Data storage methods, devices and electronic equipment

Abstract

This invention discloses a data storage method, apparatus, and electronic device, relating to the field of semiconductor technology. The method includes: in a data storage stage, acquiring the current device parameter value of a target device parameter; comparing the current device parameter value with the latest persistent parameter value of the target device parameter in a hot data layer to obtain a change value of the target parameter; and storing the current device parameter value in the hot data layer based on the change value of the target parameter. The technical solution of this invention enables the storage of effective changes in the target device parameter in the hot data layer, thereby achieving precise load reduction in the hot data layer and improving the effectiveness of hot data layer storage.

Description

Technical Field

[0001] This invention relates to the field of semiconductor technology, and in particular to a data storage method, apparatus, and electronic device. Background Technology

[0002] The current common semiconductor data storage models of "periodic full write" and "memory cache + disk database" have fundamental problems that can be summarized as a vicious cycle of "low storage efficiency" and "query performance bottleneck".

[0003] In terms of data storage, the data is extremely redundant, resulting in the ineffective use of storage resources. Specifically, all equipment parameter values ​​are collected and written at a fixed frequency. For a large number of equipment parameters that remain stable over a long period during the process, this mode generates massive amounts of continuous, repetitive data. At this point, the database size expands linearly or even exponentially. This not only consumes a large amount of high-speed storage space but also dramatically increases the costs of subsequent data backup, migration, and management. Summary of the Invention

[0004] This invention provides a data storage method, apparatus, and electronic device that enables the storage of effective change values ​​of target device parameters in the hot data layer, thereby achieving precise load reduction of the hot data layer and improving the effectiveness of hot data layer storage.

[0005] According to one aspect of the present invention, a data storage method is provided, the method comprising: During the data storage phase, the current device parameter values ​​of the target device parameters are obtained; The current device parameter value is compared with the latest persistent parameter value of the target device parameter in the thermal data layer to obtain the target parameter change value; Based on the change value of the target parameter, the current device parameter value is stored in a thermal data layer.

[0006] According to another aspect of the present invention, a data storage device is provided, the device comprising: The current device parameter value acquisition module is used to acquire the current device parameter value of the target device during the data storage phase. The target parameter change value determination module is used to compare the current device parameter value with the latest persistent parameter value of the target device parameter in the thermal data layer to obtain the target parameter change value. The first target data storage module is used to store the current device parameter value in a hot data layer according to the target parameter change value.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the data storage method described in any embodiment of the present invention.

[0008] The technical solution of this invention compares the current device parameter value with the latest persistent parameter value of the target device parameter in the hot data layer during the data storage stage. Based on the change value of the target parameter, the current device parameter value is stored in the hot data layer. By judging the change value of the target parameter, the hot data layer stores the effective change value of the target device parameter, thereby achieving precise load reduction of the hot data layer and improving the effectiveness of hot data layer storage.

[0009] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart of the first data storage method provided according to Embodiment 1 of the present invention; Figure 2 This is a flowchart of the second data storage method provided according to Embodiment 2 of the present invention; Figure 3 This is a flowchart of the third data storage method provided in Embodiment 2 of the present invention; Figure 4 This is a flowchart of the fourth data storage method provided in Embodiment 2 of the present invention; Figure 5 This is a schematic diagram of the structure of a data storage device according to Embodiment 3 of the present invention; Figure 6 This is a schematic diagram of the structure of an electronic device that implements the data storage method of the present invention. Detailed Implementation

[0012] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0013] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0014] Example 1 Figure 1 This is a flowchart illustrating a data storage method according to Embodiment 1 of the present invention. This embodiment is applicable to the storage of semiconductor data at a hot data layer. The method can be executed by a data storage device, which can be implemented in hardware and / or software and can be configured in an electronic device that performs data storage functions.

[0015] See Figure 1 The data storage method shown includes: S101. During the data storage phase, obtain the current device parameter values ​​of the target device parameters.

[0016] The data storage stage stores equipment data involved in the semiconductor manufacturing process. Target equipment parameters are the process parameters of the equipment involved in the semiconductor manufacturing process. Current equipment parameter values ​​are the specific contents of the target equipment parameters at the current sampling time or within the current sampling period. The current sampling time characterizes the point in time when the target equipment parameters were acquired. The current sampling period characterizes the time period during which the target equipment parameters were acquired.

[0017] Specifically, during the data storage phase, the current device parameter values ​​of the target device are collected in real time or periodically.

[0018] S102. Compare the current device parameter value with the latest persistent parameter value of the target device parameter in the thermal data layer to obtain the target parameter change value.

[0019] The hot data layer stores valid change values ​​of target device parameters and / or current device parameter values ​​with target tags. This can be understood as the hot data layer not storing all raw sampled data, but rather storing parameter change records and context snapshots of event triggers. Therefore, the "data accuracy" of the hot data layer is not "full raw point accuracy," but rather "critical change fidelity accuracy" and "process reconstructability accuracy." The hot data layer is used for process backtracking, fault diagnosis, and compliance auditing. For example, the hot data layer may include a set of optimized tables in a relational database or a high-performance time-series database.

[0020] The latest persistent parameter value is the most recently stored device parameter value of the target device in the hot data layer. In fact, the latest persistent parameter value is the last time the target device parameter was stored in the hot data layer.

[0021] The target parameter change value characterizes the degree of change in the current device parameter value of the target device parameter. It can be understood that the larger the target parameter change value, the greater the degree of change in the current device parameter value; conversely, the smaller the target parameter change value, the smaller the degree of change in the current device parameter value. The target parameter change value is used to measure whether the current device parameter value of the target device parameter has changed effectively. It can be understood that when the target parameter change value reaches a certain threshold, the current device parameter value is considered a valid change.

[0022] Optionally, this device may also include a real-time memory layer and a cold data layer.

[0023] The real-time memory layer stores all sampled data within the current sampling period. Regardless of whether the current device parameter values ​​of the target device change effectively, the real-time data in the real-time memory layer maintains the original sampling period and original time resolution. The real-time memory layer provides a sub-second response data source for the real-time monitoring screen of the host computer user interface, meaning it supports both real-time monitoring and short-term playback. Therefore, the host computer user interface can access only the real-time memory layer, completely avoiding database queries and eliminating user interface lag. For example, the real-time memory layer can be a circular buffer in memory. Real-time data in the real-time memory layer can be automatically discarded as the buffer rolls, requiring no complex management.

[0024] The cold data layer stores cold data from the semiconductor data. This cold data is historical data obtained by migrating and aggregating data from the hot data layer. For example, it aggregates hot data with 100ms precision into mean, maximum, minimum, and sample size at a 1-minute granularity. The cold data layer retains "statistical precision" rather than pursuing instantaneous raw waveform precision. It supports large-volume, low-precision tasks such as long-term trend analysis and capacity report generation. Exemplarily, the cold data layer may include a column-oriented database or a compressed file system.

[0025] In comparison, the real-time memory layer represents the highest precision of this device; the hot data layer represents high precision but compressed process precision; and the cold data layer represents low precision but highly compressed statistical precision.

[0026] Specifically, the absolute value of the difference between the current device parameter value and the latest persistent parameter value of the target device parameter in the thermal data layer is calculated to obtain the target parameter change value of the current device parameter value.

[0027] S103. Store the current device parameter values ​​in a thermal data layer based on the target parameter change value.

[0028] Specifically, when the change in the target parameter is greater than the target change threshold, the current device parameter value is stored in the hot data layer. When the change in the target parameter is less than or equal to the target change threshold, the current device parameter value is not stored in the hot data layer. The target change threshold is used to determine whether the current device parameter value is a valid change value for the target device parameter. Correspondingly, a change in the target parameter greater than the target change threshold can be understood as the current device parameter value being a valid change value for the target device parameter.

[0029] Optionally, when the change value of the target parameter exceeds the target change threshold, the change value of the target parameter within the target delay window can be detected. If all the change values ​​of the target parameter within the target delay window are greater than the target change threshold, the current device parameter value is stored in the hot data layer. If any change value of the target parameter within the target delay window is less than or equal to the target change threshold, the current device parameter value is not stored in the hot data layer. The target delay window is a preset number of sampling periods. The preset number of periods is used to adjust the time span of the target delay window. Optionally, the preset number of periods can be set and adjusted by technicians based on experience. For example, the preset number of periods can be 3; correspondingly, the target delay window can be 3 sampling periods. This avoids noise interference during the hot data layer storage process and improves the effectiveness of target device parameter storage.

[0030] In an optional embodiment of the present invention, after obtaining the current device parameter value of the target device parameter in the data storage stage, the method further includes: performing a mark detection on the current device parameter value to obtain a target mark detection result of the current device parameter value; and storing the current device parameter value in a hot data layer according to the target mark detection result.

[0031] The target marker detection result is used to characterize whether the current device parameter value has a target marker. For example, the target marker detection result can include whether the current device parameter value has a target marker and whether the current device parameter value does not have a target marker. The presence of a target marker in the current device parameter value can be understood as indicating that the current device parameter value is of high importance in the semiconductor manufacturing process; the absence of a target marker in the current device parameter value can be understood as indicating that the current device parameter value is of low importance in the semiconductor manufacturing process. Here, the target marker is used to characterize the importance of the current device parameter value in the semiconductor manufacturing process.

