Light source machine maintenance management system and methods

Abstract

This application provides a maintenance management system and method for a light source machine. The maintenance management system includes: a real-time monitoring module for collecting real-time operating parameters and equipment status parameters of the light source machine through a sensor array; an optical parameter adaptation module for automatically retrieving the corresponding optical parameter threshold from a pre-stored model-parameter mapping relationship based on the model of the CMOS image sensor currently being tested; a diagnostic module for analyzing real-time operating parameters based on pre-stored fault judgment logic and outputting fault identifiers and maintenance suggestions; and a dynamic maintenance planning module for dynamically generating adjustment instructions for adjusting the maintenance cycle and content based on equipment status parameters and real-time operating parameters. Through the collaboration and data fusion of these modules, this application achieves a shift in the maintenance management of the light source machine from "passive and fixed" to "active, intelligent, and dynamic," effectively ensuring the accuracy and efficiency of CIS wafer testing.

Description

Technical Field

[0001] This application relates to the field of semiconductor test equipment management technology, and in particular to a light source machine maintenance management system and method. Background Technology

[0002] In the wafer testing process of CIS (CMOS image sensor) products, the light source unit, as a key component, plays a decisive role in the accuracy of the test results due to its performance stability and light output accuracy. As the core device that converts light signals into electrical signals, the performance of the CIS directly affects the image quality. Wafer testing is a crucial step in verifying whether various parameters of the CIS (such as sensitivity, dynamic range, signal-to-noise ratio, etc.) meet the standards. During this process, the light source unit needs to simulate different lighting conditions (such as light intensity, color temperature, spectral distribution, etc.) to provide a standardized input light signal to the CIS. If the performance of the light source unit is unstable—for example, due to sudden fluctuations in light intensity, color temperature shifts, or a decrease in the uniformity of the output light—it will directly lead to distortion of the light signal received by the CIS, thus causing deviations in the test results: it may misjudge qualified chips as defective products, resulting in wasted costs; or it may overlook defective chips, affecting the quality of the final product.

[0003] The current maintenance and management of light source machines has the following main shortcomings: Lack of real-time status monitoring: It is impossible to understand the key operating parameters of the light source (such as light intensity, color temperature, operating temperature, etc.) in real time, making it difficult to detect potential problems in a timely manner.

[0004] Unreasonable maintenance plans: Fixed-cycle maintenance is commonly adopted without taking into account individual differences such as the actual usage intensity and performance degradation rate of the light source, resulting in over-maintenance or under-maintenance.

[0005] Low efficiency in fault diagnosis and repair: When a fault occurs, it relies on manual experience for troubleshooting, which is time-consuming and makes it difficult to accurately locate the root cause of the fault.

[0006] Maintenance record management is chaotic: records are mostly kept on paper or in simple spreadsheets, resulting in scattered information that is difficult to find and hinders data tracing and in-depth analysis.

[0007] In summary, the existing technology for maintenance management of light source machines has deficiencies in aspects such as condition monitoring, fault diagnosis, plan generation, parameter adaptation, and effect verification. Summary of the Invention

[0008] In order to overcome at least one of the technical problems mentioned in the above technical background, this application provides an intelligent maintenance management system and method for light source machines.

[0009] To achieve the above objectives, this application provides a light source machine maintenance management system, applied to wafer testing scenarios for CMOS image sensors. The light source machine maintenance management system includes: The real-time monitoring module includes a sensor group connected to the light source machine for collecting the real-time operating parameters of the light source machine. The real-time monitoring module also includes a status recording unit for acquiring and recording the equipment status parameters of the light source machine, including the cumulative usage time and the number of times it has been used. The optical parameter adaptation module is used to call the corresponding optical parameter threshold from the pre-stored model-parameter mapping relationship according to the model of the CMOS image sensor currently being tested; The diagnostic module, connected to the real-time monitoring module, is used to analyze real-time operating parameters based on pre-stored fault judgment logic and output fault identifiers and maintenance suggestions. The dynamic maintenance plan module, connected to the real-time monitoring module, is used to dynamically generate adjustment instructions for adjusting the maintenance cycle based on equipment status parameters and / or real-time operating parameters.

[0010] In one embodiment, the sensor group includes at least one of a light intensity sensor, a temperature sensor, a color temperature sensor, a spectral sensor, and a current sensor.

[0011] In one embodiment, the optical parameter adaptation module pre-stores the mapping relationship between different CMOS image sensor models and optical parameter thresholds, including light intensity threshold, color temperature threshold and spectral parameter threshold.

[0012] In one embodiment, the pre-stored fault judgment logic includes at least one of the following: When the light intensity data continues to fluctuate beyond the first preset range and the temperature data is higher than the preset temperature, the light source aging fault indicator is triggered. When the cooling fan speed is continuously lower than a preset percentage of the rated speed and the temperature rise rate exceeds a preset value, the cooling system fault indicator is triggered.

