Vacuum process parameter intelligent configuration management system

Abstract

The present application relates to the technical field of vacuum process parameter management, and discloses a kind of vacuum process parameter intelligent configuration management system, the system includes central control unit, parameter acquisition module, configuration adjustment module, system state tracking module and interactive prompt module etc.Parameter acquisition module is to the data acquisition of pre-evacuation, vacuum maintenance and broken empty recovery stage, judges abnormal signal and sends to relevant module, configuration adjustment module correction, system state tracking module tracks configuration process.It also includes system reliability evaluation module, process cycle integration module and regional parameter abnormality evaluation module, realizes system reliability evaluation, process cycle integration analysis and cavity surface regional parameter abnormality evaluation.The system can comprehensively collect parameters, intelligently process exceptions, evaluate system reliability and process cycle, improve vacuum process management efficiency and stability.

Description

Technical Field

[0001] The present invention relates to the technical field of vacuum process parameter management, and in particular to an intelligent configuration management system for vacuum process parameters. Background Art

[0002] In modern industrial production, vacuum processes are widely used in many key areas, including semiconductor manufacturing, vacuum coating, and vacuum heat treatment. The precise configuration and effective management of process parameters directly impact product quality, production efficiency, and equipment operational stability. However, traditional methods for managing vacuum process parameters present numerous challenges that require urgent resolution.

[0003] When it comes to exception handling, traditional systems lack intelligent anomaly detection and adaptive correction mechanisms. When process parameters deviate, they are unable to quickly and accurately generate anomaly signals and make corresponding configuration adjustments. For example, during the vacuum hold phase, if parameters such as chamber temperature stability, humidity, or airflow uniformity deviate, traditional systems may not be able to detect these subtle changes in a timely manner, or even if they do, they may be unable to make effective adjustments quickly, resulting in an unstable vacuum environment and affecting product processing quality.

[0004] Furthermore, conventional systems are significantly deficient in assessing parameter anomalies across different regions of the vacuum chamber surface. They are unable to conduct detailed analysis and assessment of parameter conditions across the chamber surface, hindering timely detection of potential anomalies in specific areas, such as localized leaks and surface contamination. These issues can escalate over time, ultimately impacting the overall vacuum process. Summary of the Invention

[0005] The purpose of the present invention is to provide a vacuum process parameter intelligent configuration management system to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solutions: an intelligent configuration management system for vacuum process parameters, the system comprising: a central control unit, a parameter acquisition module, a configuration adjustment module, a system status tracking module and an interactive prompt module.

[0007] The parameter acquisition module collects data during the vacuum process execution process, which includes the pre-vacuum stage, the vacuum holding stage, and the air-breaking recovery stage. In the pre-vacuum stage, the vacuum chamber is initially evacuated and the parameter data in the chamber is collected simultaneously. After the pre-vacuum is completed, the vacuum holding stage is entered to continuously collect parameters of the stable vacuum environment. After the holding stage is completed, the air-breaking recovery stage is entered to collect parameter data during the cavity re-pressurization process;

[0008] The parameter acquisition module determines whether a pre-extraction abnormality signal, a holding abnormality signal, or a breaking air abnormality signal is generated through analysis, and sends the pre-extraction abnormality signal, the holding abnormality signal, or the breaking air abnormality signal to the configuration adjustment module and the interactive prompt module via the central control unit; the interactive prompt module displays the pre-extraction abnormality signal, the holding abnormality signal, or the breaking air abnormality signal and issues a reminder, and the configuration adjustment module adaptively corrects the data acquisition process upon receiving the pre-extraction abnormality signal, the holding abnormality signal, or the breaking air abnormality signal to optimize the configuration effect;

[0009] The system status tracking module tracks the configuration process of the configuration adjustment module, records the moment when the configuration adjustment module receives the pre-extraction abnormal signal, the holding abnormal signal or the breaking abnormal signal and marks it as the abnormal reception moment, and uses the abnormal reception moment as the time starting point to start timing until the configuration process returns to stability to obtain the response time. If the response time does not exceed the preset response time threshold, the corresponding configuration process is judged to be valid and the valid times are accumulated. If the response time exceeds the preset response time threshold, the corresponding configuration process is judged to be invalid and the invalid times are accumulated.

[0010] Preferably, the specific operation process of the parameter acquisition module is as follows:

[0011] During the pre-vacuum stage, the vacuum change rate, pressure fluctuation value and leakage rate values ​​in the cavity are collected, and the difference between the vacuum change rate and the standard value of the vacuum change rate is marked as the vacuum deviation value, and the difference between the pressure fluctuation value and the standard value of the pressure fluctuation is marked as the pressure deviation value; the difference between the leakage rate and the standard value of the leakage rate is marked as the leakage deviation value; if the vacuum deviation value, pressure deviation value or leakage deviation value exceeds the corresponding preset range, a pre-vacuum abnormality signal is generated;

[0012] During the vacuum holding phase, the temperature stability of the chamber parameter data is compared with the standard temperature and marked as the temperature offset value. The humidity value and airflow uniformity value in the chamber are collected, and the difference between the humidity value and the humidity standard value is marked as the humidity deviation value. Similarly, the airflow deviation value is obtained. If the temperature offset value, humidity deviation value or airflow deviation value exceeds the corresponding preset range, a holding abnormality signal is generated.

[0013] During the air break recovery stage, the pressure recovery rate, impurity mixing amount and surface cleanliness value in the cavity are collected, and the difference between the pressure recovery rate and the pressure recovery rate standard value is marked as the pressure deviation value. Similarly, the impurity deviation value and cleanliness deviation value are obtained; if the pressure deviation value, impurity deviation value or cleanliness deviation value exceeds the corresponding preset range, an air break abnormality signal is generated.

[0014] Preferably, the system further comprises a system reliability evaluation module in communication with the central control unit, the system reliability evaluation module collects the frequency and intensity of the pre-extraction abnormal signal, the holding abnormal signal and the air-breaking abnormal signal generated in the corresponding process and marks them as abnormal occurrence values, and collects the number of failures and the number of valid times in the corresponding process, and marks the ratio of the number of failures to the valid times as the adjustment deviation value;

[0015] The abnormal occurrence value and the adjustment deviation value are numerically calculated to obtain the system evaluation value. If the system evaluation value exceeds the preset system evaluation threshold, a system abnormality warning signal is generated. If the system evaluation value does not exceed the preset system evaluation threshold, a system normal warning signal is generated, and the system abnormality warning signal is sent to the interactive prompt terminal via the central control unit.

[0016] Preferably, the system also includes a process cycle integration module that is communicatively connected to the central control unit. The process cycle integration module is used to set a statistical period, summarize and analyze all execution processes of the vacuum process within the statistical period, generate an integrated warning signal or an integrated normal signal through analysis, and send the integrated warning signal to the interactive prompt module via the central control unit. When the interactive prompt module receives the integrated warning signal, it will issue a corresponding reminder.

[0017] Preferably, the specific analysis process of the process cycle integration module is as follows:

[0018] The configuration effect analysis is used to obtain the configuration efficiency value, and the number of times the system abnormal warning signal is generated within the statistical period is collected. The ratio of the number of occurrences of the system abnormal warning signal to the total number of process steps within the statistical period is marked as the abnormal occurrence ratio. The configuration efficiency value and the abnormal occurrence ratio are numerically calculated to obtain the process integration value; if the process integration value exceeds the preset process integration threshold, an integration warning signal is generated; if the process integration value does not exceed the preset process integration threshold, an integration normal signal is generated.

[0019] Preferably, the specific analysis process of the configuration effect analysis is as follows:

[0020] By analyzing and judging whether the corresponding process is a configuration-substandard process, the ratio of the number of configuration-substandard processes to the total number of process processes within the statistical period is marked as the substandard configuration rate, the difference between the configuration compliance value of the corresponding configuration-substandard process and the preset configuration compliance threshold is marked as the compliance deviation value, all compliance deviation values ​​of the statistical period are summed up and averaged to obtain the average deviation value, and all configuration compliance values ​​within the statistical period are summed up and averaged to obtain the average configuration value; the substandard configuration rate, the average deviation value and the average configuration value are numerically calculated to obtain the configuration efficiency value.

[0021] Preferably, the specific judgment of the configuration non-compliance process is as follows:

[0022] The number of actual configuration parameters corresponding to the process is collected, and the ratio of the number of actual configuration parameters to the number of standard configuration parameters is marked as a configuration compliance value.

