Wafer defect detection method and device

Abstract

The invention provides a wafer defect detection method and device, and belongs to the technical field of semiconductor manufacturing. The wafer defect detection method comprises the steps that defect data of the surface of each wafer are acquired, and the defect data comprise defect types and defect position coordinates; the defect position coordinates of the same batch of wafers are subjected to clustering analysis through a spatial clustering algorithm, the defect distribution mode and defect distribution density of the batch of wafers are determined, and the same production process parameters are adopted for the same batch of wafers; calculating the risk score of the batch of wafers according to the defect distribution mode and the defect distribution density of the batch of wafers and the proportion of abnormal wafers in the batch; and comparing the risk score of the batch of wafers with a preset risk threshold, and if the risk score of the batch of wafers is greater than the preset risk threshold, determining the batch of wafers as abnormal wafers. According to the invention, the batch defect of the wafer can be detected.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor manufacturing technology, and in particular to a wafer defect detection method and device. Background Art

[0002] With the continuous advancement of semiconductor manufacturing technology, defect control on the wafer surface has become particularly important. In actual production, wafer defect distribution is affected by many factors, such as front-end process fluctuations, material quality changes, and equipment maintenance. These influences may lead to specific defect distribution characteristics in the same batch of wafers, while traditional ADC (Automatic Defect Classification) models usually only focus on the defect characteristics of a single wafer, which is prone to missed detection of batch defects, resulting in the outflow of large quantities of defective products. Summary of the invention

[0003] The present invention provides a wafer defect detection method and device, which can detect batch defects of wafers.

[0004] In order to achieve the above object, the technical solution adopted by the embodiment of the present invention is:

[0005] A wafer defect detection method, comprising:

[0006] Acquire defect data on the surface of each wafer, wherein the defect data includes the type of defect and the coordinates of the defect location;

[0007] Performing cluster analysis on defect position coordinates of wafers of the same batch using a spatial clustering algorithm to determine a defect distribution pattern and a defect distribution density of the wafers of the same batch, wherein the wafers of the same batch use the same production process parameters;

[0008] Calculate the risk score of the batch of wafers according to the defect distribution pattern, defect distribution density and the proportion of abnormal wafers in the batch;

[0009] The risk score of the batch of wafers is compared with a preset risk threshold. If the risk score of the batch of wafers is greater than the preset risk threshold, the batch of wafers is determined to be abnormal wafers.

[0010] In some embodiments, obtaining defect data on the surface of each wafer includes:

[0011] Use wafer surface particle detection equipment to collect defect data on the surface of each wafer;

[0012] The collected defect data is processed to eliminate noise in the defect data and errors in data collection.

[0013] In some embodiments, after determining that the batch of wafers is abnormal wafers, the method further includes:

[0014] Matching the production process parameters of the batch of wafers with the production process parameters of the first wafer in the historical database;

[0015] Whether the production process parameters of the batch of wafers are abnormal is determined based on the matching degree between the production process parameters of the batch of wafers and the production process parameters of the first wafer, and the first wafer is the wafer determined to be abnormal.

[0016] In some embodiments, determining whether the production process parameters of the batch of wafers are abnormal according to the matching degree between the production process parameters of the batch of wafers and the production process parameters of the first wafer includes:

[0017] If the matching degree between the production process parameters of the batch of wafers and the production process parameters of the first wafer is greater than or equal to a preset matching degree, it is determined that the production process parameters of the batch of wafers are abnormal;

[0018] If the matching degree between the production process parameters of the batch of wafers and the production process parameters of the first wafer is less than a preset matching degree, it is determined that there is no abnormality in the production process parameters of the batch of wafers.

[0019] In some embodiments, after determining that the batch of wafers is abnormal wafers, the method further includes:

[0020] The batch of wafers is intercepted, and a risk report of the batch of wafers is generated, wherein the risk report includes defect modes, production process parameters and corresponding corrective measures of the batch of wafers.