[0013] In one embodiment, the dynamic maintenance plan module is configured to generate an adjustment instruction by comparing at least two of the device status parameters, namely, light intensity attenuation rate, color temperature fluctuation frequency, cumulative usage time, and number of uses, with the corresponding preset thresholds.

[0014] In one embodiment, the light source machine maintenance management system further includes a maintenance verification module, which is connected to the real-time monitoring module and the optical parameter adaptation module respectively. After maintenance is completed, the maintenance verification module acquires the light intensity and color temperature data after maintenance and compares them with the standard values ​​of the current test model called by the optical parameter adaptation module to determine whether the maintenance is qualified.

[0015] In one embodiment, the light source machine maintenance management system further includes a remote interaction module, which is connected to the diagnostic module, the dynamic maintenance plan module, and the maintenance verification module, respectively, and is used to automatically generate and send alarm or reminder information based on fault identification, adjustment instructions, or maintenance verification results.

[0016] Based on the same inventive concept, this application also provides a light source machine maintenance and management method, applied to the wafer testing scenario of CMOS image sensors, the method comprising: The operating parameters of the light source machine are collected in real time by sensors, and the equipment status parameters of the light source machine are obtained through status recording. Based on the CMOS image sensor model currently being tested, the corresponding optical parameter threshold is retrieved from the pre-stored model-parameter mapping relationship; The operating parameters are analyzed based on the pre-stored fault judgment logic, and a fault identifier is generated if the preset conditions are met. Based on equipment status parameters and / or operating parameters, adjustment instructions for adjusting maintenance cycles are dynamically generated.

[0017] In one embodiment, the method further includes: Receive and store maintenance information, including maintenance time, maintenance content, replaced parts, and performance parameters before and after maintenance; The maintenance information is associated with and stored in conjunction with the corresponding CMOS image sensor test batch information; Based on the data stored in the association, visualized analysis charts are generated to show maintenance history and performance trends.

[0018] In one embodiment, the method further includes: After maintenance is completed, the performance parameters after maintenance are obtained and compared with the standard values ​​of the current test model to determine whether the maintenance is qualified. If a fault identifier, adjustment instruction, or maintenance failure is generated, the corresponding alarm information or maintenance reminder will be automatically generated and sent to the remote terminal.

[0019] The light source machine maintenance management system provided in this application achieves "proactive, intelligent, and dynamic" management by constructing a system architecture in which a real-time monitoring module, an optical parameter adaptation module, a diagnostic module, and a dynamic maintenance plan module work together. Specifically, the real-time monitoring module automates the collection of key operating parameters and historical status data of the light source machine, overcoming the deficiency of lacking real-time status monitoring; the optical parameter adaptation module ensures accurate matching between test light source parameters and CIS models through a mapping database and automatic calling mechanism, overcoming the defects of manual configuration errors and inaccurate matching; the diagnostic module analyzes and processes real-time data based on preset rules, enabling early detection of potential faults and precise location of their root causes, transforming the maintenance mode from relying on manual experience for troubleshooting to intelligent diagnosis; and the dynamic maintenance plan module dynamically outputs maintenance cycle adjustment instructions based on the comparative analysis of equipment status parameters and preset thresholds, changing the problem of insufficient or excessive maintenance caused by fixed-cycle maintenance, and realizing on-demand maintenance based on the actual health status of the equipment. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a system block diagram of a light source machine maintenance management system according to an embodiment of this application; Figure 2 This is a system block diagram of a light source machine maintenance management system according to another embodiment of this application; Figure 3 This is a flowchart illustrating the execution of optical parameter adaptation rules in a light source machine maintenance management system according to an embodiment of this application. Figure 4 This is a schematic flowchart of a light source machine maintenance and management method according to an embodiment of this application; Figure 5 This is a flowchart illustrating a light source machine maintenance and management method according to another embodiment of this application.

[0022] Marker explanation: 100. Light source machine maintenance management system; 1. Real-time monitoring module; 11. Sensor group; 12. Status recording unit; 2. Optical parameter adaptation module; 3. Diagnostic module; 4. Dynamic maintenance plan module; 5. Maintenance verification module; 6. Maintenance record management module; 7. Remote interaction module. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0024] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0025] The maintenance management system and method for light source machines provided in this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments described herein are for the purpose of helping to understand the core concept of this application and are not intended to limit the scope of protection of this application.

[0026] The embodiments of this application provide a light source machine maintenance management system, which is applied to the wafer testing scenario of CMOS image sensors.

