Semiconductor production line anomaly detection method and device, storage medium and equipment

Abstract

The invention discloses a semiconductor production line anomaly detection method and device, a storage medium and equipment. The method comprises the following steps: acquiring abnormal object information; based on the abnormal object information, reading corresponding raw material information from a preset raw material database; and performing analysis based on the read raw material information so as to determine the abnormal reason of the semiconductor production line. By adopting the scheme, the speed of performing anomaly detection from the dimension of the raw material can be improved.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor technology, and in particular to a method, device, storage medium and equipment for detecting abnormalities in a semiconductor production line. Background Art

[0002] In the manufacturing process of semiconductor devices, there are many types of equipment involved, complex processes, and high specifications for raw materials used in production, which makes semiconductor production very difficult.

[0003] In the actual manufacturing process, production line abnormalities often occur. When production line abnormalities occur, it is necessary to detect the cause of the production line abnormality from a quality perspective. Usually, it is necessary to detect the root cause of the production line abnormality from six dimensions: human factors, equipment, raw materials, parameters, environment, and testing.

[0004] However, the existing rate of anomaly detection from the raw material dimension is low. Summary of the invention

[0005] The problem to be solved by the present invention is: how to improve the rate of abnormality detection from the raw material dimension.

[0006] To solve the above problems, an embodiment of the present invention provides a method for detecting abnormalities in a semiconductor production line, the method comprising:

[0007] Get exception object information;

[0008] Based on the abnormal object information, corresponding raw material information is read from a preset raw material database;

[0009] The raw material information read is analyzed to determine the cause of the semiconductor production line abnormality.

[0010] In a possible embodiment, the abnormal object is at least one of the following: an abnormal process, an abnormal process step, an abnormal process equipment, and an abnormal factory equipment.

[0011] In a possible embodiment, the preset raw material database includes at least one of the following raw material information:

[0012] The warehousing information of each raw material;

[0013] The outbound information of each raw material;

[0014] Usage record information of each raw material;

[0015] Shipping inspection information of each raw material;

[0016] Change record information of each raw material;

[0017] Abnormal raw material information.

[0018] In a possible embodiment, the warehousing information of the raw materials includes at least one of the following: the name, quantity, batch number, and supplier information of the raw materials.

[0019] In a possible embodiment, the delivery information of the raw materials includes at least one of the following: the name, quantity and batch number information of the raw materials.

[0020] In a possible embodiment, the usage record information of the raw material includes at least one of the following: online time information and offline time information of the raw material.

[0021] In a possible embodiment, the raw material shipment inspection information includes: inspection result information of various performance parameters of the raw materials.

[0022] In a possible embodiment, the raw material change record information includes at least one of the following: raw material model change information and manufacturer change information.

[0023] In a possible embodiment, the abnormal raw material information includes: identification information of expired raw materials, identification information of raw materials with abnormal shipment detection, and identification information of raw materials with abnormal storage environment.

[0024] In a possible embodiment, the preset raw material database also includes: corresponding relationship information between each abnormal object and each raw material information.

[0025] In a possible embodiment, the analyzing based on the read raw material information includes:

[0026] The commonality analysis is performed on the wafer measured values ​​using the raw material information read at different times to form a commonality analysis chart.

[0027] In a possible embodiment, the performing analysis based on the read raw material information further includes:

[0028] The commonality analysis chart indicates the measured values ​​using abnormal raw material wafers or the measured values ​​using raw material wafers with changes.

[0029] An embodiment of the present invention further provides a semiconductor production line abnormality detection device, the device comprising:

[0030] An acquisition unit, adapted to acquire abnormal object information;

[0031] A reading unit, adapted to read corresponding raw material information from a preset raw material database based on the abnormal object information;

[0032] The analysis unit is adapted to perform analysis based on the read raw material information to determine the cause of the semiconductor production line abnormality.

[0033] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. The computer program is executed by a processor to implement the steps of any of the above methods.

[0034] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor executes the steps of any of the above methods when running the computer program.

