ECS data processing method and device and medium

Abstract

The invention provides an ECS data processing method and device and a medium, and the method comprises the steps: obtaining an initial ECS data set of a machine in an MES, taking a rule identifier in the initial ECS data set as an index, carrying out the data aggregation of the initial ECS data set, extracting N common fields from the initial ECS data set, fields in the initial ECS data set are intercepted and spliced to generate description fields used for describing ECS data, and after language logic enhancement is conducted on the N common fields and the description fields, a target ECS data set comprising the N common fields and the description fields is created, the target ECS data set comprises ECS data of machine tables of all departments, a visual chart corresponding to the target ECS data set is displayed, the method is used for analyzing the ECS data into the visual chart, and integration and rule visualization of the ECS data are achieved.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor device data processing, and particularly to an ECS data processing method, apparatus, and medium. Background Art

[0002] An Equipment Constraint Setup (ECS) is a method for restricting the operating rules of a machine tool. Its purpose is to prevent un-released wafer batches from entering unauthorized machine tools, thereby improving product quality. However, since the ECS is set by engineering personnel through a Manufacturing Execution System (MES), usually set according to products or processes, etc., due to a very large number of setting rules and the query interface only simply displaying the rules, the readability is very poor and it is inconvenient to understand. Therefore, only manual records can be used to record which machine tools can run which products and other rules. However, manual records will have problems of latency and easy loss, and there will be differences from the actual situation. It can be seen that the prior art has problems of high complexity of ECS setting rules and difficulty in data query. Summary of the Invention

[0003] The purpose of the present invention is to provide an ECS data processing method, apparatus, and medium for parsing ECS data into a visual chart to achieve the integration and rule visualization of ECS data.

[0004] To achieve the above purpose, the present invention provides an ECS data processing method, including: obtaining an initial ECS data set of a machine tool in the MES, performing data aggregation on the initial ECS data set using the rule identifier in the initial ECS data set as an index, extracting N common fields from the initial ECS data set, and generating a description field for describing the ECS data by intercepting and splicing the original fields in the initial ECS data set. After enhancing the language logic of the N common fields and the description field, a target ECS data set including the N common fields and the description field is created. The target ECS data set includes the ECS data of machine tools in each department, and a visual chart corresponding to the target ECS data set is displayed.

[0005] In a possible embodiment, the visualization chart includes a daily ECS trend chart, which is used to represent the number of ECSs in different regions daily. Before displaying the daily ECS trend chart corresponding to the target ECS dataset, it further includes: using the rule identifier in the initial ECS dataset as an index, inserting machine and cavity information into the initial ECS dataset as machine fields and cavity information fields, and adding region fields and time fields to obtain an intermediate ECS dataset. After that, the intermediate ECS dataset is periodically crawled for information to create the target ECS dataset, which includes the N common fields, the description field, machine fields, cavity information fields, region fields, and time fields; the data in the target ECS dataset is summed by region and by day to obtain the daily ECS trend chart.

[0006] In another possible embodiment, the visualization chart includes a daily ECS variable chart, which is used to represent the change in the number of ECSs daily; before displaying the daily ECS variable chart corresponding to the target ECS dataset, it further includes: obtaining a historical ECS dataset, comparing and judging the relevant fields in the initial ECS dataset with those in the historical ECS dataset, and marking the data with changes; crawling the information of the ECS data marked every day to create the target ECS dataset; the data in the target ECS dataset is summed by region and by day to obtain the daily ECS variable chart.

[0007] In other possible embodiments, enhancing the language logic of the N common fields and the description field includes: intercepting, splicing, and adding descriptions to the N common fields and the description field, and repeating the above actions multiple times to enhance the language logic in a way of logical nesting.

[0008] In yet another possible embodiment, the visualization chart includes a filtering control for at least one parameter among machine status, department identifier, control condition type, and machine identifier; when receiving an operation by the user on the filtering control, filter and display the ECS data including the parameter corresponding to the filtering control.

[0009] In other possible embodiments, displaying the visualization chart corresponding to the target ECS dataset includes: displaying the visualization chart corresponding to the target ECS dataset, where the colors of the ECS data of the effective control types and the ineffective control types are different.

[0010] In a possible embodiment, the N common fields include the completed goods identifier, production steps, production stage, and control condition type.