[0027] like Figure 1 As shown, the light source machine maintenance management system 100 includes a real-time monitoring module 1, an optical parameter adaptation module 2, a diagnostic module 3, and a dynamic maintenance plan module 4.

[0028] The real-time monitoring module 1 includes a sensor group 11 connected to the light source unit. The sensor group 11 includes one or more of the following: light intensity sensor, temperature sensor, color temperature sensor, spectral sensor, and current sensor. The real-time monitoring module also includes a status recording unit 12 for acquiring the cumulative usage time and number of uses of the light source unit. The real-time monitoring module 1 is used to output the real-time operating parameters collected by the sensor group 11 and the equipment status parameters acquired by the status recording unit 12.

[0029] The optical parameter adaptation module 2 is used to retrieve the corresponding optical parameter thresholds from a pre-stored model-parameter mapping relationship based on the currently tested CMOS image sensor model. Specifically, the optical parameter adaptation module pre-stores mapping relationships between different CMOS image sensor models and optical parameter thresholds. Based on the currently tested CMOS image sensor model, it retrieves the corresponding optical parameter thresholds from the pre-stored model-parameter mapping database. The optical parameter thresholds include light intensity thresholds, color temperature thresholds, and spectral parameter thresholds.

[0030] The diagnostic module 3 is connected to the real-time monitoring module 1 and is used to analyze real-time operating parameters based on pre-stored fault judgment logic, and output fault identifiers and maintenance suggestions. Specifically, the diagnostic module 3 has a preset rule-based fault judgment logic, which is used to receive real-time operating parameters, analyze and process the received real-time operating parameters based on the rule-based fault judgment logic, and output the corresponding fault identifiers.

[0031] The dynamic maintenance plan module 4 is connected to the real-time monitoring module 1 and is used to dynamically generate adjustment instructions for adjusting the maintenance cycle based on equipment status parameters and / or real-time operating parameters.

[0032] Specifically, sensor group 11 includes a light intensity sensor, a temperature sensor, a color temperature sensor, a spectrum sensor, and a current sensor. The dynamic maintenance plan module 4 receives data from equipment status parameters such as light intensity decay rate, color temperature fluctuation frequency, cumulative usage time of the light source, and usage count. It then compares and analyzes the received data with preset thresholds and outputs adjustment instructions to shorten or extend the maintenance cycle based on the comparison and analysis results.

[0033] Among them, sensor group 11 supports the implementation of fault judgment logic of diagnostic module and optical parameter call of optical parameter adaptation module.

[0034] For example, the light source machine maintenance management system is deployed on the CIS wafer testing production line. The real-time monitoring module 1 continuously collects real-time operating parameters of the light source machine through sensor group 11; simultaneously, it obtains equipment status parameters such as cumulative usage time and usage count through the status recording unit 12 connected to the light source machine's main control system. The optical parameter adaptation module automatically retrieves the optical parameter thresholds corresponding to the pre-stored model-parameter mapping relationship from the built-in mapping database based on the CIS model (e.g., "CIS-A100") input in the test plan. The diagnostic module receives real-time operating parameters and runs its preset rule-based fault judgment logic for analysis. The dynamic maintenance plan module receives trend data (such as light intensity attenuation rate) from the equipment status parameters and compares and analyzes it with preset thresholds.

[0035] The light source machine maintenance management system provided in this application embodiment achieves management of the light source machine from real-time status perception, intelligent diagnostic decision-making, personalized planning to dynamic adjustment by constructing a system architecture that includes a real-time monitoring module 1, an optical parameter adaptation module 2, a diagnostic module 3, and a dynamic maintenance plan module 4 working together.

[0036] Specifically, the real-time monitoring module, through the integrated sensor group 11 and status recording unit 12, realizes the automated acquisition of key operating parameters and historical status data of the light source, overcoming the problem of lack of real-time status monitoring and providing a foundation for data-driven decision-making for the entire system; the optical parameter adaptation module, through the built-in mapping database and automatic calling mechanism, ensures that the optical parameters of the test light source can accurately match the specific requirements of the CIS model being tested, overcoming the defects of inaccurate matching between optical parameter configuration and test requirements and reliance on manual configuration; the diagnostic module analyzes and processes real-time data based on preset rule logic and outputs fault identification and maintenance suggestions, realizing early detection of potential faults and rapid and accurate location of root causes, transforming the maintenance mode from relying on manual experience to intelligent diagnosis, and improving fault handling efficiency; the dynamic maintenance plan module, based on the comparative analysis of trend data in equipment status parameters and preset thresholds, dynamically outputs maintenance cycle adjustment instructions, changing the problem of insufficient or excessive maintenance caused by fixed-cycle maintenance, and realizing on-demand maintenance based on the actual health status of the equipment. This transforms the maintenance and management of light source machines from the traditional "passive, manual, and fixed" model to a systematic and integrated "proactive, intelligent, and dynamic" model.

