Data management method, device, equipment, medium and program of semiconductor product

Abstract

The invention discloses a data management method and device for a semiconductor product, equipment, a medium and a program. The method comprises the steps of summarizing original application data related to a semiconductor product in at least one application scene into a non-relational database, and performing data cleaning on the original application data to obtain effective application data; according to a set data aggregation dimension, performing data aggregation processing on the effective application data to obtain a data aggregation result under the data aggregation dimension; and fusing the data aggregation results according to a set data fusion strategy to obtain the key application data of the semiconductor product after the format is unified. According to the embodiment of the invention, by adopting the technical scheme, the problem that different data sources and data formats of the multi-source data cannot be effectively integrated and analyzed can be solved, and the management effectiveness of the multi-source data of the semiconductor product is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, in particular to a method, device, equipment, medium and program for data management of semiconductor products. Background Art

[0002] Semiconductor product lifecycle data refers to all the data generated throughout the entire lifecycle of a semiconductor product, from initial design through raw material procurement, manufacturing, testing, packaging, and final delivery to the customer. Collecting this data from various stages of the process into a data center for unified management enables comprehensive tracking and analysis of semiconductor products, promptly identifying production process issues, optimizing process flows, and improving product quality and efficiency.

[0003] In practice, semiconductor product lifecycle data often comes from diverse sources in varying formats and characteristics. This data is widely distributed and may originate from various production equipment, test systems, and management systems, making it difficult to integrate and analyze. Existing semiconductor lifecycle data management solutions are unable to effectively integrate and analyze this multi-source data in varying formats, hindering comprehensive tracking and analysis of semiconductor products. Summary of the Invention

[0004] The present invention provides a data management method, device, equipment, medium and program for semiconductor products to solve the problem that the existing technology cannot effectively integrate and analyze different data sources and data formats of multi-source data, thereby improving the effectiveness of managing multi-source data of semiconductor products.

[0005] According to a first aspect of the present invention, there is provided a data management method for a semiconductor product, comprising:

[0006] Aggregating raw application data related to semiconductor products in at least one application scenario into a non-relational database, and performing data cleaning on each of the raw application data to obtain valid application data;

[0007] Performing data aggregation processing on the valid application data according to the set data aggregation dimension to obtain a data aggregation result under the data aggregation dimension;

[0008] The data aggregation results are fused according to the set data fusion strategy to obtain the key application data of the semiconductor product in a unified format.

[0009] According to a second aspect of the present invention, there is provided a semiconductor data management device comprising:

[0010] A warehousing module is used to aggregate the original application data involved in at least one application scenario of semiconductor products into a non-relational database, and perform data cleaning on each of the original application data to obtain valid application data;

[0011] An aggregation module is used to perform data aggregation processing on the valid application data according to a set data aggregation dimension to obtain a data aggregation result under the data aggregation dimension;

[0012] The fusion module is used to fuse the data aggregation results according to the set data fusion strategy to obtain the key application data of the semiconductor product in a unified format.

[0013] According to a third aspect of the present invention, there is provided an electronic device, comprising:

[0014] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the data management method for semiconductor products described in any embodiment of the present invention.

[0015] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the data management method for a semiconductor product according to any embodiment of the present invention when executed.

[0016] According to a fifth aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the computer program implements the data management method for a semiconductor product according to any embodiment of the present invention.

[0017] The technical solution of the embodiment of the present invention, first, by aggregating the original application data involved in semiconductor products in at least one application scenario into a non-relational database, and performing data cleaning on each of the original application data, to obtain effective application data, it is possible to build a unified and flexible multi-source data storage architecture, and provide a stable, reliable and unified data source for subsequent data processing. Secondly, according to the set data aggregation dimension, data aggregation processing is performed on the effective application data to obtain the data aggregation result under the data aggregation dimension, and data from different sources and in different formats are converted into intermediate results under the required data aggregation dimension, thereby achieving a unified data caliber. Finally, according to the set data fusion strategy, the data aggregation results are fused to obtain the key application data of the semiconductor product in a unified format, which can eliminate data conflicts, unify the key information of semiconductor data, and provide comprehensive and accurate data support for production monitoring, quality assessment and process optimization in the semiconductor industry. The present invention solves the problem that the existing technology cannot effectively integrate and analyze different data sources and data formats of multi-source data, and improves the effectiveness of the data management process.