[0032] Specifically, it checks whether the current device parameter value has a target marker. If the current device parameter value has a target marker, the target marker detection result is determined as the current device parameter value having a target marker. If the current device parameter value does not have a target marker, the target marker detection result is determined as the current device parameter value not having a target marker.

[0033] Specifically, if the target marker detection result indicates that the current device parameter value has a target marker, the current device parameter value is stored in the hot data layer. If the target marker detection result indicates that the current device parameter value does not have a target marker, the current device parameter value is not stored in the hot data layer.

[0034] Optionally, the current device parameter value can be stored in the hot data layer based on the target parameter change value and the target marker detection result. Specifically, when the target parameter change value is greater than the target change threshold, the current device parameter value is stored in the hot data layer. When the target parameter change value is less than or equal to the target change threshold, if the target marker detection result indicates that the current device parameter value has a target marker, then the current device parameter value is stored in the hot data layer; if the target marker detection result indicates that the current device parameter value does not have a target marker, the current device parameter value is not stored in the hot data layer.

[0035] This solution performs target marking and detection on the current device parameter values ​​during the data storage phase. Based on the target marking and detection results, the current device parameter values ​​are stored in the hot data layer. By using target marking and detection, the importance of the device parameter values ​​is taken into account, and supplementary judgments on the device parameter values ​​are realized, thereby improving the comprehensiveness and accuracy of the hot data layer storage.

[0036] In an optional embodiment of the present invention, the current equipment parameter value is stored in a thermal data layer based on the target marker detection result, including: storing the current equipment parameter value in a thermal data layer based on the target key event marker detection result, the target process stage boundary marker detection result, the target periodic anchor point marker detection result, and / or the target abnormal trend determination marker detection result.

[0037] The target critical event marker detection result is used to characterize whether the current equipment parameter value has a target critical event marker. For example, the target critical event marker detection result can include whether the current equipment parameter value has a target critical event marker and whether the current equipment parameter value does not have a target critical event marker. The target critical event marker is used to identify the target critical event in the semiconductor manufacturing process related to the current equipment parameter value. The target critical event is used to characterize event nodes that have a significant impact on the semiconductor manufacturing process. For example, the target critical event can include alarm triggering, process step switching, recipe switching, start / stop, cleaning start, or cleaning end, etc.

[0038] The target process stage boundary marker detection result is used to characterize whether the current equipment parameter value has a target process stage boundary marker. For example, the target process stage boundary marker detection result can include whether the current equipment parameter value has a target process stage boundary marker and whether the current equipment parameter value does not have a target process stage boundary marker. The target process stage boundary marker is used to identify the boundary of the target process stage in the semiconductor manufacturing process to which the current equipment parameter value relates. The target process stage boundary is the start and end point of the target process stage in the semiconductor manufacturing process. For example, the target process stage boundary can include the first sampling time of the current batch, the last sampling time of the current batch, the first sampling time of the current wafer, the last sampling time of the current wafer, the first sampling time of the current process step, or the last sampling time of the current process step, etc.

[0039] The target periodic anchor mark detection result is used to characterize whether the current device parameter value has a target periodic anchor mark. For example, the target periodic anchor mark detection result can include whether the current device parameter value has a target periodic anchor mark and whether the current device parameter value does not have a target periodic anchor mark. The target periodic anchor mark is used to identify whether the current sampling time of the current device parameter value is a preset periodic anchor time or whether the current sampling period of the current device parameter value includes a preset periodic anchor time. The preset periodic anchor time is used to characterize a pre-defined time in the semiconductor manufacturing process when periodic sampling is required. For example, the preset periodic anchor time can include anchor times corresponding to equipment maintenance cycles, process parameter fluctuation cycles, and market demand cycles, etc.

[0040] The target anomaly trend determination marker detection result is used to characterize whether the current device parameter value has a target anomaly trend determination marker. For example, the target anomaly trend determination marker detection result can include whether the current device parameter value has a target anomaly trend determination marker and whether the current device parameter value does not have a target anomaly trend determination marker. The target anomaly trend is used to identify whether the current device parameter value has a target anomaly trend. The target anomaly trend characterizes whether the data corresponding to the current device parameter value deviates from the normal range. For example, the rate of change, fluctuation amplitude, or continuous drift trend of the current device parameter value exceeds a preset determination condition.

[0041] Specifically, if the current equipment parameter value has any one of the following: target critical event marker, target process stage boundary marker, target periodic anchor point marker, and target abnormal trend judgment marker, the current equipment parameter value will be stored in the hot data layer; if the current equipment parameter value does not have any of the following: target critical event marker, target process stage boundary marker, target periodic anchor point marker, and target abnormal trend judgment marker, the current equipment parameter value will not be stored in the hot data layer.

[0042] Optionally, before storing the current equipment parameter value in the thermal data layer based on the target critical event marker detection results, target process stage boundary marker detection results, target periodic anchor point marker detection results, and / or target abnormal trend judgment marker detection results, the method further includes: adding a target critical event marker to the current equipment parameter value when it is detected that the current equipment parameter value involves a target critical event; adding a target process stage boundary marker to the current equipment parameter value when it is detected that the current equipment parameter value is at the first sampling time of the current batch, the last sampling time of the current batch, the first sampling time of the current wafer, the last sampling time of the current wafer, the first sampling time of the current process step, or the last sampling time of the current process step; adding a target periodic anchor point marker to the current equipment parameter value when it is detected that the current equipment parameter value reaches a preset periodic anchor point time; and adding a target abnormal trend judgment marker to the current equipment parameter value when the rate of change, fluctuation amplitude, or continuous drift trend of the current equipment parameter value exceeds a preset judgment condition.

[0043] This solution concretizes the target marker detection results into target key event marker detection results, target process stage boundary marker detection results, target periodic anchor point marker detection results, and / or target abnormal trend determination marker detection results. It takes into account the key events, process stage boundaries, periodic anchor points, and abnormal trends of the current equipment parameter values ​​in the semiconductor manufacturing process, thereby improving the storage efficiency and storage accuracy of target marker-based hot data layer storage.

[0044] The technical solution of this invention compares the current device parameter value with the latest persistent parameter value of the target device parameter in the hot data layer during the data storage stage. Based on the change value of the target parameter, the current device parameter value is stored in the hot data layer. By judging the change value of the target parameter, the hot data layer stores the effective change value of the target device parameter, thereby achieving precise load reduction of the hot data layer and improving the effectiveness of hot data layer storage.

[0045] In an optional embodiment of the present invention, after storing the current device parameter value in the thermal data layer according to the target parameter change value, the method further includes: assigning target context identifiers and target time identifiers to the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current device parameter value; during the data query stage, obtaining a data query request for the target device parameter; performing a preliminary screening of the target time identifiers of each thermal data record of the target device parameter in the thermal data layer according to the target time condition included in the data query request to obtain the main query result; identifying the identifiers of each main query result to obtain the target context condition; and performing an association query on the target context identifiers of the log record, alarm record, recipe step record, and wafer processing record corresponding to the target device parameter according to the target context condition to obtain the target query result.

[0046] The target hot data record is used to record information about the current device parameter value in the hot data layer. Optionally, the target data record may include the target device parameter, the current acquisition time, the current device parameter value, and the target parameter change value. The target device parameter represents the category to which the current device parameter value belongs. The current acquisition time represents the timestamp of the current device parameter value. The current device parameter value represents the specific numerical value of the current device parameter. The target parameter change value represents the amount of change of the current device parameter value compared to the previous stored record of the target device parameter in the hot data layer. For example, the target hot data record can be [parameter ID, timestamp, new value, change]. Here, the parameter ID is the target device parameter; the timestamp is the current acquisition time; the new value is the current device parameter value; and the change is the change in the target parameter.

[0047] The target log records are automatically generated event records arranged in chronological order during the semiconductor manufacturing process. Target log records are used to record operational actions, status changes, and error messages. The target alarm log records are data records containing alarm information and processing results triggered when abnormal conditions are detected during the semiconductor manufacturing process. Target alarm log records are used to promptly record the response process to abnormal problems. The target recipe step log records are standardized operation sequences for executing specific process flows during the semiconductor manufacturing process. Target recipe step log records are used to record the parameter settings, execution time, and execution results for each step. Target recipe step log records are used to ensure the consistency and repeatability of the semiconductor manufacturing process. The target wafer processing log records the entire production data for a single wafer during the semiconductor manufacturing process.

[0048] The target context identifier is used to merge data from different sources within the same process flow. For example, the target context identifier can be a combination of fields such as equipment identifier, cavity identifier, batch identifier, wafer identifier, recipe identifier, and process step identifier. Specifically, the equipment identifier uniquely identifies a semiconductor manufacturing equipment; the cavity identifier identifies a cavity within the semiconductor manufacturing equipment where individual processing is performed; the batch identifier uniquely identifies the corresponding production batch; the wafer identifier uniquely identifies a single wafer; the recipe identifier uniquely identifies a standardized process flow; and the process step identifier uniquely identifies a single operational step within the recipe. The target time identifier identifies the recording time of the data record. For example, the target time identifier includes a timestamp or start and end times of the data record.

