WO2021142622A1 - 确定不良原因的方法、电子设备、存储介质及*** - Google Patents

确定不良原因的方法、电子设备、存储介质及*** Download PDF

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Publication number
WO2021142622A1
WO2021142622A1 PCT/CN2020/072033 CN2020072033W WO2021142622A1 WO 2021142622 A1 WO2021142622 A1 WO 2021142622A1 CN 2020072033 W CN2020072033 W CN 2020072033W WO 2021142622 A1 WO2021142622 A1 WO 2021142622A1
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Prior art keywords
defective
substrate
production equipment
data
distribution pattern
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PCT/CN2020/072033
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English (en)
French (fr)
Inventor
王海金
薛静
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京东方科技集团股份有限公司
北京京东方光电科技有限公司
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Application filed by 京东方科技集团股份有限公司, 北京京东方光电科技有限公司 filed Critical 京东方科技集团股份有限公司
Priority to PCT/CN2020/072033 priority Critical patent/WO2021142622A1/zh
Priority to CN202080000026.XA priority patent/CN113597664A/zh
Publication of WO2021142622A1 publication Critical patent/WO2021142622A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/956—Inspecting patterns on the surface of objects
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L22/00—Testing or measuring during manufacture or treatment; Reliability measurements, i.e. testing of parts without further processing to modify the parts as such; Structural arrangements therefor

Definitions

  • the present invention relates to the field of panel production and processing, and more specifically, to a method, electronic equipment, computer-readable storage medium, and system for determining the cause of substrate failure.
  • the qualification rate of each project is often referred to as the yield rate, which reflects the proportion of qualified products in the testing process of each project.
  • the yield is directly related to the production cost. Whether the yield can be increased quickly in the shortest time determines whether the production cost can be recovered on time to a large extent.
  • Yield rate as a health indicator of factory products, has application value in all aspects of component manufacturing. A lower yield rate will lead to an increase in various costs.
  • a high-level yield is a key indicator that reflects product reliability and realizes product revenue, and is particularly important in manufacturing enterprises in the component processing industry.
  • a method for determining the cause of the defective substrate including: obtaining substrate production process data, the production process data including substrate production history data and at least two production processes Parameter data of equipment parameters; obtaining, according to the type of the substrate, a parameter data reference range of the at least two production equipment parameters corresponding to the type of the substrate; and parameter data based on the obtained at least two production equipment parameters , And the parameter data reference range of the at least two production equipment parameters, determining the defective production equipment parameter that deviates from the parameter data reference range of the at least two production equipment parameters.
  • the number of defective production equipment parameters that deviate from the parameter data reference range of the determined at least two production equipment parameters is at least two, and the method further includes: A substrate, using the substrate defective distribution pattern of each of the plurality of substrates to generate a defective distribution pattern corresponding to the production process data, wherein the defective distribution pattern shows the substrate coordinate position of the defective point; acquiring at least one A reference bad distribution pattern; and based on the bad distribution pattern and the at least one reference bad distribution pattern, determining the correlation ranking of the at least two production equipment parameters to be processed.
  • the method further includes: acquiring the production process data of the panel samples of the plurality of substrates from a distributed storage device.
  • the method further includes: displaying the defective production equipment parameters that deviate from the parameter data reference range of the at least two production equipment parameters that caused the occurrence of the defects, and/or displaying the sorted and determined defective production equipment Device parameters.
  • an electronic device includes a memory, a processor, and a computer program that is stored on the memory and can run on the processor, and is characterized in that, when the processor executes the program, the method for causing the panel failure as described above is implemented.
  • a computer-readable storage medium the computer-readable storage medium storing computer instructions, the computer instructions for causing the computer to perform the above-mentioned determination of the substrate failure Reason method.
  • a system for determining the cause of substrate failure includes a distributed storage device configured to store all production process data of all substrates within a preset time period; an electronic device as described above; and a display device configured to display the data to be displayed obtained from the electronic device Image data.
  • 1A-1B show a flow chart of a method for determining the cause of substrate failure based on a yield analysis system
  • FIG. 2 shows a schematic flowchart of a method for determining a cause of a substrate failure according to an embodiment of the present disclosure
  • FIG. 3 shows a schematic flow chart of another method for determining the cause of a defective substrate according to an embodiment of the present disclosure
  • FIGS. 4A-4B show schematic diagrams of displayed bad coordinate patterns and reference bad distribution patterns according to an embodiment of the present disclosure
  • FIG. 5 shows a structural block diagram of an electronic device for determining the cause of a defective substrate according to an embodiment of the present disclosure
  • 6A-6B show a structural block diagram of a system for determining the cause of a defective substrate according to an embodiment of the present disclosure.
  • OLED Organic Light-Emitting Diode
  • LCD Liquid Crystal Display, liquid crystal display
  • the traditional determination of the cause of the failure is usually based on the Yield Management System (YMS).
  • YMS Yield Management System
  • the yield management system can obtain the data uploaded by the production equipment when the product passes through each production equipment, such as from the Manufacturing Execution System (MES) and the Fault Detection & Classification (Fault Detection & Classification, FDC) system. These data are collectively referred to as production data.
  • the production data usually includes production process data and bad information data.
  • the production process data includes: the relevant data of the production equipment parameters obtained from the FDC system, including the time from the start of the production of the product to the current time (the following uses the glass substrate (Glass) to generate the panel (Panel) as an example).
  • Data measured by internal/external sensors in the production equipment can refer to the operation of the production equipment/ State input data.
  • the various physical/electrical state values of the production equipment are arranged in time series; the production history data obtained from the MES system, the production history data mainly includes historical data about the entire production, including historical data from the beginning.
  • the data of the executed process identifier (ID), material ID, equipment ID, equipment work specification ID, material ID, material ratio, etc. are arranged in a time series from the time of the start of the manufacture of the glass substrate to the current time.
  • the bad information data is obtained from the MES system, and after all the processes of the glass substrate are completed and/or each process is completed separately, the inspection data for the entire glass substrate includes the substrate coordinates of many defective points Location information, and optional bad type information, etc.
  • the production is based on the same production process data (for example, the same production equipment and its parameters, the same process, the same material ratio, etc.), but each batch of glass substrates
  • the bad information data of each substrate can be different.
  • the process of determining the cause of the defect based on the yield management system includes the following steps.
  • the yield management system when it is detected that an abnormally high incidence of defects (for example, multiple white spots and black spots) occurs on the final substrate to be cut into panels of a certain batch, the yield management system obtains the substrate in step S101 Corresponding production process data and bad information data.
  • the yield management system has various defect analysis functions, such as the defect map analysis function.
  • step S102 based on the obtained defect information data, the substrate coordinate position of the defective point can be intuitively mapped to a single glass substrate, and
  • the user interface of the yield management system graphically displays a substrate defect map (also including list information associated with each defect in the defect map) and/or a defect trend map on a single glass substrate.
  • step S103 the senior yield engineer checks the substrate defect map and/or the defect trend graph to understand where the defect is mainly concentrated, when, the trend of the bad coordinate, etc., and then can determine which specific production equipment is causing it based on experience High incidence of bad.
  • step S104 after determining which specific production equipment is causing the high incidence of defects in step S103 based on experience, in step S104, each parameter data of the specific production equipment in the production process data is checked one by one to determine that it is the specific production equipment. The parameter data of which of the production equipment parameters is abnormal, which leads to a high incidence of defects.
  • the embodiments of the present disclosure provide a new method, electronic device, computer-readable storage medium, and system for determining the cause of substrate failure.
  • This method uses historical production process data to obtain the parameter data reference ranges of multiple production equipment parameters in advance, so that when defects on the substrate are high, the multiple production equipment corresponding to the defects can be quickly determined at this time.
  • the parameter data of the parameter deviates from the parameter data reference range of the defective production equipment parameter.
  • the method provided by the embodiment of the present disclosure can also obtain at least one reference defective distribution pattern in advance by using historical production process data and its corresponding defective information data, and make the substrate of each defective point on the at least one reference defective distribution pattern.
  • the coordinate position is associated with its corresponding defective production equipment parameter, so that the substrate coordinate position of the defective point on the defective distribution pattern corresponding to the production process data in the actual production process at this time is correlated with at least one substrate that refers to the defective point on the defective distribution pattern
  • the coordinate position is compared, based on the correlation between the substrate coordinate position of each defective point on at least one reference defective distribution pattern obtained in advance and its corresponding defective production equipment parameter, and the corresponding defective distribution pattern according to each defective production equipment parameter.
  • the number of bad points is used to sort the correlation of each bad production equipment parameter with respect to the bad.
  • the user can more accurately and quickly determine the cause of the failure, take timely measures, and determine the correlation between the parameters of the poor production equipment and the failure. Sorting can be targeted to adjust the parameters of production equipment, while also reducing the experience and ability requirements of engineers.