[0011] In another possible embodiment, data aggregation of the initial ECS dataset includes: summarizing the data in the initial ECS dataset by department, and then summarizing the data of each department by machine.

[0012] In a second aspect, the present invention further provides an ECS data processing device, which includes modules / units for executing the method according to any one of the possible designs in the first aspect. These modules / units can be implemented by hardware or by hardware executing corresponding software.

[0013] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, which includes a computer program. When the computer program runs on an electronic device, the electronic device is caused to execute the method according to any one of the possible designs in the first aspect.

[0014] In a fourth aspect, an embodiment of the present application further provides a computer program product. When the computer program product runs on an electronic device, the electronic device is caused to execute the method according to any one of the possible designs in the first aspect.

[0015] The beneficial effects of the ECS data processing method, device, and medium provided by the present invention are as follows: parsing the ECS data into reports, realizing the integration and rule visualization of the ECS data, adding real-time ECS quantity display, daily ECS trend chart, and daily ECS variable chart, which can display the real-time ECS quantity of each department's machines, reflecting the stability of the production line through the daily change trend of the whole factory's ECS quantity. If there are large changes in the Daily ECS in a certain area, the daily ECS variable chart will display it in real time, ensuring the timeliness and accuracy of the data. This method can better monitor and control the ECS, and at the same time improve the readability and timeliness of the data, so that operators and managers can better understand and utilize these data. Description of the Drawings

[0016] Figure 1 It is a schematic flowchart of an ECS data processing method provided by an embodiment of the present invention; [[ID=2G]]

[0017] Figure 2 It is an example diagram of a dataset before and after compilation provided by an embodiment of the present invention;

[0018] Figure 3 It is a real-time ECS data view provided by an embodiment of the present invention;

[0019] Figure 4 It is a daily ECS trend chart provided by an embodiment of the present invention;

[0020] Figure 5 It is a daily ECS variable chart provided by an embodiment of the present invention;

[0021] Figure 6 Schematic diagram of an ECS data processing device provided by an embodiment of the present invention. Detailed implementation manners

[0022] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein shall have the ordinary meanings as understood by those of ordinary skill in the art in the field to which the present invention belongs. The words such as "including" used herein are intended to mean that the elements or items appearing before this word cover the elements or items listed after this word and their equivalents, without excluding other elements or items.

[0023] This embodiment is a solution for adding real-time monitoring and reporting functions to an ECS, and provides an ECS data processing method. Figure 1 Flowchart of the ECS data processing method provided for this embodiment, including the following steps:

[0024] S101, obtain the initial ECS data set of the machines in the MES.

[0025] S102, use the rule identifiers in the initial ECS data set as indexes to perform data aggregation on the initial ECS data set, extract N common fields from the initial ECS data set, and generate a description field for describing ECS data by intercepting and splicing the original fields in the initial ECS data set, where N is a positive integer.

[0026] S103, after enhancing the language logic of the N common fields and the description field, create a target ECS data set including the N common fields and the description field, and the target ECS data set includes the ECS data of the machines in each department.

[0027] S104, display the visualization chart corresponding to the target ECS data set.

[0028] It should be understood that ECS is a method for restricting the operating rules of a machine tool. The initial ECS data set includes the control conditions of various machine tools. For example, the control condition with Rule_ID of 001 is that the wafer batch with serial number 01XX cannot be processed on the machine tool ABXXX. Since each control condition in the initial ECS data set has a unique Rule_ID, the initial ECS data set can be aggregated by using Rule_ID as an index. Specifically, the data in the initial ECS data set can be summarized by department first, and then the data of each department can be summarized by machine tool. In this way, the ECS data of each machine tool in each department can be aggregated.

[0029] Considering the problem of low readability of the data in the initial ECS data set, in addition to aggregating the data in this implementation, the aggregated data is also parsed and compiled. Specifically, N common fields are extracted first. Exemplarily, the N common fields may include information such as Finish_good_ID, Step, Stage, Rule_type, etc. In addition, the original fields in the initial ECS data set are intercepted and spliced to generate a description field for describing the ECS data, and then it is enhanced by means of logical nesting. Exemplarily, the way of logical nesting may be to intercept, splice and add descriptions to the N common fields and the description field, and repeat the above actions multiple times. In this way, through multiple iterations and optimizations, the key semantics can be further highlighted, and the logicality of the language is strengthened while adding descriptions.