[0049] A data query request is used to query data records associated with target device parameters during semiconductor manufacturing. The data query request may include the target device parameters and target time conditions. The target time conditions characterize the time range of the target device parameters to be queried in the data query request. The main query result is the filtered result of thermal data records corresponding to the target device parameters in the thermal data layer based on the target time conditions. The target context condition is the identification result of the context identifier of the main query result. The target query result is the associated query result of the target device parameters determined based on the target context identifier. For example, the target query result includes target log records, target alarm records, target recipe step records, and target wafer processing records associated with the target device parameters.

[0050] Specifically, the recording times corresponding to the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current equipment parameter value are detected respectively. The target time identifiers corresponding to the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current equipment parameter value are determined respectively, and target time identifiers are assigned to the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current equipment parameter value.

[0051] Specifically, the semiconductor manufacturing equipment involved in the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current equipment parameter value is detected respectively, and the equipment identifier of the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current equipment parameter value is determined based on the identifier of the semiconductor manufacturing equipment.

[0052] Specifically, the cavity inside the semiconductor manufacturing equipment that is processed separately is detected for the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current equipment parameter value. Based on the identifier of the cavity processed separately inside the semiconductor manufacturing equipment, the cavity identifier of the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current equipment parameter value is determined.

[0053] Specifically, the production batch to which the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current equipment parameter value belong is detected, and the batch identifier of the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current equipment parameter value is determined based on the production batch identifier.

[0054] Specifically, the wafers involved in the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current equipment parameter value are detected respectively, and the wafer identifiers of the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current equipment parameter value are determined based on the wafer identifiers.

[0055] Specifically, the standardized process flows involved in the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current equipment parameter value are detected respectively. Based on the identifier of the standardized process flow, the recipe identifier of the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current equipment parameter value is determined respectively.

[0056] Specifically, the system detects individual operation steps in the recipes of the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current equipment parameter value. Based on the identifier of the individual operation step in the recipe, the system determines the process step identifier of the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current equipment parameter value.

[0057] Specifically, for a single data record in the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current equipment parameter value, the fields of equipment identifier, cavity identifier, batch identifier, wafer identifier, recipe identifier, and process step identifier are combined to obtain the target context identifier corresponding to the data record, and the corresponding target context identifier is assigned to the data record.

[0058] Specifically, during the data query phase, the data query request for the target device parameters initiated by the data querying party is obtained. The target time identifier of each thermal data record of the target device parameters in the thermal data layer is compared with the target time condition included in the data query request. Thermal data records whose target time identifiers fall within the range of the target time condition are taken as the main query results. The context identifiers of each main query result are identified. The context identifier with the highest proportion is determined as the target context condition.

[0059] Specifically, the target context identifiers of the log records, alarm records, recipe step records, and wafer processing records corresponding to the target context conditions and target device parameters are compared. The log records, alarm records, recipe step records, and wafer processing records corresponding to the target device parameters that match the target context identifiers are determined as the target query results.

[0060] This solution assigns target context identifiers and target time identifiers to target thermal data records, target log records, target alarm records, target recipe step records, and target wafer processing records corresponding to the current equipment parameter values ​​during the data storage phase. This configures a unified context primary key for the current equipment parameter values, enabling unified merging of data from different sources within the same process flow. During the data query phase, based on the target time conditions included in the data query request and the target time identifiers of each thermal data record for the target equipment parameter in the thermal data layer, initial screening of the target thermal data records for the target equipment parameter is achieved. By identifying the context identifiers of each main query result, target context conditions are obtained. Based on the target context conditions and target context identifiers, associated queries on the log records, alarm records, recipe step records, and wafer processing records corresponding to the target equipment parameters are achieved, enabling root cause tracing of the target equipment parameters.

[0061] In an optional embodiment of the present invention, after storing the current device parameter value in the hot data layer according to the target parameter change value, the method further includes: when the target hot data in the edge-side hot data layer is detected to have reached the migration trigger condition, uploading the target hot data from the edge-side hot data layer to the cloud cold data layer; simultaneously, caching the target hot data in the edge-side hot data layer, adding a data migration identifier to the target hot data in the edge-side hot data layer, and retaining the current query position of the target hot data as the edge-side hot data layer; after the target hot data in the edge-side hot data layer has been uploaded to the cloud cold data layer, deleting the data migration identifier of the target hot data, adjusting the current query position of the target hot data to the cloud cold data layer, and storing the target cold data index corresponding to the target hot data in the edge-side cold data index layer.

[0062] The edge-side hot data layer is a hot data layer deployed at the edge. It is deployed in a local database or high-performance time-series library on the edge side to store change records and event-triggered context snapshots, supporting process backtracking, fault diagnosis, and compliance auditing. The edge side can be the host computer side of the device. Optionally, a real-time memory layer and a cold data index layer can also be deployed on the edge side.

[0063] The edge-side real-time memory layer is a real-time memory layer deployed on the edge side. Deployed in the host computer's local memory, it is used for full data sampling within the current sampling period and is directly used for real-time monitoring and short-term playback of the host computer's user interface.

[0064] At this point, the host computer acquires device parameters at a fixed high frequency, and the data from the current sampling period first enters the real-time memory layer. Simultaneously, the current device parameter values ​​are compared with the most recently persisted parameter values ​​in the parameter status mapping table of the edge-side hot data layer, and only data that meets the change conditions or event triggering conditions is written to the edge-side hot data layer. In this way, real-time control and high-frequency preprocessing are entirely completed locally at the edge, without relying on the cloud link. When the query target is real-time monitoring, short-term playback, or recent high-precision diagnostics, the real-time memory layer or hot data layer on the edge side is accessed first. Prioritizing responses through local edge-side queries avoids introducing cloud access into the real-time control link.

[0065] The edge-side cold data index layer is a cold data index layer deployed on the edge side. This layer only retains the cold data index and not all long-term historical data.

[0066] The cloud-based cold data layer is a layer of cold data deployed in the cloud. It is used to store long-term historical data, aggregated statistics, and archived files migrated from the hot data layer. The cloud-based cold data layer can include cloud objects, data lakes, or centralized columnar databases.

[0067] Migration trigger conditions are the conditions that trigger data migration from the hot data layer to the cold data layer. For example, migration trigger conditions may include time conditions, access conditions, or hot data layer storage utilization conditions. For instance, a time condition is that the data generation time is greater than a preset generation time threshold. The preset generation time threshold is a pre-set minimum data generation time for data migration. For example, the preset generation time threshold could be 30 days. Another example is an access condition where the number of data accesses within a preset time period is less than or equal to a preset number of accesses. The preset time period is the time period corresponding to the pre-set access condition. For example, the preset time period could be within 7 days. The preset number of accesses is the pre-set maximum number of data accesses for data migration. A third example is a hot data layer storage utilization condition where the hot data layer storage utilization is greater than a preset utilization threshold. The preset utilization threshold is a pre-set minimum hot data layer storage utilization for data migration. For example, the preset utilization threshold could be 80%.

[0068] The target hot data is a single hot data record stored in the edge-side hot data layer. A data migration identifier indicates that the target hot data is undergoing data migration. For example, the data migration identifier could be "Migrable". The current query location is the location where the data is queried.

[0069] The target cold data index is the data index of the target data corresponding to the target hot data in the cloud cold data layer. For example, the target cold data index may include a target cold data identifier and the target storage location of the target cold data in the cloud cold data layer. The target cold data identifier identifies the target cold data corresponding to the target hot data in the cloud cold data layer. The target storage location is the storage location of the target cold data in the cloud cold data layer.

[0070] Optionally, during the migration of target hot data from the edge-side hot data layer to the cloud-side cold data layer, time aggregation, compression encoding, summary generation, and index building can be completed simultaneously.

[0071] This solution deploys the cold data layer to the cloud and the hot data layer to the edge, forming a two-tier "edge-cloud" architecture that enables distributed cloud-edge collaboration. Furthermore, by retaining a lightweight index on the edge, even after the primary copy of the target cold data has been migrated to the cloud, the edge retains the target cold data index, allowing for quick determination of whether data query requests need to be forwarded to the cloud. This improves both edge storage utilization and cloud data query efficiency. Simultaneously, during data migration, the target hot data is cached in the edge hot data layer, and its current query position is maintained at the edge hot data layer. Once the target hot data in the edge hot data layer has been uploaded to the cloud cold data layer, its current query position is adjusted to the cloud cold data layer. By prioritizing edge reading during data migration and switching the current query position only after the cloud write of the target hot data is complete, query interruptions during migration are avoided, improving the stability of semiconductor data queries.