  • FIG. 2 is a schematic flowchart of a method 200 for determining a cause of a defective substrate according to an embodiment of the present disclosure.
  • the method for determining the cause of the failure according to the embodiment of the present disclosure obtains the parameter data reference range of multiple production equipment parameters in advance by using historical production process data, so that it can quickly determine the time when the failure on the substrate is high.
  • the bad production equipment parameter deviates from the reference range of the parameter data.
  • the method of determining the cause of the failure of the panel includes the following steps.
  • step S201 the production process data of the substrate is acquired, where the production process data includes the production history data of the substrate and parameter data of at least two production equipment parameters.
  • step S202 according to the type of the substrate, a parameter data reference range of at least two production equipment parameters corresponding to the type of the substrate is acquired.
  • the parameter data reference ranges of the production equipment parameters corresponding to the different substrate types may be different.
  • obtaining the parameter data reference range of at least two production equipment parameters corresponding to the type of the substrate includes: obtaining multiple sets of production process data of multiple substrate samples of the same type in the historical data, wherein the multiple substrate samples include the first A number of positive substrate samples and a second number of negative substrate samples; and based on the first number of positive substrate samples and their production process data, and the second number of negative substrate samples and their production process data, determine the at least two production The parameter data reference range of the device parameter.
  • determining the parameter data reference range of the at least two production equipment parameters includes: training the production equipment using a first number of positive substrate samples and production process data, and a second number of negative substrate samples and production process data. Parameter model; and using the production equipment parameter model to generate at least two parameter data reference ranges for the production equipment parameters.
  • a substrate includes multiple panels, and the positive and negative substrate samples can be distinguished according to the proportion of defective panels on each substrate.
  • the proportion of defective panels is less than 20%.
  • substrate samples, and the substrates with defective panels accounting for more than 20% are negative substrate samples.
  • the production equipment parameter model may be a convolutional neural network, linear regression, decision tree, random forest, support vector machine (SVM) or any other machine learning model.
  • SVM support vector machine
  • a test substrate sample can also be used to verify the reliability of the model.
  • the test substrate sample can be imported into the trained production equipment parameter model, so as to test the trained production equipment parameter model, and the ratio of the test substrate sample to the training substrate sample can be 2:8.
  • There are many ways to judge the quality of the model such as using mean absolute error (mean_absolute_error), and cross validation error (cross_validation_error), and so on.
  • the parameters and weights in the trained production equipment parameter model are further adjusted based on the judgment result of the measured substrate sample on the model quality (for example, the error back propagation algorithm used in the convolutional neural network model).
  • step S203 based on the acquired parameter data of the at least two production equipment parameters and the parameter data reference range of the at least two production equipment parameters, determine the defective production equipment parameter that deviates from the parameter data reference range of the at least two production equipment parameters .
  • the method may further include obtaining multiple sets of production process data of multiple substrate samples from the distributed storage device for training to obtain the production equipment parameter model.
  • the related content of the distributed storage device will be described in detail later.
  • the parameter data reference range of the production equipment parameter is obtained in advance, so that the parameter data of the production equipment parameter in the actual production process data can be directly obtained. Compare with the reference range of the parameter data to quickly determine whether there is an abnormality in the production equipment parameter.
  • FIG. 3 shows a flowchart of another method 300 for determining the cause of the defective substrate.
  • the method 300 may include the following steps.
  • Steps S301-S303 are similar to S201-S203 in method 200, and will not be repeated here.
  • step S304 for a plurality of substrates produced by using the production process data, a defective distribution pattern corresponding to the production process data is generated using the defective distribution pattern of each substrate in the plurality of substrates, wherein the defective distribution pattern shows defective points The coordinate position of the substrate.
  • the substrate defect distribution pattern of each substrate is obtained according to its corresponding defect information data.
  • Bad information data can be obtained from the MES system, as described above.
  • generating a defective distribution pattern corresponding to the production process data by using the defective distribution pattern of each substrate in the plurality of substrates includes: stacking the defective distribution patterns of the multiple substrates to obtain the defective distribution corresponding to the production process data pattern.
  • step S305 at least one reference bad distribution pattern is acquired.
  • acquiring at least one reference defective distribution pattern includes: acquiring a defective substrate distribution pattern of each defective substrate in a plurality of defective substrates in the historical data, wherein the defective substrate distribution pattern shows the substrate coordinate position of the defective point, and each The substrate coordinate positions of the defective points are associated with their corresponding defective production equipment parameters; and the defective substrate distribution patterns of the plurality of defective substrates in the historical data are stacked in at least one group to obtain at least one reference defective distribution pattern.
  • the bad distribution pattern and at least one reference bad distribution pattern are displayed by a display device.
  • the defective production equipment parameter associated with the defective point at the coordinate position of the substrate in the at least one reference defective distribution pattern can be determined by the user (for example, an experienced yield analysis engineer) according to the at least one reference defective distribution pattern.
  • the coordinate position of the substrate is directly determined in advance, or the parameter data of the production equipment parameter in the production process data corresponding to the defective point at the coordinate position of the substrate in the at least one reference defective distribution pattern can be compared with the parameter data reference range to obtain
  • the defective production equipment parameter that exceeds the reference range of the parameter data is taken as the defective production equipment parameter associated with the defective point at the coordinate position of the substrate in the at least one reference defective distribution pattern.
  • step S306 based on the bad distribution pattern and at least one reference bad distribution pattern, determine the correlation ranking of each bad production equipment parameter and the bad.
  • determining the correlation ranking of each bad production equipment parameter and the bad includes: for each bad point in the bad distribution pattern, determining the substrate coordinate position of the bad point, And obtain the defective production equipment parameter associated with the defective point at the coordinate position of the substrate in at least one reference defective distribution pattern; for each determined defective production equipment parameter, determine the number of corresponding defective points; and The number of defective points corresponding to each of the determined defective production equipment parameters is sorted by correlation for the determined defective production equipment parameters.
  • determining the correlation ranking of each bad production equipment parameter and the bad includes: for each bad point in the bad distribution pattern, determining the substrate coordinate position of the bad point, And obtain the defective production equipment parameter associated with the defective point at the coordinate position of the substrate in at least one reference defective distribution pattern; for each determined defective production equipment parameter, determine the number of panels distributed by the corresponding defective point ; And according to the number of panels distributed by the respective defective points corresponding to the determined defective production equipment parameters, the determined defective production equipment parameters are sorted in correlation.
  • the method 300 may further include step S307: displaying the deviation of the parameter data of the at least two production equipment parameters that caused the failure.
  • the above-mentioned content is displayed to the user through a display device.
  • the historical data includes a large amount of production process data and bad information data of defective substrates over a long period of time, it can be considered that the coordinate positions of the defective points in the defective distribution pattern in the actual production process are referred to the defective distribution. There are also bad spots on the pattern here.
  • step S302 to step S303 can be omitted. That is to say, after obtaining the production process data of the substrates on which defects are high, proceed directly to step S304, that is, use the substrate of each substrate in the plurality of substrates (for example, a batch of substrates) corresponding to the production process data.
  • the defective distribution pattern generates a defective distribution pattern corresponding to the production process data, wherein the defective distribution pattern shows the substrate coordinate position of the defective point, and then steps S305 to S307 are similarly performed.
  • the following illustrates an example process of a method for determining a cause of a substrate failure according to an embodiment of the present disclosure.
  • 4A-4B show schematic diagrams of displayed bad coordinate patterns and reference bad distribution patterns according to an embodiment of the present disclosure.
  • the production process data of this batch of substrates can be obtained from the MES system and the FDC system.
  • the production process data includes the production history of the substrates Data (for example, process 1, process 2, material type, material ratio) and parameter data of at least two production equipment parameters (for example, the operating parameters of each production equipment, including temperature, humidity, pressure, working time, etc.).
  • the bad information data of each substrate can also be obtained from the MES system.
  • the parameter data reference range of the known at least two production equipment parameters is obtained by training in advance based on the production process data of multiple historical substrate samples, wherein the substrate samples include a first number of positive substrate samples and a second number of substrate samples. Negative substrate sample.
  • the substrate coordinate position of the defective point on the substrate defective distribution pattern of the substrate 1 is (20,20-panel 1) (15,30-panel 1), and the position of the substrate 2
  • the substrate coordinate position of the defective point on the substrate defective distribution pattern is (20,40-panel 1) (55,60-panel 2), and the substrate coordinate position of the defective point on the substrate defective distribution pattern of the substrate 3 is (100,100-panel 5) (120, 150-Panel 6), etc., stack the defective distribution pattern of each substrate in the multiple substrates of this batch to generate the defective distribution pattern (MAP R) corresponding to the production process data.