[0023] Preferably, the system further comprises a region parameter abnormality evaluation module, and the process cycle integration module sends an integrated normal signal to the region parameter abnormality evaluation module through the data storage unit; when the integrated normal signal is received, the region parameter abnormality evaluation module analyzes the parameter conditions of each region of the vacuum cavity surface in all process cycles in the statistical cycle, determines whether to generate a surface parameter abnormality signal through the analysis, and sends the surface parameter abnormality signal to the interactive prompt module through the data storage unit.

[0024] Preferably, the specific analysis process of the region parameter abnormality evaluation module is as follows:

[0025] A plurality of detection regions are set on the surface of the vacuum cavity, and parameter deviation values of the detection regions are collected in the process; the parameter deviation values of all the detection regions are summed and averaged to obtain a characteristic reference value; the parameter deviation values of the detection regions are subtracted from the characteristic reference value and the absolute values are taken to obtain region deviation values; if the region deviation values exceed a preset region deviation threshold, the corresponding detection regions are marked as abnormal regions.

[0026] The total execution time of the vacuum process in the statistical cycle is collected, and the number of times that the corresponding detection regions are marked as abnormal regions in the statistical cycle is collected and marked as an abnormal frequency value; the abnormal frequency value is compared with the total execution time to obtain an abnormal frequency ratio; the corresponding detection regions are defined as a high abnormal region, a medium abnormal region or a low abnormal region through comparison and analysis; if the surface of the vacuum cavity has a high abnormal region, a surface parameter abnormality signal is generated; if the surface of the vacuum cavity does not have a high abnormal region, the ratio of the number of medium abnormal regions to the number of low abnormal regions is marked as a surface evaluation value; if the surface evaluation value exceeds a preset surface evaluation threshold, a surface parameter abnormality signal is generated.

[0027] Preferably, the specific analysis process of the comparison and analysis is as follows:

[0028] If the abnormal frequency ratio exceeds the maximum value of a preset abnormal frequency range, the corresponding detection region is defined as a high abnormal region; if the abnormal frequency ratio does not exceed the minimum value of the preset abnormal frequency range, the corresponding detection region is defined as a low abnormal region; if the abnormal frequency ratio is within the preset abnormal frequency range, the corresponding detection region is defined as a medium abnormal region.

[0029] Compared with the prior art, the present application has the following advantages:

[0030] The system collects data from all stages of the vacuum process through the parameter acquisition module, covering the pre-vacuuming, vacuum holding, and air-breaking recovery stages. A number of key parameters can be accurately collected at each stage, such as the vacuum degree change rate, pressure fluctuation value, and leakage rate during the pre-vacuuming stage, ensuring comprehensive acquisition of data information from the process and providing a solid data foundation for subsequent abnormality judgment and configuration adjustments. When the parameter acquisition module analyzes and finds that the parameter deviation exceeds the preset range, it can promptly generate a pre-vacuuming, holding, or air-breaking abnormality signal and send it to the configuration adjustment module and interactive prompt module through the central control unit. The interactive prompt module displays and reminds of the abnormality in real time, and the configuration adjustment module quickly makes adaptive corrections to the data acquisition process. This intelligent abnormality handling mechanism can quickly respond to process abnormalities, effectively improve process stability, and reduce product quality issues caused by parameter abnormalities.

[0031] The system status tracking module tracks the entire configuration adjustment process, recording the moment of abnormal reception and calculating the response time. It then compares the effectiveness of the configuration process against a preset threshold and accumulates the number of valid and invalid responses. This function enables a quantitative assessment of the effectiveness of configuration adjustments, providing clear data support for system optimization and helping to continuously improve the efficiency and accuracy of configuration adjustments.

[0032] The newly added system reliability assessment module collects anomaly occurrence values ​​(frequency and intensity of abnormal signals) and adjustment deviation values ​​(the ratio of failures to valid times), calculates them, and generates system evaluation values ​​based on these values. This module comprehensively assesses system reliability from multiple dimensions, enabling operators to promptly understand the overall system operating status, proactively identify potential issues, and take appropriate measures to ensure stable system operation.

[0033] The process cycle integration module sets a statistical cycle, summarizes and analyzes all process executions within the cycle, and generates integrated warnings or normal signals. By calculating the configuration efficiency value and the abnormality occurrence ratio, and then calculating the process integration value to judge the process status, it achieves overall control of the process cycle, helps to identify long-term trends and potential problems in the process, and provides a basis for continuous process optimization, improving production efficiency and product quality consistency.

[0034] The configuration effect analysis process accurately evaluates the configuration effect by calculating the substandard configuration rate, average deviation value and average configuration value, providing detailed data support for optimizing the configuration strategy, making the configuration adjustment more scientific and reasonable, and further improving the accuracy of process parameter configuration.

[0035] After receiving the integrated normal signal, the regional parameter anomaly assessment module conducts an in-depth analysis of the parameter conditions of each detection area on the vacuum chamber surface. By calculating characteristic baseline values ​​and regional deviation values, combined with the anomaly frequency ratio, the detection area is divided into high, medium, and low anomaly zones, and a surface parameter anomaly signal is generated accordingly. This module enables refined management of the chamber surface regional parameters and can promptly detect local anomaly areas, such as the presence of high anomaly areas or the ratio of medium to low anomaly areas exceeding a threshold. This facilitates targeted maintenance and repair by operators, preventing local problems from escalating, extending equipment life, and ensuring the stable execution of vacuum processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a working principle diagram of the vacuum process parameter intelligent configuration management system of the present invention;

[0037] Figure 2 Schematic diagram of the system reliability evaluation module;

[0038] Figure 3 Flowchart for integrating modules into process cycle;

[0039] Figure 4 This is a flowchart for configuration effect analysis. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] See also Figure 1-Figure 4 The present invention relates to an intelligent configuration management system for vacuum process parameters, which includes: a central control unit, a parameter acquisition module, a configuration adjustment module, a system status tracking module, and an interactive prompt module. The specific implementation steps are as follows:

[0042] The parameter acquisition module collects data during the vacuum process, which includes the pre-vacuum stage, the vacuum hold stage, and the vacuum recovery stage. During the pre-vacuum stage, the vacuum chamber is initially evacuated and parameter data is collected simultaneously. After the pre-vacuum stage is complete, the vacuum hold stage continues, continuously collecting parameters while maintaining a stable vacuum environment. After the hold stage ends, the vacuum recovery stage begins, collecting parameter data during the chamber's re-pressurization process.

[0043] The parameter acquisition module analyzes and determines whether a pre-extraction anomaly signal, a hold anomaly signal, or a breakout anomaly signal is generated. These anomaly signals are then sent via the central control unit to the configuration adjustment module and the interactive prompt module. The interactive prompt module displays the anomaly signals and issues a reminder. Upon receiving the anomaly signals, the configuration adjustment module adaptively modifies the data acquisition process to optimize the configuration.

[0044] The system status tracking module tracks the configuration process of the configuration adjustment module, records the moment the configuration adjustment module receives an abnormal signal and marks it as the abnormal reception moment. This moment is used as the starting point to count the response time until the configuration process returns to stability. If the response time does not exceed the preset response time threshold, the corresponding configuration process is considered valid and the number of valid times is accumulated. If the response time exceeds the preset response time threshold, the corresponding configuration process is considered invalid and the number of invalid times is accumulated.

[0045] Example 1:

[0046] The parameter acquisition module of this system has specific and clear operating procedures at different stages. During the pre-vacuum stage, the module will collect a number of key parameters in the vacuum chamber, including the vacuum degree change rate, pressure fluctuation value, and leakage rate value. For each collected parameter, the module will compare it with the corresponding standard value to calculate the difference value. Among them, the difference between the vacuum degree change rate and the vacuum degree change rate standard value is marked as the vacuum deviation value, the difference between the pressure fluctuation value and the pressure fluctuation standard value is marked as the pressure deviation value, and the difference between the leakage rate value and the leakage rate standard value is marked as the leakage deviation value. It should be made clear here that each parameter has its corresponding preset range, which is determined based on the standard requirements of the vacuum process and actual operating experience. When any of the vacuum deviation value, pressure deviation value, or leakage deviation value exceeds the corresponding preset range, it indicates that an abnormality has occurred in the pre-vacuum stage, and the parameter acquisition module will generate a pre-vacuum abnormality signal.