[0035] Compared with the prior art, the technical solution of the embodiment of the present invention has the following advantages:

[0036] By applying the solution of the present invention, by pre-setting a raw material database, when abnormal object information is obtained, the corresponding raw material information can be read from the preset raw material database, so that analysis can be performed based on the read raw material information to determine the cause of the abnormality in the semiconductor production line. Compared with the method of manually determining the raw material information corresponding to the abnormal object information and analyzing it, the corresponding raw material information can be quickly locked by a computer and automatically analyzed, so that it can be quickly determined whether the raw materials cause the abnormality in the semiconductor production line, effectively improving the rate of abnormality detection from the raw material dimension. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the related factors that lead to abnormalities in semiconductor production lines;

[0038] Figure 2 is a flow chart of a semiconductor production line abnormality detection method in an embodiment of the present invention;

[0039] Figure 3 is a schematic diagram of the architecture of a raw material database in an embodiment of the present invention;

[0040] Figure 4 is a schematic diagram of a commonality analysis chart in an embodiment of the present invention;

[0041] Figure 5 is a schematic diagram of another commonality analysis chart in an embodiment of the present invention;

[0042] Figure 6 is a schematic diagram of another commonality analysis chart in an embodiment of the present invention;

[0043] Figure 7 It is a structural schematic diagram of a semiconductor production line abnormality detection device in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] Figure 1 A schematic diagram of the factors that lead to abnormalities in semiconductor production lines. Figure 1 When a production line abnormality occurs, it is necessary to detect the cause of the production line abnormality from a quality perspective. Usually, it is necessary to detect the root cause of the production line abnormality from six dimensions: human factors (people), equipment (machine), raw materials (materials), parameters (methods), environment (environment), and detection (measurement).

[0045] Among them, the detection of the four dimensions of equipment (machine), parameters (method), detection (measurement), and environment (environment) are supported by corresponding mature online systems in major manufacturing companies, which facilitates rapid detection and analysis when production line abnormalities occur. However, human factors have a smaller impact in automated production companies and cannot be controlled by the system. Only in the raw material (material) dimension, it is necessary to manually retrieve the corresponding raw material information from multiple systems and analyze it, resulting in low detection efficiency.

[0046] To address this problem, the present invention provides a semiconductor production line anomaly detection method. According to this method, when abnormal object information is obtained, the corresponding raw material information is directly read from a preset raw material database and analyzed, thereby realizing rapid anomaly detection in the raw material dimension.

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0048] Reference Figure 2 The embodiment of the present invention provides a method for detecting abnormalities in a semiconductor production line, and the method may include the following steps:

[0049] Step 21, obtain abnormal object information.

[0050] In a specific implementation, when an abnormality occurs in a semiconductor production line, it may be that a certain process is abnormal, or a certain process step is abnormal, or a certain process equipment or factory equipment (Fac) is abnormal. Therefore, in one embodiment of the present invention, the abnormal object is at least one of the following: abnormal process, abnormal process step, abnormal process equipment, abnormal factory equipment. Among them, factory equipment refers to equipment that provides special gases.

[0051] Step 22: Based on the abnormal object information, corresponding raw material information is read from a preset raw material database.

[0052] In a specific implementation, after the abnormal object is determined, the corresponding raw material information can be read from the preset raw material database. The preset raw material database includes each abnormal object, raw material information, and the corresponding relationship information between each abnormal object and the raw material information. Figure 3As shown, in the preset raw material database, each abnormal object is bound to the raw material information.

[0053] In a specific implementation, the preset raw material database can store information of various raw materials involved in the semiconductor production line, wherein the raw materials may include: chemicals, photoresists, slurries, special gases, target materials, etc.

[0054] Specifically, the information of the raw materials may include at least one of the following:

[0055] The warehousing information of each raw material;

[0056] The outbound information of each raw material;

[0057] Usage record information of each raw material;

[0058] Shipping inspection information of each raw material;

[0059] Change record information of each raw material;

[0060] Abnormal raw material information.

[0061] In a specific implementation, the warehousing information of the raw materials refers to the information recorded when the raw materials are put into the warehouse, that is, the information recorded when the raw materials are stored in the warehouse, and may specifically include at least one of the following: the name, quantity, batch number, and supplier information of the raw materials.

[0062] In a specific implementation, the raw material outbound information refers to the information recorded when the raw materials are outbound, that is, the information recorded when the raw materials stored in the warehouse are provided to the production line, and may specifically include at least one of the following: the name, quantity and batch number information of the raw materials.