[0018] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 This is a flow chart of a data management method for a semiconductor product provided in accordance with a first embodiment of the present invention;

[0021] Figure 2 This is a flow chart of a data management method for a semiconductor product provided in accordance with a second embodiment of the present invention;

[0022] Figure 3 This is a schematic structural diagram of a data management device for a semiconductor product provided according to a third embodiment of the present invention;

[0023] Figure 4 The figure is a schematic structural diagram of an electronic device for implementing the data management method for semiconductor products according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] Example 1

[0027] Figure 1 A flowchart of a semiconductor product data management method is provided for the first embodiment of the present invention. This embodiment is applicable to the management of data throughout the entire life cycle of a semiconductor product. The method can be executed by a semiconductor product data management device, which can be implemented in the form of hardware and / or software. The semiconductor product data management device can be configured in a semiconductor product manufacturing process management system / platform. Figure 1 As shown, the method includes:

[0028] S101. Aggregate original application data related to a semiconductor product in at least one application scenario into a non-relational database, and perform data cleaning on each of the original application data to obtain valid application data.

[0029] It's important to note that the entire lifecycle of a semiconductor product encompasses multiple, closely interrelated stages, including wafer fabrication, chip etching, wafer-level testing, chip-level testing, system-level testing, and chip shipment. Each stage generates a vast amount of complex data, which serves as a critical record of the semiconductor production process and is therefore extremely valuable. For example, in wafer fabrication, wafer production data reflects process parameters, equipment operating status, and other information during the manufacturing process. In chip-level testing, chip test data includes chip performance metrics and defect status. Data from these various stages is a crucial basis for production optimization, quality control, and yield improvement within the semiconductor industry. However, data generated in different application scenarios may utilize different encoding rules and timestamp formats, making it difficult to directly correlate and analyze data from the entire semiconductor product lifecycle.

[0030] Among them, the application scenario can be the production and manufacturing links involved in the entire life cycle of a semiconductor product, and can represent different data sources. For example, the application scenario can be the wafer manufacturing, chip etching, wafer-level testing, chip-level testing, system-level testing and other links of a semiconductor product. The original application data can be the original data generated in the corresponding application scenario. For example, the original application data can be the production data of the wafer corresponding to the wafer manufacturing link, or the test data of the chip corresponding to the chip-level testing link. In addition, the format of the original application data can be CSV, JSON, database table, Kafka message stream and other formats. This application does not limit the format of the original application data.

[0031] It should be further explained that the different raw application data generated by semiconductor products in different application scenarios can be recorded by the corresponding systems in the corresponding application scenarios. For example, wafer production data generated during wafer manufacturing can be recorded by the Manufacturing Execution System (MES); chip test data generated during chip-level testing can be recorded by the test system.

[0032] Non-relational databases can be used to store large-scale, distributed, and diversely structured raw application data. For example, a non-relational database might be ClickHouse or another existing non-relational OLAP database. Effective application data can be obtained by performing preliminary data cleansing and standardization on the raw application data and stored in the detail data layer (DWD layer) of the non-relational database.

[0033] For example, raw application data corresponding to the application scenario can be obtained from at least one system (e.g., a test system, MES system, or WIP system). This data from various sources can be aggregated and uploaded to a non-relational database such as Click House, serving as a unified data source for subsequent data processing. Furthermore, once all raw application data has been uploaded to the non-relational database, data cleansing can be achieved by performing operations such as deduplication, format conversion, null-filling, and time standardization on the raw application data, ensuring data consistency and uniqueness and ultimately generating valid application data.

[0034] Furthermore, after obtaining the valid application data, the valid application data can be written into the detailed data layer of the non-relational database, and the meta information of the original application data can be retained to facilitate tracing and auditing of the original application data.

[0035] S102: Perform data aggregation processing on the valid application data according to the set data aggregation dimension to obtain a data aggregation result under the data aggregation dimension.

[0036] The data aggregation dimensions may be pre-set based on the user's aggregation requirements, for example, dimensions such as customer, factory, equipment, and product of semiconductor products.

[0037] Data aggregation results can be statistical results obtained by aggregating partial data information of corresponding valid application data under the corresponding data aggregation dimension. They can include multi-source data statistics, time information, batch information, and quality indicators. For example, the same data source identifier in each valid application data under the corresponding data aggregation dimension can be aggregated to obtain the corresponding number of occurrences of each data source.

[0038] For example, after detecting that data cleaning is complete, valid application data can be obtained from the detailed data layer (DWD layer) of the non-relational database. Then, in the dimension data layer of the non-relational database, the valid application data corresponding to the set data dimension is determined, and part of the data information in the valid application data corresponding to the set data dimension is aggregated to obtain the required data aggregation result. For example, taking the customer as the set data aggregation dimension, based on the basic attribute information of semiconductor products in the customer dimension, the valid application data corresponding to different customer information can be determined, and part of the information of the valid application data under each different customer information (such as the source identifier or the wafer ID involved) can be aggregated to generate the data aggregation result under the customer dimension.