[0047] After entering the vacuum holding stage, the focus of the parameter acquisition module changes. At this time, the module will pay attention to the temperature stability of the parameter data in the cavity, compare it with the standard temperature, and calculate the deviation value. This deviation value is marked as the temperature offset value. At the same time, the module will also collect the humidity value and airflow uniformity value in the cavity. Similarly, the difference between the humidity value and the humidity standard value will be marked as the humidity deviation value, and the difference between the airflow uniformity value and the airflow uniformity standard value will be marked as the airflow deviation value. During the vacuum holding stage, temperature stability, humidity, and airflow uniformity are all important factors in maintaining a stable vacuum environment. Therefore, corresponding preset ranges are also set for these three parameters. When the temperature offset value, humidity deviation value, or airflow deviation value exceeds the corresponding preset range, it means that the environmental stability of the vacuum holding stage has been affected, and the parameter acquisition module will generate a holding abnormality signal.

[0048] During the air break recovery phase, the parameter acquisition module collects parameters related to the cavity re-pressurization process, including the pressure recovery rate, the amount of impurity inclusion, and the surface cleanliness value. Similar to the previous phase, the difference between the pressure recovery rate and the standard value of the pressure recovery rate is marked as the pressure deviation value, the difference between the amount of impurity inclusion and the standard value of the impurity inclusion is marked as the impurity deviation value, and the difference between the surface cleanliness value and the standard value of the surface cleanliness is marked as the cleanliness deviation value. The air break recovery phase is the last phase of the vacuum process. Whether the pressure recovery rate is reasonable, whether the amount of impurity inclusion is within a controllable range, and whether the surface cleanliness meets the requirements are all directly related to the final effect of the entire vacuum process. Therefore, strict preset ranges are also set for these three parameters. When the pressure deviation value, impurity deviation value, or cleanliness deviation value exceeds their respective preset ranges, it means that an abnormality has occurred in the air break recovery phase, and the parameter acquisition module will generate an air break abnormality signal.

[0049] The parameter acquisition module continuously collects and analyzes various parameters in real time throughout the three stages of the vacuum process. By comparing the collected data with standard values, calculating deviations, and comparing them with preset ranges, it determines whether any anomalies occur at each stage. If an anomaly is detected, a corresponding abnormality signal is generated and transmitted via the central control unit to the configuration adjustment module and the interactive prompt module. This design enables the system to promptly identify problems during vacuum process execution, providing an accurate basis for subsequent configuration adjustments and abnormality notifications.

[0050] During the pre-vacuum stage, the parameter acquisition module begins operating with the initial pumping of the vacuum chamber. During the pumping process, the module monitors changes in the vacuum level in real time, calculating its rate of change while also monitoring pressure fluctuations and chamber leaks. Comprehensive analysis of these three parameters enables timely detection of potential problems during the initial pumping process, such as insufficient pumping rate, unstable pressure, or chamber leaks. If a parameter's deviation exceeds the preset range, a pre-vacuum anomaly signal is generated, promptly notifying the relevant modules for processing and preventing the problem from escalating further.

[0051] The vacuum maintenance phase is crucial for ensuring the effectiveness of the vacuum process. During this phase, the parameter acquisition module continuously monitors temperature, humidity, and airflow uniformity. Unstable temperature can affect the physical and chemical properties of the materials within the chamber, abnormal humidity can cause condensation, and uneven airflow can affect process consistency. By monitoring and analyzing these three parameters in real time, a maintenance anomaly signal is generated when temperature, humidity, or airflow deviations exceed preset ranges, enabling timely configuration adjustments to maintain a stable vacuum environment.

[0052] The air break recovery phase is the final stage of the vacuum process. The parameter acquisition module is crucial for monitoring the pressure recovery rate, impurity ingress, and surface cleanliness. A pressure recovery rate that is too fast or too slow can affect subsequent process operations. Excessive impurity ingress can lead to chamber contamination, and substandard surface cleanliness can affect product quality. By monitoring and analyzing these parameters and generating air break anomaly signals, problems can be promptly identified during the final stage, ensuring the smooth completion of the entire vacuum process.

[0053] The parameter acquisition module follows strict logic and procedures during the operation of each stage. From parameter collection, comparison with standard values, calculation of deviation values, to comparison with preset ranges and generation of abnormal signals, each link is closely linked and interconnected. This meticulous and comprehensive design enables the system to accurately capture abnormal conditions at each stage of the vacuum process execution, providing a solid foundation for the stable operation and efficient work of the entire vacuum process parameter intelligent configuration management system. Through the effective operation of the parameter acquisition module, the system can promptly discover and feedback problems, provide accurate adjustment basis for the configuration adjustment module, and at the same time send reminders to operators through the interactive prompt module, thereby realizing the intelligent configuration and management of vacuum process parameters, improving the stability and reliability of the vacuum process, and ensuring that the process effect meets the expected requirements.

[0054] When the configuration adjustment module receives a hold anomaly signal, it will modify the data collection process during the vacuum hold phase. The system will first retrieve the current temperature, humidity, and airflow uniformity acquisition frequency and sensor sampling accuracy parameters for that phase. If the temperature offset exceeds the preset range, the temperature sensor sampling frequency will be increased, the data acquisition interval will be shortened, and the sensor's measurement reference will be calibrated to ensure more accurate acquisition of actual temperature values. If the humidity deviation value is abnormal, the humidity sensor's monitoring threshold range will be adjusted to better reflect the humidity fluctuation characteristics of the current process environment, reducing misjudgments due to environmental interference. When the configuration adjustment module receives an abnormal air breaking signal, for the air breaking recovery stage, if the pressure deviation value exceeds the preset range, the response sensitivity of the pressure sensor will be optimized to speed up the capture of pressure changes during the cavity re-pressurization process. At the same time, the filtering algorithm of the pressure data will be adjusted to reduce the impact of instantaneous pressure fluctuations on the acquisition results; if the impurity deviation value or the cleanliness deviation value is abnormal, the acquisition density of the impurity and cleanliness detection points on the cavity surface will be increased, and the duration of a single data acquisition will be extended to ensure that the impurity changes and surface cleanliness status inside the cavity during the re-pressurization process can be fully captured. In this way, the data acquisition process is adapted to the current process abnormality state, thereby optimizing the configuration effect.

[0055] Example 2:

[0056] This system is equipped with a system reliability evaluation module that is in communication with the central control unit. The operating logic of this module revolves around abnormal signals and configuration adjustment effects in the process. The system reliability evaluation module will collect the frequency and intensity of pre-extraction abnormal signals, holding abnormal signals and breaking air abnormal signals generated in the corresponding process in real time. These data are uniformly marked as abnormal occurrence values. Among them, frequency refers to the number of times each type of abnormal signal appears in a specific process, and the intensity reflects the degree of parameter deviation or the severity of the problem represented by the abnormal signal. At the same time, the module will also collect the number of failures and valid times in the corresponding process. The number of failures here refers to the number of times the system status tracking module determines that the configuration process has failed, and the valid number is the number of times the configuration process is determined to be valid. The module marks the ratio of the number of failures to the valid number as the adjustment deviation value.

[0057] After obtaining the anomaly occurrence value and the adjustment deviation value, the system reliability assessment module performs numerical calculations on these two sets of data to obtain the system assessment value. The numerical calculation method here is based on the logical rules of the system design and aims to comprehensively evaluate the reliability of the system through quantitative calculations. If the system assessment value exceeds the preset system assessment threshold, it means that the current system is experiencing frequent anomalies during operation or the configuration adjustment is ineffective, and reliability is at risk. At this time, the module will generate a system anomaly warning signal. If the system assessment value does not exceed the preset system assessment threshold, it indicates that the system operation is relatively stable and the reliability is within the normal range. The module will generate a system normal warning signal. The generated system anomaly warning signal will be sent to the interactive prompt terminal through the central control unit so that relevant personnel can be notified of system anomalies in a timely manner.