[0063] In a specific implementation, the raw material usage record information refers to the time information when the raw material is used, which may include at least one of the online time information and offline time information of the raw material. The online time information and offline time information of the raw material can be automatically collected through sensors, scanning equipment, etc.

[0064] In a specific implementation, the shipment inspection information of the raw materials includes: the inspection result information of each performance parameter of the raw materials. Among them, the shipment inspection information can be obtained through the shipment inspection report (Coa) of the raw materials. The so-called shipment inspection report is a report on the inspection of the performance parameters of the raw materials provided by the raw material supplier when providing the raw materials to the semiconductor factory. The shipment inspection report contains the inspection results of various performance parameters of the raw materials. For example, the shipment inspection report of the substrate wafer can include the measured values ​​of key parameters such as the diameter, thickness, warpage, resistivity, etc. of the substrate wafer.

[0065] In a specific implementation, the change record information of the raw materials refers to the information recorded when the raw materials are changed, and may specifically include at least one of the following: the model change information of the raw materials, the manufacturer change information, the production location change information, and other change information that affects the performance of the raw materials. Among them, the model change information of the raw materials may include: the change time, the model information before the change, and the model information after the change. The raw material manufacturer change information may include: the change time, the manufacturer identification information before the change, and the manufacturer identification information after the change.

[0066] In a specific implementation, the abnormal raw material information refers to the identification information of raw materials in which various abnormal situations occur. Specifically, it may include various abnormal situations and the identification information of raw materials corresponding to various abnormal situations. Among them, abnormal situations may include: expiration, abnormal shipment inspection, abnormal storage environment, etc. Expiration means that the raw materials have exceeded the safe use period. Abnormal shipment inspection refers to the existence of abnormalities in the raw material shipment inspection report. Abnormal storage environment refers to the existence of abnormalities in the storage environment before the raw materials are put into storage, such as being stored in a high temperature or high pressure environment for a period of time.

[0067] In one embodiment, the abnormal raw material information may include at least one of the following: identification information of expired raw materials, identification information of raw materials with abnormal shipment detection, and identification information of raw materials with abnormal storage environment.

[0068] In a specific implementation, the preset raw material database may only include any one of the warehousing information of each raw material, the outbound information of each raw material, the usage record information of each raw material, the shipment inspection information of each raw material, the change record information of each raw material and the abnormal raw material information, or it may include two or more of them, or it may include all of the above information.

[0069] It can be understood that the more raw material information there is in the preset raw material database, the more conducive it is to the semiconductor production line anomaly detection, and the higher the efficiency of the semiconductor production line anomaly detection will be.

[0070] In a specific implementation, the preset raw material database can be dynamically maintained. For example, the raw material information in the preset raw material database can be updated in real time. That is, once the raw material information changes, the raw material information in the preset raw material database can also be updated according to a certain period (such as one week), thereby ensuring the accuracy of the raw material information in the preset raw material database.

[0071] Step 23: performing analysis based on the read raw material information to determine the cause of the semiconductor production line abnormality.

[0072] In a specific implementation, the raw material information read can be analyzed in a variety of ways, which are not limited here, as long as the cause of the abnormality of the semiconductor production line can be determined.

[0073] In one embodiment, commonality analysis may be performed on wafer measured values ​​using raw material information read at different times to form a commonality analysis chart, thereby based on the commonality analysis chart.

[0074] Specifically, it is possible to receive externally input analysis target information, and based on the analysis target information, classify the read raw material information, thereby obtaining the actual measured values ​​of wafers used for various types of raw material information at different times, and form a common analysis chart. The common analysis chart can be displayed to the user, and through the common analysis chart, it is possible to more intuitively determine whether various types of raw material information are abnormal.

[0075] That is, the input device can be used to receive external input analysis target information, that is, to specify the analysis target, so as to graphically display the raw material information according to the specified analysis target. The analysis target can be any attribute of the raw material information in the preset raw material database, such as the name, batch number, model, manufacturer, etc. of the raw material.