  • the defective distribution pattern shows the coordinate positions of all defective points on the substrate, that is, the size of the defective distribution pattern corresponds to the size of the substrate, and the defective points are located at (20,20)(15,30)(20,40). )(55,60)(100,100)(120,150) and so on at the coordinate position of the substrate, as shown in Figure 4A.
  • these specific coordinate positions of the substrate are only examples, and other positions of the substrate may also have defects.
  • the defective point at the substrate coordinate position (20, 20) in the defective distribution pattern obtain the known defective point at the substrate coordinate position (20, 20) in the at least one reference defective distribution pattern
  • the defective point at the substrate coordinate position (15, 30) in the defective distribution pattern obtain the known defective point at the substrate coordinate position (15, 30) in the at least one reference defective distribution pattern
  • the defective point at the substrate coordinate position (20, 40) in the defective distribution pattern obtain the known defective point at the substrate coordinate position (20, 40) in the at least one reference defective distribution pattern
  • Production equipment parameters knowing that the temperature of equipment 2 and the pressure of equipment 3 caused the failure to occur.
  • Production equipment parameters knowing that the pressure of equipment 3 and the humidity of equipment 4 caused the failure.
  • the number of defective points corresponding to each defective production equipment parameter can be obtained, that is, the number of occurrences of each defective production equipment parameter on the defective distribution pattern, so as to sort the determined defective production equipment parameters. More specifically, for several exemplary substrate coordinate positions in the above-mentioned bad distribution pattern, device 1-temperature affects three bad points (20, 20), (20, 40) and (55, 60), device 1- Humidity affects (20,40) a bad point, equipment 2-pressure affects (15,30) and (20,40) two bad points, equipment 2-temperature affects (20,20) a point, equipment 2- Temperature affects one point (100,100), equipment 3-pressure affects (100,100) and (120,150) two bad points, equipment 4-humidity affects (55,60) and (120,150) two bad points.
  • the correlation order of the various bad production equipment parameters that lead to the occurrence of the substrate failure is: equipment 1-temperature; equipment 2-pressure, equipment 4-humidity (parallel); equipment 2-humidity, equipment 3-pressure, Equipment 1-Humidity (tie).
  • the above sorting is based on the number of bad points corresponding to each bad production equipment parameter. Of course, as mentioned above, it can also be sorted according to the number of panels distributed by bad points corresponding to each bad production equipment parameter. This will not be described in detail here.
  • the process of obtaining at least one reference bad distribution pattern is: obtaining the bad distribution pattern of each bad substrate in the multiple bad substrates (Glass 1,..., Glass n-1, Glass n) on which there are bad substrates in the historical data , Wherein each substrate defective distribution pattern shows the substrate coordinate position of one or more defective points on it, and the substrate coordinate position of each defective point is associated with its corresponding defective production equipment parameter; and the historical data
  • the substrate defective distribution patterns of multiple defective substrates are stacked according to at least one group. Taking one group as an example, a reference defective distribution pattern (MAP REF) is obtained.
  • the reference defective distribution pattern (MAP REF) includes all the substrates on the multiple substrates. The substrate coordinate position of the defective point.
  • the defective production equipment parameters associated with the substrate coordinate position of each defective point are pre-calculated and stored, so that they are known, and the acquisition process is: display a sample reference defective distribution pattern on the display device, as shown in Figure 4B As shown, the coordinate position of each substrate corresponding to each defective point of the defective distribution pattern as shown in FIG. 4A is determined through comparison, and then the user according to the coordinate position of each substrate in the reference defective distribution pattern, Compare the parameter data of the production equipment parameters in the production process data corresponding to the defective point at each substrate coordinate position in the reference defective distribution pattern with the parameter data reference range, and obtain the correlation with the substrate coordinate position of each defective point Bad production equipment parameters. Therefore, the defective production equipment parameters associated with the substrate coordinate position of each defective point are pre-calculated and stored. When the actual defective distribution pattern is obtained, the operation as described above is performed on the defective distribution pattern. Known, defective production equipment parameters associated with the substrate coordinate position of each defective point are used as the basis of the analysis.
  • the actual defective distribution pattern is coordinated with the reference defective distribution pattern to obtain the actual
  • the correlation of the defective production equipment parameters associated with each defective point in the defective distribution pattern is determined by determining the number of defective points caused by each defective production equipment parameter. Therefore, it is convenient for the less experienced yield analysis engineers to quickly check Sort the correlation between the equipment parameters and the defects that caused the substrate defects.
  • the defective distribution pattern and the substrate coordinate position of the defective point in the reference defective distribution pattern are described in a graphical manner, but the text form may also be adopted.
  • the substrate coordinate positions of all defective points on multiple defective substrates on which defective substrates exist in the historical data are stored in text form.
  • each actual The substrate coordinate position and the stored substrate coordinate position are compared in text to determine which of the stored substrate coordinate positions the actual substrate coordinate position corresponds to, and the corresponding defective production equipment parameters are obtained.
  • an electronic device 500 is also provided.
  • FIG. 5 shows a structural block diagram of an electronic device 500 according to an embodiment of the present disclosure.
  • the electronic device 500 includes a memory 501 and a processor 502.
  • a computer program is stored on the memory, and when the computer program runs on the processor, the method for determining the cause of the substrate failure as described with reference to FIGS. 2 to 3 is implemented.
  • a system 600 for determining the cause of substrate failure there is also provided a system 600 for determining the cause of substrate failure.
  • FIG. 6A-6B show a structural block diagram of a system 600 according to an embodiment of the present disclosure.
  • the system includes a distributed storage device 610, an electronic device 620 (for example, the electronic device 500 described with reference to FIG. 5), and a display device 630.
  • the distributed storage device 610 is configured to store all production process data and bad information data of all substrates within a preset time period. For example, it is possible to store all production process data and bad information data corresponding to all substrates in the process of producing all substrates within two years.
  • the distributed storage device stores relatively complete data (such as a database), and the distributed storage device includes multiple hardware memories, and different hardware memories are distributed in different physical locations (such as in different factories, or in different Different production lines), and realize the transfer of information between each other through the network, so that the data is distributed, but logically constitute a database based on big data technology.
  • data such as a database
  • the distributed storage device includes multiple hardware memories, and different hardware memories are distributed in different physical locations (such as in different factories, or in different Different production lines), and realize the transfer of information between each other through the network, so that the data is distributed, but logically constitute a database based on big data technology.
  • the raw data of a large number of different factory equipment are stored in the corresponding manufacturing system, such as the relational system of yield management system (YMS), error detection and classification (FDC) system, manufacturing execution system (MES), etc. Database (such as Oracle, Mysql, etc.), and these raw data can be extracted from the original table by data extraction tools (such as Sqoop, kettle, etc.) to be transmitted to distributed storage devices (such as Hadoop Distributed File System, HDFS, Hadoop Distributed File System, HDFS) ), in order to reduce the load on the factory equipment and manufacturing system, and facilitate the subsequent data reading of the analysis equipment.
  • YMS relational system of yield management system
  • FDC error detection and classification
  • MES manufacturing execution system
  • Database such as Oracle, Mysql, etc.
  • data extraction tools such as Sqoop, kettle, etc.
  • distributed storage devices such as Hadoop Distributed File System, HDFS, Hadoop Distributed File System, HDFS)
  • the data in the distributed storage device can be stored in Hive tool or Hbase database format.
  • Hive tool the above raw data is first stored in the data lake; later, in order to reduce the learning cost of data cognition and the unity of business realization, you can continue to perform data in the Hive tool according to the application theme and scenario of the data.
  • Preprocessing such as cleaning and data conversion to obtain data warehouses with different themes (for example, production history data theme, bad detection data theme, bad point measurement data theme, and production equipment parameter data theme), and different scenarios (such as sudden Data marts for bad sex scenes and correlation analysis scenes.
  • the data topics and scenarios are not limited to the above examples. New data topics and scenarios can be added or reconstructed according to business needs. For example, the subject of bad detection data and the subject of bad point measurement data can be merged into the production history data. Topic.
  • the above data mart can be connected to display devices, electronic devices, etc. through different API interfaces to realize data interaction with these devices.
  • the data volume of the above raw data is very large.
  • the raw data generated by all factory equipment every day may be several hundred G, and the data generated every hour may also be tens of G.
  • RDBMS relational database management Relational Database Management System
  • DFS distributed File System
  • the grid computing of RDBMS divides the problem that requires very huge computing power into many small parts, and then distributes these parts to many computers for separate processing, and finally combines these calculation results.
  • Oracle RAC Real Application Cluster
  • Oracle RAC is the core technology of grid computing supported by the Oracle database, in which all servers can directly access all data in the database.
  • the RDBMS grid computing application system cannot meet user requirements when the amount of data is large. For example, due to the limited expansion space of the hardware, when the data is increased to a large enough order of magnitude, the input/output bottleneck of the hard disk will cause The efficiency of processing data is very low.