[0058] In the system reliability assessment module, if the frequencies of pre-extraction anomaly signals, holding anomaly signals, and air-breaking anomaly signals collected are 5, 3, and 2, respectively, and their corresponding intensity values ​​are converted to 0.8, 0.6, and 0.4, respectively, the anomaly occurrence value is calculated by weighting the frequency and intensity: for example, (5 × 0.8 + 3 × 0.6 + 2 × 0.4) = 4 + 1.8 + 0.8 = 6.6. Furthermore, if the configuration adjustment module has a valid count of 15 and a failure count of 5, the adjustment deviation is 5 ÷ 15 ≈ 0.33. In this case, the system assessment value is calculated by multiplying the anomaly occurrence value by the adjustment deviation: 6.6 × 0.33 ≈ 2.18. If the preset system assessment threshold is 2.0, since 2.18 exceeds this threshold, the system reliability assessment module generates a system anomaly warning signal, which is sent to the interactive prompt terminal via the central control unit to alert the operator of the current system reliability risk. In the process cycle integration module, if the total number of process steps in the statistical period is 50 and the number of system abnormality warning signals generated is 8, the abnormality occurrence ratio is 8 ÷ 50 = 0.16. In the configuration effect analysis, if the number of substandard configuration processes in the statistical period is 10, the substandard configuration rate is 10 ÷ 50 = 0.2. The differences between the configuration compliance values ​​of these substandard processes and the preset configuration compliance thresholds are 0.1, 0.2, 0.15, etc., respectively. The average deviation is 0.15 after summing and averaging. The average value of all configuration compliance values ​​is 0.85. The configuration efficiency value is obtained by calculating (1-0.2) × 0.85 - 0.15 = 0.8 × 0.85 - 0.15 = 0.68 - 0.15 = 0.53. The process integration value is obtained by summing the configuration efficiency value and the abnormality occurrence ratio, that is, 0.53 + 0.16 = If the preset process integration threshold is 0.69, then because 0.69 exceeds this threshold, the process cycle integration module generates an integrated warning signal and sends it to the interactive prompt module, prompting the operator to pay attention to the overall process status during this statistical period. These numerical calculation methods are based on the logical rules of system design and aim to comprehensively evaluate system reliability and process cycle status through quantitative calculations, providing data support for intelligent management of vacuum processes.

[0059] In addition, the system also includes a process cycle integration module that is in communication with the central control unit. The core function of this module is to periodically summarize and analyze the execution process of the vacuum process. The process cycle integration module first needs to set the statistical period, which can be flexibly set according to the actual process requirements and management needs. For example, it can be one day, one week, or one month. After setting the statistical period, the module will summarize all the execution processes of the vacuum process within the period, including the relevant data of the pre-vacuum stage, vacuum holding stage, and air-breaking recovery stage in each execution process, as well as various abnormal signals generated by the parameter acquisition module, the adjustment process of the configuration adjustment module, and the number of valid times and failure times recorded by the system status tracking module.

[0060] The process cycle integration module analyzes the aggregated data to generate an integrated warning signal or an integrated normal signal. When the analysis results indicate that there are many abnormalities in the process execution during the statistical period or that the configuration adjustment effect is unsatisfactory, the module will generate an integrated warning signal. If the analysis results indicate that the process execution is generally normal and all indicators are within a reasonable range, an integrated normal signal will be generated. The generated integrated warning signal will be sent to the interactive prompt module through the central control unit. After receiving the integrated warning signal, the interactive prompt module will issue a corresponding reminder to the operator in a set reminder method (such as flashing lights, sound prompts, or screen displays) so that the operator can promptly understand the overall status of the process execution within a certain period.

[0061] The System Reliability Assessment Module and the Process Cycle Integration Module do not operate independently; instead, they interact and collaborate through a central control unit. The data collected by the System Reliability Assessment Module, such as abnormality occurrence values ​​and adjustment deviation values, as well as the system abnormality warning signals and normal system warning signals generated by the module, serve as an important basis for analysis by the Process Cycle Integration Module. For example, after setting a statistical cycle, the Process Cycle Integration Module will collect the number of system abnormality warning signals generated by the System Reliability Assessment Module within that cycle and combine this with other data for comprehensive analysis.

[0062] During actual operation, the system reliability assessment module continuously evaluates the reliability of each process in real time, while the process cycle integration module provides macroscopic control of process execution over a longer timeframe. When the system reliability assessment module detects a high number of abnormality occurrences and a large adjustment deviation in a process, it generates a system abnormality warning signal, which is then transmitted to the process cycle integration module. The process cycle integration module aggregates the number of occurrences of these abnormality warning signals within a statistical period and calculates this with the total number of processes within that period to determine the abnormality occurrence ratio. By numerically calculating the abnormality occurrence ratio with the configuration effectiveness value (derived from configuration effect analysis), the process integration value is determined, which is then used to determine whether to generate an integrated warning signal.

[0063] The configuration performance value is a comprehensive index obtained by multi-dimensional analysis of the configuration effect of the process in the statistical period, and is used for quantitative evaluation of the overall effect of configuration adjustment. The calculation is based on three key parameters: the non-compliance configuration rate, the average deviation value and the average configuration value. The non-compliance configuration rate is the ratio of the number of processes with non-compliant configuration to the total number of processes in the statistical period, reflecting the overall proportion of non-compliant configuration. The average deviation value is the average of the sum of the difference between the configuration compliance value and the preset configuration compliance threshold in all non-compliant processes, representing the average deviation of non-compliant processes from the standard. The average configuration value is the average of the configuration compliance values of all processes in the statistical period, representing the average level of overall configuration.

[0064] When performing numerical operations, for example, in a certain statistical period, there are a total of 20 vacuum heat treatment processes, of which 4 are non-compliant processes. The non-compliance configuration rate is 4 ÷ 20 = 0.2. The difference between the configuration compliance value and the preset configuration compliance threshold for these 4 non-compliant processes is 0.12, 0.08, 0.15 and 0.05, respectively. The sum is 0.4, and the average deviation value is 0.4 ÷ 4 = 0.1. At the same time, the configuration compliance values of the 20 processes are 0.85, 0.92, 0.78, … (other values are omitted), the sum is 17.6, and the average configuration value is 17.6 ÷ 20 = 0.88. Assuming the numerical operation formula is (1-non-compliance configuration rate) × average configuration value - average deviation value, the configuration performance value of this statistical period is (1-0.2) × 0.88 - 0.1 = 0.8 × 0.88 - 0.1 = 0.704 - 0.1 = 0.604. Through such operation, the configuration performance value of this period is 0.604, which directly reflects the comprehensive effect of configuration adjustment at this stage.

[0065] This double evaluation mechanism enables the system to comprehensively monitor the running status of vacuum processes from both micro and macro perspectives. The system reliability evaluation module ensures fine control of each process, timely discovering problems in individual processes; the process period integration module analyzes the overall trend of process execution from the period perspective, avoiding the accumulation of systemic problems due to the neglect of individual process anomalies. The two complement each other and work together to provide more comprehensive and reliable protection for intelligent configuration management of vacuum process parameters, helping operators to timely discover potential reliability problems of the system and take appropriate optimization measures to improve the stability and production efficiency of vacuum processes.

[0066] When setting statistical cycles, the process cycle integration module considers factors such as process type, production pace, and management requirements. For vacuum processes with a faster production pace, a shorter statistical cycle may be set to facilitate timely identification and adjustment of issues. For processes with a slower production pace or extremely high process stability requirements, a longer statistical cycle may be set to observe the overall performance of the process over a longer period. During the summary analysis process, the module standardizes all process execution data within the statistical cycle to ensure comparability across different processes, resulting in more accurate and reliable analysis results.

[0067] Through the collaborative work of the System Reliability Assessment Module and the Process Cycle Integration Module, the system enables dynamic evaluation and optimization of vacuum process parameter configuration management. Whether it's abnormal fluctuations within a single process or periodic process trend changes, they can be captured and analyzed in a timely manner, providing data support for continuous improvement and optimization of the system, ensuring that the vacuum process always operates reliably and stably.

[0068] Example 3:

[0069] The specific analysis process of the process cycle integration module in this system is closely related to the configuration effect analysis. After setting the statistical period, the process cycle integration module will comprehensively process the vacuum process execution data within that period. The configuration efficiency value is obtained through configuration effect analysis, and the number of system abnormality warning signals generated within the statistical period is collected at the same time. The system abnormality warning signal here is generated by the system reliability evaluation module and reflects the occurrence of abnormal conditions in the process within that period. The module marks the ratio of the number of occurrences of system abnormality warning signals to the total number of process processes within the statistical period as the abnormality occurrence ratio. This ratio can intuitively reflect the frequency of abnormal conditions within the period.

