Production data management device

Abstract

The application discloses a kind of production data management equipment, the production data management equipment includes: data processing module, the data processing module is configured with the operating environment of distributed data storage system, the data processing module is used to collect multiple wafer manufacturers provided wafer production data, and for multiple wafer manufacturers provided wafer production data is stored in local server with the production data management equipment network connection;Data analysis module, the data analysis module is connected with the data processing module, the data analysis module is used to call at least one wafer manufacturer provided wafer production data, and at least one wafer manufacturer provided wafer production data is analyzed according to multiple dimensions to form visual analysis result.Using the equipment can realize the big data analysis comparison across wafer manufacturer, ensure data storage time and security.

Description

Technical Field

[0001] This invention relates to the field of production data management equipment technology, and in particular to a production data management device. Background Technology

[0002] In related technologies, the analysis of wafer manufacturers' production data mainly relies on the production data uploaded by each wafer manufacturer's MES (Manufacturing Execution System) and FTP (File Transfer Protocol). Moreover, it can only perform data analysis on a single wafer manufacturer based on the production data provided by the wafer manufacturer, and cannot perform cross-wafer manufacturer big data analysis and comparison, thus failing to ensure data storage time and security. Summary of the Invention

[0003] This invention aims to at least solve one of the technical problems existing in the prior art. Therefore, one object of this invention is to provide a production data management device that enables big data analysis and comparison across wafer manufacturers, ensuring data storage time and security.

[0004] To address the aforementioned problems, a first aspect of the present invention provides a production data management device, comprising: a data processing module configured with a distributed data storage system operating environment, the data processing module being used to collect wafer production data provided by multiple wafer manufacturers and to store the wafer production data provided by the multiple wafer manufacturers in a local server network-connected to the production data management device; and a data analysis module connected to the data processing module, the data analysis module being used to retrieve wafer production data provided by at least one wafer manufacturer and to perform multi-dimensional analysis based on the wafer production data provided by at least one wafer manufacturer to generate visualized analysis results.

[0005] According to an embodiment of the present invention, the production data management device, based on the structure of a data processing module and a data analysis module, retrieves wafer production data provided by at least one wafer manufacturer from the data processing module through the data analysis module for multi-dimensional analysis. Thus, the production data management device of this application performs multi-dimensional analysis on wafer production data provided by different wafer manufacturers through the data analysis module, realizing cross-wafer manufacturer big data analysis and comparison, and ensuring data storage time and security.

[0006] In some embodiments, the wafer production data includes at least one or more of the production data test data selected from CP yield test data, bump manufacturing yield test data, chip packaging yield test data, FT yield test data, and SLT yield test data.

[0007] In some embodiments, the data analysis module is further configured to establish a data association model for performance benchmarking, and retrieve wafer production data provided by at least one wafer manufacturer based on the data association model, wherein the data involved in performance benchmarking includes at least one or more of the wafer manufacturer, wafer production batch number, and wafer production line number.

[0008] In some embodiments, the multi-dimensional dimension includes at least one or more of the following dimensions: wafer manufacturer dimension, wafer production model dimension, wafer production time dimension, wafer production efficiency dimension, wafer yield dimension, and low yield cause dimension.

[0009] In some embodiments, the device further includes a process automation module that supports users in submitting online requests for changes to wafer manufacturing issues.

[0010] In some embodiments, the device further includes a daily management module connected to the data analysis module, the daily management module being used to manage the performance evaluation results of each wafer manufacturer based on the visualization analysis results.

[0011] In some embodiments, the routine management module is also used to manage changes in wafer manufacturers.

[0012] In some embodiments, the device further includes an early warning module, which is used to remind users who upload data to upload data based on the data upload time.

[0013] In some embodiments, the early warning module is connected to the data processing module, and the early warning module is further used to perform yield identification on the wafer production data of each wafer manufacturer to determine the yield identification result, and to issue a yield early warning prompt when the yield identification result is abnormal.