[0072] Example 2 Figure 2This is a flowchart of a data storage method provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment of the present invention further specifies "comparing the current device parameter value with the latest persistent parameter value of the target device parameter in the thermal data layer to obtain the target parameter change value" as "comparing the current device parameter value with the latest persistent parameter value of the target device parameter in the thermal data layer according to the target dimension, target range, target allowable deviation range, target change preference, and target operating condition sensitivity of the target device parameter to determine the target parameter change value; wherein, the target parameter change value includes the absolute change value or the relative change value of the target parameter." It also specifies "storing the current device parameter value in the thermal data layer according to the target parameter change value" as "using the target..." The target change threshold for equipment parameters is used to detect changes in target parameters individually, obtaining a target-specific detection result for the current equipment parameter value. Based on this result, the current equipment parameter value is stored in a thermal data layer. The target change threshold includes either an absolute change threshold or a relative change threshold. By considering the target dimensions, range, allowable deviation range, change preference, and sensitivity to operating conditions of the target equipment parameters, target parameter changes are categorized as either absolute or relative changes, and target change thresholds are also categorized as either absolute or relative change thresholds. This improves the flexibility and accuracy of current equipment parameter value detection. It should be noted that parts not detailed in this embodiment can be found in other embodiments.

[0073] See Figure 2 The data storage method shown includes: S201. During the data storage phase, obtain the current device parameter values ​​of the target device parameters.

[0074] S202. Based on the target dimensions, target range, target allowable deviation range, target change preference, and target operating condition sensitivity of the target equipment parameters, compare the current equipment parameter values ​​with the latest persistent parameter values ​​of the target equipment parameters in the thermal data layer to determine the target parameter change values.

[0075] The target dimension refers to the dimension of the target equipment parameter. Optionally, the type of target variation threshold can be determined based on whether the target dimension is fixed. A fixed dimension can be understood as the dimension of the target equipment parameter remaining constant and not changing with variations in measurement method, unit selection, or external conditions. A variable dimension can be understood as the dimension of the target equipment parameter changing due to variations in measurement method, unit selection, or external conditions.

[0076] The target range is the range of target equipment parameters. Optionally, the type of target variation threshold can be determined based on the span of the target range.

[0077] The target allowable deviation range refers to the permissible fluctuation range of the target equipment parameters. Optionally, the type of target change threshold can be determined based on whether the target allowable deviation range is clearly defined. A clearly defined target allowable deviation range can be understood as explicitly specifying the permissible deviation range of the target equipment parameters. For example, the allowable deviation range can be characterized by specific numerical values ​​or upper and lower limits, such as a length allowable deviation of ±2mm, or a temperature fluctuation range not exceeding ±5℃. An unclear target allowable deviation range can be understood as not explicitly specifying the permissible deviation range of the target equipment parameters, or merely vaguely defining the acceptable fluctuation range of the target equipment parameters, lacking a quantitative standard for the target allowable deviation range. For example, merely vaguely defining the acceptable fluctuation range of the target equipment parameters could include "the target equipment parameters are within a reasonable range" or "the target equipment parameters are as close to the target as possible," etc.

[0078] Target change preference is used to characterize the parameter change preference of the target device parameters. For example, target change preference can include sensitivity to fixed value offset and sensitivity to proportional change. Sensitivity to fixed value offset can be understood as the target device parameters being more sensitive to changes in absolute values. Sensitivity to proportional change can be understood as the target device parameters being more sensitive to relative changes, while changes in absolute values ​​can be compensated for or mitigated by other parts of the system.

[0079] Target condition sensitivity refers to the degree to which the target equipment parameters are sensitive to different operating conditions. Optionally, the type of target change threshold can be determined based on the degree of difference between the target condition sensitivity of the target equipment parameters under different operating conditions.

[0080] The target parameter change values ​​include both absolute and relative changes. The absolute change value characterizes the absolute degree of change in the current device parameter value. The relative change value characterizes the relative degree of change in the current device parameter value.

[0081] Specifically, the following criteria should be considered: Whether the target dimensions of the target equipment parameters are fixed. Whether the span of the target range of the target equipment parameters exceeds the preset range. Whether the target allowable deviation range of the target equipment parameters is clearly defined. Whether the target variation preference of the target equipment parameters is sensitivity to fixed value deviations or proportional changes. Whether the difference in the sensitivity of the target equipment parameters under different operating conditions exceeds the preset difference level. The preset range measures the span of the target range. The preset difference level measures the difference in the sensitivity of the target equipment parameters under different operating conditions. The preset range and preset difference level can be set and adjusted by technicians based on experience.

[0082] Specifically, when the target dimension of the target device parameter is a fixed dimension, the target allowable deviation range is clear, or the target change preference of the target device parameter is a fixed numerical offset, the absolute value of the difference between the current device parameter value and the latest persistent parameter value of the target device parameter in the thermal data layer is calculated to obtain the absolute change value of the target parameter of the current device parameter.

[0083] Specifically, when the target range of the target device parameter is greater than the preset range, or when the difference between the target operating condition sensitivity of the target device parameter under different operating conditions is greater than the preset difference, the absolute value of the ratio between the difference between the current device parameter value and the latest persistent parameter value of the target device parameter in the thermal data layer and the latest persistent parameter value is calculated to obtain the relative change value of the target parameter of the current device parameter value.

[0084] S203. Using the target change threshold of the target equipment parameters, the target parameter change value is detected separately to obtain the target separate detection result of the current equipment parameter value, and the current equipment parameter value is stored in the thermal data layer based on the target separate detection result.

[0085] The target change threshold includes the target absolute change threshold and the target relative change threshold. The target absolute change threshold is used to detect the absolute change in the target parameter corresponding to the current device parameter value. The target relative change threshold is used to detect the relative change in the target parameter corresponding to the current device parameter value.

[0086] The target-individual detection result is the detection result of individually detecting the current device parameter value using either the target absolute change threshold or the target relative change threshold. When the target parameter change value is an absolute change value, the target-individual detection result includes either the target parameter absolute change value being greater than the target absolute change threshold or the target parameter absolute change value being less than or equal to the target absolute change threshold. When the target parameter change value is a relative change value, the target-individual detection result includes either the target parameter relative change value being greater than the target relative change threshold or the target parameter relative change value being less than or equal to the target relative change threshold.

[0087] Specifically, when the change value of the target parameter is the absolute change value of the target parameter, the absolute change value of the target parameter is compared separately with the target absolute change threshold of the target device parameter to obtain the target individual detection result of the current device parameter value. If the target individual detection result shows that the absolute change value of the target parameter is greater than the target absolute change threshold, the current device parameter value is stored in the hot data layer. If the target individual detection result shows that the absolute change value of the target parameter is less than or equal to the target absolute change threshold, the current device parameter value is not stored in the hot data layer.

[0088] For example, the following formula can be used to represent the detection process of the current device parameter value using a target absolute change threshold: ; In the formula, The current device parameter value for the target device; This is the latest persistent parameter value of the target device in the thermal data layer; The target parameter absolute change value is the current device parameter value. The target absolute change threshold for the target device parameters.

[0089] Specifically, when the change value of the target parameter is a relative change value, the relative change value of the target parameter is compared separately with the target relative change threshold of the target device parameter to obtain the target individual detection result of the current device parameter value. If the target individual detection result shows that the relative change value of the target parameter is greater than the target relative change threshold, the current device parameter value is stored in the hot data layer. If the target individual detection result shows that the relative change value of the target parameter is less than or equal to the target relative change threshold, the current device parameter value is not stored in the hot data layer.

[0090] For example, the following formula can be used to represent the detection process of the current device parameter value using a target relative change threshold: ; In the formula, The current device parameter value for the target device; This is the latest persistent parameter value of the target device in the thermal data layer; This represents the relative change of the target parameter value relative to the current device parameter value. The target relative change threshold for the target device parameters.

[0091] In an optional embodiment of the present invention, before comparing the current device parameter value with the latest persistent parameter value of the target device parameter in the thermal data layer based on the target dimensions, target range, target allowable deviation range, target change preference, and target operating condition sensitivity of the target device parameter to determine the target parameter change value, the method further includes: detecting whether the target device parameter is a preset key parameter; when the target device parameter is a preset key parameter, comparing the current device parameter value with the latest persistent parameter value of the target device parameter in the thermal data layer to determine the target parameter absolute change value and the target parameter absolute change value; correspondingly, storing the current device parameter value in the thermal data layer based on the target parameter change value further includes: when the target device parameter is a preset key parameter, using the target absolute change threshold and the target relative change threshold of the target device parameter to jointly detect the target parameter absolute change value and the target parameter absolute change value, obtaining the target joint detection result of the current device parameter value, and storing the current device parameter value in the thermal data layer based on the target joint detection result.

[0092] The preset key parameters are parameters that are manually set during the process. The target joint detection result is the detection result of the current equipment parameter value jointly detected using the target absolute change threshold and the target relative change threshold.

[0093] Specifically, it checks whether the parameters of the target device are the preset key parameters.

[0094] Specifically, when the target device parameter is a preset key parameter, the absolute value of the difference between the current device parameter value and the latest persistent parameter value of the target device parameter in the thermal data layer is calculated to obtain the absolute change value of the target parameter for the current device parameter. The absolute value of the ratio between the difference between the current device parameter value and the latest persistent parameter value of the target device parameter in the thermal data layer and the latest persistent parameter value is calculated to obtain the relative change value of the target parameter for the current device parameter value.