[0070] The process cycle integration module performs numerical calculations on the configuration efficiency value and the anomaly occurrence ratio to obtain the process integration value. The numerical calculations here are based on the system's preset logical rules and aim to comprehensively evaluate the process execution status within the cycle in a quantitative manner. If the process integration value exceeds the preset process integration threshold, it means that during the statistical period, there may be many substandard configurations or a high frequency of anomalies during the process execution process. At this time, the module generates an integration warning signal. If the process integration value does not exceed the preset process integration threshold, it indicates that the overall process execution within the cycle is normal, and the module generates an integration normal signal.

[0071] The specific process of configuration effect analysis involves a multi-dimensional assessment of the configuration compliance status in the process. First, it is necessary to analyze and determine whether the corresponding process is a process that does not meet the configuration standards. This judgment is based on the calculation of the configuration compliance value. The number of standard configuration parameters and the actual number of configuration parameters of the corresponding process are collected, and the ratio of the actual number of configuration parameters to the standard number of configuration parameters is marked as the configuration compliance value. The standard number of configuration parameters is determined according to the design requirements and quality standards of the vacuum process, and represents the number of parameter configurations that should be achieved under ideal conditions; the actual number of configuration parameters is the number of parameters that are actually configured during the process execution. If the configuration compliance value does not exceed the preset configuration compliance threshold, it means that the number of parameters actually configured has failed to meet the standard requirements. At this time, the corresponding process is marked as a process that does not meet the configuration standards.

[0072] During the statistical period, configuration effectiveness analysis requires summarizing the configuration compliance status of all process steps. The ratio of the number of processes with substandard configurations to the total number of processes during the statistical period is labeled the substandard configuration rate. This indicator reflects the overall proportion of processes with substandard configurations during the period. At the same time, for each substandard process, the difference between its configuration compliance value and the preset configuration compliance threshold is calculated. This difference is labeled the compliance deviation value and is used to measure the size of the configuration gap of a single substandard process. All compliance deviation values ​​within the statistical period are summed and averaged to obtain the average deviation value, which reflects the average gap level of the overall substandard configuration process.

[0073] Configuration effectiveness analysis provides a positive assessment of configuration compliance within a statistical period. All configuration compliance values ​​within the statistical period (including those for both on- and off-target processes) are summed and averaged to obtain the average configuration value. This metric comprehensively reflects the overall level of configuration parameter completion within the period. Finally, the three indicators—non-compliant configuration rate, average deviation, and average configuration value—are numerically calculated to obtain the configuration effectiveness value. This calculation combines negative indicators (non-compliant configuration rate and average deviation) with positive indicators (average configuration value) to comprehensively reflect the effectiveness of configuration within the period.

[0074] Determining whether a process has substandard configuration is fundamental to analyzing configuration effectiveness. After each process is completed, the system automatically collects the number of standard configuration parameters and the number of actual configuration parameters for that process. For example, if a vacuum process has 10 standard configuration parameters and 8 are actually configured, the ratio of the actual number of configuration parameters to the standard number of configuration parameters is 0.8. The preset configuration compliance threshold is usually set to no less than 1.0 (or adjusted according to process requirements). If the ratio does not exceed the threshold (e.g., 0.8 < 1.0), the process is determined to have substandard configuration. This judgment mechanism can accurately identify processes that fail to fully meet configuration requirements, providing a basis for subsequent analysis and optimization.

[0075] The process cycle integration module uses configuration efficiency and anomaly rate as core metrics during analysis. The configuration efficiency reflects the overall quality of the process configuration within a cycle, while the anomaly rate reflects the frequency of anomalies. The combination of these two metrics provides a more comprehensive assessment of process execution. For example, if the configuration efficiency is low and the anomaly rate is high, this indicates not only poor configuration performance but also frequent anomalies. In this case, the process integration value is likely to exceed the threshold, generating an integration warning signal. If the configuration efficiency is high and the anomaly rate is low, the process integration value is generally within the normal range, generating an integration normal signal.

[0076] The entire analysis process encompasses everything from determining whether a single process's configuration meets specifications to calculating and evaluating overall cycle metrics, forming a complete data analysis chain. By processing multi-dimensional data such as substandard configuration rates, average deviation values, and average configuration values, the system can deeply identify issues within the configuration process and provide accurate data support for process optimization. Furthermore, data interaction between the process cycle integration module and the system reliability assessment module enables the incorporation of abnormal signal occurrences into cycle analysis, further enhancing the comprehensiveness and accuracy of the assessment.

[0077] In practice, the process cycle integration module's preset thresholds (such as process integration thresholds and configuration compliance thresholds) can be dynamically adjusted based on process requirements, production experience, and quality standards. For example, for high-precision vacuum processes, the configuration compliance threshold can be set higher to ensure the process meets more stringent configuration standards. Thresholds can also be adjusted to suit management needs during different production phases (such as pilot production and stable production). This flexibility allows the system to better adapt to actual production scenarios and achieve more accurate process assessment and management.

[0078] Through the collaborative operation of the process cycle integration module and configuration effect analysis, the system can continuously track and evaluate the configuration effects and abnormal conditions of the vacuum process from a time dimension, providing operators with periodic process execution reports and early warning information. This helps operators promptly identify changes in process trends, take targeted improvement measures, and continuously optimize the configuration management of vacuum process parameters, thereby improving process stability and product quality.

[0079] The specific method for judging whether the configuration does not meet the standards is as follows: the number of standard configuration parameters and the actual number of configuration parameters of the corresponding process are collected, and the ratio of the actual number of configuration parameters to the standard number of configuration parameters is marked as the configuration compliance value. If the configuration compliance value does not exceed the preset configuration compliance threshold, the corresponding process is marked as a configuration does not meet the standards process.

[0080] Example 4:

[0081] The system also includes a regional parameter anomaly assessment module, which communicates with the process cycle integration module via a data storage unit. After the process cycle integration module completes a summary analysis of the vacuum process execution within the statistical period and generates an integrated normal signal, it sends this signal to the regional parameter anomaly assessment module via the data storage unit. When the regional parameter anomaly assessment module receives the integrated normal signal, it indicates that the overall status of the process execution within the statistical period is normal at the macro level. However, a more detailed analysis of the parameter status of each area on the surface of the vacuum chamber is still required to determine whether any localized parameter anomalies exist.

[0082] The analysis process of the regional parameter anomaly assessment module begins by setting up several detection areas on the surface of the vacuum chamber. The setting of these detection areas needs to be determined based on the structural characteristics of the vacuum chamber, the process requirements, and the possible parameter distribution patterns. For example, multiple areas can be evenly divided at different locations such as the top, bottom, and sidewalls of the chamber. More detection areas can also be set for key areas prone to parameter anomalies. During the process, the regional parameter anomaly assessment module will collect the parameter deviation values ​​of the corresponding detection areas. The parameter deviation values ​​here refer to the difference between the various parameters (such as temperature, pressure, humidity, etc.) of each detection area during the process and the corresponding standard values. The specific parameter type can be set according to different process stages and chamber surface characteristics.

[0083] After obtaining the parameter deviation values ​​for all detection areas, the regional parameter anomaly assessment module processes this data. First, the parameter deviation values ​​for all detection areas are summed and averaged to obtain a characteristic baseline value. This characteristic baseline value represents the overall average level of parameter deviations for all detection areas on the vacuum chamber surface. Next, the parameter deviation value for each detection area is subtracted from the characteristic baseline value and the absolute value is taken to obtain the regional deviation value. The regional deviation value reflects the degree to which the parameter deviation of a single detection area deviates from the overall average level.

[0084] Next, the regional parameter anomaly assessment module compares the regional deviation value with the preset regional deviation threshold. The preset regional deviation threshold is set based on process requirements and the allowable fluctuation range of cavity surface parameters and is used to determine whether the detection area is an abnormal area. If the regional deviation value of a detection area exceeds the preset regional deviation threshold, it indicates that the parameter deviation in that area is significantly higher than the overall average level and a local anomaly exists. In this case, the corresponding detection area is marked as an abnormal area. If the regional deviation value does not exceed the preset regional deviation threshold, the detection area is considered normal.

[0085] During the statistical period, the regional parameter anomaly assessment module also collects two key data points: the total execution time of the vacuum process during the statistical period, and the number of times the corresponding inspection area was marked as an abnormal area during the statistical period, which is recorded as the anomaly frequency value. The module then records the ratio of the anomaly frequency value to the total execution time as the anomaly frequency ratio. The anomaly frequency ratio reflects the frequency of anomalies in the inspection area per unit time and is an important indicator for determining the degree of anomaly in the inspection area.

