Defect monitoring sampling method and computer readable storage medium
By calculating the difference in the combination of sample wafer numbers during semiconductor manufacturing, the defect monitoring method is optimized to ensure that all process equipment cavities are covered. This solves the problem of low product yield and low efficiency caused by unreasonable sampling in existing technologies, and achieves a highly efficient and stable production process.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CSMC TECH FAB2 CO LTD
- Filing Date
- 2024-11-20
- Publication Date
- 2026-05-22
AI Technical Summary
In the existing technology, the sampling methods for defect monitoring in semiconductor manufacturing are difficult to quantify as reasonable, resulting in a lack of sampling in some process equipment cavities, which may lead to low product yield and reduced production efficiency.
By calculating the cumulative difference between the expected and actual sample size of different sampling piece number combinations in the target process section, the sampling piece number combination with the smallest cumulative difference is selected as the sampling piece number combination for the defect monitoring equipment, ensuring that all process machine cavities are covered.
It enables real-time monitoring of all process machine cavities, ensuring high efficiency and stability of the production process, reducing production costs and improving production efficiency.
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Figure CN122072933A_ABST
Abstract
Description
Technical Field
[0001] This application relates to semiconductor manufacturing, and more particularly to a sampling method for defect monitoring, and also to a computer-readable storage medium. Background Technology
[0002] The semiconductor integrated circuit (IC) industry has experienced exponential growth. However, with increasing complexity in semiconductor processes and longer production cycles, process quality control is crucial for high-quality semiconductor operations. An inline defect monitor step (IMP) refers to a defect inspection station located after a specific process loop in the semiconductor wafer manufacturing process. It uses a defect scanning machine to directly monitor the process stability of the wafer product in real time. Figure 1 As shown.
[0003] The sampling rate of online defect scanning can be expressed as the number of wafers sampled. For example, for a batch of 25 wafers, 3 wafers are sampled, that is, the number of wafers sampled is 3.
[0004] The sampling selection strategy for exemplary online defect scanning is mainly set by engineers based on experience. For example, a fixed combination of sampling wafer numbers is used (e.g., for a batch of 25 wafers, wafers number 3, 10, and 20 are selected for defect scanning). This strategy is not adjusted based on different process flows of the product, and it is difficult to quantify the rationality of the sampling wafer number combination. Summary of the Invention
[0005] Therefore, it is necessary to provide a sampling method for defect monitoring that can quantify the rationality of sampling.
[0006] A sampling method for defect monitoring includes: acquiring target data, the target data including shipment data of process equipment in a target process segment; acquiring, based on the target data, the cumulative difference between the expected and actual sample quantities of different sampling piece number combinations in the target process segment within a statistical period; and using the sampling piece number combination with the smallest cumulative difference as the sampling piece number combination for defect monitoring equipment in the target process segment for defect monitoring.
[0007] The above-mentioned sampling method for defect monitoring calculates the cumulative difference between the actual sample size and the expected sample size of different sampling piece number combinations in the target process section, and selects the sampling piece number combination with the smallest cumulative difference as the sampling piece number combination. This achieves the goal of covering different cavities of different process machines as much as possible for defect monitoring, so that the process stability of all cavities in the production process is under monitoring, ensuring efficient and stable production operation.
[0008] In one embodiment, the expected sample size for each process chamber of the process equipment is the total throughput of each process chamber multiplied by λ, where λ is the number of sampled wafers per batch divided by the number of wafers per batch; the cumulative difference is the cumulative value of the first difference of all process chambers of the process equipment, where the first difference is the expected sample size of the process chamber minus the actual sample size, and the cumulative difference is not included when the first difference of the process chamber is negative.
[0009] In one embodiment, the sampling method for defect monitoring is applied to online defect monitoring, and the defect monitoring equipment is used to monitor defects in the target process segment.
[0010] In one embodiment, the defect monitoring device includes a measurement unit configured to measure sampled wafers.
[0011] In one embodiment, the target data also includes product shipment information and cavity classification data.
[0012] In one embodiment, the method further includes the step of determining the target process segment based on the process flow of the target product.
[0013] In one embodiment, the number of wafers in each batch is 25, and the number of sample wafers in each batch is 3.
[0014] It is also necessary to provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method in any of the above embodiments.