[0095] Specifically, the absolute change value of the target parameter is compared with the target absolute change threshold of the target device parameter, and the relative change value of the target parameter is compared with the target relative change threshold of the target device parameter to obtain the joint detection result of the target.

[0096] Specifically, when the joint detection result shows that the absolute change value of the target parameter is greater than the target absolute change threshold of the target device parameter, and the relative change value of the target parameter is greater than the target relative change threshold of the target device parameter, the current device parameter value is stored in the hot data layer. When the joint detection result shows that the absolute change value of the target parameter is less than or equal to the target absolute change threshold of the target device parameter, and / or the relative change value of the target parameter is less than or equal to the target relative change threshold of the target device parameter, the current device parameter value is not stored in the hot data layer.

[0097] Specifically, when the target equipment parameter is not a preset key parameter, the current equipment parameter value is compared with the latest persistent parameter value of the target equipment parameter in the thermal data layer based on the target dimensions, target range, target allowable deviation range, target change preference, and target operating condition sensitivity of the target equipment parameter to determine the target parameter change value. Using the target change threshold of the target equipment parameter, the target parameter change value is detected individually to obtain the target-specific detection result of the current equipment parameter value. Based on the target-specific detection result, the current equipment parameter value is stored in the thermal data layer.

[0098] This solution introduces the detection of whether the target device parameters are preset key parameters. By using the target absolute change threshold and the target relative change threshold for joint detection of the preset key parameters, the accuracy of the hot data layer storage of the preset key parameters is further improved.

[0099] In an optional embodiment of the present invention, before using the target change threshold of the target equipment parameter to detect the change value of the target parameter individually and obtain the target individual detection result of the current equipment parameter value, the method further includes: dynamically adjusting the target change threshold according to the target equipment operating condition, target process stage and / or historical statistical characteristics corresponding to the target equipment parameter.

[0100] The target equipment operating condition characterizes the operating condition corresponding to the current equipment parameter values. Different target equipment operating conditions have different noise levels or service sensitivity, and correspondingly, the target change threshold is not fixed. For example, based on noise level, the target equipment operating condition can include high-fluctuation operating conditions and steady-state critical operating conditions. For example, high-fluctuation operating conditions can include heating, vacuuming, or instantaneous switching. Low-fluctuation operating conditions can include etching steady-state or endpoint control phases. For example, based on service sensitivity, the target equipment operating condition can also include fault recovery operating conditions.

[0101] The target process stage refers to the process stage to which the current equipment parameter values ​​belong. For example, the target process stage may include a vacuum stage, a voltage stabilization stage, a precursor introduction stage, an etching stage, a purging stage, a cooling stage, or a transition stage. The vacuum stage removes gases and impurities from the process chamber to ensure a vacuum environment. The voltage stabilization stage maintains stable pressure within the process chamber, ensuring precise control of equipment parameters. The precursor introduction stage introduces reactive gases or precursor materials into the process chamber to provide raw materials for subsequent chemical reactions or physical deposition. The etching stage removes unwanted materials from the wafer surface using physical or chemical methods to form the desired circuit patterns or structures. The purging stage uses high-purity gases to clean the process chamber and piping and remove impurities. The cooling stage reduces the temperature of the process chamber or equipment. Transition stages include stages between different operation steps on the same wafer, cleaning stages between different wafers, and wafer charge removal stages.

[0102] Historical statistical features are the statistical results of the current device parameter values ​​within a historical sliding time window. The historical sliding time window is a fixed-length time interval sliding from the current acquisition time or the current acquisition period. Historical device parameter values ​​are the target device parameter values ​​prior to the current acquisition time or the current acquisition period. Examples of historical statistical features include mean, standard deviation, absolute deviation of the median, mean rate of change, frequency of fluctuation, or duration of continuous drift.

[0103] Specifically, the fluctuation level of the target equipment's operating condition is compared with a preset fluctuation level. If the fluctuation level exceeds the preset level, the target equipment is classified as a high-fluctuation condition, and the target change threshold is increased. If the fluctuation level is less than or equal to the preset level, the target equipment is classified as a low-fluctuation condition, and the target change threshold is decreased. The service sensitivity of the target equipment's operating condition is also tested. If the service sensitivity is less than or equal to the preset sensitivity, the target equipment is classified as a fault recovery condition, and the target change threshold is decreased. The preset fluctuation level measures the fluctuation of the target equipment's operating condition. The preset service sensitivity measures whether the target equipment is in a fault recovery condition.

[0104] Specifically, the system checks whether the target process stage corresponding to the current equipment parameter value contains critical process steps. If the target process stage contains critical process steps, the target change threshold is lowered. It also checks whether the target process stage corresponding to the current equipment parameter value is a transition stage. If the target process stage is a transition stage, the target change threshold is raised. Finally, it checks whether the target process stage corresponding to the current equipment parameter value is within a preset sampling period before and after the process step switch. If the target process stage is within the preset sampling period before and after the process step switch, the target change threshold is lowered. Optionally, for preset critical parameters, target change thresholds corresponding to different process steps can be preset.

[0105] Specifically, the historical device parameter values ​​within the historical sliding time window can be obtained. Based on each historical device parameter value, the historical statistical characteristics of the current device parameter value are calculated. Using the historical statistical characteristic adjustment formula, the target adjustment value of the target change threshold is determined based on the historical statistical characteristics, and the target adjustment value is used to adjust the target change threshold.

[0106] For example, the historical statistical feature adjustment formula can be represented by the following formula: ; In the formula, The target adjustment value is the threshold value for the target change. Weights for historical statistical features; Historical statistical characteristics; The weights of the current device parameters; This represents the current device parameter value.

[0107] Optionally, there can be multiple historical statistical features. Correspondingly, a target statistical feature can be selected from among the historical statistical features based on the target equipment parameter type. Using the historical statistical feature adjustment formula, a target adjustment value for the target change threshold is determined based on the target statistical feature. This target adjustment value is then used to linearly adjust the target change threshold. Here, the target equipment parameter type refers to the parameter type of the target equipment parameter.

[0108] This solution dynamically adjusts the target change threshold based on the target equipment operating conditions, target process stages, and / or historical statistical characteristics corresponding to the target equipment parameters. This improves the adaptability between the target change threshold and the target equipment operating conditions, target process stages, and historical statistical characteristics, thereby enhancing the accuracy of the target change threshold.

[0109] The technical solution of this invention distinguishes target parameter changes into absolute or relative changes by considering the target dimensions, target range, target allowable deviation range, target change preference, and target operating condition sensitivity of the target equipment parameters. It also distinguishes target change thresholds into absolute or relative change thresholds. By comparing the current equipment parameter value with the latest persistent parameter value of the target equipment parameter in the thermal data layer, the target parameter change value is determined. Using the target change threshold, the target parameter change value is detected individually to obtain a target-specific detection result for the current equipment parameter value. Based on this target-specific detection result, the current equipment parameter value is stored in the thermal data layer, thus improving the flexibility and accuracy of current equipment parameter value detection.

[0110] The current common semiconductor data storage models of "periodic full write" and "physical media tiering" have fundamental problems that can be summarized as a vicious cycle of "low storage efficiency" and "query performance bottleneck".

[0111] In terms of data writing, there is extreme data redundancy, resulting in the ineffective use of storage resources. Specifically, regardless of whether the actual values ​​change, all equipment parameter values ​​are collected and written at a fixed frequency (e.g., 100ms). For a large number of equipment parameters that remain stable over a long period during the process (such as basic equipment status and fixed process settings), this pattern generates massive amounts of continuous, repetitive data. At this point, the database size expands linearly or even exponentially (as the number of parameter points increases). This not only consumes a large amount of high-speed storage space but also dramatically increases the costs of subsequent data backup, migration, and management.

[0112] In terms of data storage, the lack of differentiation based on data value leads to the inefficient use of high-efficiency storage media. While a layered approach of "memory cache + disk database" exists, it essentially relies on differences in hardware media performance and lacks logical layering based on the value of the data for business purposes. Different business scenarios, such as real-time monitoring, historical data review, and long-term trend analysis, have drastically different requirements for data access frequency, response speed, and accuracy. Treating all data "equally" and piling it into the same database or simple cache results in high-value real-time / hot data being overwhelmed by massive amounts of cold data. Valuable memory and high-speed storage resources are squeezed out by historical data that has not been accessed for a long time, failing to provide optimal performance for real-time business.

[0113] In terms of data querying, the inability to optimize the query path leads to response delays and system lag. Any historical query requires scanning a massive single table containing all redundant data. The query engine cannot predict or determine the time range and value level of the required data, and can only perform full table or wide-range scans. Query time increases significantly with the amount of data, especially when generating complex reports or performing long-term analysis, easily causing database I / O (input / output) bottlenecks and sluggish response on the host computer interface, affecting the efficiency of engineers' debugging and diagnostics.