[0086] The regional parameter anomaly assessment module defines the corresponding detection area as a high anomaly area, a medium anomaly area, or a low anomaly area by analyzing the anomaly frequency ratio. The specific judgment criteria are: if the anomaly frequency ratio exceeds the maximum value of the preset anomaly frequency range, it means that the number of anomalies in the detection area per unit time is large, and the anomaly is more serious, and it is defined as a high anomaly area; if the anomaly frequency ratio does not exceed the minimum value of the preset anomaly frequency range, it means that the number of anomalies in the detection area per unit time is small, or even almost no anomalies, and it is defined as a low anomaly area; if the anomaly frequency ratio is within the preset anomaly frequency range, it means that the anomaly frequency of the detection area is at a medium level, and it is defined as a medium anomaly area. The preset anomaly frequency range here is pre-set based on process experience and cavity surface characteristics, and is used to divide areas with different degrees of anomaly.

[0087] After defining the degree of abnormality for all inspection areas, the regional parameter anomaly assessment module comprehensively assesses the distribution of abnormal areas on the vacuum chamber surface. If high-abnormality areas exist on the vacuum chamber surface, the module generates a surface parameter anomaly signal, as these areas indicate severe and frequent parameter anomalies, potentially significantly impacting the vacuum process and the chamber itself. If no high-abnormality areas exist, the module further analyzes the relationship between the number of medium and low-abnormality areas and labels the ratio of the number of medium to low-abnormality areas as the surface assessment value. The surface assessment value measures the distribution of abnormal areas on the chamber surface. If the surface assessment value exceeds the preset threshold, it indicates a relatively high number of medium-abnormal areas. While there are no severe high-abnormal areas, moderate-abnormal areas are widespread and may impact the process. In this case, the module generates a surface parameter anomaly signal. If the surface assessment value does not exceed the threshold, it indicates a relatively small number of abnormal areas on the chamber surface and a low degree of abnormality. No surface parameter anomaly signal is generated.

[0088] The generated surface parameter abnormality signal will be sent to the interactive prompt module through the data storage unit. After receiving the signal, the interactive prompt module will issue a reminder to the operator in a set manner (such as screen display, light prompt, etc.). At the same time, it may also display information such as the location and degree of abnormality of the abnormal area, so that the operator can accurately understand the parameter abnormality of the cavity surface and take corresponding maintenance or adjustment measures in time.

[0089] For example, suppose 10 inspection zones are set up on the surface of a vacuum chamber. Within a statistical period (e.g., 7 days), the total process execution time is 100 hours. After calculating the parameter deviation value for inspection zone A, the zone deviation value exceeds the preset zone deviation threshold, marking it as an abnormal zone. During these 7 days, this zone was marked as an abnormal zone 20 times, with an abnormal frequency ratio of 20 times / 100 hours = 0.2 times / hour. If the preset abnormal frequency range has a minimum value of 0.1 times / hour and a maximum value of 0.3 times / hour, then 0.2 times / hour falls within this range, and inspection zone A is defined as a moderately abnormal zone. Inspection zone B, however, has an abnormal frequency ratio of 0.4 times / hour, exceeding the preset maximum value and defining it as a highly abnormal zone. The zone parameter abnormality assessment module generates a surface parameter abnormality signal. Upon receiving this alert, the operator can inspect inspection zone B for high abnormality, such as checking for sealing issues or impurities, and take appropriate action.

[0090] Another scenario is when there are no high-abnormal areas on the vacuum chamber surface, and all abnormal areas are medium or low-abnormal areas. Assuming there are four medium-abnormal areas and two low-abnormal areas, and the ratio of medium to low-abnormal areas is 2, if the preset surface evaluation threshold is 1.5 and 2 exceeds this threshold, the regional parameter abnormality evaluation module will also generate a surface parameter abnormality signal, prompting the operator to pay attention to the overall parameter distribution. Comprehensive cleaning of the chamber or adjustment of process parameters may be necessary to reduce the number of medium-abnormal areas.

[0091] By running the regional parameter anomaly assessment module, the system can promptly detect parameter anomalies in localized areas of the vacuum chamber surface while the overall process is executing normally, preventing overall process problems caused by the accumulation of local anomalies. This meticulous macro-to-micro assessment mechanism further enhances the comprehensiveness and accuracy of the system's management of vacuum process parameter configuration, providing strong support for the stable operation of the vacuum process and chamber maintenance.

[0092] Example 5:

[0093] The regional parameter anomaly assessment module in this system performs a comparative analysis to define the degree of anomaly in the inspection area. This process is based on the relationship between the anomaly frequency ratio and the preset anomaly frequency range. The anomaly frequency ratio is calculated by dividing the number of times the corresponding inspection area is marked as an anomaly area (the anomaly frequency value) by the total execution time of the vacuum process within a statistical period. It reflects the frequency of anomalies in the inspection area per unit time. The preset anomaly frequency range is pre-set based on factors such as the characteristics of the vacuum process, the chamber structure, and historical operating data, and is used to classify the degree of anomaly in the inspection area.

[0094] During comparative analysis, if the abnormal frequency ratio of a detection area exceeds the maximum value of the preset abnormal frequency range, it means that the number of abnormalities in this area per unit time is relatively high and the abnormality is more prominent. In this case, the corresponding detection area will be defined as a high abnormality area. For example, the preset abnormal frequency range of a vacuum chamber is 0.1 times / hour to 0.3 times / hour. If the total execution time of detection area C in a statistical cycle is 80 hours and the number of times it is marked as an abnormal area is 30 times, then the abnormal frequency ratio is 30 ÷ 80 = 0.375 times / hour. This value exceeds the maximum value of the preset range of 0.3 times / hour. Therefore, detection area C will be defined as a high abnormality area. In this case, the abnormality in this area is more serious and requires special attention and treatment.

[0095] If the abnormal frequency ratio of a detection area does not exceed the minimum value of the preset abnormal frequency range, it means that the number of abnormalities in the area per unit time is very small, or even almost no abnormalities occur. In this case, the corresponding detection area is defined as a low abnormality area. For example, if the total execution time of detection area D in the statistical period is 120 hours, the abnormal frequency value is 10 times, and the abnormal frequency ratio is 10 ÷ 120 ≈ 0.083 times / hour, which is less than the minimum value of the preset range of 0.1 times / hour, then detection area D will be defined as a low abnormality area. Such areas have fewer abnormalities and usually do not require immediate targeted treatment, but their changes still need to be monitored regularly.

[0096] When the abnormal frequency ratio of a detection area falls within the preset abnormal frequency range, it indicates that the abnormal frequency in that area is at a medium level, and the corresponding detection area is defined as a medium abnormality zone. For example, the abnormal frequency ratio of detection area E is calculated to be 0.2 times / hour, which is within the preset range of 0.1 times / hour to 0.3 times / hour, and is therefore defined as a medium abnormality zone. Abnormalities in the medium abnormality zone require attention, and whether they are developing into a high abnormality zone should be observed.

[0097] In practical applications, the setting of the preset abnormal frequency range requires comprehensive consideration of multiple factors. For precision processes with extremely high vacuum requirements, the maximum value of the preset abnormal frequency range may be set lower to more strictly control the abnormal frequencies in the detection area; for processes with relatively low vacuum requirements, the maximum value of the preset abnormal frequency range may be appropriately increased. At the same time, the preset range will also be dynamically adjusted based on factors such as the chamber's usage time and maintenance status. For example, a newly commissioned chamber may have a stricter preset range, while a chamber with a longer usage history may have a wider preset range based on historical data.

[0098] Taking a vacuum chamber used in semiconductor manufacturing as an example, its preset abnormal frequency range is set to 0.05 times / hour to 0.25 times / hour. During a statistical period of one month, the total execution time of the chamber is 600 hours. For example, the number of times detection area F was marked as an abnormal area during the process was 15 times, and the abnormal frequency ratio was 15÷600=0.025 times / hour, which did not exceed the minimum value of the preset range of 0.05 times / hour, so it was defined as a low abnormal area; the abnormal frequency value of detection area G was 180 times, and the abnormal frequency ratio was 180÷600=0.3 times / hour, which exceeded the maximum value of the preset range of 0.25 times / hour, and was defined as a high abnormal area; the abnormal frequency ratio of detection area H was 0.15 times / hour, which was within the preset range and was defined as a medium abnormal area.