  • the Hive tool is a Hadoop-based data warehouse tool that can be used for data extraction, transformation and loading (ETL).
  • the Hive tool defines a simple SQL-like query language, and it also allows custom MapReduce mappers and reducers to be used by default tools. Complex analysis work.
  • the Hive tool does not have a special data storage format, nor does it create an index for the data. Users can freely organize the tables in it and process the data in the database. It can be seen that the parallel processing of distributed file management can meet the storage and processing requirements of massive data. Users can process simple data through SQL queries, and use custom functions for complex processing. Therefore, when analyzing the massive data of the factory when all the substrates are produced, it is necessary to extract the data of the factory database into the distributed file system, which will not damage the original data on the one hand, and improve the efficiency of data analysis on the other hand.
  • the electronic device 620 may include one or more processors, and the one or more processors are configured to perform operations for determining the correlation.
  • the electronic device 620 includes a processor (such as a CPU) with data processing capabilities, and may also have a memory (such as a hard disk) storing required programs.
  • the processor and the memory are connected through I/O to realize information interaction, so that the processor The required operation can be performed according to the program stored in the memory to realize the operation of determining the correlation.
  • the electronic device 620 may be the electronic device 500 described with reference to FIG. 5.
  • the display device 630 has a display function for displaying the image data to be displayed obtained from the electronic device.
  • the display data may include a bad distribution pattern and at least one reference bad distribution pattern, a bad production equipment parameter that deviates from its parameter data reference range among a plurality of production equipment parameters that causes a bad occurrence, and/or a display experience Sort the determined parameters of the bad production equipment.
  • the display device 630 may include one or more displays, including one or more terminals with display functions, so that the electronics can send the calculated display data to the display device, and the display device will then display it.
  • the display device can also be used to display an "interactive interface", which can include a sub-interface that displays the determined result (such as poor production equipment parameters, correlation), and is used to control the system to perform the required work (such as task setting) sub-interface, as well as the sub-interface for controlling each production equipment (such as modifying its production equipment parameters).
  • an "interactive interface” can include a sub-interface that displays the determined result (such as poor production equipment parameters, correlation), and is used to control the system to perform the required work (such as task setting) sub-interface, as well as the sub-interface for controlling each production equipment (such as modifying its production equipment parameters).
  • the user can fully interact with the system that determines the cause of the substrate failure (control and receive results).
  • the distributed storage device can efficiently realize the collection and preliminary processing of the raw data of multiple production equipment through big data, and the electronic device can easily obtain the required data from the distributed storage device.
  • the defective production equipment parameters beyond the reference range of the parameter data of the production equipment parameters can be calculated by calculation, and the correlation between the defective production equipment parameters and the bad can be further obtained and displayed by the display device. Therefore, the embodiment of the present disclosure can automatically determine the cause of the substrate defect, so as to locate the cause of the defect, adjust the production process, and so on.
