requires hundreds of processes, and different types of defects only exist in certain different processes. Please refer to Figure 5 , such as etching processes, which usually produce polymer residues, partial etching (such as Figure 5 (a) in the figure), corrosive substances (such as Figure 5 In the photolithography process, for example, there will be defects such as residual development (such as Figure 5 (c) in the figure), photoresist residue (as shown in Figure 5 In short, process characteristics are the process information that produces the corresponding defects. Different processes (i.e. different conditions of process characteristics), such as Fig. 9"MetalETCH (metal etching)", "Oxide ETCH (oxide etching)", "LTHO (photolithography)", "WET (wet etching)", "Furnace (furnace tube)", "IMP (ion implantation)", "CVD (chemical vapor deposition)", "PVD (physical vapor deposition)" and "CMP (chemical mechanical polishing)" etc., will form different process characteristics, and each process characteristic can be associated with at least one (for example, 4 or 5) defect types.

[0062] For example, please refer to Fig. 9 In the defect type database used in the present invention, the defect types associated with the "Metal ETCH" process feature are "metal residue", "arcing", "metal roughness", "surface", and "metal grain". The defect types associated with the "Oxide ETCH" process feature are "surface", "Si residue", "arcing", "tiny", and "crystal". The defect types associated with the "LITHO" process feature are "defocus", "repeating", "PR residue", "surface", and "discolor". The defect types associated with the "WET" process feature are "PR residue", "Si damage", "corrosion", "crystal", and "pits". The defect types associated with the "Furnace" process feature are "burried", "crush", "scratch", "film peeling", and "discolor". The defect types associated with the "IMP" process feature are "scratch", "surface", and "film peeling". The defect types associated with the "CVD" process feature are "film peeling", "bubble", "Oil ball", "burried", and "tiny". The defect types associated with the "PVD" process feature are "metal grain", "metal roughness", "crack", "burried", and "surface". The defect types associated with the “CMP” process feature are “surface”, “discolor”, “Si damage”, “metal residue”, and “scratch”.

[0063] 3) Distribution characteristics. Depending on the characteristics of different processes and equipment, defects may be formed at specific locations on the wafer. For example, when performing photolithography on the wafer, defects are likely to form in repeated areas, such as Figure 6As shown, or as in chemical mechanical polishing, the difference in pressure between the middle and the edge may cause edge polishing residue, such as Figure 7 As shown in the figure, this type of feature is called a distribution feature (also called a "position feature"). Simply put, the distribution feature is the distribution position information of the defect on the wafer. Different distribution positions or distribution ranges (that is, different conditions of the distribution feature), such as Fig. 9 The “random (random distribution)”, “line (linear distribution)”, “cluster (cluster distribution)”, “edge (distributed at the edge of the wafer)”, “center (distributed at the center of the wafer)”, and “middle (distributed in the middle of the wafer)” will form different distribution characteristics.

[0064] For example, please refer to Fig. 9 In the defect type database used in the present invention, the defect types associated with the “random” distribution feature are “burried”, “surface”, “oil ball”, “large particle”, and “metalroughness”. The defect types associated with the “line” distribution feature are “scratch” and “PR residue”. The defect types associated with the “cluster” distribution feature are “defocus”, “arcing”, “corrosion”, “crack”, and “crush”. The defect types associated with the “edge” distribution feature are “Si residue”, “crack”, “Si damage”, “film peeling”, and “discolor”. The defect types associated with the “center” distribution feature are “metal roughness”, “tiny”, “pits”, “crystal”, and “PR residue”. The defect types associated with the “middle” distribution feature are “crush”, “voild”, “discolor”, “metal grain”, and “bubble”.

[0065] 4) Size characteristics. Because the formation mechanism of defects is different, the size of defects will be different. For example, mechanical scratches are characterized by long strips in the wafer map formed after scanning the wafer. Figure 8 , such features are called dimensional features. Simply put, dimensional features are the size of the defect, different sizes (i.e. different conditions of dimensional features), such as Fig. 9 The “<100nm”, “100-500nm”, “500-1000nm”, “1μm-5μm”, “>5μm”, etc. in the diagram will form different size features.

[0066] For example, please refer to Fig. 9 In the defect type database used in the present invention, the defect types associated with the "<100nm" size feature are "pits", "tiny", "metal roughness", "crystal", and "voild". The defect types associated with the "100-500nm" size feature are "crack", "metal grain", "PR residue", "metal damage", and "repeating". The defect types associated with the "500-1000nm" size feature are "Si damage", "burried", "defocus", "metal residue", and "corrosion". The defect types associated with the "1μm-5μm" size feature are "surface", "crush", "crack", "oil ball", and "scratch". The defect types associated with the ">5μm" size feature are "large particle", "arcing", "film peeling", "bubble", and "discolor".