[0014] In some embodiments, the device further includes a dashboard module connected to the data analysis module. The dashboard module is used to determine the wafer evaluation results for each wafer manufacturer based on the visualization analysis results and to display the wafer evaluation results. The wafer evaluation results include at least one or more of the following: wafer production quantity, detained batch processing progress, MRB review results, and CAR improvement report for each wafer manufacturer.

[0015] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0016] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a structural block diagram of a production data management device according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the operation of a production data management device according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the visualization analysis results according to an embodiment of the present invention; Figure 4 This is a structural block diagram of a production data management device according to another embodiment of the present invention; Figure 5 This is a structural block diagram of a production data management device according to another embodiment of the present invention; Figure 6 This is a structural block diagram of a production data management device according to another embodiment of the present invention; Figure 7 This is a structural block diagram of a production data management device according to another embodiment of the present invention.

[0017] Figure label: 10 production data management devices; Data processing module 1; Data analysis module 2; Process automation module 3; Daily management module 4; Early warning module 5; Kanban module 6. Detailed Implementation

[0018] The embodiments of the present invention are described in detail below. The embodiments described with reference to the accompanying drawings are exemplary. The embodiments of the present invention are described in detail below.

[0019] Currently, fabless companies manage the production and operations of wafer foundries primarily through daily meetings, report presentations, and email communication. While this management approach is efficient and fast, it also has significant drawbacks. For example, meeting times cannot be guaranteed, report content is entirely dependent on the wafer foundry, and report content is subject to delays. Furthermore, email communication content is untraceable.

[0020] To address the aforementioned issues, a first aspect of this invention provides a production data management device that enables cross-wafer manufacturer big data analysis and comparison, ensuring data storage time and security.

[0021] The following is for reference. Figure 1 The production data management device 10 according to an embodiment of the present invention is described as follows: Figure 1 As shown, the production data management device 10 includes: a data processing module 1 and a data analysis module 2.

[0022] The data processing module 1 is configured with a distributed data storage system environment. This system can be HDFS (Hadoop Distributed File System) or cloud storage to support the storage and rapid access of massive amounts of data. The distributed data storage system consists of multiple basic databases. The data processing module 1 collects wafer production data from multiple wafer manufacturers and stores this data on a local server connected to the production data management device 10 via a network. This involves periodically backing up the wafer production data from multiple manufacturers to the local server to ensure data security and integrity. The data analysis module 2 is connected to the data processing module 1. It retrieves wafer production data from at least one wafer manufacturer and performs multi-dimensional analysis based on this data to generate visualized analysis results. The production data management device 10 can display these visualized analysis results. Data interaction between multiple wafer manufacturers and the production data management device 10 is mediated by Alibaba Cloud, ensuring the security of the production data management device 10. Figure 2 As shown, the wafer manufacturer logs into the system to upload wafer production data and backs up the wafer production data to the local server via Alibaba Cloud. The user of the production data management device 10 can obtain the wafer production data through the local server. In addition, wafer production data can also be uploaded via FPT / Mail in this application.

[0023] Specifically, to address the aforementioned issues, the production data management device 10 in this application uses a data analysis module 2 to perform multi-dimensional analysis on wafer production data provided by at least one wafer manufacturer to generate visualized analysis results. This enables cross-wafer manufacturer big data analysis and comparison, ensuring data storage time and security. In other words, when the data processing module 1 performs cross-wafer manufacturer big data analysis and comparison, it retrieves wafer production data from multiple wafer manufacturers from its local server and stores this data in a distributed data storage system. This allows the data analysis module 2 to retrieve wafer production data from at least one wafer manufacturer from the data processing module 1 and perform multi-dimensional analysis based on this data to generate visualized analysis results, such as yield analysis based on the wafer production data provided by at least one wafer manufacturer. Therefore, the production data management device 10 in this application performs multi-dimensional analysis on wafer production data provided by at least one wafer manufacturer through the data analysis module 2, realizes big data analysis and comparison across wafer manufacturers, thereby optimizing resource allocation, improving overall operational efficiency and product quality, ensuring data storage time and security, and realizing the assessment of synergistic effects among wafer manufacturers.