[0076] For example, taking the photolithography process as an example, the raw material corresponding to the photolithography process is photoresist. After reading the photoresist information from the preset raw material database, the photoresist can be classified according to the batch number, and the actual measured line width of the photolithography pattern on the wafer used at different times for each batch number can be obtained to form a common analysis chart, such as Figure 4 shown.

[0077] Reference Figure 4 , the horizontal axis represents time, the vertical axis represents the measured value of the line width of the photolithography pattern on the wafer, 2KA028, 2KA030 and 2KA035 are photoresist batch numbers, straight line 41 is the target upper limit value of the line width of the photolithography pattern, straight line 42 is the target lower limit value of the line width of the photolithography pattern, and straight line 43 is the ideal value of the line width of the photolithography pattern. Figure 4It can be seen that the photoresist line width on the wafer with batch number 2KA035 basically exceeds the target upper limit of the photoresist line width. Therefore, it can be determined that the photoresist with batch number 2KA035 is one of the reasons for the production line abnormality.

[0078] In some embodiments, after forming a commonality analysis chart, the measured values ​​of using abnormal raw material wafers or the measured values ​​of using raw material wafers with changes can also be identified in the commonality analysis chart. Specifically, the measured values ​​of using abnormal raw material wafers can be identified based on the abnormal raw material information in the preset raw material database. For example, the measured values ​​of using expired raw material wafers in the commonality analysis chart are identified, or the measured values ​​of using raw material wafers with changed models in the commonality analysis chart are identified. In this way, the cause of the raw material abnormality can be determined more intuitively, further improving the efficiency of abnormality detection in semiconductor production lines.

[0079] For example, refer to Figure 5 Assuming that the batch number 2KA035 is expired raw material when used for some wafers, when displaying the commonality analysis chart, it can be displayed on the commonality analysis chart which part of the wafers actually measured values ​​use expired photoresist. Among them, the actual measured values ​​of the wafers using expired photoresist are distributed in area 51.

[0080] For example, refer to Figure 6 , assuming that the raw material batches corresponding to the photolithography process are 2KA028, 2KA030 and WCA012, among which, the photoresists with batch numbers 2KA028 and 2KA030 are provided by manufacturer A, and the photoresist with batch number WCA012 is provided by manufacturer B. Compared with the photoresists with batch numbers 2KA028 and 2KA030, the photoresist with batch number WCA012 has changed manufacturers. In this case, the measured values ​​of the wafers with changed manufacturers can be marked on the commonality analysis chart, as shown in area 61. Figure 6 It can be seen that the actual measured values ​​of the wafers corresponding to the photoresist provided by manufacturer B all exceed the target upper limit, which is likely to be the main reason for the abnormality of the raw materials.

[0081] In a specific implementation, semiconductor production line anomalies may exist not only in the raw material dimension, but also in other dimensions, such as parameters (methods), environment (environment), etc. The causes of semiconductor production line anomalies can be obtained in combination with other dimensions.

[0082] From the above content, it can be seen that the semiconductor production line anomaly detection method in the embodiment of the present invention can improve the efficiency of semiconductor production line anomaly detection in the raw material dimension by setting up a raw material database, thereby preventing production line anomalies from affecting semiconductor production.

[0083] In order to enable those skilled in the art to better understand and implement the present invention, the device, test system, electronic device and computer-readable storage medium corresponding to the above method are described in detail below.

[0084] Reference Figure 7 The embodiment of the present invention further provides a semiconductor production line abnormality detection device, which may include: an acquisition unit 71, a reading unit 72 and an analysis unit 73. Among them:

[0085] The acquisition unit 71 is adapted to acquire abnormal object information;

[0086] The reading unit 72 is adapted to read corresponding raw material information from a preset raw material database based on the abnormal object information;

[0087] The analysis unit 73 is adapted to perform analysis based on the read raw material information to determine the cause of the semiconductor production line abnormality.

[0088] In some embodiments, the semiconductor production line anomaly detection device may further include: a raw material database, which may store various raw material information related to the semiconductor production line. The reading unit 72 may be connected to the raw material database, so as to read the raw material information in the raw material database for anomaly analysis.

[0089] Regarding the acquisition unit 71, the reading unit 72 and the analysis unit 73, specific reference may be made to the above descriptions of step 21 to step 23, which will not be repeated here.