[0039] S103 , fusing the data aggregation results according to a set data fusion strategy to obtain key application data of the semiconductor product in a unified format.

[0040] Data fusion strategies can be used to combine data aggregation results from different data aggregation dimensions. Key application data can include globally unique identifiers for semiconductor products, as well as statistics from multiple sources throughout the semiconductor product lifecycle, including time information, batch information, and quality indicators.

[0041] It should be noted that a semiconductor product's globally unique identifier can be determined by combining basic attribute information of the semiconductor product within the data aggregation dimension. For example, taking the data aggregation dimensions of customer, factory, and equipment as an example, a semiconductor product's globally unique identifier can be formed by combining the semiconductor product's customer information, the manufacturing plant's attribute information (such as the factory address), and the attribute information of the testing equipment.

[0042] For example, after detecting that the data aggregation processing is completed, the data aggregation results under different data aggregation dimensions can be integrated through a pre-set data fusion strategy to generate a globally unique identifier for the semiconductor product, and the summarized relevant multi-source data statistics, time information, batch information and quality indicators can be associated with the globally unique identifier of the semiconductor product as key application data after the format of the semiconductor product is unified.

[0043] The technical solution of the embodiment of the present invention, first, by aggregating the original application data involved in semiconductor products in at least one application scenario into a non-relational database, and performing data cleaning on each of the original application data, to obtain effective application data, it is possible to build a unified and flexible multi-source data storage architecture, and provide a stable, reliable and unified data source for subsequent data processing. Secondly, according to the set data aggregation dimension, data aggregation processing is performed on the effective application data to obtain the data aggregation result under the data aggregation dimension, and data from different sources and in different formats are converted into intermediate results under the required data aggregation dimension, thereby achieving a unified data caliber. Finally, according to the set data fusion strategy, the data aggregation results are fused to obtain the key application data of the semiconductor product in a unified format, which can eliminate data conflicts, unify the relevant information of semiconductor data, and provide comprehensive and accurate data support for production monitoring, quality assessment and process optimization in the semiconductor industry. The present invention solves the problem that the existing technology cannot effectively integrate and analyze different data sources and data formats of multi-source data, and improves the effectiveness of the data management process.

[0044] Based on the above embodiment, the present invention further provides an optional embodiment, which can further optimize the process of aggregating original application data in step S101 of the above embodiment, and may include:

[0045] Obtaining original application data in at least one application scenario;

[0046] Parsing the original application data through a preset adapter to determine the source identifier, update method, and original dimension structure key information of the original application data;

[0047] The original application data is classified according to the data key information, and the classified original application data is aggregated into the non-relational database.

[0048] Adapters can include relational databases (such as MySQL and Oracle), NoSQL databases (such as MongoDB and Redis), and file systems (such as HDFS and local disks). Key data information can include the source identifier, update method, and original dimensions of the original application data. The source identifier identifies the source of the original application data. The update method can be real-time write or batch import. Original dimensions can include the batch, wafer, and chip levels of the semiconductor products involved in the original application data.

[0049] Exemplarily, during the data collection phase, the present invention can access multiple data sources through adapters to obtain original application data. The adapter can be a relational database (such as MySQL, Oracle), a NoSQL database (such as MongoDB, Redis), a file system (such as HDFS, a local disk), etc., which can be compatible with the data formats of multiple data sources, ensuring that data with different levels of structure can be correctly imported, and parsing the source identifier, update method, and original dimension corresponding to each original application data. Furthermore, the original application data from different sources can be classified and stored according to their corresponding source identifiers (such as the `data_source` field), update methods (such as real-time writing or batch import), and original dimensions (such as wafer level, batch level, Die level), and uploaded to non-relational OLAP databases such as ClickHouse.

[0050] It should be noted that before the original application data is uploaded to the non-relational database, the adapter can be used to automatically identify and repair abnormal data in the original application data during the data access process. For example, operations such as null value filling, field type conversion, and time standardization can be performed to ensure the consistency and integrity of the original application data before it is entered into the database.

[0051] The benefit of this setup is that it can acquire raw application data from multiple sources, including test systems, MES systems, WIP systems, and wafer map configurations, ensuring that raw application data of varying degrees of structure can be correctly parsed and imported. Furthermore, during the raw application data access process, it automatically identifies and corrects abnormal data to ensure data consistency and integrity.