[0114] Based on the above embodiments, the present invention also provides a preferred embodiment of a data storage method. For example... Figure 3 and Figure 4 As shown, this method uses dynamic layering to prevent semiconductor data from being statically accumulated. Instead, it dynamically migrates data across three logical layers based on changes in the data's value over time. The three logical layers of this invention include a real-time memory layer, a hot data layer, and a cold data layer.

[0115] 1. The real-time memory layer provides the highest precision.

[0116] The real-time memory layer stores all sampled data from the most recent period (i.e., the current sampling period). Regardless of changes in device parameter values, the original sampling period and original time resolution are maintained. The real-time memory layer provides a sub-second response data source for the real-time monitoring screen of the device's host computer user interface, enabling both real-time monitoring and short-term playback. The user interface only accesses the real-time memory layer, completely avoiding database queries and thus eliminating interface lag. For example, the real-time memory layer can be a circular buffer in memory. Data in the real-time memory layer can be automatically discarded as the buffer rolls, requiring no complex management.

[0117] 2. The hot data layer reflects high precision but is compressed process precision.

[0118] The hot data layer does not store all original sampling points; instead, it stores records of parameter changes generated by the change-aware engine, as well as context snapshots triggered by events. Therefore, the "accuracy" of the hot data layer is not "accuracy of all original points," but rather "accuracy in preserving critical changes" and "accuracy for process reconstructibility." The hot data layer is used to provide high-precision, high-value data required for process backtracking, fault diagnosis, and compliance audits. Data is retained in the hot data layer for several days to several weeks until migration trigger conditions are met. For example, the hot data layer may include a set of optimized tables in a relational database or a high-performance time-series database.

[0119] The "Change Awareness Engine" filters redundancy from the hot data layer at the source. This is the first intelligent checkpoint for data entering the hot data layer, ensuring that only "valuable changes" are persistently saved to the hot data layer.

[0120] Specifically, such as Figure 4 As shown, the system still collects all target device parameters at a fixed high frequency (e.g., 100ms), but for each current device parameter value... It will be compared with the latest persistent parameter value of the target device parameter in the parameter status mapping table in the hot data layer. A comparison is made to determine the target parameter change value. A target change threshold is used to detect the target parameter change value and determine whether the current device parameter value is a valid change. For valid changes, a lightweight target hot data record is generated, containing: [parameter ID, timestamp, new value, change amount]. Fixed data acquisition ensures real-time monitoring without loss, while change determination achieves precise reduction of storage load. The two are decoupled, balancing the real-time performance and cost-effectiveness of semiconductor data storage.

[0121] Optionally, the target change threshold can be configured individually for each target device parameter. .

[0122] For example, the target parameter change value includes the target parameter absolute change value and the target parameter relative change value. The target change threshold includes the target absolute change threshold and the target relative change threshold.

[0123] For example, for target equipment parameters with fixed dimensions, clearly defined allowable deviation ranges, or sensitivity to deviations from fixed values, a target absolute change threshold is used to determine the absolute change value of the target parameter and whether to persistently store the current equipment parameter value in the thermal data layer. For instance, target equipment parameters with fixed dimensions, clearly defined allowable deviation ranges, or sensitivity to deviations from fixed values ​​include pressure, temperature setpoints, valve opening, position quantities, status quantities, on / off quantities, and enumerated parameters.

[0124] For example, for target equipment parameters with a large measurement range, significant differences in sensitivity to target operating conditions under different operating conditions, or sensitivity to proportional changes, a target relative change threshold is used to determine the relative change value of the target parameter and whether to persistently store the current equipment parameter value to the thermal data layer. For example, target equipment parameters with a large measurement range, significant differences in sensitivity to target operating conditions under different operating conditions, or sensitivity to proportional changes include flow rate, power, rotational speed, concentration, and current.

[0125] For example, for preset key parameters (i.e. parameters set manually in the process), a method of jointly determining the target absolute change threshold and the target relative change threshold is adopted. That is, the target parameter absolute change value and the target parameter relative change value are calculated separately. When the target parameter absolute change value meets the target absolute change threshold and the target parameter relative change value meets the target relative change threshold, the corresponding current equipment parameter value is persistently stored in the thermal data layer.

[0126] The logic for determining absolute change is as follows: ; In the formula, The current device parameter value for the target device; This is the latest persistent parameter value of the target device in the thermal data layer; The target parameter absolute change value is the current device parameter value. The target absolute change threshold for the target device parameters.

[0127] The logic for judging relative changes is as follows: ; In the formula, The current device parameter value for the target device; This is the latest persistent parameter value of the target device in the thermal data layer; This represents the relative change of the target parameter value relative to the current device parameter value. The target relative change threshold for the target device parameters.

[0128] Optionally, to prevent noise interference, anti-jitter processing is performed when the current device parameter value is first detected to exceed the target change threshold. This involves starting a configurable target delay window (e.g., for 3 sampling periods), and the current device parameter value is considered a valid change only if the change is continuously confirmed within the target delay window.

[0129] For example, the target change threshold can be a fixed threshold or a dynamic threshold. The dynamic threshold can be adjusted based on the target equipment operating conditions, the target process stage, and / or historical statistical characteristics.

[0130] 1) Dynamically adjust the target change threshold based on the target equipment operating conditions.

[0131] Different equipment operating conditions have different noise levels and business sensitivity, and the corresponding target change thresholds also differ. For example, target equipment operating conditions may include standby, heating, vacuuming, air intake, steady-state processing, cleaning, alarm recovery, and shutdown.

[0132] Specifically, for highly fluctuating operating conditions (such as temperature rise, vacuuming, or instantaneous switching), the target change threshold can be increased to reduce noise writes. For steady-state critical operating conditions (such as etching steady state or endpoint control stage), the target change threshold can be decreased to improve the ability to capture minute offsets. For fault recovery operating conditions, the target change threshold can be decreased and anchor point writes can be increased to preserve a more complete recovery process.

[0133] 2) Dynamically adjust the target change threshold based on the target process stage.

[0134] For example, the target process stages may include a evacuation stage, a pressure stabilization stage, a precursor introduction stage, an etching stage, a purging stage, a cooling stage, and a transition stage. The target variation threshold for the same target equipment parameter may differ in different target process stages. For instance, the allowable fluctuation for the same chamber pressure parameter may be only ±0.2 in the etching stage, while a larger fluctuation is allowed in the evacuation stage.

[0135] Specifically, for critical process steps, the target change threshold can be lowered. For transition phases, the target change threshold can be increased. Within the preset sampling period before and after step switching, the target change threshold can be adjusted.

[0136] 3) Dynamically adjust the target change threshold based on historical statistical characteristics.

[0137] Examples of historical statistical characteristics include mean, standard deviation, absolute deviation of median, mean rate of change, frequency of fluctuation, or duration of continuous drift.

[0138] Specifically, historical device parameter values ​​within a historical sliding time window can be obtained for the current device parameter value. The mean, standard deviation, median absolute deviation, mean rate of change, fluctuation frequency, or duration of continuous drift for each historical device parameter value can be statistically analyzed to obtain the historical statistical characteristics of the current device parameter value. Based on the target device parameter type, a target statistical feature can be selected from the historical statistical features. Using the historical statistical feature adjustment formula, a target adjustment value for the target change threshold is determined based on the target statistical feature. This target adjustment value is then used to linearly adjust the target change threshold.

[0139] Optionally, the influence of the target equipment operating condition, the target process stage, and historical statistical characteristics can be weighted and summed to obtain a target adjustment value for the target change threshold. This target adjustment value is then used to dynamically adjust the target change threshold. Thus, the target change threshold is no longer a single fixed value, but a dynamic threshold associated with "target equipment parameters + target equipment operating condition + target process stage + historical statistical characteristics".

[0140] In the data storage phase, the decision to store the current equipment parameter value in the hot data layer is not solely based on whether the target parameter change between the current equipment parameter value and the latest persistent parameter value in the parameter status mapping table exceeds the target change threshold. It can also combine at least one of the following: critical event markers, process stage boundary markers, periodic anchor point markers, and abnormal trend judgment markers, to jointly determine the current equipment parameter value.

[0141] Specifically, when the current equipment parameter value has at least one of the following markers: target critical event marker, target process stage boundary marker, target periodic anchor point marker, and target abnormal trend judgment marker, the current equipment parameter value is stored in the thermal data layer. For example, target critical events include alarm triggering, process step switching, recipe switching, start / stop, cleaning start or end, etc. Target process stage boundaries include the first or last sampling time of the current batch, the current wafer, or the current process step, etc. Target abnormal trends are used to characterize whether the data corresponding to the current equipment parameter value deviates from the normal range. For example, the rate of change, fluctuation amplitude, or continuous drift trend of the current equipment parameter value exceeds a preset judgment condition.

[0142] Through the above-mentioned multi-condition joint triggering mechanism, the hot data layer can not only save change points, but also key event points, process stage boundary points, periodic anchor points, and abnormal trend judgment points, thereby balancing storage compression rate and process traceability.

[0143] 3. The cold data layer exhibits statistical accuracy with low precision but high compression.