[0090] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. The computer program is executed by a processor to implement the steps of any of the above methods.

[0091] In a specific implementation, the computer-readable storage medium may include: ROM, RAM, magnetic disk or optical disk, etc.

[0092] An embodiment of the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor executes the steps of any of the above methods when running the computer program.

[0093] Regarding the various modules / units included in the various devices and products described in the above embodiments, they can be software modules / units, or hardware modules / units, or they can be partially software modules / units and partially hardware modules / units. For example, for various devices and products applied to or integrated in a chip, the various modules / units included therein can all be implemented in the form of hardware such as circuits, or at least some of the modules / units can be implemented in the form of software programs, which run on a processor integrated inside the chip, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated in a chip module, the various modules / units included therein can all be implemented in the form of hardware such as circuits, and different modules / units can be located in the same component (such as a chip, circuit module, etc.) or different components of the chip module, or at least some of the modules / units can be implemented in the form of hardware such as circuits. The element can be implemented in the form of a software program, which runs on a processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated in the terminal, the various modules / units contained therein can be implemented in the form of hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or in different components in the terminal, or, at least some modules / units can be implemented in the form of a software program, which runs on a processor integrated inside the terminal, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits.

[0094] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the scope defined by the claims.

Claims

1. A semiconductor production line abnormality detection method, characterized in that: include: Get exception object information; Based on the abnormal object information, corresponding raw material information is read from a preset raw material database; The raw material information read is analyzed to determine the cause of the semiconductor production line abnormality.

2. The semiconductor production line abnormality detection method according to claim 1, characterized in that: The abnormal object is at least one of the following: abnormal process, abnormal process step, abnormal process equipment, and abnormal factory equipment.

3. The semiconductor production line abnormality detection method according to claim 1, characterized in that: The preset raw material database includes at least one of the following raw material information: The warehousing information of each raw material; The outbound information of each raw material; Usage record information of each raw material; Shipping inspection information of each raw material; Change record information of each raw material; Abnormal raw material information.

4. The semiconductor production line abnormality detection method according to claim 3, characterized in that: The warehousing information of the raw materials includes at least one of the following: the name, quantity, batch number and supplier information of the raw materials.

5. The semiconductor production line abnormality detection method according to claim 3, characterized in that: The outbound information of the raw materials includes at least one of the following: the name, quantity and batch number of the raw materials.

6. The semiconductor production line abnormality detection method according to claim 3, characterized in that: The usage record information of the raw materials includes at least one of the following: online time information and offline time information of the raw materials.

7. The semiconductor production line abnormality detection method according to claim 3, characterized in that: The raw material shipment inspection information includes: inspection result information of various performance parameters of the raw materials.

8. The semiconductor production line abnormality detection method according to claim 3, characterized in that: The change record information of the raw materials includes at least one of the following: model change information and manufacturer change information of the raw materials.

9. The semiconductor production line abnormality detection method according to claim 3, characterized in that: The abnormal raw material information includes: identification information of expired raw materials, identification information of raw materials with abnormal shipment detection, and identification information of raw materials with abnormal storage environment.

10. The semiconductor production line abnormality detection method according to claim 1, characterized in that: The preset raw material database also includes: corresponding relationship information between each abnormal object and each raw material information.

11. The semiconductor production line abnormality detection method according to claim 1, characterized in that: The analysis based on the read raw material information includes: The commonality analysis is performed on the wafer measured values ​​using the raw material information read at different times to form a commonality analysis chart.

12. The semiconductor production line abnormality detection method according to claim 11, characterized in that: The analysis based on the read raw material information also includes: The commonality analysis chart indicates the measured values ​​using abnormal raw material wafers or the measured values ​​using raw material wafers with changes.

13. A semiconductor production line abnormality detection device, characterized in that: include: An acquisition unit, adapted to acquire abnormal object information; A reading unit, adapted to read corresponding raw material information from a preset raw material database based on the abnormal object information; The analysis unit is adapted to perform analysis based on the read raw material information to determine the cause of the semiconductor production line abnormality.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the steps of the method according to any one of claims 1 to 12.

15. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor runs the computer program, the steps of the method according to any one of claims 1 to 12 are performed.

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