[0030] Exemplarily, as Figure 2 shown, Figure 2 in (a) is the initial ECS data set. The machine tool ID corresponding to this ECS data set is ABXXX. This machine tool has multiple control conditions, and each control condition has unique corresponding information such as Rule_ID, rule name, rule version, rule type, and rule description. After the above aggregation and compilation of the initial ECS data set, the visual chart includes at least one parameter filtering control of machine tool status, department identifier, control condition type, and machine tool identifier. When receiving an operation of the user on the filtering control, the ECS data including the parameter corresponding to the filtering control is filtered and displayed. For example, Figure 2 in (b) is the visual chart obtained after compilation. When the user inputs ABXXX in the machine tool identifier control, the relevant control condition information of the machine tool ABXXX is filtered out. Optionally, in order to further improve the query efficiency, the filtered data can also be generated as Figure 3The bar chart shown adds color management to the real-time ECS view by department, using different colors to distinguish between effective and ineffective ECS data. For example, negative control conditions are shown in light red, while effective control conditions are shown in blue, facilitating understanding and reading by on-site personnel.

[0031] In a possible scenario, when the visual chart to be displayed is the daily ECS trend chart, the daily ECS trend chart is used to represent the ECS quantities in different regions daily. For the daily ECS trend chart, based on the above S101 to S104, the following steps can also be added: Using the rule identifier in the initial ECS dataset as an index, insert the machine and cavity information into the initial ECS dataset as the machine field and cavity information field, and add a region field and a time field to obtain an intermediate ECS dataset. It should be understood that the region field refers to different physical regions in the workshop. For example, the wet etching machine is located in region 1 of the workshop, and the lithography machine is located in region 2 of the workshop. In this way, the information in the intermediate ECS dataset can be periodically fetched to create the target ECS dataset. At this time, the target ECS dataset includes the N common fields, the description field, the machine field, the cavity information field, the region field, and the time field. Therefore, the data in the target ECS dataset can be summed by region and by day to obtain the daily ECS trend chart. The time field helps users select the viewing interval, and the stability of the production line can be reflected through the daily change trend of the total ECS quantity in the whole factory. As Figure 4 shown, the daily ECS quantities are summarized to obtain the daily ECS trend chart (Daily ECS). This daily ECS trend table reflects the daily ECS quantities and can reflect the stability of the production line.

[0032] In another possible scenario, when the visual chart to be displayed is the daily ECS variable chart, the daily ECS variable chart is used to represent the changes in the daily ECS quantities. For the daily ECS variable chart, based on the above S101 to S104, before displaying the daily ECS variable chart corresponding to the target ECS dataset, a historical ECS dataset can be obtained, the relevant fields in the initial ECS dataset and the historical ECS dataset are compared and judged, and the data with changes are marked; the information of the ECS data marked every day is fetched to create the target ECS dataset. As Figure 5As shown, the data in the target ECS dataset is summed by region and by day to obtain a daily ECS variable graph (ECSDaily change). In this embodiment, by presenting the controlled conditions that change (newly added, modified, or removed) each day by region, it is convenient for on-line personnel to quickly obtain the changes in the controlled conditions in the relevant regions, improving the shipping efficiency and quality. At the same time, this data also reflects the stability of the production line to a certain extent. If there are a large number of changes in the ECS of a certain region, the report will be displayed in real time, with real-time and accuracy.