[0144] The cold data layer stores historical semiconductor data migrated from the hot data layer and processed through time aggregation. For example, it aggregates raw data with 100ms precision into statistical values ​​with a 1-minute granularity. These statistical values ​​include the mean, maximum, minimum, or sample size. In other words, the cold data layer retains "statistical precision" rather than pursuing instantaneous raw waveform-level precision. The cold data layer supports large-volume, low-precision tasks such as long-term trend analysis and capacity report generation, significantly saving storage space. It is also used for long-term or permanent archiving. For example, the cold data layer may include a column-oriented database or a compressed file system.

[0145] When the migration trigger condition is met, data is migrated from the hot data layer to the cold data layer. The migration trigger condition can include time-related conditions, access conditions, or hot data layer storage utilization conditions. For example, the time-related condition could be the data generation time. (e.g., 30 days). Access criteria are based on the data being accessed most recently. Number of visits within 7 days (e.g.) The hot data layer storage utilization condition is the hot data layer storage utilization rate. (e.g., 80%).

[0146] During the data query phase, when a user initiates a data query request, the system no longer blindly scans the entire database, but instead plans the optimal path like intelligent navigation.

[0147] Specifically, the process begins by analyzing the time range, target device parameters, and data precision requirements of the data query request. Based on the analysis results, preset rules are applied to determine the target data layer to access.

[0148] For example, if the query time range The time window for the real-time memory layer only accesses the real-time memory layer, resulting in the fastest response.

[0149] For example, if raw accuracy and a suitable time span are required, routing is performed to the thermal data layer to obtain the target thermal data record. After storing the current device parameter value in the thermal data layer, target context identifiers can be assigned to the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current device parameter value. The target context identifier includes a combination of fields such as device identifier, cavity identifier, batch identifier, wafer representation, recipe identifier, and process step identifier. Target time identifiers are added to the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current device parameter value. The target time identifier includes a timestamp or start and end times. Correspondingly, during the data query phase, a data query request for the target device parameters is obtained. Based on the target time conditions included in the data query request, the data in the thermal data layer is initially screened to obtain the main query results. Each main query result is identified to obtain the target context conditions. Based on the target context conditions, a correlation query is performed on the log record, alarm record, recipe step record, and wafer processing record corresponding to the target device parameters to obtain the target query results.

[0150] For example, if performing long-term trend analysis (such as monthly reports), the data is first routed to the cold data layer to obtain aggregated data, which is extremely efficient.

[0151] This solution introduces a "change-aware" mechanism to store frequently changing data, reducing invalid writes by 60%-85% at the source. This curbs storage bloat, reduces data volume at the source, and improves storage efficiency. Furthermore, it establishes a three-tier logical architecture based on data business value and access frequency, enabling intelligent tiering of data storage. This ensures high-value data resides in the efficient storage layer, optimizes resource allocation, and achieves optimal resource configuration. Simultaneously, intelligent routing is designed to automatically route queries to the optimal data layer based on query intent, avoiding unnecessary full database scans, ensuring stable query response times, and guaranteeing query performance.

[0152] Specifically, addressing the problem of extreme data redundancy and storage resource waste caused by periodic full writes in existing technologies, and considering that most process parameters in semiconductor devices change slowly under steady-state conditions, traditional methods write the same data 10 times per second, resulting in 60%-90% invalid storage. This invention introduces an adaptive data writing mechanism based on parameter change awareness. By generating storage records only when parameter values ​​undergo "valid changes," it reduces most redundant data writing at the source, lowering the amount of effective stored data by an order of magnitude, significantly slowing down database expansion, and directly reducing storage costs. It also significantly reduces the database write frequency and disk I / O pressure, reduces system I / O load, frees up valuable computing and communication resources for the host computer, and improves overall system stability. Through a matching "parameter status mapping table" recording the last valid value of each parameter, it ensures that even when only storing "change points," the system can completely trace back the device status at any historical moment, resolving the contradiction between "storing less" and "seeing more," and maintaining data semantic integrity.

[0153] To address the issues of mixed hot and cold data and rigid resource allocation in existing simple physical tiers like "memory cache + disk database," and considering that traditional architectures cannot distinguish between high-value real-time monitoring data, mid-frequency analytical backtracking data, and low-frequency archived data, leading to inefficient use of high-efficiency storage media, a logical three-tier dynamic storage architecture based on data business value and timeliness is introduced. This architecture intelligently allocates data to the real-time memory layer, hot data layer, and cold data layer according to access frequency and accuracy requirements, ensuring that high-value data always resides on high-performance storage media, maximizing system resource utilization, and achieving optimal storage resource allocation. Furthermore, it decouples business logic from hardware dependencies; the storage tiers are defined based on data value rather than physical media, making the system architecture more flexible and adaptable to different hardware environments (such as changing database types or adding distributed storage), improving the scalability and portability of the solution. Finally, clear data tiering provides explicit path planning and pruning conditions for subsequent intelligent query routing, laying the foundation for efficient queries and serving as a prerequisite for query acceleration.

[0154] To address the issue of linearly decreasing query performance with increasing data volume caused by full table scans required for historical queries in existing technologies, this paper introduces an intelligent multi-path query routing algorithm based on query intent recognition. This algorithm automatically routes queries to the most suitable data layer (e.g., recent data in memory, precise historical data in the hot layer, and long-term trends in the cold layer) by analyzing the time range and precision requirements of the query request. This avoids invalid scans of non-target data layers, reducing complex query response time from minutes to seconds, achieving an order-of-magnitude improvement in query performance. Furthermore, the real-time monitoring interface is explicitly configured to access only the real-time memory layer, completely isolating it from database I / O pressure. This fundamentally eliminates the lag in the host computer interface caused by resource contention for historical data queries, ensuring absolutely smooth real-time monitoring. Finally, a transparent user experience is provided. The routing process is completely transparent to the user; they do not need to understand the details of data layering to obtain optimal query performance, simplifying operational complexity.

[0155] Example 3 Figure 5 This is a schematic diagram of a data storage device according to Embodiment 3 of the present invention. This embodiment of the present invention is applicable to the storage of semiconductor data in a hot data layer. The device can execute a data storage method and can be implemented in hardware and / or software. The device can be configured in an electronic device that carries data storage functionality.

[0156] See Figure 5 The data storage device shown includes: a current device parameter value acquisition module 501, a target parameter change value determination module 502, and a first target data storage module 503. The current device parameter value acquisition module 501 is used to acquire the current device parameter value of the target device parameter during the data storage phase; the target parameter change value determination module 502 is used to compare the current device parameter value with the latest persistent parameter value of the target device parameter in the hot data layer to obtain the target parameter change value; and the first target data storage module 503 is used to store the current device parameter value in the hot data layer based on the target parameter change value.

[0157] The technical solution of this invention compares the current device parameter value with the latest persistent parameter value of the target device parameter in the hot data layer during the data storage stage. Based on the change value of the target parameter, the current device parameter value is stored in the hot data layer. By judging the change value of the target parameter, the hot data layer stores the effective change value of the target device parameter, thereby achieving precise load reduction of the hot data layer and improving the effectiveness of hot data layer storage.

[0158] In an optional embodiment of the present invention, the target parameter change value determination module 502 includes: a first target parameter change value determination unit, used to compare the current device parameter value with the latest persistent parameter value of the target device parameter in the thermal data layer based on the target dimension, target range, target allowable deviation range, target change preference, and target operating condition sensitivity of the target device parameter, and determine the target parameter change value; wherein, the target parameter change value includes the absolute change value of the target parameter or the relative change value of the target parameter; correspondingly, the first target data storage module 503 includes: a first target data storage unit, used to perform separate detection of the target parameter change value using the target change threshold of the target device parameter, obtain the target separate detection result of the current device parameter value, and store the current device parameter value in the thermal data layer based on the target separate detection result; wherein, the target change threshold includes the target absolute change threshold or the target relative change threshold.

[0159] In an optional embodiment of the present invention, the target parameter change value determination module 502 further includes: a preset key parameter detection unit, used to detect whether the target device parameter is a preset key parameter before comparing the current device parameter value with the latest persistent parameter value of the target device parameter in the thermal data layer according to the target dimension, target range, target allowable deviation range, target change preference and target operating condition sensitivity of the target device parameter; a second target parameter change value determination unit, used to compare the current device parameter value with the latest persistent parameter value of the target device parameter in the thermal data layer when the target device parameter is a preset key parameter, to determine the absolute change value of the target parameter and the absolute change value of the target parameter; correspondingly, the first target data storage module 503 further includes: a second target data storage unit, used to perform joint detection of the absolute change value of the target parameter and the absolute change value of the target parameter using the target absolute change threshold and the target relative change threshold of the target device parameter when the target device parameter is a preset key parameter, to obtain the target joint detection result of the current device parameter value, and to store the current device parameter value in the thermal data layer based on the target joint detection result.