[0052] Based on the above embodiment, the present invention further provides an optional embodiment, which can further optimize the data cleaning process in step S101 of the above embodiment and may include:

[0053] After receiving the first message instruction sent through the message mechanism, the original application data in the non-relational database is deduplicated and cleaned, and data format is processed to obtain intermediate processed data;

[0054] The intermediate processed data are reduced according to the set reduction logic to obtain corresponding valid application data.

[0055] The messaging mechanism can be a Kafka message-driven approach. This allows Kafka to be used as a message middleware, with each layer communicating via specific messages. Each module listens for corresponding messages to trigger tasks. The first message instruction can indicate the completion of the aggregation process for raw application data and can be used to drive data cleansing tasks.

[0056] Exemplarily, after aggregating the original application data into a non-relational database, a DATA_UPLOADED message (first message instruction) can be sent via Kafka to trigger a data cleaning task. Thus, by performing cleaning tasks on the original application data in the non-relational database, operations such as supplementing missing time information, merging and deduplicating multi-valued fields, and standardizing time fields can be performed to obtain intermediate processed data after data cleaning and standardization. On this basis, the present invention also introduces a dynamic map-reduce algorithm to map the field values ​​in the original application data into key-value pairs, such as `map(DATA_SOURCE, value)`, and reduce the mapped data through `arrayReduce('maxMap')` or `arrayReduce('sumMap')` to extract the corresponding maximum value or perform summation processing to adapt to different business scenarios.

[0057] The advantage of this setting is that the dynamic mapping-reduction algorithm not only improves data processing efficiency but also enhances the flexibility and scalability of the system.

[0058] It should be further explained that the message mechanism used in this process of the present invention can be applied to the data management method of the entire semiconductor product. For example, when the original application data is aggregated, the DATA_UPLOADED (first message instruction) message can be automatically sent to trigger the data cleaning task; after the data cleaning task is completed, the DWD_FINISHED message can be sent to start the data aggregation task; after the data aggregation task is completed, the DWS_FINISHED message is sent to drive the execution of the final fusion task.

[0059] The benefit of this setup is that the entire data management process is driven by Kafka messages, achieving loose coupling and event-driven transactions across all layers. The asynchronous communication approach based on message-based middleware not only improves system responsiveness but also enhances fault tolerance and scalability.

[0060] Based on the above embodiment, the present invention further provides an optional embodiment. This optional embodiment can further optimize after obtaining the key application data of the semiconductor product in a unified format in the above embodiment, and can include:

[0061] Converting the key application data into a relational data format and storing it in a relational database, and connecting the relational database to the business system via a network application program interface;

[0062] According to the received data query instruction, the query result is determined from the key application data stored in the relational database and displayed in the business application interface associated with the business system.

[0063] The relational data format may be the data storage format required by a relational database. The network application program interface may be used to transmit key application data to an external business system. The business system may be a front-end system for querying and displaying production data related to semiconductor products. The data query instruction may be a data query instruction sent by a user of the business system and may include the semiconductor product information desired by the user. The query result may be a statistical result corresponding to the semiconductor product information desired in the data query instruction. The business application interface may be a system page in the business system for displaying information.

[0064] For example, after determining the critical application data for the entire life cycle of a semiconductor product in a non-relational database, this data can be converted to a relational data format through a format conversion operation and stored in the relational database. Then, through a pre-defined REST API or JDBC interface, it can be connected to an external BI system, quality analysis system, or production monitoring system, allowing users to understand the desired semiconductor product data based on data query instructions through visualization tools in the corresponding business system.

[0065] Example 2

[0066] Figure 2This is a flowchart of a data management method for a semiconductor product provided in the second embodiment of the present invention. This embodiment can further optimize step S102 in the above-mentioned first embodiment, and can include: obtaining at least one data aggregation dimension pre-set according to the application scenario of the semiconductor product; for each data aggregation dimension, determining the aggregation processing logic involved in the data aggregation dimension, and performing data aggregation processing on the effective application data according to the aggregation processing logic to obtain corresponding data aggregation results. Figure 2 As shown, the method includes:

[0067] S201: Aggregate original application data related to a semiconductor product in at least one application scenario into a non-relational database, and perform data cleaning on each of the original application data to obtain valid application data.

[0068] S202: Obtain at least one data aggregation dimension pre-set according to an application scenario of the semiconductor product.