[0067] Obviously, each defect feature in the defect type database used in the present invention is associated with at least one corresponding defect type, or in other words, each defect feature has a defect type that meets its corresponding conditions. The more defect features under different conditions, the more accurate the classification of wafer defects using the defect type database.

[0068] Exemplarily, the defect types in the defect type database of this embodiment include multiple or all of tiny particle defects, metal grain defects, pit defects, crystallization defects, repeatability defects, chromatic aberration defects, metal surface roughness defects, corrosion defects, crack defects, void defects, buried defects, metal residue defects, defocus defects, bubble defects, silicon damage defects, surface defects, arc defects, photoresist residue defects, metal damage defects, silicon residue defects, large particle defects, spherical defects, film peeling defects, scratch defects and fragmentation defects.

[0069] It should be understood that the defect type database used in the present invention can collect data in the database based on the user's own process characteristics and is not limited to the above examples, thereby achieving a higher degree of production freedom.

[0070] Please refer to Fig.10 In the wafer defect classification method of this embodiment, after scanning wafer defects, the following steps are performed for each defect scanned (i.e., the specific process of implementing the above-mentioned step S20):

[0071] S21, directly acquiring multiple defect features of the defect from the scan data of the defect, for example, one or more or all of signal features, process features, distribution features, and size features;

[0072] S22, retrieving defect types respectively associated with various defect features of the defect from a pre-established defect type database, which are referred to as "target defect types";

[0073] S23, filtering out common defect types associated with multiple or all defect features of the defect from the search results, and classifying the defect into the common defect type with the highest matching degree, thereby completing the classification of the defect.

[0074] By executing the above steps S21 to S23 for each defect scanned on the wafer, classification of all defects scanned on the wafer can be completed, thereby obtaining a defect classification result for the entire wafer.

[0075] Among them, in step S23, multiple common defect types may be screened out. At this time, the common defect types can be sorted by the degree of matching according to the number of defect features associated with each common defect type. The more defect features associated with a common defect type, the higher its matching degree.

[0076] Combine the following Fig.11 The example shown is used to further illustrate the wafer defect classification method of the present invention. After completing the wafer defect scanning, the defects scanned on the wafer are classified. In step S21, the defect features extracted from the scanning data of a defect are: the signal feature is 60, the process feature is LITHO (i.e., lithography), the distribution feature is cluster, and the size feature is 800nm; in step S22, the defect type database starts to search based on the extracted defect features, thereby retrieving the defect type associated with each signal feature extracted from the defect, such as Fig.11As shown in the "matching defect types" column in, no further details are given here. In step S23, the common defect type "defous" associated with all four defect features of the defect, and the common defect type "burried" associated with the signal feature and size feature of the defect can be screened out from the search results. Since the common defect type "defocus" is associated with all the defect features of the defect, its matching degree is 100%, and the common defect type "burried" is associated with half of the defect features of the defect, its matching degree is 50%, therefore, the common defect type "defocus" has the highest matching degree, and in step S23, the defect can be further classified into the defect type "defocus". In addition, the "burried" defect type can be further used as a reference item for classifying the defect.

[0077] In other embodiments of the present invention, the matching degree of each common defect type may be determined by any other appropriate method, which is not specifically limited in the present invention.

[0078] Please refer to Figure 2 and Fig.10 This embodiment also provides an electronic device, which includes at least one processor and at least one memory communicatively connected to the at least one processor, and the at least one memory stores a corresponding program. When the program is executed by the at least one processor, the at least one processor implements the wafer defect classification method as described in this embodiment, that is, implements the above steps S21 to S23.

[0079] Based on the same invention concept, please refer to Fig.12 , an embodiment of the present invention further provides a wafer defect classification system 1, which comprises:

[0080] A defect type database 10, each defect type in the defect type database 10 is associated with at least one defect feature, and each defect feature in the defect type database is associated with at least one defect type;

[0081] The defect classification module 11 is used to perform the following steps on each defect scanned by the wafer defect scanning device 2 after the wafer defect scanning device 2 completes scanning of the wafer defects: Fig.10 Steps S21 to S23 shown are:

[0082] S21, directly acquiring a plurality of defect features of the defect from the scan data of the defect;

[0083] S22, retrieving target defect types respectively associated with each of the defect features of the defect from the defect type database 10;

[0084] S23, screening out common defect types associated with multiple or all of the defect features of the defect from the search results, and classifying the defect into the common defect type with the highest matching degree.

[0085] Among them, the construction and content of the defect type database 10 can refer to the corresponding content described in the wafer defect classification method above, and will not be repeated here.