[0015] It is also necessary to provide a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the method described in any of the foregoing embodiments.
[0016] It is also necessary to provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the method as described in any of the foregoing embodiments.
[0017] It is also necessary to provide a method for defect monitoring sampling using a computer program, including: acquiring target data, the target data including the shipment data of process equipment in the target process segment; based on the target data, acquiring the cumulative difference between the expected sample size and the actual sample size of different sampling piece number combinations in the target process segment within a statistical period; and using the sampling piece number combination with the smallest cumulative difference as the sampling piece number combination of the defect monitoring equipment in the target process segment for defect monitoring.
[0018] The above-mentioned method of defect monitoring sampling using computer programs calculates the cumulative difference between the actual sample size and the expected sample size of different sampling piece number combinations in the target process section, and selects the sampling piece number combination with the smallest cumulative difference as the sampling piece number combination. This achieves the goal of covering different cavities of different process machines as much as possible for defect monitoring, so that the process stability of all cavities in the production process is under monitoring, ensuring efficient and stable production operation.
[0019] In one embodiment, the expected sample size for each process chamber of the process equipment is the total throughput of each process chamber multiplied by λ, where λ is the number of sampled wafers per batch divided by the number of wafers per batch; the cumulative difference is the cumulative value of the first difference of all process chambers of the process equipment, where the first difference is the expected sample size of the process chamber minus the actual sample size, and the cumulative difference is not included when the first difference of the process chamber is negative.
[0020] In one embodiment, the sampling method for defect monitoring is applied to online defect monitoring, and the defect monitoring equipment is used to monitor defects in the target process segment.
[0021] In one embodiment, the defect monitoring device includes a measurement unit configured to measure sampled wafers.
[0022] In one embodiment, the target data also includes product shipment information and cavity classification data.
[0023] In one embodiment, the method further includes the step of determining the target process segment based on the process flow of the target product.
[0024] In one embodiment, the number of wafers in each batch is 25, and the number of sample wafers in each batch is 3. Attached Figure Description
[0025] To better describe and illustrate embodiments and / or examples of the inventions disclosed herein, reference may be made to one or more accompanying drawings. Additional details or examples used to describe the drawings should not be considered as limiting the scope of any of the disclosed inventions, the currently described embodiments and / or examples, or the best mode of these inventions as currently understood.
[0026] Figure 1 This is a schematic diagram of online defect monitoring;
[0027] Figure 2 This is a flowchart of a sampling method for defect monitoring in one embodiment of this application. Detailed Implementation
[0028] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.
[0030] It should be understood that when an element or layer is referred to as "on," "adjacent to," "connected to," or "coupled to" other elements or layers, it may be directly on, adjacent to, connected to, or coupled to other elements or layers, or there may be intervening elements or layers. Conversely, when an element is referred to as "directly on," "directly adjacent to," "directly connected to," or "directly coupled to" other elements or layers, there are no intervening elements or layers. It should be understood that although the terms first, second, third, etc., may be used to describe various elements, components, areas, layers, and / or portions, these elements, components, areas, layers, and / or portions should not be limited by these terms. These terms are only used to distinguish one element, component, area, layer, or portion from another element, component, area, layer, or portion. Therefore, without departing from the teachings of this invention, the first element, component, area, layer, or portion discussed below may be referred to as the second element, component, area, layer, or portion.
[0031] Spatial relation terms such as “below,” “under,” “below,” “under,” “above,” “above,” etc., are used herein for convenience of description to describe the relationship between one element or feature shown in the figure and other elements or features. It should be understood that, in addition to the orientation shown in the figure, spatial relation terms are intended to also include different orientations of the device in use and operation. For example, if the device in the figure is flipped, then the element or feature described as “below” or “under” the other element or feature will be oriented “above” the other element or feature. Therefore, the exemplary terms “below” and “under” can include both upper and lower orientations. The device may be otherwise oriented (rotated 90 degrees or otherwise) and the spatial descriptive terms used herein will be interpreted accordingly.
[0032] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “compose” and / or “comprising,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.