[0069] It should be noted that the entire life cycle of a semiconductor product involves multiple application scenarios, including wafer manufacturing, chip etching, wafer-level testing, chip-level testing, and system-level testing. While the application scenarios of semiconductor products continue to change over the life cycle of a semiconductor product, the fixed attribute data of the semiconductor product does not change accordingly. For example, the specific customer information for a semiconductor product, determined by the business of the semiconductor product, does not change with changes in the semiconductor product's life cycle or application scenarios. Therefore, the specific customer information for a semiconductor product can be the fixed attribute data of the semiconductor product.

[0070] Optionally, the data aggregation dimension may be a data dimension corresponding to the fixed attribute data of the semiconductor product. The present invention proposes that at least one fixed attribute data that does not change with the lifecycle of the semiconductor product can be determined in advance based on the application scenario of the semiconductor product, and the data dimension of the determined fixed attribute data can be used as the data aggregation dimension.

[0071] For example, specific customer information for semiconductor product delivery can be determined based on the product's business information. Furthermore, in various semiconductor product application scenarios, the corresponding customer information remains constant throughout the product's lifecycle. Therefore, the customer dimension can be set as the data aggregation dimension. Similarly, data aggregation dimensions can include the semiconductor product's factory location, testing area, equipment, batch number, and other data dimensions.

[0072] S203: For each data aggregation dimension, determine the aggregation processing logic involved in the data aggregation dimension, and perform data aggregation processing on the valid application data according to the aggregation processing logic to obtain a corresponding data aggregation result.

[0073] The aggregation processing logic may be a function for aggregating partial data information of valid application data. Optionally, the aggregation processing logic may include: aggregating data from multiple sources, deduplicating and splicing data based on wafer identification, aggregating data based on the earliest upload time of a batch, calculating data based on the latest upload time of a batch, and aggregating data based on a set calculation indicator.

[0074] Exemplarily, when it is determined that the aggregation processing logic involved in the data aggregation dimension is multi-source data aggregation, the present invention can perform data aggregation processing on the effective application data through a preset multi-source data statistical function to obtain the number of occurrences of each data source.

[0075] The preset multi-source data statistical function may be:

[0076] sql

[0077] -- Count whether or how many times each data source appears

[0078] data_source = toString(arrayReduce(

[0079] 'sumMap',

[0080] arrayConcat(

[0081] groupUniqArray(data_source),

[0082] [map('CP(Map)', 0), map('CP(InklessMap)', 0), map('Test RawData', 0), map('MES', 0)])

[0083] It should be noted that groupUniqArray(data_source) can be used to aggregate `data_source` within the same group into an array and ensure uniqueness; arrayConcat(..., [map(...)]) can be used to supplement missing data sources (retaining keys even if they do not appear); arrayReduce('sumMap', ...) can be used to merge all maps and count the number of times each key appears; toString(...) can be used to convert the result to string format for output.

[0084] When it is determined that the aggregation processing logic involved in the data aggregation dimension is deduplication and splicing based on wafer identification, the present invention can perform data aggregation processing on the valid application data through a preset wafer Lot ID deduplication and splicing function to obtain wafer lot ID information.

[0085] The preset wafer Lot ID deduplication and splicing function may be:

[0086] sql

[0087] -- Wafer Lot ID array after deduplication

[0088] wafer_lot_id = arrayStringConcat(

[0089] arrayFilter(x -> x != '',

[0090] arraySort(arrayDistinct(arrayFlatten(groupUniqArray(wafer_lot_id)))) )

[0092] It should be noted that groupUniqArray(wafer_lot_id) can be used to aggregate wafer lot IDs (wafer_lot_id) within the same group into an array and ensure uniqueness.

[0093] arrayFlatten(...) can be used to flatten a nested array into a one-dimensional array; arrayDistinct(...) can be used to remove duplicate values; arraySort(...) can be used to sort an array; arrayFilter(x ->x != '') can be used to filter out empty strings; arrayStringConcat(..., ',') can be used to concatenate the final array into a string with commas.

[0094] When it is determined that the aggregation processing logic involved in the data aggregation dimension is aggregation based on the earliest batch upload time, the present invention can perform data aggregation processing on the valid application data through a preset earliest batch upload time calculation function to obtain the earliest batch upload time information.

[0095] The preset batch upload earliest time calculation function can be:

[0096] `sql

[0097] upload_time_min = LEAST(

[0098] COALESCE(upload_time_mes_min,toDateTime('9999-12-31 23:59:59')),

[0099] COALESCE(upload_time_map_min,toDateTime('9999-12-31 23:59:59')),

[0100] COALESCE(upload_time_rawdata_min,toDateTime('9999-12-31 23:59:59')))

[0101] It should be noted that this function can use COALESCE(...) to obtain the minimum time among the three data sources as the earliest upload time of the batch.