[0099] When a high-abnormality area exists on the surface of the vacuum chamber, the regional parameter anomaly assessment module generates a surface parameter anomaly signal, regardless of the conditions in other areas. For example, if detection area G is defined as a high-abnormality area in the example above, the module generates a surface parameter anomaly signal and sends it to the interactive prompt module via the data storage unit. The interactive prompt module alerts the operator to the presence of a high-abnormality area on the chamber surface and requires a focused inspection of detection area G. The operator can then inspect this area for issues such as component wear, seal failure, and localized contamination, and take measures such as replacing components, resealing, or cleaning.

[0100] If there are no high anomaly areas on the vacuum chamber surface, further analysis is needed to determine the quantitative relationship between medium and low anomaly areas. The ratio of the number of medium anomaly areas to the number of low anomaly areas is marked as the surface evaluation value. If the surface evaluation value exceeds the preset surface evaluation threshold, it indicates that there are a relatively large number of medium anomaly areas, which may have a certain impact on the vacuum process. In this case, a surface parameter anomaly signal is generated. For example, if a chamber surface has no high anomaly areas, but has five medium anomaly areas and two low anomaly areas, the surface evaluation value is 5 ÷ 2 = 2.5. If the preset surface evaluation threshold is 2, and 2.5 exceeds the threshold, the module will generate a surface parameter anomaly signal, prompting the operator that there are a large number of medium anomaly areas and that a comprehensive inspection of the parameter distribution on the chamber surface is necessary, and process parameters may need to be adjusted or overall maintenance may be performed.

[0101] The comparative analysis process is a key step in the regional parameter anomaly assessment module's ability to identify cavity surface parameter anomalies. It quantifies the frequency of anomalies in the detection area and categorizes them into different levels, enabling operators to clearly understand the degree of anomalies in different areas and take targeted measures. This data-based analysis method avoids subjective judgment errors and improves the accuracy and reliability of system assessments.

[0102] By comparing and analyzing abnormal frequency ratios, the system can assess the abnormal stability of inspection areas over time. For example, if the abnormal frequency ratio of an inspection area remains consistently high over multiple statistical cycles, it indicates a persistent problem and requires thorough overhaul. If the abnormal frequency ratio of an inspection area suddenly rises to the high abnormal range within a certain period, it may be due to temporary process fluctuations or component anomalies, requiring prompt investigation of these temporary factors.

[0103] Comparative analysis works closely with other components of the regional parameter anomaly assessment module to form a complete anomaly detection system. From setting the detection area and collecting parameter deviation values, to calculating characteristic baseline values ​​and regional deviation values, to analyzing anomaly frequency ratios and defining anomaly regions, each step provides a basis for generating the final surface parameter anomaly signal.

[0104] Example 6:

[0105] In a certain semiconductor chip manufacturing vacuum etching process, parameter acquisition during the pre-vacuum stage is led by the system's parameter acquisition module, enabling real-time and accurate collection of vacuum change rate, pressure fluctuation, and leak rate. Upon process startup, the parameter acquisition module immediately activates the connected vacuum sensor, pressure sensor, and leak detection unit, all of which enter operation synchronously to ensure parallel data acquisition. The vacuum sensor uses a high-frequency sampling mode, recording the chamber vacuum value every 0.2 seconds. For example, if the vacuum level is 50 Pa at the first second and 45 Pa at the second.2 seconds, the parameter acquisition module calculates (45-50) / 0.2 = -25 Pa / s to determine the vacuum change rate during this period. This value is then compared with the preset vacuum change rate standard value of -20 Pa / s, resulting in a vacuum deviation of -5 Pa / s. At the same time, the pressure sensor collected intracavity pressure data every 0.1 seconds. Over a continuous 10-second acquisition period, it recorded a maximum pressure of 52 Pa and a minimum of 48 Pa. This resulted in a calculated pressure fluctuation of 4 Pa. This, when compared with the standard pressure value of 5 Pa, yielded a pressure deviation of -1 Pa. The leak detection unit monitored the pressure rise caused by the leak per unit time. Combined with parameters such as the cavity volume of 100 L, it calculated a leak rate of 0.005 Pa・L / s. Comparing this with the standard leak rate of 0.003 Pa・L / s, the resulting leak deviation was 0.002 Pa・L / s.

[0106] During the continuous acquisition process, the parameter acquisition module continuously assesses deviations in the three parameters mentioned above. At the 30th second mark, the vacuum rate of change suddenly dropped to -15 Pa / s, a deviation of 5 Pa / s from the standard value of -20 Pa / s, exceeding the preset range of ±3 Pa / s. The module immediately generated a pre-vacuum abnormality signal, which was transmitted synchronously via the central control unit to the configuration adjustment module and the interactive prompt module. Upon receiving the signal, the interactive prompt module displayed "Abnormal vacuum rate of change during pre-vacuum phase" on the user interface and issued a beeping alert. The configuration adjustment module then quickly initiated adaptive corrections, first increasing the vacuum sensor sampling frequency to every 0.1 second to capture more detailed vacuum changes. It also utilized correction parameters from similar operating conditions in the historical database and optimized the vacuum rate of change calculation model, incorporating real-time pressure fluctuation data as a compensation factor. For example, if the pressure fluctuation exceeded 3 Pa, a dynamic correction of ±0.5 Pa / s was applied to the vacuum rate of change. For pressure fluctuations, the module activates a filtering algorithm to remove abnormal data points caused by transient sensor jitter. For example, a pressure value of 60 Pa collected on a particular occasion that significantly deviates from the normal range is identified as interference data and removed. The pressure fluctuation value is then recalculated to 3 Pa, bringing the pressure deviation back to the normal range of -2 Pa. For leak rate, the configuration adjustment module calibrates the pressure sensor in the leak detection unit to eliminate measurement errors caused by temperature drift, improving the leak rate calculation accuracy to ±0.0005 Pa・L / s and ensuring that the leak deviation remains stable within 0.001 Pa・L / s.

[0107] The system status tracking module marked the moment the configuration adjustment module received the pre-evacuation anomaly signal (at the 30th second) as the anomaly reception time and began counting from that moment. After a 15-second correction, at the 45th second, the vacuum rate of change stabilized at -19 Pa / s, the deviation was 1 Pa / s, the pressure fluctuation was 3 Pa, and the leakage rate was 0.003 Pa・L / s. The deviations of all three parameters were within the preset ranges, indicating that the configuration process had returned to stability. The response time of 15 seconds did not exceed the preset threshold of 20 seconds, thus determining that the configuration process was valid and the valid count was incremented by 1. In the subsequent pre-vacuum stage, the parameter acquisition module continues to operate in the optimized mode. The vacuum sensor maintains a sampling frequency of once every 0.1 seconds, the pressure sensor's filtering algorithm continues to be effective, and the calibration parameters of the leak detection unit are updated in real time to ensure that the vacuum change rate, pressure fluctuation value, and leak rate are always in a precise acquisition state. Until the pre-vacuum stage is completed and the vacuum level in the cavity reaches the target value of 1Pa, the parameter acquisition module automatically switches to the acquisition mode of the vacuum holding stage. During the entire process, the acquisition error of the three key parameters is controlled within 5%, meeting the high-precision requirements of the semiconductor etching process for vacuum environment parameters. Through such collaborative work, the system realizes real-time monitoring and precise acquisition of key parameters in the pre-vacuum stage, providing reliable data support for the stable operation of the entire vacuum process.