[0024] According to an embodiment of the present invention, the production data management device 10, based on the structure of the data processing module 1 and the data analysis module 2, retrieves wafer production data provided by at least one wafer manufacturer from the data processing module 1 through the data analysis module 2 for multi-dimensional analysis. Thus, the production data management device 10 of this application performs multi-dimensional analysis on wafer production data provided by different wafer manufacturers through the data analysis module 2, realizes cross-wafer manufacturer big data analysis and comparison, and ensures data storage time and security.

[0025] In some embodiments, wafer production data includes at least one or more of the following test data: CP (Chip Probe) yield test data, bump manufacturing yield test data, assembly yield test data, FT (Final Test) yield test data, and SLT (System Level Test) yield test data.

[0026] In the chip manufacturing process, CP (Content Delivery) testing occurs between wafer fabrication and packaging. It tests each bare chip on the entire wafer, typically including voltage, current, timing, and functional verification. Therefore, CP yield test data should at least include yield data for voltage, current, timing, and functionality. Chip packaging yield test data refers to the yield during chip packaging and assembly, including wire bonding and flip chip yield. FT (Firmware Testing) is the final testing stage after chip packaging. FT testing includes environmental compatibility testing, aging testing, reliability testing, parameter testing, and functional testing. Therefore, CP yield test data includes data from environmental compatibility testing, aging testing, reliability testing, parameter testing, and functional testing. SLT (System-in-Package) testing checks whether the functions of each module of the chip are normal. It primarily tests system-in-package (SiP) chips, so SLT yield test data can reflect whether the functions of each module are normal.

[0027] Therefore, this application uses multi-dimensional analysis of wafer production data throughout the entire wafer manufacturing process, which improves the accuracy of data analysis for different wafer manufacturers and enhances the completeness of wafer production data.

[0028] In some embodiments, the data analysis module 2 is further configured to establish a data association model for performance benchmarking, and retrieve wafer production data provided by at least one wafer manufacturer based on the data association model, wherein the data involved in performance benchmarking includes at least one or more of the wafer manufacturer, wafer production batch number and wafer production line number.

[0029] Specifically, because the granularity of wafer production data varies among different wafer manufacturers, when comparing wafer production data across different wafer manufacturers, it is necessary to retrieve wafer production data from at least one wafer manufacturer through the data association model of data analysis module 2. For example, the wafer production data of each wafer manufacturer can be directly compared across different wafer manufacturers; alternatively, wafer production data from different wafer manufacturers can be integrated based on wafer production batch numbers to compare wafer production data across different wafer manufacturers; or, wafer production data from different wafer manufacturers can be integrated based on wafer production line numbers to compare wafer production data across different wafer manufacturers according to wafer production line numbers. Therefore, data analysis module 2 in this application achieves cross-factory comparison of wafer production data by establishing a data association model for performance benchmarking, thereby accurately reflecting the production differences between different wafer manufacturers.

[0030] Furthermore, Nudz can be used to integrate wafer production data from different wafer manufacturers. Data warehouse technology, such as Hive, Impala, or Snowflake, can also be used to centrally integrate data from different factories and build a unified data view. Data analysis module 2 can also retrieve wafer production data from different stations for analysis.

[0031] In some embodiments, the multi-dimensional dimension includes at least one or more of the following dimensions: wafer manufacturer dimension, wafer production model dimension, wafer production time dimension, wafer production efficiency dimension, wafer yield dimension, and low yield cause dimension. That is, the multi-dimensional dimension may include the wafer manufacturer dimension, wafer production model dimension, wafer production time dimension, wafer production efficiency dimension, wafer yield dimension, and low yield cause dimension, respectively; or, the multi-dimensional dimension may include at least the wafer manufacturer dimension and wafer production model dimension; or, the multi-dimensional dimension may include at least the wafer manufacturer dimension, wafer production model dimension, and wafer production time dimension; or, the multi-dimensional dimension may include at least the wafer manufacturer dimension, wafer production model dimension, wafer production time dimension, wafer production efficiency dimension, wafer yield dimension, and low yield cause dimension. It should be noted that multiple dimensions, including the wafer manufacturer dimension, wafer production model dimension, wafer production time dimension, wafer production efficiency dimension, wafer yield dimension, and low yield cause dimension, can be arbitrarily combined.