  • a computer-readable storage medium stores computer instructions for causing the computer to execute the method for determining the cause of the defective substrate as described with reference to FIGS. 2 to 3.

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Abstract

一种确定导致基板不良的原因的方法，包括：获取基板的生产过程数据（S201），所述生产过程数据包括基板的生产履历数据以及至少两个生产设备参数的参数数据；根据所述基板的类型，获取与所述基板的类型对应的所述至少两个生产设备参数的参数数据参考范围（S202）；基于所获取的至少两个生产设备参数的参数数据、以及所述至少两个生产设备参数的参数数据参考范围，确定所述至少两个生产设备参数中偏离其参数数据参考范围的不良生产设备参数（S203）。通过该方法，能够快速方便地进行导致基板不良的原因的定位。

Description

确定不良原因的方法、电子设备、存储介质及*** 技术领域
本发明涉及面板生产加工领域，更具体地，涉及一种确定导致基板不良的原因的方法、电子设备、计算机可读存储介质及***。
背景技术
化工、电子等工业生产中，各工程的合格率常被称为良率，其反应了在各工程的检测环节合格产品的比例。良率直接关系到生产成本，能否在最短的时间内实现快速提高良率，很大程度上决定了能否按时收回生产成本。良率作为工厂产品的健康指标，在元件生产制造的各个环节都有应用价值，较低的良率会导致各类成本的增加。高水平的良率是体现产品可靠性和实现产品收益的关键指标，在元件加工行业生产制造型企业中尤为重要。
随着科技的发展，运用计算机来处理和分析生产制造中所得到的数据，运用程序帮助人工分析，推理判断和决策，对于提高生产效率，提升产品良率有着极大的帮助。
发明内容
根据本公开的第一个方面，提供了一种确定导致基板(GLASS)不良的原因的方法，包括：获取基板的生产过程数据，所述生产过程数据包括基板的生产履历数据以及至少两个生产设备参数的参数数据；根据所述基板的类型，获取与所述基板的类型对应的所述至少两个生产设备参数的参数数据参考范围；以及基于所获取的至少两个生产设备参数的参数数据、以及所述至少两个生产设备参数的参数数据参考范围，确定所述至少两个生产设备参数中偏离其参数数据参考范围的不良生产设备参数。
可选地，所确定的所述至少两个生产设备参数中偏离其参数数据参考范围的不良生产设备参数的数量为至少两个，所述方法还包括：对于采用所述生产过程数据生产的多个基板，利用所述多个基板中每个基板的基板不良分布图案生成与所述生产过程数据对应的不良分布图案，其中所述不良分布图案示出了不良点的基板坐标位置；获取至少一个参考不良分布图案；以及基 于所述不良分布图案以及所述至少一个参考不良分布图案，确定所述至少两个待处理生产设备参数的相关性排序。
可选地，所述方法还包括：从分布式存储设备获取所述多个基板的面板样本的生产过程数据。
可选地，所述方法还包括：显示导致不良发生的、所述至少两个生产设备参数中偏离其参数数据参考范围的不良生产设备参数，和/或显示经排序的所确定的各不良生产设备参数。
根据本公开的第二个方面，提供了一种电子设备。该电子设备包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述程序时实现如上所述的导致面板不良的原因的方法。
根据本公开的第三个方面，提供了一种计算机可读存储介质，所述计算机可读存储介质存储计算机指令，所述计算机指令用于使所述计算机执行如上所述的确定导致基板不良的原因的方法。
根据本公开的第四个方面，提供了一种确定导致基板不良的原因的***。所述***包括分布式存储设备，被配置为存储预设时间段内的所有基板的所有生产过程数据；如上所述的电子设备；以及显示设备，被配置为显示从电子设备获得的要显示的图像数据。
附图说明
通过结合附图对本公开实施例进行更详细的描述，本公开的上述以及其它目的、特征和优势将变得更加明显。附图用来提供对本公开实施例的进一步理解，并且构成说明书的一部分，与本公开实施例一起用于解释本公开，并不构成对本公开的限制。在附图中，相同的参考标号通常代表相同部件或步骤。
图1A-1B示出了一种基于良率分析***来确定导致基板不良的原因的方法的流程图；
图2示出了根据本公开实施例的确定导致基板不良的原因的方法的示意流程图；
图3示出了根据本公开实施例的确定导致基板不良的原因的另一方法的示意流程图；
图4A-4B示出了根据本公开实施例的显示的不良坐标图案以及参考不良 分布图案的示意图；
图5示出了根据本公开实施例的确定导致基板不良的原因的电子设备的结构框图；以及
图6A-6B示出了根据本公开实施例的确定导致基板不良的原因的***的结构框图。
具体实施方式
下面将结合本公开实施例中的附图，对本公开实施例中的技术方案进行清楚、完整地描述。显然，所描述的实施例仅是本公开一部分的实施例，而不是全部的实施例。基于本公开中的实施例，本领域普通技术人员在无需创造性劳动的前提下所获得的所有其他实施例，都属于本公开保护的范围。
一些产品，例如，OLED(Organic Light-Emitting Diode，有机发光二极管)面板和LCD(Liquid Crystal Display，液晶显示器)面板的生产工序高度整合，从最初的玻璃基板到最终由基板切割的面板涉及到的工序、工艺和设备繁多，生产线突发已知不良和未知不良较多，这些生产线的不良都会体现在要被切割为面板的最终的基板上。并且，不良本质上都是由生产设备引起的。因此，如何在最终的基板出现异常高发的不良时确定产生该不良的原因是需要迫切解决的问题。
传统的确定导致不良的原因通常是基于良率管理***(Yield Management System，YMS)。
良率管理***能够获取到产品经过各个生产设备时生产设备上传的数据，例如从制造执行***(Manufacturing Execution System，MES)以及错误侦测及分类(Fault Detection&Classification，FDC)***等获得，后文将这些数据统称为生产数据。该生产数据通常包括生产过程数据和不良信息数据。生产过程数据包括：从FDC***获得的生产设备参数的相关数据，包括指从产品的制造开始的时间到目前时间(下文以从玻璃基板(Glass)生成面板(Panel)为例)的过程中通过生产设备中的内部/外部传感器测量的数据(诸如，设备操作时间、温度、湿度、压力、振动的次数、特定材料的比率等)，即生产设备参数相关的数据可以指关于生产设备的操作/状态的输入数据，在该数据中，关于生产设备的各种物理/电气状态值按时间序列排列；从MES***获得的生产履历数据，生产履历数据主要包括关于整个生产的历史数据包括从 最初的玻璃基板的制造开始的时间到目前时间按时间序列排列的执行的工序标识符(Identifier，ID)、物料ID、设备ID、设备工作规范ID、物料ID、物料配比等的数据。不良信息数据从MES***获得，并且是在对玻璃基板的各项工艺全部进行完以后和/或各项工艺各自进行完以后，对于整片玻璃基板的检测数据，包括了很多不良点的基板坐标位置信息，以及可选的不良类型信息等。此外，对于同一批玻璃基板，一般认为是基于相同的生产过程数据(例如，同样的生产设备及其参数、同样的工序、同样的物料比等)进行生产的，但是该批玻璃基板中的每个基板各自的不良信息数据可以不同。
基于良率管理***的确定导致不良的原因的过程包括如下步骤。
如图1A所示，当检测到某一批次的要被切割为面板的最终基板上出现异常高发的不良(例如，多个白点和黑点)时，良率管理***在步骤S101获得基板对应的生产过程数据和不良信息数据。良率管理***具有各种缺陷分析功能，例如缺陷图(Defect Map)分析功能，在步骤S102基于所获得的不良信息数据，可以直观地将不良点的基板坐标位置映射到单个玻璃基板中，并在该良率管理***的用户界面上图形化显示单个玻璃基板上的基板缺陷图(还包括缺陷图中各个缺陷相关联的列表信息)和/或不良趋势图。在步骤S103，高级良率工程师查看基板缺陷图和/或不良趋势图而了解该不良主要集中在什么位置、集中在什么时候、不良坐标的趋势等，然后根据经验可以确定是哪个特定生产设备导致不良高发。
此外，上述过程一般只能定位到设备别，然而还可能需要确定导致不良高发的具体的生产设备参数。因此，如图1B所示，在步骤S103根据经验确定是哪个特定生产设备导致不良高发之后，在步骤S104对生产过程数据中该特定生产设备的各个参数数据挨个排查，来确定是该特定生产设备的哪个或哪些生产设备参数的参数数据发生异常从而导致不良高发。
采用上述过程，在最终的基板出现异常高发的不良之后，良率管理***运行缺陷图分析功能直到生成要显示在用户界面上的结果也需要很多时间。此外，如果需要确定具体是哪个生产设备参数异常，需要多层级地分析，例如，工程师在确定到是设备A发生故障后，还需要进一步对设备A的各个参数进行确认，过程复杂且效率低。同时，数据获得的方式和类型因人而异，提供决策的数据基础不足，决策判断完全依靠人员的经验，不良原因分析经验未***固化，人为因素比重较大，并且对良率分析工程师的要求比较高， 如果不是经验丰富的工程师，并不能正确地确定导致不良的原因。
因此，本公开的实施例提供了一种新的确定导致基板不良的原因的方法、电子设备、计算机可读存储介质和***。该方法通过利用历史的生产过程数据，而预先得到多个生产设备参数的参数数据各自的参数数据参考范围，从而能够在基板上的不良高发时，快速地确定此时不良对应的多个生产设备参数的参数数据中偏离其参数数据参考范围的不良生产设备参数。此外，本公开的实施例提供的方法还可以通过利用历史的生产过程数据及其对应的不良信息数据预先得到至少一个参考不良分布图案并使该至少一个参考不良分布图案上每个不良点的基板坐标位置与其对应的不良生产设备参数相关联，从而将此时实际生产过程中的生产过程数据对应的不良分布图案上的不良点的基板坐标位置与至少一个参考不良分布图案上的不良点的基板坐标位置进行比对，基于预先得到的至少一个参考不良分布图案上的每个不良点的基板坐标位置与其对应的不良生产设备参数的关联性，根据各不良生产设备参数分别对应的不良分布图案上的不良点的数量来对各个不良生产设备参数相对于不良的相关性进行排序。
