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 defect detection method comprises the steps that defect data of the surface of each wafer are acquired, and the defect data comprise the type, size and position coordinates of defects; the wafers of the same batch are partitioned according to a unified rule, each wafer is divided into I regions, the defect density value of each region is calculated, the defect density value of the i-th region is determined by defect data of the i-th region and the area of the i-th region, and the defect data of the i-th region is summarized by the defect data of all the wafers of the same batch in the i-th region; and comparing the defect density value of each region with a defect density threshold value, and if the defect density value of the existing region is more than N times of the corresponding defect density threshold value, or the defect density value of the same region of more than M wafers in the batch is greater than the corresponding defect density threshold value, judging the wafer in the batch as an abnormal wafer. 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. Existing wafer inspection methods mostly focus on defect density analysis of single wafers. For some defects that may not be significant on a single wafer, such as tiny cluster distribution, it is difficult to identify them in single wafer inspection. However, when this distribution appears repeatedly throughout a batch of wafers, it may indicate hidden process or equipment risks, such as increased particle density in a specific area caused by dirt accumulation in a certain device or wear of mechanical parts. Therefore, a method for analyzing the distribution and aggregation of defects on the surface of an entire batch of wafers is needed to identify these batch defects and trigger production risk warnings. 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, size and location coordinates of the defect;

[0007] The wafers of the same batch are partitioned in a unified rule, each wafer is divided into I regions, and the defect density value of each region is calculated, wherein the defect density value of the i-th region is determined by the defect data of the i-th region and the area of ​​the i-th region, and the defect data of the i-th region is the summary of the defect data of all wafers of the same batch in the i-th region, and i is greater than 0 and less than 1;

[0008] The defect density value of each area is compared with the defect density threshold. If the defect density value of an area is more than N times the corresponding defect density threshold, or the defect density value of the same area of ​​more than M wafers in the batch is greater than the corresponding defect density threshold, the batch of wafers is judged as abnormal wafers, where M and N are positive integers.

[0009] In some embodiments, calculating the defect density value of each region includes:

[0010] The kernel density estimation method is used to calculate the defect density value of each area based on the defect data on the surface of each wafer.

[0011] In some embodiments, obtaining the defect density threshold comprises:

[0012] The defect data of historical normal batches of wafers are obtained, and the defect density threshold of each area is calculated based on the defect data on the surface of the normal wafers using the kernel density estimation method.

[0013] In some embodiments, N=3, M=3.

[0014] In some embodiments, obtaining defect data on the surface of each wafer includes:

[0015] Using a wafer surface particle detection device to collect the defect data on the surface of each wafer;

[0016] The collected defect data is processed to eliminate noise in the defect data and errors in data collection.

[0017] In some embodiments, after determining that the batch of wafers is abnormal wafers, the method further includes:

[0018] 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.

[0019] An embodiment of the present invention further provides a wafer defect detection device, comprising:

[0020] An acquisition module is used to acquire defect data on the surface of each wafer, wherein the defect data includes the type, size and position coordinates of the defect;

[0021] A calculation module is used to partition the same batch of wafers in a uniform and regular manner, where each wafer is divided into I regions, and a defect density value of each region is calculated, wherein the defect density value of the i-th region is determined by the defect data of the i-th region and the area of ​​the i-th region, and the defect data of the i-th region is a summary of the defect data of all wafers of the same batch in the i-th region, where i is greater than 0 and less than 1;

[0022] A processing module is used to compare the defect density value of each area with the defect density threshold. If the defect density value of an area is more than N times the corresponding defect density threshold, or the defect density value of the same area of ​​more than M wafers in the batch is greater than the corresponding defect density threshold, the batch of wafers is determined to be abnormal wafers, where M and N are positive integers.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] The beneficial effects of the present invention are:

[0027] In this embodiment, the defect data of each wafer surface is obtained, the wafers are partitioned, the defect density value of each area of ​​the wafer is calculated, the defect density value of each area is compared with the defect density threshold, and the abnormal wafer is determined according to the comparison result. This embodiment can reduce the defect comparison and diagnosis process involving manual participation, and improve the accuracy and efficiency of defect identification; this embodiment can realize the defect risk identification at the batch level, and then identify potential process parameter problems or equipment abnormalities, reduce production losses caused by failure to timely discover potential risks, avoid the spread of wafer quality problems in the production chain, and arrange maintenance in time to improve the stability of equipment and product yield; by conducting risk assessment and shipment interception at the batch level, it is effective to prevent the occurrence of batch quality accidents and avoid the outflow of large quantities of defective wafers. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A schematic diagram showing a flow chart of a wafer defect detection method according to an embodiment of the present invention;

[0029] Figure 2 A schematic diagram showing how a wafer is divided into a plurality of regions according to an embodiment of the present invention;

[0030] Figure 3 and Figure 4 A schematic diagram showing the aggregation of cluster defects according to an embodiment of the present invention;

[0031] Figure 5 A schematic diagram showing the structure of a wafer defect detection device according to an embodiment of the present invention;

[0032] Figure 6 A schematic diagram showing the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] 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.

[0034] The present invention provides a wafer defect detection method and device, which can detect batch defects of wafers.

[0035] The embodiment of the present invention provides a wafer defect detection method, such as Figure 1 As shown, including:

[0036] Step 101: Obtaining defect data on the surface of each wafer, wherein the defect data includes the type, size and position coordinates of the defect;

[0037] Specifically, a wafer surface particle detection device (such as SP5 or SP7) can be used to detect the surface of each wafer, obtain defect data such as the location coordinates, size, type, etc. of each defect, and generate a map of defect distribution. In some embodiments, the collected defect data can also be processed to eliminate noise in the defect data and errors in data collection, which can improve the accuracy of defect detection.

[0038] In this embodiment, the defect data of all wafers in a batch (Lot) can also be imported into the data management system, the defect distribution of multiple wafers can be summarized in batches, and a batch defect aggregation database can be created for further aggregation detection and comparative analysis.

[0039] Step 102: performing uniform partitioning on the wafers of the same batch, each wafer is divided into I regions, and calculating the defect density value of each region, wherein the defect density value of the i-th region is determined by the defect data of the i-th region and the area of ​​the i-th region, the defect data of the i-th region is a summary of the defect data of all wafers of the same batch in the i-th region, and i is greater than 0 and less than 1;

[0040] In this embodiment, the same batch of wafers can be partitioned in a uniform and regular manner, and the wafer surface can be divided into I different areas, such as Figure 2As shown. Each wafer is divided into region 1, region 2, region 3, ..., region 1. For wafers of the same batch, the positions and sizes of regions with the same number are the same. In some embodiments, the wafer surface can be divided into a central area, an edge area, a specific corner area, etc. After the wafer is partitioned, the defect density value of each area is calculated. Specifically, the defect density value of each area can be calculated using the kernel density estimation method to evaluate the concentration of defects in each area.

[0041] Step 103: Compare the defect density value of each area with the defect density threshold. If the defect density value of an area is more than N times the corresponding defect density threshold, or the defect density value of the same area of ​​more than M wafers in the batch is greater than the corresponding defect density threshold, the batch of wafers is determined to be abnormal wafers, where M and N are positive integers.

[0042] In this embodiment, the defect data of historical normal batches of wafers can be obtained, and the defect density threshold of each area can be calculated based on the defect data of the normal wafer surface using the kernel density estimation method. In this embodiment, a standard density deviation model can be established based on the defect data of historical normal batches of wafers, including the normal defect density range and its upper and lower limits of fluctuations in different regions. Combined with the defect aggregation trend of adjacent wafers at the same position in the batch data, the characteristic differences between normal and abnormal distributions are identified through machine learning models (such as clustering algorithms and anomaly detection algorithms).