[0032] Specifically, multi-dimensional analysis is performed on wafer production data provided by at least one wafer manufacturer to generate visualized analysis results. That is, wafer production data from at least one wafer manufacturer is analyzed to create visualized analysis results; wafer production data from at least one wafer manufacturer is analyzed according to the wafer production model dimension to create visualized analysis results for wafer production data of the same manufacturer at that dimension, allowing for a direct understanding of the changes in wafer production data from different wafer manufacturers; or, wafer production data from at least one wafer manufacturer is analyzed according to the wafer production time dimension to create visualized analysis results for wafer production data of the same manufacturer at that dimension. The wafer production time dimension can include monthly, quarterly, or other timeframes to allow for a direct understanding of the changing trends of wafer production data from different wafer manufacturers over time. Figure 3 The chart shows the monthly yield variation of wafer production model S4D, and Table 1 shows the monthly yield variation of S4D calculated based on good and scrap products. Alternatively, wafer production data provided by at least one wafer manufacturer can be analyzed according to the wafer production efficiency dimension to form a visualized analysis result of wafer production data of wafer manufacturers in the wafer production efficiency dimension. This allows for understanding the wafer production efficiency of different wafer manufacturers, and adjustments can be made to the wafer manufacturers' production based on the wafer production efficiency of different wafer manufacturers to improve their wafer production efficiency. Alternatively, analyze wafer production data provided by at least one wafer manufacturer according to the wafer yield dimension to form a visualized analysis result of wafer production data of wafer manufacturers in the wafer yield dimension, so as to intuitively understand the yield of wafers produced by different wafer manufacturers; or, based on the wafer production data provided by at least one wafer manufacturer, analyze the reasons for low yield of wafers produced by at least one wafer manufacturer, that is, analyze the reasons for low yield of wafers produced by at least one wafer manufacturer, so that each wafer manufacturer can improve its production measures for the reasons for low yield of its wafers. Therefore, this application analyzes wafer production data provided by at least one wafer manufacturer through one or more of multiple dimensions, enabling cross-factory comparison of wafer production data from different wafer manufacturers across multiple dimensions, which can effectively save manpower, material resources, and time costs for cross-factory comparison.

[0033]

[0034] Table 1 In this embodiment, a big data computing framework is used to perform large-scale parallel computing on wafer production efficiency and wafer yield. The big data computing framework can be Spark or Flink to achieve efficient comparison and correlation analysis of data across factories.

[0035] In some embodiments, such as Figure 4 As shown, the production data management device 10 also includes a process automation module 3.

[0036] Among them, the process automation module 3 supports users to apply for changes to wafer production issues online.

[0037] Specifically, the process automation module 3 is used to process changes to wafer manufacturing issues. These changes include the raw materials, methods, equipment, and environment used in wafer manufacturing. Staff initiate the change request process online through the production data management device 10. Managers confirm the request online through the production data management device 10, organize internal approvals, and finally send the factory review results. This ensures that change request information is shared, significantly improves production efficiency, and ensures that change requests are processed in a timely and accurate manner.

[0038] For example, in order to ensure stable wafer delivery and supply, a change application process can be applied for through the process automation module 3, that is, to enter the substrate origin application change process.

[0039] In addition, the application change process interface requires you to fill in the application change identifier, application change date, change purpose, change type, change description, impact of the change, supplementary description of the change type, and supplementary description of the change purpose.

[0040] In this embodiment, the process automation module 3 also includes an anomaly feedback module, which is used to calculate the anomaly results based on the wafer production data and provide feedback, so as to realize the function of online feedback of factory anomalies and online processing notification to the factory for handling.