[0102] When it is determined that the aggregation processing logic involved in the data aggregation dimension is based on the latest batch upload time statistics, the present invention can perform data aggregation processing on the valid application data through a preset latest batch upload time calculation function to obtain the latest batch upload time information.

[0103] sql

[0104] end_time_max = GREATEST(

[0105] COALESCE(end_time_mes_max,toDateTime('1970-01-01 00:00:00')),

[0106] COALESCE(end_time_map_max,toDateTime('1970-01-01 00:00:00')),

[0107] COALESCE(end_time_rawdata_max,toDateTime('1970-01-01 00:00:00')) )

[0109] It should be noted that this function can use COALESCE(...) to obtain the maximum time among the three data sources as the latest upload time of the batch.

[0110] When it is determined that the aggregation processing logic involved in the data aggregation dimension is aggregation according to the set calculation indicators, the present invention can perform data aggregation processing on the effective application data through the preset ECID pass number and total number statistical function to obtain quality indicators at each stage.

[0111] The preset ECID passing number and total number statistical function may be:

[0112] sql

[0113] pass_cnt_ecid = SUM(FINAL_PASS_CNT) WHERE DATA_SOURCE ='MES'

[0114] total_cnt_ecid = SUM(FINAL_TOTAL_CNT) WHERE DATA_SOURCE ='MES'

[0115] It should be noted that FINAL_PASS_CNT and FINAL_TOTAL_CNT are fields from the original application data. SUM() can be used to sum the field data that meets the conditions to obtain the final pass count and total number, thereby determining the yield of each chip in the wafer of the semiconductor product and thus determining the corresponding quality indicators.

[0116] S204 , fusing the data aggregation results according to a set data fusion strategy to obtain key application data of the semiconductor product in a unified format.

[0117] Based on the above, the present invention can combine the attribute data fields under each data aggregation dimension for each semiconductor product to obtain a globally unique identifier for the semiconductor product. For example, the globally unique batch identifier can be generated by combining the attribute data fields of the customer, sub-customer, factory, plant area, test area, equipment ID, and batch ID of the semiconductor product. In addition, the number of occurrences of each data source obtained by the aggregation process, wafer batch ID information, earliest batch upload time, latest batch upload time, and instruction indicators of each stage are associated with the globally unique batch identifier to obtain key application data in a unified format.

[0118] The technical solution of the embodiment of the present invention is to pre-set at least one data aggregation dimension according to the application scenario of the semiconductor product, and for each data aggregation dimension, determine the aggregation processing logic involved under the data aggregation dimension, and perform data aggregation processing on the valid application data according to the aggregation processing logic to obtain the corresponding data aggregation result. According to the set data fusion strategy, the data aggregation results are integrated to obtain the key application data of the semiconductor product in a unified format. The multi-source heterogeneous data of the semiconductor product can be converted into the intermediate results required for data analysis, and the key application data is formed with the global unique batch identifier of the semiconductor product as the main data, forming a core view of the entire life cycle of semiconductor data, so that it can not only adapt to the current existing data source structure, but also have good scalability, can dynamically access new data sources, and meet the data integration needs in complex and changeable semiconductor manufacturing scenarios. In addition, the use of a globally unique identifier can also ensure that each batch of data remains consistent and traceable throughout the entire life cycle, avoiding information inconsistency problems caused by manual maintenance.

[0119] Based on the above embodiment, the present invention further provides an optional embodiment, which can further optimize step S204 in the above embodiment and may include:

[0120] Perform field merging processing on each of the data aggregation results according to the set field merging strategy;

[0121] Performing time-axis unified processing on each of the data aggregation results according to a set time unification strategy;

[0122] According to the set data conflict handling strategy, multi-source data conflict handling is performed on each of the data aggregation results;

[0123] The processed data results are determined as key application data of the semiconductor product in a unified format.

[0124] The field merging strategy can be used to merge attribute data fields under each data aggregation dimension. The time unification strategy can be used to unify the earliest and latest batch upload times under each data aggregation dimension.