[0033] In this embodiment, a solution for adding real-time monitoring and visualization chart functions to ECS is to classify and summarize by product, technology, machine tool, etc., effectively integrating and analyzing the ECS data in MES. The report can be used to clearly display the ECS rules. For complex setting rules, the visualization chart can provide an easy-to-understand visual representation, including which machine tools can run which products and the associations between various rules, etc. Through the report, the changes in ECS can be monitored in real time, including which machine tools are running which products and whether there are any violations of the ECS rules. This can help discover and solve problems, and at the same time provide real-time feedback on the stability of the production line. It should be understood that the report generated according to the above method in this embodiment can not only provide real-time ECS data but also perform in-depth data analysis. For example, by analyzing historical data, it can be found which machine tools are prone to problems and which products require more attention in the production process, etc. These reports can help improve the production process and product quality. Furthermore, through the report, behaviors that do not conform to the ECS rules can also be detected and actions can be taken before problems may occur, such as reminding the operator that a certain wafer batch is entering an unauthorized machine tool or reminding the engineer that a certain ECS rule needs to be modified, etc. The visualization chart can provide a summary view, enabling both operators and engineers to easily understand the current ECS settings and status, facilitating simplified operations and understanding. Therefore, compared with the prior art, it can eliminate manual records, reduce latency and the problem of being easily lost. In addition, the visualization chart can also provide decision-making support for management. For example, by analyzing the ECS data, management can decide whether more resources need to be added to meet production requirements, or whether certain machine tools need to be upgraded or replaced, etc. Generally speaking, the visualization chart is a powerful tool that can help solve problems such as ECS data integration, visualization, monitoring, analysis, and reporting. By using the report, the efficiency of the production process and product quality can be improved, while at the same time improving the work efficiency and accuracy of operators and engineers.

[0034] In some embodiments of the present application, as Figure 6 shown, the device for implementing the above method includes:

[0035] An acquisition unit 601, configured to acquire an initial ECS data set of a machine in MES;

[0036] A compilation unit 602, configured to use the rule identifiers in the initial ECS data set as indexes to perform data aggregation on the initial ECS data set, extract N common fields from the initial ECS data set, and generate a description field for describing ECS data by intercepting and splicing the original fields in the initial ECS data set, where N is a positive integer;

[0037] A creation unit 603, configured to perform language logic enhancement on the N common fields and the description field, and then create a target ECS data set including the N common fields and the description field, where the target ECS data set includes ECS data of machines in each department;

[0038] A display unit 604, configured to display a visualization chart corresponding to the target ECS data set.

[0039] The above Figure 1 All relevant content of each step involved in the above-described method embodiment can be cited in the function description of the corresponding unit module, and will not be elaborated here.

[0040] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a computer, the method described in the above method embodiment is implemented.

[0041] An embodiment of the present application also provides an electronic device, including a processor and a memory. Among them, the memory is used to store one or more computer programs; when the one or more computer programs stored in the memory are executed by the processor, the electronic device can implement the method described in the above method embodiment.

[0042] The present invention also provides a computer program product, and when the computer program product is executed by a computer, the method described in the above method embodiment is implemented.

[0043] Although the embodiments of the present invention have been described in detail above, it is obvious to those skilled in the art that various modifications and changes can be made to these embodiments. However, it should be understood that such modifications and changes are all within the scope and spirit of the present invention described in the claims. Moreover, the present invention described herein can have other embodiments and can be implemented or realized in various ways.

Claims

1. An ECS data processing method, characterized in that, Including: Obtain the initial ECS dataset of the machines in the MES; Using the rule identifiers in the initial ECS dataset as indexes, perform data aggregation on the initial ECS dataset, extract N common fields from the initial ECS dataset, and generate a description field for describing ECS data by intercepting and splicing the original fields in the initial ECS dataset, where N is a positive integer; After enhancing the language logic of the N common fields and the description field, create a target ECS dataset including the N common fields and the description field, and the target ECS dataset includes the ECS data of the machines in each department; Display the visualization chart corresponding to the target ECS dataset.

2. The method according to claim 1, wherein The visualization chart includes a daily ECS trend chart, and the daily ECS trend chart is used to represent the ECS quantities in different regions daily; Before displaying the daily ECS trend chart corresponding to the target ECS dataset, it further includes: Using the rule identifiers in the initial ECS dataset as indexes, insert machine and cavity information into the initial ECS dataset as machine fields and cavity information fields, and add region fields and time fields to obtain an intermediate ECS dataset; Creating a target ECS dataset including the N common fields and the description field, including: Periodically perform information scraping on the intermediate ECS dataset to create the target ECS dataset, and the target ECS dataset includes the N common fields, the description field, machine fields, cavity information fields, region fields and time fields; Sum the data in the target ECS dataset by region and by day to obtain a daily ECS trend chart.