[0041] In this embodiment, the process automation module 3 also includes a TPRF (test program release form) module, which is used to plan the handling of abnormalities in the wafer foundry and to connect with the wafer foundry to realize the online application, approval and automatic sending of test program releases after completion.

[0042] In some embodiments, such as Figure 5 As shown, the production data management device 10 also includes: a daily management module 4.

[0043] Among them, the daily management module 4 is connected to the data analysis module 2. The daily management module 4 is used to manage the assessment results of each wafer manufacturer based on the visualization analysis results. The assessment results include quarterly assessments, annual assessments, wafer production efficiency assessments, and wafer yield assessments, etc., to realize the full-process visualization management function of wafer manufacturers' factories and identify best practices and improvement opportunities.

[0044] In this embodiment, the daily management module 4 also includes a dedicated personnel and equipment module, which is used to manage anomalies caused by changes in personnel and equipment at the wafer manufacturer's factory.

[0045] In some embodiments, the daily management module 4 is also used to manage changes in wafer manufacturers. For example, the daily management module 4 manages the daily management of wafer manufacturer factories, including managing the dedicated personnel / equipment of wafer manufacturer factories, or it is used to change wafer manufacturers and introduce new wafer manufacturers.

[0046] In some embodiments, such as Figure 6 As shown, the production data management device 10 also includes an early warning module 4.

[0047] Among them, the early warning module 4 is used to remind users who upload data based on the data upload time.

[0048] Specifically, in the existing technology, wafer manufacturers send production data via email, which results in poor initiative in storage and uploading, and a lack of effective channels for supervision and traceability. To solve this problem, this application sets up an early warning module 4 to remind wafer manufacturers to upload production data. In other words, the early warning module 4 sends the data upload time to the data upload user, which is the wafer manufacturer, to remind the data upload user to upload production data, thereby ensuring the timeliness and accuracy of data upload and improving work efficiency.

[0049] In some embodiments, the early warning module 4 is connected to the data processing module 1. The early warning module 4 is also used to perform yield identification on the wafer production data of each wafer manufacturer to determine the yield identification result, and to issue a yield early warning when the yield identification result is abnormal. That is, the early warning module 4 obtains the production data of each wafer manufacturer from the data processing module 1, and performs yield identification on the wafer production data of each wafer manufacturer to determine the yield identification result. That is, it compares the yield of each wafer manufacturer with a set threshold to obtain the yield identification result of each wafer manufacturer. When the yield identification result is abnormal, a yield early warning is issued. That is, if the yield of the corresponding wafer manufacturer in the yield identification result is lower than the set threshold, the yield identification result is abnormal. Then, the early warning module 4 issues an early warning for the yield identification result and assigns it to the appropriate personnel for processing according to the warning.

[0050] In some embodiments, such as Figure 7 As shown, the production data management device 10 also includes a Kanban module 6.

[0051] The Kanban module 6 is connected to the data analysis module 2. The Kanban module 6 is used to determine the wafer evaluation results of each wafer manufacturer based on the visualization analysis results and to display the wafer evaluation results. The wafer evaluation results include at least one or more of the following: wafer production quantity, processing progress of detained batches, MRB (Material Review Board) review results, and CAR (Corrective Action Requirement) improvement report for each wafer manufacturer.

[0052] Specifically, Kanban module 6 obtains and displays the wafer production quantity of each wafer manufacturer through data analysis module 2; Kanban module 6 obtains and displays the yield of each wafer manufacturer through data analysis module 2; Kanban module 6 obtains and displays the processing progress of detained batches of each wafer manufacturer through data analysis module 2, where the processing progress of detained batches can be displayed as a curve by date; Kanban module 6 obtains and displays the MRB review results of each wafer manufacturer through data analysis module 2, where the MRB review results are the results of a detailed review and evaluation of non-conforming products in the wafers; Kanban module 6 obtains and displays the CAR improvement report of each wafer manufacturer through data analysis module 2, where the CAR improvement report includes a report recording and analyzing various errors and problems in the wafer production process. In addition, Kanban module 6 can also display pie charts of orders for each wafer manufacturer and orders for each site, as well as pie charts of material audits for each wafer manufacturer and bar charts of corrective and preventive action summaries for each wafer manufacturer.