采用本公开的实施例提供的确定导致不良的原因的方法，用户能够更准确的更快速地判断导致不良的原因，及时采取措施，并且通过对导致不良的不良生产设备参数与不良的相关性的排序可以对生产设备参数有针对性的调整，同时还可以降低对工程师的经验和能力要求。
在下文中，将参照附图2-5以及6A-6B更详细地描述本公开的实施例。
图2是根据本公开的实施例的确定导致基板不良的原因的方法200的示意流程图。根据本公开的实施例的确定导致不良的原因的方法通过采用历史的生产过程数据，预先得到多个生产设备参数的参数数据参考范围，从而能够在基板上的不良高发时，快速地确定此时不良对应的多个生产设备参数的参数数据中偏离其参数数据参考范围的不良生产设备参数。
更具体地，如图2所示，确定导致面板的不良的原因的方法包括以下步骤。
在步骤S201，获取基板的生产过程数据，其中该生产过程数据包括基板的生产履历数据以及至少两个生产设备参数的参数数据。
生产履历数据以及生产设备参数的参数数据的示例或解释在前文已经介绍，这里不再重复描述。当然，本领域技术人员也可以对生产过程数据按照 其他方式进行划分，并还可以包括其他类型的数据。
在步骤S202，根据基板的类型，获取与基板的类型对应的至少两个生产设备参数的参数数据参考范围。
可选地，不同的基板类型各自对应的生产设备参数的参数数据参考范围可以不同。
可选地，获取与基板的类型对应的至少两个生产设备参数的参数数据参考范围包括：获取历史数据中同种类型的多个基板样本的多组生产过程数据，其中多个基板样本包括第一数量的正基板样本和第二数量的负基板样本；以及基于第一数量的正基板样本及其生产过程数据、以及第二数量的负基板样本及其生产过程数据，确定该至少两个生产设备参数的参数数据参考范围。
可选地，确定该至少两个生产设备参数的参数数据参考范围包括：利用第一数量的正基板样本及其生产过程数据、以及第二数量的负基板样本及其生产过程数据，训练生产设备参数模型；以及利用生产设备参数模型，产生至少两个生产设备参数的参数数据参考范围。
可选地，一个基板上包括多个面板，正负基板样本可以按照每个基板上存在不良的面板的占比来进行区分，例如，存在不良的面板的占比为20％以下的基板为正基板样本，并且存在不良的面板的占比大于20％的基板为负基板样本。正负基板样本的比例可以为第一数量：第二数量＝7:3。
可选地，该生产设备参数模型可以是卷积神经网络、线性回归，决策树，随机森林，支持向量机(Support Vector Machine，SVM)或任何其他机器学习模型。
可选地，还可以用测试基板样本来检验模型的可靠程度。该测试基板样本可以导入训练后的生产设备参数模型，从而用来对训练得到的生产设备参数模型进行测试，并且测试基板样本与训练基板样本的比例可以为2:8。判断模型质量的方法有很多种，例如采用平均绝对误差(mean_absolute_error)，以及交叉验证误差(cross_validation_error)等等。基于测量基板样本对模型质量的判断结果来对训练的生产设备参数模型内部的参数以及权重等进行进一步地调整(例如在卷积神经网络模型中采用的误差反向传播算法)。
在步骤S203，基于所获取的至少两个生产设备参数的参数数据、以及至少两个生产设备参数的参数数据参考范围，确定至少两个生产设备参数中偏离其参数数据参考范围的不良生产设备参数。
可选地，该方法还可以包括从分布式存储设备获取多个基板样本的多组生产过程数据，以用于训练得到生产设备参数模型。分布式存储设备的相关内容将在后文详细描述。
因此，通过本公开实施例提供的用于确定导致基板不良的原因的上述方法，通过预先获得生产设备参数的参数数据参考范围，从而可以直接将实际的生产过程数据中的生产设备参数的参数数据与该参数数据参考范围进行比较，快速地确定是否存在生产设备参数异常。
另一方面，在实际分析过程中，还可以对该各个不良生产设备参数与采用相同的生产过程数据的这一批基板上发生的不良的相关性进行排序。图3示出了确定导致基板不良的原因的另一种方法300的流程图。
显然，只有在不良生产设备参数的参数数据的数量为至少两个时，才需要进行相关性的排序。该方法300可以包括以下步骤。
步骤S301-S303与方法200中的S201-S203类似，这里不再重复。
在步骤S304，对于采用所述生产过程数据生产的多个基板，利用多个基板中每个基板的基板不良分布图案生成与生产过程数据对应的不良分布图案，其中不良分布图案示出了不良点的基板坐标位置。
其中，每个基板的基板不良分布图案是根据其对应的不良信息数据而得到的。不良信息数据可以从MES***得到，如前文所述。
可选地，利用多个基板中每个基板的基板不良分布图案生成与生产过程数据对应的不良分布图案包括：将多个基板的基板不良分布图案进行堆叠，获得与生产过程数据对应的不良分布图案。
在步骤S305，获取至少一个参考不良分布图案。
可选地，获取至少一个参考不良分布图案包括：获取历史数据中多个不良基板中每个不良基板的基板不良分布图案，其中基板不良分布图案示出了不良点的基板坐标位置，并且每个不良点的基板坐标位置与其对应的不良生产设备参数相关联；以及将历史数据中多个不良基板的基板不良分布图案按照至少一个组进行堆叠，获得至少一个参考不良分布图案。
可选地，通过显示设备来显示不良分布图案以及至少一个参考不良分布图案。
可选地，在至少一个参考不良分布图案中的该基板坐标位置处的不良点相关联的不良生产设备参数可以通过用户(例如经验丰富的良率分析工程师) 根据至少一个参考不良分布图案中的该基板坐标位置直接预先确定，或者可以将至少一个参考不良分布图案中的该基板坐标位置处的不良点对应的生产过程数据中的生产设备参数的参数数据与其参数数据参考范围进行比较，而得到超过其参数数据参考范围的不良生产设备参数，作为至少一个参考不良分布图案中的该基板坐标位置处的不良点相关联的不良生产设备参数。
在步骤S306，基于不良分布图案以及至少一个参考不良分布图案，确定各不良生产设备参数与不良的相关性排序。
可选地，基于不良分布图案以及至少一个参考不良分布图案，确定各不良生产设备参数与不良的相关性排序包括：对于不良分布图案中的每个不良点，确定该不良点的基板坐标位置，并且获取在至少一个参考不良分布图案中的该基板坐标位置处的不良点相关联的不良生产设备参数；对于每个所确定的不良生产设备参数，确定其对应的不良点的数量；并且根据所确定的各不良生产设备参数分别对应的不良点的数量，对所确定的各不良生产设备参数进行相关性排序。
可选地，基于不良分布图案以及至少一个参考不良分布图案，确定各不良生产设备参数与不良的相关性排序包括：对于不良分布图案中的每个不良点，确定该不良点的基板坐标位置，并且获取在至少一个参考不良分布图案中的该基板坐标位置处的不良点相关联的不良生产设备参数；对于每个所确定的不良生产设备参数，确定其对应的不良点所分布的面板的数量；并且根据所确定的各不良生产设备参数分别对应的不良点所分布的面板的数量，对所确定的各不良生产设备参数进行相关性排序。
此外，为了更好地提升用户交互体验以便于用户对不良生产设备参数的参数数据进行调整，该方法300可以进一步包括步骤S307：显示导致不良发生的、至少两个生产设备参数中偏离其参数数据参考范围的不良生产设备参数，和/或显示经排序的所确定的各不良生产设备参数。
可选地，通过显示设备向用户显示上述内容。
此外，应当理解，由于历史数据包括了长时间内的不良基板的大量生产过程数据以及不良信息数据，因此可以认为对于实际生产过程中的不良分布图案中的不良点的基板坐标位置，参考不良分布图案上在此处也存在不良点。
值得注意的是，在可替代的实施方式中，例如，可以直接得到导致基板不良的生产设备参数与该不良的相关性，因此步骤S302至步骤S303是可以 省略的。也就是说，在获取了不良在其上高发的基板的生产过程数据之后，直接进入步骤S304，即，利用采用该生产过程数据对应的多个基板(例如一批基板)中每个基板的基板不良分布图案生成与所述生产过程数据对应的不良分布图案，其中所述不良分布图案示出了不良点的基板坐标位置，然后类似地执行步骤S305至S307。
下面举例说明根据本公开实施例的确定导致基板不良的原因的方法的示例过程。图4A-4B示出了根据本公开实施例的显示的不良坐标图案以及参考不良分布图案的示意图。
当发现经过了所有工艺流程但尚未被切割为面板的一批基板上的不良高发时，可以从MES***以及FDC***获得获取这批基板的生产过程数据，该生产过程数据包括了基板的生产履历数据(例如，工序1、工序2、物料类型、物料比)以及至少两个生产设备参数的参数数据(例如各个生产设备的运行参数，包括温度、湿度、压力、工作时间等)。同时，还可以从MES***获得各个基板的不良信息数据(例如最终基板1(Glass 1)的不良坐标位置，最终基板2(Glass 2)的不良坐标位置等)。
将至少两个生产设备参数的参数数据与已知的至少两个生产设备参数的参数数据参考范围进行比较，确定该至少两个生产设备参数的参考数据中偏离其参数数据参考范围的不良生产设备参数，例如，确定例如设备1-温度、设备1-压力、设备2-温度、设备2-湿度、设备3-压力、设备4-湿度超过了其参考范围。其中，已知的至少两个生产设备参数的参数数据参考范围是预先根据历史多个基板样本的生产过程数据训练得到的，其中所述基板样本包括第一数量的正基板样本和第二数量的负基板样本。
然后，当经验不够丰富的良率分析工程师需要对目前的不良生产设备参数的参数数据与发生的不良的相关性进行分析时，还可以继续进行或替换地进行下列操作。对于采用相同的生产过程数据生产的这一批基板，基板1的基板不良分布图案上的不良点的基板坐标位置为(20,20-面板1)(15,30-面板1)，基板2的基板不良分布图案上的不良点的基板坐标位置为(20,40-面板1)(55,60-面板2)，基板3的基板不良分布图案上的不良点的基板坐标位置为(100,100-面板5)(120,150-面板6)等等，将这一批基板的多个基板中每个基板的基板不良分布图案进行堆叠，生成与该生产过程数据对应的不良分布图案(MAP R)，其中所述不良分布图案(MAP R)示出了所有不良点的基板坐标 位置，即，不良分布图案的大小与基板的大小对应，且不良点位于(20,20)(15,30)(20,40)(55,60)(100,100)(120,150)等等基板坐标位置处，如图4A所示。当然，这些具体的基板坐标位置仅仅是示例，基板的其他位置也可能存在不良。