[0033] In integrated circuit manufacturing, different wafer numbers within each batch are often processed by different cavities on the same process equipment. Therefore, when sampling wafers for defect scanning, products from the same batch but with different numbers can reflect the process status of multiple cavities. If the sampling strategy for online defect scanning ensures that the sampled wafers are always processed by cavities on a subset of process equipment, then the remaining cavities are at risk of not being sampled. This could lead to defective products not being detected in time by online defect scanning, resulting in low yield, increased production costs, and reduced production efficiency. We aim to find an optimal sampling strategy that ensures the products scanned by the defect scanning equipment fully represent all cavities on the process equipment, allowing for real-time monitoring of the process stability of all cavities in the production process.
[0034] This application provides a sampling method for defect monitoring. By establishing an intelligent sampling and selection model for defect monitoring, key statistics are identified, and the results of different sampling and selection strategies are quantified. Relying on the material handling records of each process cavity of the machine tool, and based on the process flow of different products and the cavity classification of the machine tool, a reasonable sampling and selection strategy is made to achieve the goal of covering different process cavities of different machine tools as much as possible during the defect monitoring process. Figure 2 This is a flowchart of a sampling method for defect monitoring in one embodiment of this application, including the following steps:
[0035] S210, Obtain target data, which includes the shipment data of the process equipment in the target process segment.
[0036] Based on the shipment records of each cavity of the process equipment in the target process section, a corresponding sampling and selection strategy is formulated to cover as many cavities of the process equipment in the target process section as possible.
[0037] S220, obtain the cumulative difference between the expected sample size and the actual sample size for different sampling piece number combinations in the target process section.
[0038] Based on the target data obtained in step S210, calculate the cumulative difference between the expected and actual sample size for different combinations of sampled wafer numbers in the target process segment within a certain statistical period (e.g., one week). The expected sample size for each process chamber of the process equipment is the total throughput of each process chamber multiplied by λ, where λ is the number of sampled wafers per batch divided by the total number of wafers per batch. For example, for a batch of 25 wafers, if 3 wafers are sampled for defect inspection, then λ = 3 ÷ 25 = 0.12.
[0039] S230 uses the sample piece number combination with the smallest cumulative difference as the sample piece number combination for defect monitoring.
[0040] The sample piece number combination with the smallest cumulative difference is used as the sample piece number combination for the defect monitoring equipment (defect monitoring equipment site) of the target process section for defect monitoring.
[0041] The above-mentioned sampling method for defect monitoring calculates the cumulative difference between the actual sample size and the expected sample size of different sampling piece number combinations in the target process section, and selects the sampling piece number combination with the smallest cumulative difference as the sampling piece number combination. This achieves the goal of covering different cavities of different process machines as much as possible for defect monitoring, so that the process stability of all cavities in the production process is under monitoring, ensuring efficient and stable production operation.
[0042] In one embodiment of this application, the cumulative difference between the expected sample size and the actual sample size is the cumulative value of the first difference of all process cavities of the process equipment in the target process segment. The first difference is the expected sample size of the process cavity minus the actual sample size, and when the first difference of the process cavity is negative, it is not included in the cumulative difference.
[0043] In one embodiment of this application, the sampling method for defect monitoring is applied to online defect monitoring.
[0044] In one embodiment of this application, the defect monitoring device includes a measurement machine for measuring sampled wafers.
[0045] In one embodiment of this application, the target data also includes product shipment information and cavity classification data.
[0046] In one embodiment of this application, before step S210, a step of determining the target process segment based on the process flow of the target product is further included.
[0047] The following describes the technical solution of the sampling method for defect monitoring in this application, using a specific embodiment as an example.
[0048] The defect monitoring sampling method in this application is based on a mathematical model of big data analysis. It identifies the most critical statistical quantities, measures the representativeness of different sampling and selection strategies under different conditions to each cavity of the target process machine, and finally derives a measurement machine sampling and selection strategy that can fully represent all cavities of the process machine.
[0049] I. Data Extraction
[0050] Data is integrated from the Fab (semiconductor manufacturing plant) database to obtain variable data that influences the sampling and selection strategy. This includes acquiring shipment data for each cavity of the process equipment in the target process segment, as well as product shipment information and cavity classification data.