[0125] Exemplarily, after obtaining the number of occurrences of each data source under each data aggregation dimension, wafer batch ID information, earliest batch upload time, latest batch upload time and instruction indicators of each stage, the attribute data fields under each data aggregation dimension are merged through a field merging strategy to generate a globally unique identifier for the semiconductor product. At the same time, the time axis of the earliest batch upload time and the latest batch upload time can be unified through the set time unification strategy, and the source data conflicts of each data aggregation result in each data aggregation dimension can be automatically identified and resolved through a data conflict handling strategy, so that the number of occurrences of each data source in a unified format, wafer batch ID information, earliest batch upload time, latest batch upload time and instruction indicators of each stage are associated with the globally unique batch identifier to obtain key application data of the semiconductor product in a unified format.

[0126] Example 3

[0127] Figure 3 This is a schematic diagram of the structure of a semiconductor data management device provided by the third embodiment of the present invention. Figure 3 As shown, the device includes:

[0128] The storage module 31 can be used to aggregate the original application data involved in at least one application scenario of the semiconductor product into a non-relational database, and perform data cleaning on each of the original application data to obtain valid application data.

[0129] The aggregation module 32 may be configured to perform data aggregation processing on the valid application data according to a set data aggregation dimension, and obtain a data aggregation result under the data aggregation dimension.

[0130] The fusion module 33 may be configured to fuse the data aggregation results according to a set data fusion strategy to obtain key application data of the semiconductor product in a unified format.

[0131] The technical solution of the embodiment of the present invention, first, by aggregating the original application data involved in semiconductor products in at least one application scenario into a non-relational database, and performing data cleaning on each of the original application data, to obtain effective application data, it is possible to build a unified and flexible multi-source data storage architecture, and provide a stable, reliable and unified data source for subsequent data processing. Secondly, according to the set data aggregation dimension, data aggregation processing is performed on the effective application data to obtain the data aggregation result under the data aggregation dimension, and data from different sources and in different formats are converted into intermediate results under the required data aggregation dimension, thereby achieving a unified data caliber. Finally, according to the set data fusion strategy, the data aggregation results are fused to obtain the key application data of the semiconductor product in a unified format, which can eliminate data conflicts, unify the key information of semiconductor data, and provide comprehensive and accurate data support for production monitoring, quality assessment and process optimization in the semiconductor industry. The present invention solves the problem that the existing technology cannot effectively integrate and analyze different data sources and data formats of multi-source data, and improves the effectiveness of the data management process.

[0132] Optionally, the warehousing module 31 can be specifically used to obtain original application data in at least one application scenario; parse the original application data through a preset adapter to determine the source identification, update method and original dimension of the original application data, and classify the original application data according to the data key information, and aggregate the classified original application data into the non-relational database.

[0133] Optionally, the warehousing module 31 may be further configured to, after receiving a first message instruction sent via a message mechanism, perform deduplication and cleansing and data format processing on the original application data in the non-relational database to obtain intermediate processed data;

[0134] The intermediate processed data are reduced according to the set reduction logic to obtain corresponding valid application data.

[0135] Optionally, the aggregation module 32 may be specifically configured to obtain at least one data aggregation dimension pre-set according to an application scenario of the semiconductor product;

[0136] For each data aggregation dimension, the aggregation processing logic involved in the data aggregation dimension is determined, and data aggregation processing is performed on the valid application data according to the aggregation processing logic to obtain a corresponding data aggregation result.

[0137] Optionally, the data aggregation dimension is a data dimension corresponding to fixed attribute data of the semiconductor product;

[0138] The aggregation processing logic includes: aggregating data from multiple sources, deduplicating and splicing data based on wafer identification, aggregating data based on the earliest time of batch upload, performing statistics based on the latest time of batch upload, and aggregating data based on set calculation indicators.

[0139] Optionally, the fusion module 33 may be specifically configured to perform field merging processing on each of the data aggregation results according to a set field merging strategy;

[0140] Performing time axis unification processing on each of the data aggregation results according to the set time unification strategy; performing multi-source data conflict processing on each of the data aggregation results according to the set data conflict processing strategy;

[0141] The processed data results are determined as key application data of the semiconductor product after the format is unified.

[0142] Optionally, the device may further include: a display module;

[0143] A presentation module may be used to convert the key application data into a relational data format, store the data in a relational database, and connect the relational database to a business system via a network application program interface;

[0144] According to the received data query instruction, the query result is determined from the key application data stored in the relational database and displayed in the business application interface associated with the business system.