对于该不良分布图案中的基板坐标位置(20,20)处的不良点，通过获取已知的、在所述至少一个参考不良分布图案中的该基板坐标位置(20,20)处的不良点相关联的不良生产设备参数，而知道是设备1的温度和设备2的湿度导致该不良发生的。
对于该不良分布图案中的基板坐标位置(15,30)处的不良点，通过获取已知的、在所述至少一个参考不良分布图案中的该基板坐标位置(15,30)处的不良点相关联的不良生产设备参数，而知道是设备1的湿度和设备2的压力导致该不良发生的。
对于该不良分布图案中的基板坐标位置(20,40)处的不良点，通过获取已知的、在所述至少一个参考不良分布图案中的该基板坐标位置(20,40)处的不良点相关联的不良生产设备参数，而知道是设备1的温度和设备2的压力导致该不良发生的。
对于该不良分布图案中的基板坐标位置(55,60)处的不良点，通过获取已知的、在所述至少一个参考不良分布图案中的该基板坐标位置(55,60)处的不良点相关联的不良生产设备参数，而知道是设备1的温度和设备4的湿度导致该不良发生的。
对于该不良分布图案中的基板坐标位置(100,100)处的不良点，通过获取已知的、在所述至少一个参考不良分布图案中的该基板坐标位置(100,100)处的不良点相关联的不良生产设备参数，而知道是设备2的温度和设备3的压力导致该不良发生的。
对于该不良分布图案中的基板坐标位置(120,150)处的不良点，通过获取已知的、在所述至少一个参考不良分布图案中的该基板坐标位置(120,150)处的不良点相关联的不良生产设备参数，而知道是设备3的压力和设备4的湿度导致该不良发生的。
通过进行上述操作，可以得到每个不良生产设备参数对应的不良点的数量，即，该不良分布图案上的各个不良生产设备参数的出现次数，来对所确定的各不良生产设备参数进行排序。更具体地，对于上述不良分布图案中示 例性的几个基板坐标位置，设备1-温度影响了(20,20)、(20,40)和(55,60)三个不良点，设备1-湿度影响了(20,40)一个不良点，设备2-压力影响了(15,30)和(20,40)两个不良点，设备2-温度影响(20,20)一个点，设备2-温度影响(100,100)一个点，设备3-压力影响(100,100)和(120,150)两个不良点，设备4-湿度影响(55,60)和(120,150)两个不良点。因此，可以确定导致该基板的不良发生的各不良生产设备参数的相关性排序为：设备1-温度；设备2-压力，设备4-湿度(并列)；设备2-湿度，设备3-压力，设备1-湿度(并列)。
上述的排序是通过各个不良生产设备参数对应的不良点的数量来进行排序的，当然，如前面所述，也可以根据各个不良生产设备参数对应的不良点所分布的面板的数量来进行排序，这里对此不再详细描述。
此外，至少一个参考不良分布图案的获取过程为：获取历史数据中其上存在不良的多个不良基板(Glass 1,…,Glass n-1,Glass n)中每个不良基板的基板不良分布图案，其中每个基板不良分布图案示出了其上一个或多个不良点的基板坐标位置，并且每个不良点的基板坐标位置与其对应的不良生产设备参数相关联；以及将所述历史数据中多个不良基板的基板不良分布图案按照至少一个组进行堆叠，以一个组为例，获得一个参考不良分布图案(MAP REF)，该参考不良分布图案(MAP REF)包括了所有多个基板上的不良点的基板坐标位置。
与每个不良点的基板坐标位置相关联的不良生产设备参数是预先计算并存储，从而是已知的，并且获取过程为：在显示设备上显示示例的一个参考不良分布图案，如图4B所示，通过比对确定该参考不良分布图案上与如图4A所述不良分布图案的每个不良点对应的每个基板坐标位置，然后用户根据该参考不良分布图案中的每个基板坐标位置，将该参考不良分布图案中的每个基板坐标位置处的不良点对应的生产过程数据中的生产设备参数的参数数据与其参数数据参考范围进行比较，而得到与每个不良点的基板坐标位置相关联的不良生产设备参数。因此，与每个不良点的基板坐标位置相关联的不良生产设备参数是预先计算并存储，当获得了实际的不良分布图案时，对该不良分布图案进行如前面所述的操作，而该已知的、与每个不良点的基板坐标位置相关联的不良生产设备参数作为分析的基础。
通过采用上述该方法，通过预先获得参考不良分布图案以及其中每个不 良点的基板坐标位置相关联的不良生产设备参数，将实际的不良分布图案与参考不良分布图案进行坐标比对而得到实际的不良分布图案中每个不良点相关联的不良生产设备参数，通过对每个不良生产设备参数导致不良点的数量，来确定相关性，因此能够便于经验不够丰富的良率分析工程师来快速地对导致基板不良的各个不良生产生设备参数与不良的相关性进行排序。
同时，在本公开的实施例中，对于不良分布图案以及参考不良分布图案中的不良点的基板坐标位置是采用图形化的方式进行了描述，但是，也可以采用文本形式。例如，对于历史数据中其上存在不良的多个不良基板的所有不良点的基板坐标位置用文本形式存储，当得到实际生产过程中的不良点的实际基板坐标位置时，此时将每一个实际基板坐标位置与所存储的基板坐标位置进行文本比对，来确定该实际基板坐标位置与哪一个存储的基板坐标位置对应，而得到相应的不良生产设备参数。
根据本公开的另一方面，还提供了一种电子设备500。
图5示出了根据本公开实施例的电子设备500的结构框图。该电子设备500包括存储器501和处理器502。在存储器上存储有计算机程序，并且该计算机程序在处理器上运行时实现如参考图2至图3描述的确定导致基板不良的原因的方法。
根据本公开的再一方面，还提供了一种确定导致基板不良的原因的***600。
图6A-6B示出了根据本公开实施例的***600的结构框图。如图6A所示，该***包括分布式存储设备610、电子设备620(例如，参考图5描述的电子设备500)、显示设备630。
分布式存储设备610被配置为存储预设时间段内的所有基板的所有生产过程数据以及不良信息数据。例如，可以存储两年内生产所有基板的过程中的、所有基板各自对应的所有生产过程数据和不良信息数据。
其中，分布式存储设备中存储有相对完整的数据(如一个数据库)，而且，分布式存储设备包括多个硬件的存储器，且不同的硬件存储器分布在不同物理位置(如在不同工厂，或在不同生产线)，并通过网络实现相互之间信息的传递，从而其数据是分布式关系的，但在逻辑上构成一个基于大数据技术的数据库。
参考图6B，大量不同工厂设备的原始数据存储在相应的生产制造***中， 如良率管理***(YMS)、错误侦测及分类(FDC)***、制造执行***(MES)等***的关系型数据库(如Oracle、Mysql等)中，而这些原始数据可通过数据抽取工具(如Sqoop、kettle等)进行原表抽取以传输给分布式存储设备(如Hadoop Distributed File System，HDFSHadoop Distributed File System，HDFS)，以降低对工厂设备和生产制造***的负载，便于后续分析设备的数据读取。
分布式存储设备中的数据可采用Hive工具或Hbase数据库格式存储。例如，根据Hive工具，以上原始数据先存储在数据湖中；之后，为了减小数据认知的学习成本及业务实现的统一性，可继续在Hive工具中按照数据的应用主题、场景等进行数据清洗、数据转换等预处理，得到具有不同主题(例如，生产履历数据主题、不良检测数据主题、不良点位测量数据主题以及生产设备参数数据主题)的数据仓库，以及具有不同场景(如突发性不良场景、相关性分析场景)的数据集市。需要说明的是，数据主题以及场景并不限于以上示例，可根据业务需求增加或重构新的数据主题以及场景，例如，可以将不良检测数据主题、不良点位测量数据主题合并到生产履历数据主题中。以上数据集市可再通过不同的API接口，与显示设备、电子设备等连接，以实现与这些设备间的数据交互。
其中，由于涉及多个工厂的多个工厂设备，故以上原始数据的数据量是很大的。例如，所有工厂设备每天产生的原始数据可能有几百G，每小时产生的数据也可能有几十G。
对海量结构化数据实现存储与计算主要有两种方案：RDBMS关系型数据库管理(Relational Database Management System,RDBMS)的网格计算方案；分布式文件管理***(Distributed File System,DFS)的大数据方案。
其中，RDBMS的网格计算是把需要非常巨大的计算能力的问题分成许多小部分，然后把这些部分分配给许多计算机分别处理，最后把这些计算结果综合起来。例如，作为一种具体例子，Oracle RAC(真正应用集群)是Oracle数据库支持的网格计算的核心技术，其中所有服务器都可直接访问数据库中的所有数据。但是，RDBMS的网格计算的应用***在数据量很大时无法满足用户要求，例如，由于硬件的扩展空间有限，故数据增加到足够大的数量级后，会因为硬盘的输入/输出的瓶颈使得处理数据的效率非常低。
分布式文件管理为基础的大数据技术，则允许采用多个廉价硬件设备构建大型集群，以对海量数据进行处理。如Hive工具是基于Hadoop的数据仓 库工具，可用来进行数据提取转化加载(ETL)，Hive工具定义了简单的类SQL查询语言，同时也允许通过自定义的MapReduce的mapper和reducer来默认工具无法完成的复杂的分析工作。Hive工具没有专门的数据存储格式，也没有为数据建立索引，用户可以自由的组织其中的表，对数据库中的数据进行处理。可见，分布式文件管理的并行处理可满足海量数据的存储和处理要求，用户可通过SQL查询处理简单数据，而复杂处理时可采用自定义函数来实现。因此，在对生产所有基板时工厂的海量数据分析时，需要将工厂数据库的数据抽取到分布式文件***中，一方面不会对原始数据造成破坏，另一方面提高了数据分析效率。