[0053] Therefore, this application displays wafer evaluation results through Kanban module 6 to provide an intuitive understanding of the wafer evaluation results of each wafer manufacturer, enabling managers to identify key areas for improvement.

[0054] In this embodiment, the Kanban module 6 displays the wafer evaluation results of each wafer manufacturer in the form of charts, dashboards, etc., which makes it easier for enterprise leaders and managers to intuitively understand the differences and commonalities between various factories, and assists in strategic decision-making and continuous improvement.

[0055] In this embodiment, the Kanban module 6 uses BI (Business Intelligence) tools to monitor and issue early warnings for the wafer evaluation results of each wafer manufacturer in real time, thereby ensuring that the production and operation status across the factory can be responded to and adjusted in a timely manner.

[0056] In this embodiment, after data processing module 1 collects wafer production data from the automated equipment of various wafer manufacturers, including big data on production, operations, quality, and energy consumption, it performs data cleaning to remove duplicate data, fill in missing values, and correct errors, ensuring the quality and consistency of data from each factory. Different wafer manufacturers may use different data formats and standards, so data standardization is also required to convert and unify the wafer production data from multiple manufacturers, facilitating subsequent analysis and comparison.

[0057] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example.

[0058] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A production data management device, characterized in that, include: The data processing module is configured with a distributed data storage system operating environment. The data processing module is used to collect wafer production data provided by multiple wafer manufacturers and to store the wafer production data provided by multiple wafer manufacturers in a local server connected to the production data management device network. The data analysis module is connected to the data processing module. The data analysis module is used to retrieve wafer production data provided by at least one wafer manufacturer and perform multi-dimensional analysis based on the wafer production data provided by at least one wafer manufacturer to form a visual analysis result.

2. The production data management equipment according to claim 1, characterized in that, The wafer production data includes at least one or more of the following test data: CP yield test data, bump manufacturing yield test data, chip packaging yield test data, FT yield test data, and SLT yield test data.

3. The production data management equipment according to claim 1, characterized in that, The data analysis module is also used to establish a data association model for performance benchmarking, and to retrieve wafer production data provided by at least one wafer manufacturer based on the data association model. The data involved in performance benchmarking includes at least one or more of the wafer manufacturer, wafer production batch number, and wafer production line number.

4. The production data management equipment according to claim 1, characterized in that, The multi-dimensional dimensions include at least one or more of the following: wafer manufacturer dimension, wafer production model dimension, wafer production time dimension, wafer production efficiency dimension, wafer yield dimension, and reasons for low yield dimension.

5. The production data management equipment according to any one of claims 1-4, characterized in that, The device also includes: The process automation module supports users in submitting online requests for changes related to wafer production.

6. The production data management equipment according to claim 5, characterized in that, The device also includes: A daily management module, which is connected to the data analysis module, is used to manage the performance evaluation results of each wafer manufacturer based on the visualization analysis results.

7. The production data management equipment according to claim 6, characterized in that, The daily management module is also used to manage changes in wafer manufacturers.

8. The production data management equipment according to claim 7, characterized in that, The device also includes: The early warning module is used to remind users who upload data based on the data upload time.

9. The production data management equipment according to claim 8, characterized in that, The early warning module is connected to the data processing module. The early warning module is also used to identify the yield of each wafer manufacturer's wafer production data to determine the yield identification result, and to issue a yield early warning prompt when the yield identification result is abnormal.

10. The production data management equipment according to claim 9, characterized in that, The device also includes: A dashboard module, connected to the data analysis module, is used to determine the wafer evaluation results for each wafer manufacturer based on the visualization analysis results, and to display the wafer evaluation results. The wafer evaluation results include at least one or more of the following: wafer production quantity, detained batch processing progress, MRB review results, and CAR improvement report for each wafer manufacturer.

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