[0145] The semiconductor data management device provided by the embodiment of the present invention can execute the semiconductor data management method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0146] Example 4

[0147] Figure 4 A schematic diagram of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0148] like Figure 4As shown, electronic device 40 includes at least one processor 41 and memory, such as read-only memory (ROM) 42 and random access memory (RAM) 43, communicatively connected to at least one processor 41. The memory stores computer programs executable by the at least one processor. Processor 41 can perform various appropriate actions and processes based on the computer programs stored in ROM 42 or loaded from storage unit 48 into RAM 43. RAM 43 can also store various programs and data required for the operation of electronic device 40. Processor 41, ROM 42, and RAM 43 are interconnected via bus 44. An input / output (I / O) interface 45 is also connected to bus 44.

[0149] Multiple components in the electronic device 40 are connected to the I / O interface 45, including an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a magnetic disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0150] Processor 41 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, digital signal processors (DSPs), and any other suitable processor, controller, microcontroller, etc. Processor 41 executes the various methods and processes described above, such as the data management method for semiconductor products.

[0151] In some embodiments, the data management method for a semiconductor product can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the data management method for a semiconductor product described above can be performed. Alternatively, in other embodiments, processor 41 can be configured to execute the data management method for a semiconductor product in any other suitable manner (e.g., via firmware).

[0152] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0153] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0154] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or apparatus. A computer-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0155] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device that has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0156] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0157] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0158] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0159] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A data management method for semiconductor products, characterized in that: include: Aggregating raw application data related to semiconductor products in at least one application scenario into a non-relational database, and performing data cleaning on each of the raw application data to obtain valid application data; Performing data aggregation processing on the valid application data according to the set data aggregation dimension to obtain a data aggregation result under the data aggregation dimension; The data aggregation results are fused according to the set data fusion strategy to obtain the key application data of the semiconductor product in a unified format.

2. The method according to claim 1, characterized in that The aggregating raw application data related to the semiconductor product in at least one application scenario into a non-relational database includes: Obtaining original application data in at least one application scenario; Parsing the original application data through a preset adapter to determine the source identifier, update method, and original dimension structure key information of the original application data; The original application data is classified according to the data key information, and the classified original application data is aggregated into the non-relational database.

3. The method according to claim 1, characterized in that The step of performing data cleaning on each of the original application data to obtain valid application data includes: After receiving the first message instruction sent through the message mechanism, the original application data in the non-relational database is deduplicated and cleaned, and data format is processed to obtain intermediate processed data; Each of the intermediate processed data is reduced according to the set reduction logic to obtain corresponding valid application data.

4. The method according to claim 1, wherein The performing data aggregation processing on the valid application data according to the set data aggregation dimension to obtain the data aggregation result under the data aggregation dimension includes: Acquire at least one data aggregation dimension pre-set according to an application scenario of the semiconductor product; For each data aggregation dimension, the aggregation processing logic involved in the data aggregation dimension is determined, and data aggregation processing is performed on the valid application data according to the aggregation processing logic to obtain a corresponding data aggregation result.

5. The method according to claim 4, characterized in that The data aggregation dimension is a data dimension corresponding to the fixed attribute data of the semiconductor product; The aggregation processing logic includes: aggregating data from multiple sources, deduplicating and splicing data based on wafer identification, aggregating data based on the earliest time of batch upload, performing statistics based on the latest time of batch upload, and aggregating data based on set calculation indicators.

6. The method according to claim 1, wherein The step of fusing the data aggregation results according to the set data fusion strategy to obtain the key application data of the semiconductor product in a unified format includes: Perform field merging processing on each of the data aggregation results according to the set field merging strategy; Performing time-axis unified processing on each of the data aggregation results according to a set time unification strategy; According to the set data conflict handling strategy, multi-source data conflict handling is performed on each of the data aggregation results; The processed data results are determined as key application data of the semiconductor product after the format is unified.

7. The method according to claim 1, characterized in that After obtaining the key application data of the semiconductor product in a unified format, the method further includes: Converting the key application data into a relational data format and storing it in a relational database, and connecting the relational database to the business system via a network application program interface; According to the received data query instruction, the query result is determined from the key application data stored in the relational database and displayed in the business application interface associated with the business system.

8. A semiconductor data management device, characterized in that: include: A warehousing module is used to aggregate the original application data involved in at least one application scenario of semiconductor products into a non-relational database, and perform data cleaning on each of the original application data to obtain valid application data; An aggregation module is used to perform data aggregation processing on the valid application data according to a set data aggregation dimension to obtain a data aggregation result under the data aggregation dimension; The fusion module is used to fuse the data aggregation results according to the set data fusion strategy to obtain the unified key application data of the semiconductor product format.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to execute the semiconductor product data management method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the semiconductor product data management method according to any one of claims 1 to 7 when executed.

11. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the data management method for a semiconductor product according to any one of claims 1 to 7.

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