[0021] An embodiment of the present invention further provides a wafer defect detection device, comprising:

[0022] An acquisition module is used to acquire defect data on the surface of each wafer, wherein the defect data includes the type of defect and the coordinates of the defect position;

[0023] A cluster analysis module, used to perform cluster analysis on defect position coordinates of wafers of the same batch using a spatial clustering algorithm to determine a defect distribution pattern and defect distribution density of the wafers of the same batch, wherein the wafers of the same batch use the same production process parameters;

[0024] A calculation module, used for calculating the risk score of the batch of wafers according to the defect distribution pattern of the batch of wafers, the defect distribution density and the proportion of abnormal wafers in the batch;

[0025] The determination module is used to compare the risk score of the batch of wafers with a preset risk threshold, and if the risk score of the batch of wafers is greater than the preset risk threshold, the batch of wafers is determined to be abnormal wafers.

[0026] In some embodiments, if the batch of wafers is determined to be abnormal wafers, the device further includes:

[0027] A matching module, used for matching the production process parameters of the batch of wafers with the production process parameters of the first wafer in the historical database;

[0028] The determination module is further used to determine whether the production process parameters of the batch of wafers are abnormal according to the matching degree between the production process parameters of the batch of wafers and the production process parameters of the first wafer, wherein the first wafer is the wafer determined to be abnormal.

[0029] An embodiment of the present invention further provides an electronic device, comprising: 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 wafer defect detection method as described above.

[0030] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the wafer defect detection method as described above are implemented.

[0031] An embodiment of the present invention further provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the wafer defect detection method as described above.

[0032] The beneficial effects of the present invention are:

[0033] In this embodiment, the defect data of each wafer surface is obtained, and the defect position coordinates of the wafers of the same batch are clustered and analyzed using a spatial clustering algorithm to determine the defect distribution pattern and defect distribution density of the wafers of the batch. According to the defect distribution pattern, defect distribution density and the proportion of abnormal wafers in the batch, the risk score of the wafers of the batch is calculated, and the risk score of the wafers of the batch is compared with the preset risk threshold to determine the abnormal wafers. This embodiment can realize the defect risk identification at the batch level, and then identify potential process parameter problems or equipment abnormalities; by performing risk assessment and shipment interception at the batch level, it can effectively prevent the occurrence of batch quality accidents and avoid the large-scale outflow of defective wafers. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic diagram showing a flow chart of a wafer defect detection method according to an embodiment of the present invention;

[0035] Figure 2 A schematic diagram showing defect distribution in an embodiment of the present invention;

[0036] Figure 3A schematic diagram showing the structure of a wafer defect detection device according to an embodiment of the present invention;

[0037] Figure 4 A schematic diagram showing the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0039] The present invention provides a wafer defect detection method and device, which can detect batch defects of wafers.

[0040] The embodiment of the present invention provides a wafer defect detection method, such as Figure 1 As shown, including:

[0041] Step 101: Acquire defect data on the surface of each wafer, wherein the defect data includes the defect type and defect position coordinates;

[0042] Specifically, the wafer surface particle detection equipment can be used to collect defect data on the surface of each wafer; and the collected defect data can be processed to eliminate noise in the defect data and errors in data collection, so as to improve the accuracy of defect detection.

[0043] Step 102: performing cluster analysis on defect position coordinates of wafers of the same batch using a spatial clustering algorithm to determine a defect distribution pattern and defect distribution density of the wafers of the same batch, wherein the wafers of the same batch use the same production process parameters;

[0044] In this embodiment, a spatial clustering algorithm (such as DBSCAN or K-Means) can be used to perform cluster analysis on the defect position coordinates of the same batch of wafers to identify the specific distribution pattern of defects at the batch level. Figure 2 As shown, the left side of the figure is a schematic diagram of the defect distribution of a wafer, and the right side of the figure is a schematic diagram of the distribution of defects of wafers from the same batch after superposition. The significant spatial characteristics of defects at the batch level can be extracted through the spatial clustering algorithm.