电子设备620可以包括一个或多个处理器，一个或多个处理器被配置为执行确定相关性的操作。电子设备620包括具有数据处理能力的处理器(如CPU)，还可具有存储有所需程序的存储器(如硬盘)，处理器与存储器通过I/O连接从而能实现信息交互，由此处理器可根据存储器中存储的程序进行所需运算，以实现确定相关性的操作。
电子设备620可以是参考图5所描述的电子设备500。
显示设备630具有显示功能，用于将从电子设备获得的要显示的图像数据显示出来。根据本公开的实施例，显示数据可以包括不良分布图案以及至少一个参考不良分布图案，导致不良发生的、多个生产设备参数中偏离其参数数据参考范围的不良生产设备参数，和/或显示经排序的所确定的各不良生产设备参数。
显示设备630可包括一个或多个显示器，包括一个或多个具有显示功能的终端，从而电子可将其计算得到的显示数据发送给显示设备，显示设备再将其显示出来。
在一些实施例中，显示设备还可用于显示“交互界面”，该交互界面可包括显示确定的结果(如不良生产设备参数、相关性)的子界面，用于控制该***进行所需工作(如任务设定)的子界面，以及对各生产设备进行控制(如修改其生产设备参数)的子界面等。
也就是说，通过该显示设备的“交互界面”，可实现用户与确定导致基板不良的原因的***的完全交互(控制和接收结果)。
本公开实施例中，分布式存储设备可通过大数据方式高效率的实现对多个生产设备的原始数据的收集和初步处理，电子设备则可从分布式存储设备 方便的获取所需的数据，以计算得到超出生产设备参数的参数数据参考范围的不良生产设备参数，并且可以进一步得到不良生产设备参数与不良的相关性，并供显示设备显示。由此，本公开实施例可自动化的确定导致基板不良的原因，以进行不良原因定位、调整生产流程等。
根据本公开的又一方面，还提供了一种计算机可读存储介质。该计算机可读存储介质存储计算机指令，所述计算机指令用于使所述计算机执行如参考图2至图3描述的确定导致基板不良的原因的方法。
虽然已经针对本主题的各种具体示例实施例详细描述了本主题，但是每个示例通过解释而不是限制本公开来提供。本领域技术人员在得到对上述内容的理解后，可以容易地做出这样的实施例的变更、变化和等同物。因此，本发明并不排除包括将对本领域普通技术人员显而易见的对本主题的这样的修改、变化和/或添加。例如，作为一个实施例的一部分图示或描述的特征可以与另一实施例一起使用，以产生又一实施例。因此，意图是本公开覆盖这样的变更、变化和等同物。
本申请使用了特定词语来描述本申请的实施例。如“一个实施例”、“一实施例”、和/或“一些实施例”意指与本申请至少一个实施例相关的某一特征、结构或特点。因此，应强调并注意的是，本说明书中在不同位置两次或多次提及的“一实施例”或“一个实施例”或“一替代性实施例”并不一定是指同一实施例。此外，本申请的一个或多个实施例中的某些特征、结构或特点可以进行适当的组合。
本领域技术人员可以理解，本申请的各方面可以通过若干具有可专利性的种类或情况进行说明和描述，包括任何新的和有用的工序、机器、产品或物质的组合，或对他们的任何新的和有用的改进。相应地，本申请的各个方面可以完全由硬件执行、可以完全由软件(包括固件、常驻软件、微码等)执行、也可以由硬件和软件组合执行。以上硬件或软件均可被称为“数据块”、“模块”、“引擎”、“单元”、“组件”或“***”。此外，本申请的各方面可能表现为位于一个或多个计算机可读介质中的计算机产品，该产品包括计算机可读程序编码。
除非另有定义，这里使用的所有术语(包括技术和科学术语)具有与本公开所属领域的普通技术人员共同理解的相同含义。还应当理解，诸如在通常字典里定义的那些术语应当被解释为具有与它们在相关技术的上下文中的 含义相一致的含义，而不应用理想化或极度形式化的意义来解释，除非这里明确地这样定义。
以上是对本公开的说明，而不应被认为是对其的限制。尽管描述了本公开的若干示例性实施例，但本领域技术人员将容易地理解，在不背离本公开的新颖教学和优点的前提下可以对示例性实施例进行许多修改。因此，所有这些修改都意图包含在权利要求书所限定的本公开范围内。应当理解，上面是对本公开的说明，而不应被认为是限于所公开的特定实施例，并且对所公开的实施例以及其他实施例的修改意图包含在所附权利要求书的范围内。本公开由权利要求书及其等效物限定。

Claims (15)

  1. 一种确定导致基板不良的原因的方法，包括：
    获取基板的生产过程数据，所述生产过程数据包括基板的生产履历数据以及至少两个生产设备参数的参数数据；
    根据所述基板的类型，获取与所述基板的类型对应的所述至少两个生产设备参数的参数数据参考范围；以及
    基于所获取的至少两个生产设备参数的参数数据、以及所述至少两个生产设备参数的参数数据参考范围，确定所述至少两个生产设备参数中偏离其参数数据参考范围的不良生产设备参数。
  2. 根据权利要求1所述的方法，其中，所确定的所述至少两个生产设备参数中偏离其参数数据参考范围的不良生产设备参数的数量为至少两个，
    所述方法还包括：
    对于采用所述生产过程数据生产的多个基板，利用所述多个基板中每个基板的基板不良分布图案来生成与所述生产过程数据对应的不良分布图案，其中所述不良分布图案示出了不良点的基板坐标位置；
    获取至少一个参考不良分布图案；以及
    基于所述不良分布图案以及所述至少一个参考不良分布图案，确定各不良生产设备参数与不良的相关性排序。
  3. 根据权利要求1所述的方法，其中，所述根据所述基板的类型，获取与所述基板的类型对应的所述至少两个生产设备参数的参数数据参考范围还包括：
    获取历史数据中同种类型的多个基板样本的多组生产过程数据，其中所述多个基板样本包括第一数量的正基板样本和第二数量的负基板样本；以及
    基于所述第一数量的正基板样本及其生产过程数据、以及所述第二数量的负基板样本及其生产过程数据，确定所述至少两个生产设备参数的参数数据参考范围。
  4. 根据权利要求3所述的方法，其中，确定所述至少两个生产设备参数的参数数据参考范围包括：
    利用所述第一数量的正基板样本及其生产过程数据、以及所述第二数量的负基板样本及其生产过程数据，训练生产设备参数模型；以及
    利用所述生产设备参数模型，产生所述至少两个生产设备参数的参数数据参考范围。
  5. 根据权利要求2所述的方法，其中，利用所述多个基板中每个基板的基板不良分布图案来生成与所述生产过程数据对应的不良分布图案包括：
    获取采用所述生产过程数据生产的多个基板各自的不良信息数据，并基于每个基板各自的不良信息数据来生成每个基板的基板不良分布图案；并且
    将所述每个基板的基板不良分布图案进行堆叠，获得与所述生产过程数据对应的不良分布图案。
  6. 根据权利要求2所述的方法，其中，获取至少一个参考不良分布图案包括：
    获取历史数据中多个不良基板中每个不良基板的基板不良分布图案，其中所述基板不良分布图案示出了不良点的基板坐标位置，并且每个不良点的基板坐标位置与其对应的不良生产设备参数相关联；以及
    将所述历史数据中多个不良基板的基板不良分布图案按照至少一个组进行堆叠，获得所述至少一个参考不良分布图案。
  7. 根据权利要求6所述的方法，其中，基于所述不良分布图案以及所述至少一个参考不良分布图案，确定各不良生产设备参数与不良的相关性排序包括：
    对于所述不良分布图案中的每个不良点，确定该不良点的基板坐标位置，并且获取在所述至少一个参考不良分布图案中的该基板坐标位置处的不良点相关联的不良生产设备参数；
    对于每个所确定的不良生产设备参数，确定其对应的不良点的数量；并且
    根据所确定的各不良生产设备参数分别对应的不良点的数量，对所确定的各不良生产设备参数与不良的相关性进行排序。
  8. 根据权利要求6所述的方法，其中，基于所述不良分布图案以及所述至少一个参考不良分布图案，确定各不良生产设备参数与不良的相关性排序包括：
    对于不良分布图案中的每个不良点，确定该不良点的基板坐标位置，并且获取在至少一个参考不良分布图案中的该基板坐标位置处的不良点相关联的不良生产设备参数；
    对于每个所确定的不良生产设备参数，确定其对应的不良点所分布的面板的数量；并且
    根据所确定的各不良生产设备参数分别对应的不良点所分布的面板的数量，对所确定的各不良生产设备参数进行相关性排序。
  9. 根据权利要求7或8所述的方法，其中，获取在所述至少一个参考不良分布图案中的该基板坐标位置处的不良点相关联的不良生产设备参数包括：
    显示所述不良分布图案以及所述至少一个参考不良分布图案；
    将至少一个参考不良分布图案上的该基板坐标位置处的不良点对应的生产过程数据中的生产设备参数的参数数据与其参数数据参考范围进行比较，而得到超过其参数数据参考范围的不良生产设备参数，作为至少一个参考不良分布图案中的该基板坐标位置处的不良点相关联的不良生产设备参数。
  10. 根据权利要求4所述的方法，其中，
    从分布式存储设备抽取所述多个基板样本的多组生产过程数据。
  11. 根据权利要求10所述的方法，其中，分布式存储设备获得所述多个基板样本的多组生产过程数据之后，首先对每组生产过程数据进行数据预处理，以得到符合预设格式的生产过程数据。
  12. 根据权利要求7所述的方法，还包括：
    显示导致不良发生的、所述至少两个生产设备参数的参数数据中偏离其参数数据参考范围的不良生产设备参数，和/或显示经排序的所确定的各不良生产设备参数。
  13. 一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述程序时实现如权利要求1至12任一项所述的导致基板不良的原因的方法。
  14. 一种计算机可读存储介质，存储计算机指令，所述计算机指令用于使所述计算机执行权利要求1至12任一项所述的确定导致基板不良的原因的方法。
  15. 一种确定导致基板不良的原因的***，包括：
    分布式存储设备，被配置为存储预设时间段内的所有基板的所有生产过程数据；
    如权利要求13所述的电子设备；以及
    显示设备，被配置为显示从电子设备获得的要显示的图像数据。
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