[0086] Exemplarily, the defect types in the defect type database 10 include multiple or all of tiny particle defects, metal grain defects, pit defects, crystallization defects, repeatability defects, chromatic aberration defects, metal surface roughness defects, corrosion defects, crack defects, void defects, buried defects, metal residue defects, defocus defects, bubble defects, silicon damage defects, surface defects, arc defects, photoresist residue defects, metal damage defects, silicon residue defects, large particle defects, spherical defects, film peeling defects, scratch defects and fragmentation defects.

[0087] Optionally, the defect classification module 11 directly obtains multiple defect features of the defect including at least two of signal features, process features, distribution features and size features; wherein,

[0088] The signal feature is a corresponding signal value obtained when scanning the defect, and the signal value is obtained by receiving optical signals from the surface of the wafer from different angles through multiple signal receivers when scanning the defect, and further performing comprehensive calculations on the optical signals received by the multiple signal receivers;

[0089] The process feature is process information that produces the defect;

[0090] The distribution feature is the distribution position information of the defect on the wafer;

[0091] The dimensional characteristic is the size of the defect.

[0092] Optionally, the defect classification module 11 determines the matching degree of each common defect type selected according to the number of defect features associated with each common defect type selected, and the more defect features associated with a common defect type, the higher the matching degree.

[0093] Alternatively, please refer to Fig.12The defect classification module 11 includes a data receiving unit 111, a feature extraction unit 112, a retrieval unit 113, a screening unit 114 and a classification output unit 115. The data receiving unit 111 can be connected to the wafer defect scanning device 2 in communication, and is used to receive the scanning data of each defect obtained by the wafer defect scanning device 2 scanning the wafer. The feature extraction unit 112 is connected to the data receiving unit 111 in communication, and is used to implement the above-mentioned step S21, that is, to directly obtain multiple defect features of the defect from the scanning data of each defect. The retrieval unit 113 is connected to the defect type database 10, the feature extraction unit 112 and the screening unit 113 in communication, and is used to implement the above-mentioned step S22, that is, to retrieve the target defect type associated with each defect feature of the defect from the defect type database 10. The screening unit 114 is used to screen out the common defect types associated with multiple or all defect features of the defect from the retrieval results of the retrieval unit 113, and determine the matching degree of each of the selected common defect types. The classification output unit 115 is coupled to the screening unit 114 and is used to classify the defect into the common defect type with the highest matching degree determined by the screening unit 114 , and output the defect classification result of the entire wafer after completing the classification of all scanned defects on the wafer.

[0094] It should be understood that the defect type database 10 and the wafer classification module 11 in the wafer defect classification system of the present embodiment can be combined in one device, or any one of the modules can be split into multiple devices, or at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one device. The data receiving unit 111, feature extraction unit 112, retrieval unit 113, screening unit 114 and classification output unit 115 in the wafer classification module 11 can be combined in one device, or any one of the units can be split into multiple devices, or at least part of the functions of one or more of these units can be combined with at least part of the functions of other units and implemented in one device.

[0095] In the wafer defect classification system according to an embodiment of the present invention, the defect type database 10 and at least one of the data receiving unit 111, the feature extraction unit 112, the retrieval unit 113, the screening unit 114 and the classification output unit 115 in the wafer classification module 11 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented in hardware or firmware such as any other reasonable way of integrating or packaging circuits, or in an appropriate combination of software, hardware and firmware.

[0096] Alternatively, the defect type database 10 and at least one of the data receiving unit 111, feature extraction unit 112, retrieval unit 113, screening unit 114 and classification output unit 115 in the wafer classification module 11 can be at least partially implemented as a computer program module. When the program is run by a computer, the functions of the corresponding module can be executed and software upgrades can be performed.

[0097] In other embodiments of the present invention, the wafer defect classification system of the present invention may further include a wafer defect scanning device 2 in addition to the defect type database 10 and the wafer classification module 11 described above.

[0098] Please continue to refer to Fig.12 , this embodiment also provides an electronic device, which has the wafer defect classification system as described in this embodiment.

[0099] In summary, the wafer defect classification method, wafer defect classification system, and electronic device provided by the present invention can directly extract multiple defect features from the scanning data after scanning the wafer defects, and use the multiple defect features to find the corresponding common defect types from the defect type database, so as to realize the classification of each defect scanned on the wafer, thereby obtaining the defect classification result of the entire wafer. There is no need to use an electron microscope to take pictures of the defects scanned on the wafer, which eliminates the electron microscope equipment and the photographing steps, greatly saves time and equipment costs, and improves the wafer defect classification rate. Moreover, there is no need to perform sample analysis through sampling, which can improve the accuracy.