[0045] Step 103: Calculate the risk score of the batch of wafers according to the defect distribution pattern, defect distribution density and the proportion of abnormal wafers in the batch;

[0046] In this embodiment, the defect recognition model can be trained using defect sample data, and the defect recognition model is used to calculate the risk score of the batch of wafers according to the defect distribution pattern, defect distribution density and the proportion of abnormal wafers in the batch. Among them, the ADC (Automatic Defect Classification) model can be used in advance to identify abnormal wafers in a batch of wafers.

[0047] In order to improve the accuracy of detection, the number of defect recognition models can be multiple, and the multiple defect recognition models can be trained independently. The weight corresponding to each defect recognition model can be set according to the actual situation, and the detection results corresponding to the multiple defect recognition models are weighted and fused to obtain the final risk score. Among them, the multiple defect recognition models can be random forest models, decision tree models, deep learning neural network models, etc.

[0048] Step 104: Compare the risk score of the batch of wafers with a preset risk threshold. If the risk score of the batch of wafers is greater than the preset risk threshold, determine the batch of wafers as abnormal wafers.

[0049] In this embodiment, the defect data of each wafer surface is obtained, and the defect position coordinates of the wafers of the same batch are clustered and analyzed using a spatial clustering algorithm to determine the defect distribution pattern and defect distribution density of the wafers of the batch. According to the defect distribution pattern, defect distribution density and the proportion of abnormal wafers in the batch, the risk score of the wafers of the batch is calculated, and the risk score of the wafers of the batch is compared with the preset risk threshold to determine the abnormal wafers. This embodiment can realize the defect risk identification at the batch level, and then identify potential process parameter problems or equipment abnormalities; by performing risk assessment and shipment interception at the batch level, it can effectively prevent the occurrence of batch quality accidents and avoid the large-scale outflow of defective wafers.

[0050] In this embodiment, potential process problems or equipment abnormalities can be identified based on the determination results of the batch of wafers. If the batch of wafers is determined to be abnormal wafers, it indicates that the production process parameters (such as crystal growth rate, temperature, pressure, etc.) corresponding to the batch of wafers have potential process problems or equipment abnormalities.

[0051] In order to improve the accuracy of the judgment, the production process parameters can be correlated and analyzed in combination with historical data to determine whether the production process parameters corresponding to the batch of wafers are abnormal. In some embodiments, after the batch of wafers is determined to be abnormal wafers, the method further includes:

[0052] Matching the production process parameters of the batch of wafers with the production process parameters of the first wafer in the historical database;

[0053] Whether the production process parameters of the batch of wafers are abnormal is determined based on the matching degree between the production process parameters of the batch of wafers and the production process parameters of the first wafer, and the first wafer is the wafer determined to be abnormal.

[0054] In some embodiments, determining whether the production process parameters of the batch of wafers are abnormal according to the matching degree between the production process parameters of the batch of wafers and the production process parameters of the first wafer includes:

[0055] If the matching degree between the production process parameters of the batch of wafers and the production process parameters of the first wafer is greater than or equal to a preset matching degree, it is determined that the production process parameters of the batch of wafers are abnormal;

[0056] If the matching degree between the production process parameters of the batch of wafers and the production process parameters of the first wafer is less than a preset matching degree, it is determined that there is no abnormality in the production process parameters of the batch of wafers.

[0057] In some embodiments, after determining that the batch of wafers is abnormal wafers, the method further includes:

[0058] The batch of wafers is intercepted, and a risk report of the batch of wafers is generated, wherein the risk report includes defect modes, production process parameters and corresponding corrective measures of the batch of wafers.

[0059] In this embodiment, abnormal wafers can be intercepted in time, and detailed risk reports and improvement suggestions can be provided, thereby effectively preventing the outflow of batch defective products; by performing risk assessment and shipment interception at the batch level, the occurrence of batch quality accidents can be effectively prevented, and the large-scale outflow of defective wafers can be avoided.

[0060] This embodiment uses a method that combines cluster analysis, morphological analysis and machine learning algorithms to comprehensively analyze defect distribution patterns from multiple dimensions and give accurate risk assessment scores, thereby greatly improving the accuracy of shipping decisions. The technical solution of this embodiment can automatically identify a variety of complex defect patterns and associate production process parameters for in-depth analysis, thereby increasing sensitivity to process fluctuations and equipment problems.