[0160] In an optional embodiment of the present invention, the first target data storage module 503 includes: a target change threshold adjustment unit, which is used to dynamically adjust the target change threshold according to the target equipment operating condition, target process stage and / or historical statistical characteristics corresponding to the target equipment parameters after determining the target change threshold based on the target dimension, target range, target allowable deviation range, target change preference and target operating condition sensitivity of the target equipment parameters.

[0161] In an optional embodiment of the present invention, the device further includes: a target marker detection module, configured to perform marker detection on the current device parameter value after obtaining the current device parameter value of the target device parameter during the data storage stage, to obtain a target marker detection result of the current device parameter value; and a second target data storage module, configured to perform hot data layer storage on the current device parameter value according to the target marker detection result.

[0162] In an optional embodiment of the present invention, the second target data storage module includes: a third target data storage unit, used to perform hot data layer storage of the current equipment parameter value based on the target key event marker detection result, the target process stage boundary marker detection result, the target periodic anchor point marker detection result and / or the target abnormal trend judgment marker detection result.

[0163] In an optional embodiment of the present invention, the device further includes: a record representation allocation module, configured to, after storing the current device parameter value in a thermal data layer according to the target parameter change value, assign target context identifiers and target time identifiers to the target thermal data record, target log record, target alarm record, target recipe step record, and target wafer processing record corresponding to the current device parameter value; a data query request acquisition module, configured to acquire a data query request for the target device parameter during the data query phase; a main query result determination module, configured to perform preliminary screening of the target time identifiers of each thermal data record of the target device parameter in the thermal data layer according to the target time condition contained in the data query request, and obtain the main query result; a main query result identifier identification module, configured to identify the identifiers of each main query result to obtain the target context condition; and a target query result determination module, configured to perform an association query on the target context identifiers of the log record, alarm record, recipe step record, and wafer processing record corresponding to the target device parameter according to the target context condition, and obtain the target query result; wherein, the target query result includes the target log record, target alarm record, target recipe step record, and target wafer processing record associated with the target device parameter.

[0164] In an optional embodiment of the present invention, the device further includes: a cold data migration module, configured to, after storing the current device parameter value in the hot data layer according to the target parameter change value, when detecting that the target hot data in the edge-side hot data layer has reached the migration trigger condition, upload the target hot data from the edge-side hot data layer to the cloud cold data layer, and simultaneously cache the target hot data in the edge-side hot data layer, add a data migration identifier to the target hot data in the edge-side hot data layer, and retain the current query position of the target hot data as the edge-side hot data layer; and a cold data index storage module, configured to, after the target hot data in the edge-side hot data layer has been uploaded to the cloud cold data layer, delete the data migration identifier of the target hot data, adjust the current query position of the target hot data to the cloud cold data layer, and store the target cold data index corresponding to the target hot data in the edge-side cold data index layer.

[0165] The data storage device provided in the embodiments of the present invention can execute the data storage method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0166] In the technical solutions of this invention, the acquisition, storage, and application of current device parameter values ​​of target device parameters, etc., all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0167] Example 4 Figure 6 A schematic diagram of an electronic device 600 that can be used to implement embodiments 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 processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0168] like Figure 6As shown, the electronic device 600 includes at least one processor 601 and a memory, such as a read-only memory (ROM) 602 and a random access memory (RAM) 603, communicatively connected to the at least one processor 601. The memory stores computer programs executable by the at least one processor. The processor 601 can perform various appropriate actions and processes based on the computer program stored in the ROM 602 or loaded into the RAM 603 from storage unit 608. The RAM 603 can also store various programs and data required for the operation of the electronic device 600. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0169] Multiple components in electronic device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of displays, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows electronic device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0170] Processor 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 601 performs the various methods and processes described above, such as data storage methods.

[0171] In some embodiments, the data storage method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by processor 601, one or more steps of the data storage method described above may be performed. Alternatively, in other embodiments, processor 601 may be configured to execute the data storage method by any other suitable means (e.g., by means of firmware).

[0172] Various implementations of the systems and techniques described above herein 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), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0173] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a separate software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0174] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0175] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: 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 provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0176] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0177] A computing system can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system. It addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability.

[0178] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0179] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A data storage method, characterized in that, The method includes: During the data storage phase, the current device parameter values ​​of the target device parameters are obtained; The current device parameter value is compared with the latest persistent parameter value of the target device parameter in the thermal data layer to obtain the target parameter change value; Based on the change value of the target parameter, the current device parameter value is stored in a thermal data layer.

2. The data storage method according to claim 1, characterized in that, The step of comparing the current device parameter value with the latest persistent parameter value of the target device parameter in the thermal data layer to obtain the target parameter change value includes: Based on the target dimensions, target range, target allowable deviation range, target change preference, and target operating condition sensitivity of the target equipment parameters, the current equipment parameter value is compared with the latest persistent parameter value of the target equipment parameters in the thermal data layer to determine the target parameter change value; wherein, the target parameter change value includes the absolute change value or the relative change value of the target parameter; Accordingly, the step of storing the current device parameter value in a thermal data layer based on the target parameter change value includes: Using the target change threshold of the target device parameter, the change value of the target parameter is detected individually to obtain the target individual detection result of the current device parameter value, and the current device parameter value is stored in the hot data layer based on the target individual detection result; wherein, the target change threshold includes the target absolute change threshold or the target relative change threshold.

3. The data storage method according to claim 2, characterized in that, Before comparing the current device parameter value with the latest persistent parameter value of the target device parameter in the thermal data layer based on the target dimensions, target range, target allowable deviation range, target change preference, and target operating condition sensitivity of the target device parameters to determine the target parameter change value, the method further includes: Detect whether the target device parameters are preset key parameters; When the target device parameter is a preset key parameter, the current device parameter value is compared with the latest persistent parameter value of the target device parameter in the thermal data layer to determine the absolute change value of the target parameter and the absolute change value of the target parameter. Accordingly, the step of storing the current device parameter value in a thermal data layer based on the target parameter change value further includes: When the target device parameter is a preset key parameter, the target absolute change threshold and the target relative change threshold of the target device parameter are used to jointly detect the absolute change value of the target parameter and the absolute change value of the target parameter to obtain the target joint detection result of the current device parameter value, and the current device parameter value is stored in the hot data layer based on the target joint detection result.

4. The data storage method according to claim 2, characterized in that, Before obtaining the target individual detection result of the current device parameter value by individually detecting the target parameter change value using the target change threshold of the target device parameter, the method further includes: The target change threshold is dynamically adjusted based on the target equipment operating conditions, target process stages, and / or historical statistical characteristics corresponding to the target equipment parameters.

5. The data storage method according to claim 1, characterized in that, After obtaining the current device parameter value of the target device parameter during the data storage phase, the method further includes: The current device parameter value is marked and detected to obtain the target mark detection result of the current device parameter value; Based on the target marker detection results, the current device parameter values ​​are stored in a thermal data layer.

6. The data storage method according to claim 5, characterized in that, The step of storing the current device parameter values ​​in a hot data layer based on the target marker detection result includes: Based on the target critical event marker detection results, target process stage boundary marker detection results, target periodic anchor point marker detection results, and / or target abnormal trend determination marker detection results, the current equipment parameter values ​​are stored in a thermal data layer.

7. The data storage method according to claim 1, characterized in that, After storing the current device parameter value in a thermal data layer based on the target parameter change value, the method further includes: Assign target context identifiers and target time identifiers to the target thermal data records, target log records, target alarm records, target recipe step records, and target wafer processing records corresponding to the current equipment parameter values; During the data query phase, a data query request is obtained to retrieve the parameters of the target device; Based on the target time condition included in the data query request, the target time identifier of each thermal data record of the target device parameter in the thermal data layer is initially screened to obtain the main query result; The main query results are identified to obtain the target context conditions; Based on the target context conditions, the target context identifiers of the log records, alarm records, recipe step records, and wafer processing records corresponding to the target equipment parameters are correlated and queried to obtain target query results; wherein, the target query results include the target log records, target alarm records, target recipe step records, and target wafer processing records associated with the target equipment parameters.

8. The data storage method according to claim 1, characterized in that, After storing the current device parameter value in a thermal data layer based on the target parameter change value, the method further includes: When the target hot data in the edge hot data layer is detected to have reached the migration trigger condition, the target hot data is uploaded from the edge hot data layer to the cloud cold data layer. At the same time, the target hot data is cached in the edge hot data layer, a data migration identifier is added to the target hot data in the edge hot data layer, and the current query position of the target hot data is retained as the edge hot data layer. After the target hot data in the edge-side hot data layer has been uploaded to the cloud cold data layer, the data migration identifier of the target hot data is deleted, the current query position of the target hot data is adjusted to the cloud cold data layer, and the target cold data index corresponding to the target hot data is stored in the edge-side cold data index layer.

9. A data storage device, characterized in that, The device includes: The current device parameter value acquisition module is used to acquire the current device parameter value of the target device during the data storage phase. The target parameter change value determination module is used to compare the current device parameter value with the latest persistent parameter value of the target device parameter in the thermal data layer to obtain the target parameter change value. The first target data storage module is used to store the current device parameter value in a hot data layer according to the target parameter change value.

10. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the data storage method according to any one of claims 1-8.

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