[0100] The above description is only a description of the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention. Any changes or modifications made by a person skilled in the art in the field of the present invention based on the above disclosure shall fall within the scope of the technical solution of the present invention.

Claims

1. A wafer defect classification method, characterized in that: After scanning wafer defects, the following steps are performed for each defect scanned: directly acquiring a plurality of defect features of the defect from the scan data of the defect; Retrieving target defect types respectively associated with each of the defect features of the defect from a pre-established defect type database; Common defect types associated with multiple or all of the defect features of the defect are screened out from the search results, and the defect is classified into the common defect type with the highest matching degree.

2. The wafer defect classification method according to claim 1, characterized in that: The plurality of defect characteristics of the defect include at least two of a signal characteristic, a process characteristic, a distribution characteristic and a size characteristic; wherein, The signal feature is a corresponding signal value obtained when scanning the defect, and the signal value is obtained by receiving optical signals from the surface of the wafer from different angles through multiple signal receivers when scanning the defect, and further performing comprehensive calculations on the optical signals received by the multiple signal receivers; The process feature is process information that produces the defect; The distribution feature is the distribution position information of the defect on the wafer; The dimensional characteristic is the size of the defect.

3. The wafer defect classification method according to claim 1 or 2, characterized in that: Each defect type in the defect type database is associated with at least one corresponding defect feature, and each defect feature in the defect type database is associated with at least one defect type.

4. The wafer defect classification method according to claim 1, characterized in that: Each defect feature directly obtained from the defect belongs to the top-level feature of the wafer; and / or, the defect types in the defect type database include tiny particle defects, metal grain defects, pit defects, crystallization defects, repeatability defects, chromatic aberration defects, metal surface roughness defects, corrosion defects, crack defects, void defects, buried defects, metal residue defects, defocus defects, bubble defects, silicon damage defects, surface defects, arc defects, photoresist residue defects, metal damage defects, silicon residue defects, large particle defects, spherical defects, film peeling defects, scratch defects and fragmentation defects.

5. The wafer defect classification method according to claim 1, wherein: The matching degree of each of the common defect types screened out is determined according to the number of defect features associated with each of the common defect types screened out, and the more defect features associated with the common defect types, the higher the matching degree.

6. A wafer defect classification system, characterized in that: include: A defect type database, each defect type in the defect type database is associated with at least one corresponding defect feature, and each defect feature in the defect type database is associated with at least one defect type; The defect classification module is used to perform the following steps for each defect scanned after scanning the wafer defects: directly acquiring a plurality of defect features of the defect from the scan data of the defect; Retrieving target defect types respectively associated with each of the defect features of the defect from the defect type database; Common defect types associated with multiple or all of the defect features of the defect are screened out from the search results, and the defect is classified into the common defect type with the highest matching degree.

7. The wafer defect classification system according to claim 6, characterized in that: The defect classification module includes: A data receiving unit, used for receiving scanning data of each defect obtained by a wafer defect scanning device scanning the wafer; A feature extraction unit, used for directly acquiring a plurality of defect features of the defect from the scanning data of each defect; A retrieval unit, configured to retrieve target defect types respectively associated with each of the defect features of the defect from the defect type database; a screening unit, configured to screen out common defect types associated with multiple or all of the defect features of the defect from the search results of the search unit, and determine a matching degree of each of the common defect types screened out by the screening unit for the defect; The classification output unit is used to classify the defects into the common defect type with the highest matching degree determined by the screening unit, and output the defect classification result of the entire wafer after the defect classification module completes the classification of all scanned defects on the wafer.

8. The wafer defect classification system according to claim 6 or 7, characterized in that: The defect classification module directly obtains multiple defect features of the defect including at least two of signal features, process features, distribution features, and size features; wherein, The signal feature is a corresponding signal value obtained when scanning the defect, and the signal value is obtained by receiving optical signals from the surface of the wafer from different angles through multiple signal receivers when scanning the defect, and further performing comprehensive calculations on the optical signals received by the multiple signal receivers; The process feature is process information that produces the defect; The distribution feature is the distribution position information of the defect on the wafer; The dimensional characteristic is the size of the defect.

9. The wafer defect classification system according to claim 6, wherein: The defect classification module determines the matching degree of each of the screened common defect types according to the number of defect features associated with each of the screened common defect types, and the more defect features associated with the common defect types, the higher the matching degree.

10. An electronic device, characterized in that: The electronic device comprises at least one processor and at least one memory in communication with the at least one processor, wherein the at least one memory stores a corresponding program, and when the program is executed by the at least one processor, the at least one processor implements the wafer defect classification method according to any one of claims 1 to 5; Alternatively, the electronic device has a wafer defect classification system as described in any one of claims 6 to 9.

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