[0108] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0109] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent configuration and management system for vacuum process parameters, characterized in that: It includes a central control unit, a parameter acquisition module, a configuration adjustment module, a system status tracking module and an interactive prompt module; the parameter acquisition module collects data during the execution of the vacuum process. The vacuum process execution process includes a pre-vacuum stage, a vacuum holding stage and a vacuum recovery stage. In the pre-vacuum stage, the parameter acquisition module performs initial vacuum pumping on the vacuum cavity and synchronously collects parameter data in the cavity. After completing the pre-vacuum, it enters the vacuum holding stage and continuously collects parameters for a stable vacuum environment. After the vacuum holding stage is completed, it enters the vacuum recovery stage to collect parameter data during the cavity re-pressurization process. The parameter acquisition module analyzes the collected data to determine whether a pre-extraction abnormality signal, a holding abnormality signal, or a breaking air abnormality signal is generated, and sends the pre-extraction abnormality signal, the holding abnormality signal, or the breaking air abnormality signal to the configuration adjustment module and the interactive prompt module via the central control unit; the interactive prompt module displays the pre-extraction abnormality signal, the holding abnormality signal, or the breaking air abnormality signal and issues a reminder, and the configuration adjustment module adaptively corrects the data acquisition process upon receiving the pre-extraction abnormality signal, the holding abnormality signal, or the breaking air abnormality signal to optimize the configuration effect; The system status tracking module tracks the configuration process of the configuration adjustment module, records the moment when the configuration adjustment module receives the pre-extraction abnormal signal, the holding abnormal signal, or the air-breaking abnormal signal and marks it as the abnormal reception moment, and uses the abnormal reception moment as the time starting point to start timing until the configuration process returns to stability to obtain the response time. If the response time does not exceed the preset response time threshold, the corresponding configuration process is judged to be valid and the valid times are accumulated. If the response time exceeds the preset response time threshold, the corresponding configuration process is judged to be invalid and the invalid times are accumulated. The system also includes a system reliability evaluation module that is in communication with the central control unit. The system reliability evaluation module collects the frequency and intensity of the pre-extraction abnormal signal, the holding abnormal signal, and the air-breaking abnormal signal generated in the corresponding process and marks them as abnormal occurrence values, and collects the number of failures and the number of valid times in the corresponding process, and marks the ratio of the number of failures to the number of valid times as an adjustment deviation value. The abnormal occurrence value and the adjustment deviation value are numerically multiplied to obtain the system evaluation value. If the system evaluation value exceeds the preset system evaluation threshold, a system abnormality warning signal is generated. If the system evaluation value does not exceed the preset system evaluation threshold, a system normal warning signal is generated, and the system abnormality warning signal is sent to the interactive prompt terminal via the central control unit.

2. The intelligent configuration management system for vacuum process parameters according to claim 1, characterized in that: The specific operation process of the parameter acquisition module is as follows: During the pre-vacuum stage, the vacuum change rate, pressure fluctuation value and leakage rate values ​​in the cavity are collected, and the difference between the vacuum change rate and the standard value of the vacuum change rate is marked as the vacuum deviation value, and the difference between the pressure fluctuation value and the standard value of the pressure fluctuation is marked as the pressure deviation value; the difference between the leakage rate and the standard value of the leakage rate is marked as the leakage deviation value; if the vacuum deviation value, pressure deviation value or leakage deviation value exceeds the corresponding preset range, a pre-vacuum abnormality signal is generated; During the vacuum holding phase, the deviation of the actual temperature value in the chamber parameter data from the standard temperature is marked as the temperature offset value. The humidity value and airflow uniformity value in the chamber are collected, and the difference between the humidity value and the humidity standard value is marked as the humidity deviation value. The difference between the airflow uniformity value and the airflow uniformity standard value is marked as the airflow deviation value. If the temperature offset value, humidity deviation value or airflow deviation value exceeds the corresponding preset range, a holding abnormality signal is generated. During the air-break recovery phase, the pressure recovery rate, impurity mixing amount, and surface cleanliness value in the cavity are collected, and the difference between the pressure recovery rate and the corresponding pressure recovery rate standard value is marked as the pressure deviation value, the difference between the impurity mixing amount and the impurity mixing amount standard value is marked as the impurity deviation value, and the difference between the surface cleanliness value and the surface cleanliness standard value is marked as the cleanliness deviation value; If the pressure deviation value, impurity deviation value or cleanliness deviation value exceeds the corresponding preset range, an air-breaking abnormal signal is generated.

3. The intelligent configuration management system for vacuum process parameters according to claim 1, characterized in that: It also includes a process cycle integration module that is communicatively connected to the central control unit. The process cycle integration module is used to set a statistical period, summarize and analyze all execution processes of the vacuum process within the statistical period, generate an integrated warning signal or an integrated normal signal through analysis, and send the integrated warning signal to the interactive prompt module via the central control unit. When the interactive prompt module receives the integrated warning signal, it will issue a corresponding reminder.

4. The intelligent configuration management system for vacuum process parameters according to claim 3, characterized in that: The specific analysis process of the process cycle integration module is as follows: The configuration effectiveness value is obtained through configuration effect analysis, and the number of system abnormality warning signals generated during the statistical period is collected. The ratio of the number of system abnormality warning signals to the total number of process steps during the statistical period is marked as the abnormality occurrence ratio. The configuration effectiveness value and the abnormality occurrence ratio are numerically calculated to obtain the process integration value. If the process integration value exceeds a preset process integration threshold, an integration warning signal is generated; If the process integration value does not exceed the preset process integration threshold, an integration normal signal is generated.

5. The intelligent configuration and management system for vacuum process parameters according to claim 4, characterized in that: The specific analysis process of configuration effect analysis is as follows: By analyzing and judging whether the corresponding process is a configuration-substandard process, the ratio of the number of configuration-substandard processes to the total number of process processes within the statistical period is marked as the substandard configuration rate, the difference between the configuration compliance value of the corresponding configuration-substandard process and the preset configuration compliance threshold is marked as the compliance deviation value, all compliance deviation values ​​of the statistical period are summed up and averaged to obtain the average deviation value, and all configuration compliance values ​​within the statistical period are summed up and averaged to obtain the average configuration value; the substandard configuration rate, the average deviation value and the average configuration value are numerically calculated to obtain the configuration efficiency value.

6. The intelligent configuration and management system for vacuum process parameters according to claim 5, characterized in that: The specific judgment process of configuration failure is as follows: The number of standard configuration parameters and the actual number of configuration parameters of the corresponding process are collected, and the ratio of the actual number of configuration parameters to the standard number of configuration parameters is marked as the configuration compliance value. If the configuration compliance value does not exceed the preset configuration compliance threshold, the corresponding process is marked as a configuration non-compliant process.

7. The intelligent configuration and management system for vacuum process parameters according to claim 3, characterized in that: It also includes a regional parameter abnormality evaluation module. The process cycle integration module sends the integrated normal signal to the regional parameter abnormality evaluation module through the data storage unit. When the regional parameter abnormality evaluation module receives the integrated normal signal, it analyzes the parameter conditions of each area on the surface of the vacuum cavity during all process steps in the statistical period, and determines whether a surface parameter abnormality signal is generated through analysis, and sends the surface parameter abnormality signal to the interactive prompt module through the data storage unit.

8. The intelligent configuration and management system for vacuum process parameters according to claim 7, characterized in that: The specific analysis process of the regional parameter anomaly assessment module is as follows: Several detection areas are set on the surface of the vacuum chamber. During the process, parameter deviation values ​​of the corresponding detection areas are collected. The parameter deviation values ​​of all detection areas are summed and averaged to obtain a characteristic reference value. The parameter deviation value of the corresponding detection area is subtracted from the characteristic reference value and the absolute value is taken to obtain a regional deviation value. If the regional deviation value exceeds a preset regional deviation threshold, the corresponding detection area is marked as an abnormal area. The total execution time of the vacuum process within the statistical period is collected, and the number of times the corresponding detection area in the statistical period is marked as an abnormal area is collected and marked as the abnormal frequency value, and the ratio of the abnormal frequency value to the total execution time is marked as the abnormal frequency ratio; Through comparative analysis, the corresponding detection area is defined as a high anomaly area, a medium anomaly area or a low anomaly area. If a high anomaly area exists on the surface of the vacuum cavity, a surface parameter anomaly signal is generated; if no high anomaly area exists on the surface of the vacuum cavity, the ratio of the number of medium anomaly areas to the number of low anomaly areas is marked as a surface evaluation value. If the surface evaluation value exceeds the preset surface evaluation threshold, a surface parameter anomaly signal is generated.

9. The intelligent configuration and management system for vacuum process parameters according to claim 8, characterized in that: The specific analysis process of the comparative analysis is as follows: If the abnormal frequency ratio exceeds the maximum value of the preset abnormal frequency range, the corresponding detection area is defined as a high abnormal area; if the abnormal frequency ratio does not exceed the minimum value of the preset abnormal frequency range, the corresponding detection area is defined as a low abnormal area; if the abnormal frequency ratio is within the preset abnormal frequency range, the corresponding detection area is defined as a medium abnormal area.

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