[0051] II. Establishment of Sampling Selection Model
[0052] To establish an intelligent sampling and selection model for metrology equipment at an online defect monitoring site, the key statistical measures for evaluating the model's performance must first be identified, quantifying the results of different sampling and selection strategies. This model aims to sample sufficient products from the process chambers of each machine tool, using a defect scanning machine to scan for product defects, thereby enabling real-time monitoring of the process stability of the process chambers. Based on this, the following statistical measures are established:
[0053] (1)
[0054] Where x ij This refers to the quantity of product (wafer) with wafer number j processed in machine cavity number i. For example, the quantity of wafer number 3 processed in cavity A of machine 01 in each batch is x. 1A 3 H represents the combination of sample piece numbers. For example, H=(3, 11, 20) indicates that pieces numbered 3, 11, and 20 are randomly selected from each batch of products for defect monitoring. This refers to the total throughput of wafers numbered 3, 11, and 20 in the i-type machine cavity. λ is the ideal sampling ratio for measurement in each batch of products, which is equal to the number of wafers sampled per batch divided by the total number of wafers in each batch. For example, for a batch of 25 wafers, if 3 wafers are sampled for defect inspection, then λ = 3 ÷ 25 = 0.12. From this, the statistic y is derived. H The actual meaning is: under the H sampling model, the sum of the deviations between the expected sample size and the actual sample size of the target process section. In actual operation, if This indicates that the actual sample size exceeded the expected sample size. In this case, the sample size meets the requirements and does not need to be considered. Therefore, the following formula is derived:
[0055] (2)
[0056] Therefore, the statistic y HThis can be simply understood as the cumulative difference between the expected sample size and the actual sample size not being reached under the H sampling model. H The smaller the value, the more closely the sampled piece number combination H matches the expected value. In y H In the sampling selection model, specific product types and corresponding process equipment can be further selected to quickly formulate the required sampling selection strategy for measurement equipment.
[0057] Table 1 shows the data for one week for a certain process segment in the actual production data of the Fab, specifically for the sample wafer number combination H=(3, 11, 20):
[0058] Table 1
[0059]
[0060] Table 2 shows the results when using other combinations of sample card numbers. H Data:
[0061] Table 2
[0062]
[0063] As can be seen from the weekly data of this process segment, for H=(3, 11, 20), the y... H The smallest sample number combination, i.e., sampling pieces numbered 3, 11, and 20 for defect monitoring, showed the best performance. To further verify the stability of this model and the sample number combinations, we calculated y using data from one month and three months of the same process segment. H .
[0064] Table 3 shows the results obtained based on one month's historical data:
[0065] Table 3
[0066]
[0067] Table 4 shows the results obtained based on three months of historical data:
[0068] Table 4
[0069]
[0070] Comparing the data in Tables 2, 3, and 4, the top-ranked sample number combinations show relatively little change, and the optimal sample number combination H=(3, 11, 20) remains stable over a long period. Therefore, the reliability and stability of the sampling selection model can be confirmed.
[0071] This application embodiment, based on historical shipment data of the target process segment's machine cavity and the product's process type, formulates a key statistical quantity y to measure the merits of a sampling selection strategy. HThis refers to the cumulative difference between the expected sample size and the actual sample size. Based on the process machine cavity of the target process section, relevant historical data is obtained, and y is calculated using Formula 2 for different wafer selection combinations. H This method involves developing a sampling and selection strategy. The goal is to ensure that the measurement process for defect monitoring covers different cavities on different process machines as much as possible, allowing for real-time monitoring of the process stability of all cavities in the production process, thus guaranteeing efficient and stable production operations. This method offers rapid engineering response; it only requires understanding the product's process flow and the shipment records of each cavity on the target process machine to develop a targeted measurement sampling and selection strategy that maximizes coverage. It is characterized by its rapid response and flexibility.
[0072] Based on all the above embodiments, this application provides a sampling method for defect monitoring based on big data analysis. It establishes three main dimensions influencing wafer selection: the cavity classification of the process equipment, the product's process flow, and the type of measurement equipment. Based on the historical batch data of each wafer's shipment records at the target process segment's process equipment, it analyzes and calculates the impact of different sample wafer number combinations on the measurement performance of the measurement equipment at the defect monitoring site, establishing optimal sampling selection models corresponding to different process flows. This achieves the goal of developing corresponding wafer selection strategies for different process flows and process segments, ensuring that the defect monitoring process covers the entire production process as much as possible. It accurately and promptly reflects problems in each cavity of the process equipment during the production process, providing a reference for engineers' judgment and decision-making.