3. The method according to claim 1, wherein The visualization chart includes a daily ECS variable chart, and the daily ECS variable chart is used to represent the change in the ECS quantity daily; Before displaying the daily ECS variable chart corresponding to the target ECS dataset, it further includes: Obtain a historical ECS dataset, compare and judge the relevant fields in the initial ECS dataset with those in the historical ECS dataset, and mark the data with changes; Perform information scraping on the ECS data marked every day to create the target ECS dataset; Sum the data in the target ECS dataset by region and by day to obtain a daily ECS variable chart.

4. The method according to any one of claims 1 to 3, characterized in that, Enhancing the language logic of the N common fields and the description field includes: Intercept, splice and add descriptions to the N common fields and the description field, and repeat the above actions multiple times to enhance the language logic in a way of logical nesting.

5. The method according to any one of claims 1 to 3, characterized in that, The visualization chart includes a filtering control for at least one parameter among machine status, department identifier, card control condition type, machine identifier; After receiving the operation of the user on the filtering control, filter and display the ECS data including the parameters corresponding to the filtering control.

6. The method according to any one of claims 1 to 3, characterized in that Displaying the visualization chart corresponding to the target ECS dataset includes: Displaying the visualization chart corresponding to the target ECS dataset, where the colors of the ECS data of the effective card control type and the ineffective card control type are different.

7. The method according to any one of claims 1 to 3, characterized in that The N common fields include the completed goods identifier, production steps, production stage, and control condition type.

8. The method according to any one of claims 1 to 3, characterized in that, Perform data aggregation on the initial ECS data set, including: Summarize the data in the initial ECS data set by department, and then summarize the data of each department by machine.

9. An ECS data processing device, characterized in that, Including: An acquisition unit for acquiring the initial ECS data set of the machine in the MES; A compilation unit for using the rule identifier in the initial ECS data set as an index to perform data aggregation on the initial ECS data set, extract N common fields from the initial ECS data set, and generate a description field for describing the ECS data by intercepting and splicing the original fields in the initial ECS data set, where N is a positive integer; A creation unit for creating a target ECS data set including the N common fields and the description field after enhancing the language logic of the N common fields and the description field, where the target ECS data set includes the ECS data of each department's machine; A display unit for displaying the visualization chart corresponding to the target ECS data set.

10. The device according to claim 9, wherein, When the visualization chart includes a daily ECS trend chart, the daily ECS trend chart is used to represent the ECS quantity in different regions daily; Before displaying the daily ECS trend chart corresponding to the target ECS data set, the compilation unit is further used to: use the rule identifier in the initial ECS data set as an index, insert the machine and cavity information into the initial ECS data set, and add a region field and a time field to obtain an intermediate ECS data set; When creating the target ECS data set including the N common fields and the description field, the creation unit is specifically used for: Periodically grab information from the intermediate ECS data set to create the target ECS data set, where the target ECS data set includes the N common fields, the description field, the machine field, the cavity information field, the region field, and the time field; When the display unit displays the visualization chart corresponding to the target ECS data set, it is specifically used for: Display the daily ECS trend chart corresponding to the target ECS data set, where the daily ECS trend chart is obtained by summing the data in the target ECS data set by region and by day.

11. The device according to claim 9, characterized in that, When the visualization chart includes a daily ECS variable chart, the daily ECS variable chart is used to represent the change in the ECS quantity daily; Before displaying the daily ECS variable chart corresponding to the target ECS data set, the acquisition unit is further used to: acquire the historical ECS data set; the compilation unit is further used to compare and judge the relevant fields in the initial ECS data set and the historical ECS data set, and mark the data with changes; The creation unit is further used to grab information from the ECS data marked every day to create the target ECS data set, the target ECS data set; When the display unit displays the visualization chart corresponding to the target ECS data set, it is specifically used for: Display the daily ECS variable graph corresponding to the target ECS dataset, where the daily ECS variable graph is obtained by summing the data in the target ECS dataset by region and by day.

12. A computer-readable storage medium storing a computer program therein, characterized in that, When the computer program is executed by a processor, it implements the method according to any one of claims 1 to 8.

13. An electronic device, characterized in that, It includes a memory and a processor, and a program that can run on the processor is stored on the memory. When the program is executed by the processor, the electronic device is caused to implement the method according to any one of claims 1 to 8.

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