[0061] The embodiment of the present invention also provides a wafer defect detection device, such as Figure 3 As shown, including:

[0062] An acquisition module 21 is used to acquire defect data on the surface of each wafer, wherein the defect data includes the type of defect and the coordinates of the defect position;

[0063] A cluster analysis module 22 is used to perform cluster analysis on the defect position coordinates of the same batch of wafers using a spatial clustering algorithm to determine the defect distribution pattern and defect distribution density of the batch of wafers, wherein the same batch of wafers adopts the same production process parameters;

[0064] A calculation module 23, used to calculate the risk score of the batch of wafers according to the defect distribution pattern, defect distribution density and the proportion of abnormal wafers in the batch;

[0065] The determination module 24 is used to compare the risk score of the batch of wafers with a preset risk threshold, and if the risk score of the batch of wafers is greater than the preset risk threshold, determine the batch of wafers as abnormal wafers.

[0066] In this embodiment, the defect data of each wafer surface is obtained, and the defect position coordinates of the wafers of the same batch are clustered and analyzed using a spatial clustering algorithm to determine the defect distribution pattern and defect distribution density of the wafers of the batch. According to the defect distribution pattern, defect distribution density and the proportion of abnormal wafers in the batch, the risk score of the wafers of the batch is calculated, and the risk score of the wafers of the batch is compared with the preset risk threshold to determine the abnormal wafers. This embodiment can realize the defect risk identification at the batch level, and then identify potential process parameter problems or equipment abnormalities; by performing risk assessment and shipment interception at the batch level, it can effectively prevent the occurrence of batch quality accidents and avoid the large-scale outflow of defective wafers.

[0067] In some embodiments, the acquisition module 21 is specifically used to collect defect data on the surface of each wafer using a wafer surface particle detection device; and process the collected defect data to eliminate noise in the defect data and errors in data collection.

[0068] In some embodiments, if the batch of wafers is determined to be abnormal wafers, the device further includes:

[0069] A matching module, used for matching the production process parameters of the batch of wafers with the production process parameters of the first wafer in the historical database;

[0070] The determination module is further used to determine whether the production process parameters of the batch of wafers are abnormal according to the matching degree between the production process parameters of the batch of wafers and the production process parameters of the first wafer, wherein the first wafer is the wafer determined to be abnormal.

[0071] In some embodiments, the judgment module is specifically used to determine that the production process parameters of the batch of wafers are abnormal if the degree of matching between the production process parameters of the batch of wafers and the production process parameters of the first wafer is greater than or equal to a preset matching degree; if the degree of matching between the production process parameters of the batch of wafers and the production process parameters of the first wafer is less than a preset matching degree, determine that the production process parameters of the batch of wafers are not abnormal.

[0072] In some embodiments, the device further comprises:

[0073] The interception module is used to intercept the batch of wafers and generate a risk report for the batch of wafers, wherein the risk report includes defect modes, production process parameters and corresponding corrective measures for the batch of wafers.

[0074] Please refer to Figure 4 An embodiment of the present invention further provides an electronic device 30, including a processor 31, a memory 32, and a computer program stored in the memory 32 and executable on the processor 31. When the computer program is executed by the processor 31, each process of the above-mentioned wafer defect detection method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0075] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above-mentioned wafer defect detection method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. Among them, the computer-readable storage medium includes permanent and non-permanent, removable and non-removable media, and information storage can be realized by any method or technology. The information can be a computer-readable instruction, a data structure, a module of a program, 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 technology, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape magnetic disk storage or other magnetic storage terminal device to be detected or any other non-transmission medium, which can be used to store information that can be accessed by the terminal device to be detected. According to the definition in this article, computer-readable storage media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0076] The present application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the above Figure 1 The various processes of the method embodiment shown can achieve the same technical effect, and will not be described again here to avoid repetition.

[0077] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0078] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an 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, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0079] In the various method embodiments of the present disclosure, the serial numbers of the steps cannot be used to limit the sequence of the steps. For ordinary technicians in this field, without paying any creative work, changes to the sequence of the steps are also within the protection scope of the present disclosure.