[0043] In some embodiments, the value of N may be 3, and the value of M may be 3. Of course, the values ​​of N and M are not limited to 3, and may also be other values.

[0044] This embodiment can perform batch aggregation detection on all wafers in the batch, and identify abnormal regional aggregation phenomena by comparing the defect density value and defect density threshold of each area. In some embodiments, once the defect density value of a certain area exceeds the defect density threshold by more than 3 times, the batch of wafers is judged as abnormal wafers. Alternatively, if the defect density value of the same area of ​​more than 3 wafers in the batch is greater than the corresponding defect density threshold, the batch of wafers is judged as abnormal wafers. In this way, when an area with high-density defect aggregation repeatedly appears at the same position on multiple wafers in the batch, and the aggregation characteristics of adjacent wafers are similar (such as a slight scratch in the lower right corner), a batch aggregation abnormality warning can be triggered.

[0045] This embodiment can also use morphological analysis methods to identify the defect cluster distribution morphology of the area with high defect density value. Determine whether the area meets the characteristics of abnormal clusters (such as linear distribution or point clusters) (such as Figure 3 and Figure 4To further analyze whether its distribution pattern has batch risk characteristics.

[0046] As batch data accumulates, this embodiment can also optimize and adjust the defect density threshold so that the interception rule is more in line with the actual production environment.

[0047] In this embodiment, the defect data of each wafer surface is obtained, the wafers are partitioned, the defect density value of each area of ​​the wafer is calculated, the defect density value of each area is compared with the defect density threshold, and the abnormal wafer is determined according to the comparison result. This embodiment can reduce the defect comparison and diagnosis process involving manual participation, and improve the accuracy and efficiency of defect identification; this embodiment can realize the defect risk identification at the batch level, and then identify potential process parameter problems or equipment abnormalities, reduce production losses caused by failure to timely discover potential risks, avoid the spread of wafer quality problems in the production chain, and arrange maintenance in time to improve the stability of equipment and product yield; by conducting risk assessment and shipment interception at the batch level, it is effective to prevent the occurrence of batch quality accidents and avoid the outflow of large quantities of defective wafers.

[0048] 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.

[0049] In order to improve the accuracy of the judgment, the production process parameters can be combined with historical data for correlation analysis to determine whether the production process parameters corresponding to the batch of wafers are abnormal. After the batch of wafers is determined to be abnormal wafers, the production process parameters of the batch of wafers can be matched with the production process parameters of the first wafer in the historical database; 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 the 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 the preset matching degree, it is determined that the production process parameters of the batch of wafers are not abnormal.

[0050] In some embodiments, after determining that the batch of wafers is abnormal wafers, the method further includes:

[0051] 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.

[0052] 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 conducting risk assessment and shipment interception at the batch level, batch quality accidents can be effectively prevented, and large-scale outflow of defective wafers can be avoided. When the abnormal density of defects in a certain area or position in a batch of wafers is identified, the flow of the batch of wafers can be automatically suspended, an early warning can be triggered, and a risk alert can be sent to the operator; a detailed risk report can also be automatically generated, including the characteristics of the defect aggregation area, density, distribution form, etc. in each wafer in the batch, and the risk level can be given, and the risk report can be sent to the production and engineering team to quickly troubleshoot equipment or process problems.

[0053] The embodiment of the present invention also provides a wafer defect detection device, such as Figure 5 As shown, including:

[0054] An acquisition module 21 is used to acquire defect data on the surface of each wafer, wherein the defect data includes the type, size and position coordinates of the defect;

[0055] The calculation module 22 is used to partition the same batch of wafers in a uniform and regular manner, each wafer is divided into I regions, and the defect density value of each region is calculated, wherein the defect density value of the i-th region is determined by the defect data of the i-th region and the area of ​​the i-th region, the defect data of the i-th region is the summary of the defect data of all wafers of the same batch in the i-th region, and i is greater than 0 and less than 1;

[0056] The processing module 23 is used to compare the defect density value of each area with the defect density threshold. If the defect density value of an area is more than N times the corresponding defect density threshold, or the defect density value of the same area of ​​more than M wafers in the batch is greater than the corresponding defect density threshold, the batch of wafers is determined to be abnormal wafers, where M and N are positive integers.