[0073] It should be understood that although the steps in the flowchart of this application are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart of this application may include multiple steps or multiple stages, which are not necessarily completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps.
[0074] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.
[0075] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the above embodiments.
[0076] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the method described in any of the foregoing embodiments.
[0077] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method as described in any of the foregoing embodiments.
[0078] This application also provides a method for defect monitoring sampling using a computer program, comprising: acquiring target data, the target data including shipment data of process equipment in a target process segment; acquiring, based on the target data, the cumulative difference between the expected sample size and the actual sample size of different sampling piece number combinations in the target process segment within a statistical period; and using the sampling piece number combination with the smallest cumulative difference as the sampling piece number combination of the defect monitoring equipment in the target process segment for defect monitoring.
[0079] The above-mentioned method of defect monitoring sampling using computer programs calculates the cumulative difference between the actual sample size and the expected sample size of different sampling piece number combinations in the target process section, and selects the sampling piece number combination with the smallest cumulative difference as the sampling piece number combination. This achieves the goal of covering different cavities of different process machines as much as possible for defect monitoring, so that the process stability of all cavities in the production process is under monitoring, ensuring efficient and stable production operation.
[0080] In one embodiment, the expected sample size for each process chamber of the process equipment is the total throughput of each process chamber multiplied by λ, where λ is the number of sampled wafers per batch divided by the number of wafers per batch; the cumulative difference is the cumulative value of the first difference of all process chambers of the process equipment, where the first difference is the expected sample size of the process chamber minus the actual sample size, and the cumulative difference is not included when the first difference of the process chamber is negative.
[0081] In one embodiment, the sampling method for defect monitoring is applied to online defect monitoring, and the defect monitoring equipment is used to monitor defects in the target process segment.
[0082] In one embodiment, the defect monitoring device includes a measurement unit configured to measure sampled wafers.
[0083] In one embodiment, the target data also includes product shipment information and cavity classification data.
[0084] In one embodiment, the method further includes the step of determining the target process segment based on the process flow of the target product.
[0085] In one embodiment, the number of wafers in each batch is 25, and the number of sample wafers in each batch is 3.
[0086] In the description of this specification, references to terms such as "some embodiments," "other embodiments," and "ideal embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative descriptions of the above terms do not necessarily refer to the same embodiments or examples.
[0087] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0088] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.
Claims
1. A sampling method for defect monitoring, comprising: Acquire target data, which includes the shipment data of the process equipment in the target process segment; Based on the target data, obtain the cumulative difference between the expected sample size and the actual sample size for different sampling piece number combinations in the target process segment during the statistical period. The sample number combination with the smallest cumulative difference is used as the sample number combination of the defect monitoring equipment for the target process section for defect monitoring.
2. The sampling method for defect monitoring according to claim 1, characterized in that, The expected sample size for each process chamber of the process equipment is the total throughput of each process chamber multiplied by λ, where λ is the number of sampled wafers per batch divided by the number of wafers per batch. The cumulative difference is the cumulative value of the first difference of all process cavities of the process equipment. The first difference is the expected sample size of the process cavity minus the actual sample size. When the first difference of the process cavity is negative, it is not included in the cumulative difference.
3. The sampling method for defect monitoring according to claim 1, characterized in that, The sampling method for defect monitoring is applied to online defect monitoring, and the defect monitoring equipment is used to monitor defects in the target process segment.
4. The sampling method for defect monitoring according to claim 3, characterized in that, The defect monitoring equipment includes a measurement machine configured to measure sampled wafers.
5. The sampling method for defect monitoring according to claim 1, characterized in that, The target data also includes product shipment information and cavity classification data.
6. The sampling method for defect monitoring according to claim 1, characterized in that, It also includes the step of determining the target process segment based on the process flow of the target product.
7. The sampling method for defect monitoring according to claim 2, characterized in that, The number of wafers in each batch is 25, and the number of sample wafers in each batch is 3.
8. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method as claimed in any one of claims 1-7.
9. A computer device comprising a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method as described in any one of claims 1-7.
10. A computer program product comprising a computer program that, when executed by a processor, implements the steps of the method as claimed in any one of claims 1-7.