[0080] It should be noted that each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the embodiments, since they are basically similar to the product embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the product embodiments.

[0081] The above is a preferred embodiment of the present disclosure. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles described in the present disclosure. These improvements and modifications should also be regarded as the scope of protection of the present disclosure.

Claims

1. A wafer defect detection method, characterized in that: include: Acquire defect data on the surface of each wafer, wherein the defect data includes the type of defect and the coordinates of the defect location; Performing cluster analysis on defect position coordinates of wafers of the same batch using a spatial clustering algorithm to determine a defect distribution pattern and a defect distribution density of the wafers of the same batch, wherein the wafers of the same batch use the same production process parameters; Calculate the risk score of the batch of wafers according to the defect distribution pattern, defect distribution density and the proportion of abnormal wafers in the batch; The risk score of the batch of wafers is compared with a preset risk threshold. If the risk score of the batch of wafers is greater than the preset risk threshold, the batch of wafers is determined to be abnormal wafers.

2. The wafer defect detection method according to claim 1, characterized in that: The step of obtaining defect data on the surface of each wafer includes: Use wafer surface particle detection equipment to collect defect data on the surface of each wafer; The collected defect data is processed to eliminate noise in the defect data and errors in data collection.

3. The wafer defect detection method according to claim 1, characterized in that: After determining that the batch of wafers is abnormal wafers, the method further includes: Matching the production process parameters of the batch of wafers with the production process parameters of the first wafer in the historical database; Whether the production process parameters of the batch of wafers are abnormal is determined based on the matching degree between the production process parameters of the batch of wafers and the production process parameters of the first wafer, and the first wafer is the wafer determined to be abnormal.

4. The wafer defect detection method according to claim 3, characterized in that: The determining whether the production process parameters of the batch of wafers are abnormal according to the matching degree between the production process parameters of the batch of wafers and the production process parameters of the first wafer comprises: If the matching degree between the production process parameters of the batch of wafers and the production process parameters of the first wafer is greater than or equal to a preset matching degree, it is determined that the production process parameters of the batch of wafers are abnormal; If the matching degree between the production process parameters of the batch of wafers and the production process parameters of the first wafer is less than a preset matching degree, it is determined that there is no abnormality in the production process parameters of the batch of wafers.

5. The wafer defect detection method according to claim 1, characterized in that: After determining that the batch of wafers is abnormal wafers, the method further includes: The batch of wafers is intercepted, and a risk report of the batch of wafers is generated, wherein the risk report includes defect modes, production process parameters and corresponding corrective measures of the batch of wafers.

6. A wafer defect detection device, characterized in that: include: An acquisition module is used to acquire defect data on the surface of each wafer, wherein the defect data includes the type of defect and the coordinates of the defect position; A cluster analysis module, used to perform cluster analysis on defect position coordinates of wafers of the same batch using a spatial clustering algorithm to determine a defect distribution pattern and defect distribution density of the wafers of the same batch, wherein the wafers of the same batch use the same production process parameters; A calculation module, used for calculating the risk score of the batch of wafers according to the defect distribution pattern of the batch of wafers, the defect distribution density and the proportion of abnormal wafers in the batch; The determination module is used to compare the risk score of the batch of wafers with a preset risk threshold, and if the risk score of the batch of wafers is greater than the preset risk threshold, the batch of wafers is determined to be abnormal wafers.

7. The wafer defect detection device according to claim 6, characterized in that: If the batch of wafers is determined to be abnormal wafers, the device further includes: A matching module, used for matching the production process parameters of the batch of wafers with the production process parameters of the first wafer in the historical database; The determination module is further used to determine whether the production process parameters of the batch of wafers are abnormal according to the matching degree between the production process parameters of the batch of wafers and the production process parameters of the first wafer, wherein the first wafer is the wafer determined to be abnormal.

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 when the program is executed by the processor, the steps of the wafer defect detection method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the wafer defect detection method according to any one of claims 1 to 5 are implemented.

10. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of the wafer defect detection method according to any one of claims 1 to 5.

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