[0057] In this embodiment, the defect data of each wafer surface is obtained, the wafers are partitioned, the defect density value of each area of ​​the wafer is calculated, the defect density value of each area is compared with the defect density threshold, and the abnormal wafer is determined according to the comparison result. This embodiment can reduce the defect comparison and diagnosis process involving manual participation, and improve the accuracy and efficiency of defect identification; this embodiment can realize the defect risk identification at the batch level, and then identify potential process parameter problems or equipment abnormalities, reduce production losses caused by failure to timely discover potential risks, avoid the spread of wafer quality problems in the production chain, and arrange maintenance in time to improve the stability of equipment and product yield; by conducting risk assessment and shipment interception at the batch level, it is effective to prevent the occurrence of batch quality accidents and avoid the outflow of large quantities of defective wafers.

[0058] In some embodiments, the calculation module 22 is specifically configured to calculate the defect density value of each region according to the defect data on the surface of each wafer using a kernel density estimation method.

[0059] In some embodiments, the calculation module 22 is further used to obtain defect data of historical normal batches of wafers, and use a kernel density estimation method to calculate a defect density threshold of each area based on the defect data on the surface of the normal wafer.

[0060] In some embodiments, N=3, M=3.

[0061] In some embodiments, the acquisition module 21 is 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.

[0062] In some embodiments, the device further comprises:

[0063] 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.

[0064] Please refer to Figure 6 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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, size and location coordinates of the defect; The wafers of the same batch are partitioned in a unified rule, each wafer is divided into I regions, and the defect density value of each region is calculated, wherein the defect density value of the i-th region is determined by the defect data of the i-th region and the area of ​​the i-th region, and the defect data of the i-th region is the summary of the defect data of all wafers of the same batch in the i-th region, and i is greater than 0 and less than 1; The defect density value of each area is compared with the defect density threshold. If the defect density value of an area is more than N times the corresponding defect density threshold, or the defect density value of the same area of ​​more than M wafers in the batch is greater than the corresponding defect density threshold, the batch of wafers is judged as abnormal wafers, where M and N are positive integers.

2. The wafer defect detection method according to claim 1, characterized in that: The calculation of the defect density value of each region includes: The kernel density estimation method is used to calculate the defect density value of each area based on the defect data on the surface of each wafer.

3. The wafer defect detection method according to claim 1, characterized in that: Obtaining the defect density threshold comprises: The defect data of historical normal batches of wafers are obtained, and the defect density threshold of each area is calculated based on the defect data on the surface of each normal wafer using the kernel density estimation method.

4. The wafer defect detection method according to any one of claims 1 to 3, characterized in that: N=3, M=3.

5. The wafer defect detection method according to any one of claims 1 to 3, characterized in that: The step of obtaining defect data on the surface of each wafer includes: Using a wafer surface particle detection device to collect the 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.

6. The wafer defect detection method according to any one of claims 1 to 3, 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.

7. 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, size and position coordinates of the defect; A calculation module is used to partition the same batch of wafers in a uniform and regular manner, where each wafer is divided into I regions, and a defect density value of each region is calculated, wherein the defect density value of the i-th region is determined by the defect data of the i-th region and the area of ​​the i-th region, and the defect data of the i-th region is a summary of the defect data of all wafers of the same batch in the i-th region, where i is greater than 0 and less than 1; A processing module is used to compare the defect density value of each area with the defect density threshold. If the defect density value of an area is more than N times the corresponding defect density threshold, or the defect density value of the same area of ​​more than M wafers in the batch is greater than the corresponding defect density threshold, the batch of wafers is determined to be abnormal wafers, where M and N are positive integers.

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 6 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 6 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 6.

Images