[0037] Specifically, the pre-stored mapping database in the optical parameter adaptation module 2 can be constructed using standard parameter tables provided by the equipment manufacturer, or it can be updated through self-learning based on successful test parameters of different CIS models in historical test data to ensure the timeliness and accuracy of its adaptation rules. When the system performs adaptation, it uses the CIS model (such as CIS-A100) entered in the current test plan as the index key to automatically retrieve and lock the corresponding set of optical parameter thresholds (such as light intensity threshold 800-1000 lux, color temperature threshold 5000-5500K, spectral half-width ≤25nm), and synchronously sends this set of thresholds to the real-time monitoring module 1 and the diagnostic module 3 as an immediate standard for subsequent data comparison and fault judgment.

[0038] The key feature of Dynamic Maintenance Planning Module 4 lies in its quantitative decision-making logic. Module 4 continuously receives and integrates two types of data: first, long-term trend data from the status recording unit 12 (such as monthly light intensity decay rate, hourly color temperature fluctuation frequency, cumulative usage time, and usage frequency); and second, real-time performance data from the sensor group 11 (such as the instantaneous deviation of light intensity / color temperature from the current CIS test standard value). The module's built-in multi-factor decision model performs parallel comparison and weighted analysis of the above data with multiple preset thresholds (such as a light intensity decay rate threshold of 0.5% / month). The final output is not a simple Boolean judgment, but a quantitative adjustment instruction (such as shortening the cycle to 70% of the original cycle) and its corresponding maintenance content optimization plan (such as adding performance testing of the light source components). This decision-making mechanism based on multi-source data fusion helps to realize the transformation from fixed-cycle to on-demand maintenance.

[0039] In some embodiments, the mapping relationships stored in the optical parameter adaptation module include at least one of the following adaptation rules: For high-pixel CMOS image sensor models, the optical parameter thresholds are set to control light intensity accuracy within ±2%, color temperature fluctuation range within ±150K, and spectral half-width ≤20nm. For low-power CMOS image sensor models, the optical parameter thresholds are used, with light intensity accuracy controlled within ±3% and color temperature fluctuation range controlled within ±200K. For infrared-sensitive CMOS image sensor models, the optical parameter thresholds with a spectral center wavelength of 850nm, a half-width of 30nm, and a light intensity dynamically adjusted within the range of 500-1200 lux are used.

[0040] Specifically, the mapping relationships stored in the optical parameter adaptation module are invoked according to differentiated rules: when testing high-pixel CIS, thresholds of light intensity ±2%, color temperature ±150K, and spectral half-width ≤20nm are invoked; when testing low-power CIS, thresholds of light intensity ±3% and color temperature ±200K are invoked; when testing infrared-sensitive CIS, spectral thresholds of center wavelength 850nm and half-width 30nm are invoked, and light intensity is allowed to be adjusted within 500-1200 lux.

[0041] Among them, by automatically calling preset differentiated parameters, the test conditions are accurately matched, ensuring the test accuracy of various CIS products from the source, and enabling the output of the light source machine to meet the test illumination requirements of different CIS products.

[0042] Furthermore, the pre-defined rule-based fault judgment logic within the diagnostic module 3 includes at least one of the following: When the light intensity data continues to fluctuate beyond the first preset range and the temperature data is higher than the preset temperature, the light source aging fault indicator is triggered. When the cooling fan speed is continuously lower than a preset percentage of the rated speed and the temperature rise rate exceeds a preset value, the cooling system fault indicator is triggered.

[0043] To further clarify the diagnostic logic, the following example is given: When the light intensity data reported by the real-time monitoring module fluctuates by more than ±3% for 3 consecutive minutes (a specific manifestation of the first preset range) and the internal temperature of the light source unit remains above 60℃ (a specific manifestation of the preset temperature), the diagnostic module will trigger the "light source aging fault" flag and suggest "checking and replacing the light source components" in the generated fault report.

[0044] The pre-defined rule-based fault judgment logic within diagnostic module 3 may include: Light source aging fault: When the light intensity data in the real-time operating parameters fluctuates by more than ±3% for 3 consecutive minutes and the temperature data is higher than 60℃, the light source aging fault indicator is triggered; correspondingly, the sensor group includes a light intensity sensor and a temperature sensor. That is, the first preset range is a fluctuation of more than ±3% for 3 consecutive minutes, and the preset temperature is 60℃.

[0045] Cooling system malfunction: When the cooling fan speed is consistently below 90% of the rated speed and the temperature rise rate exceeds 3℃ / min, a cooling system malfunction indicator is triggered. Correspondingly, the sensor group includes a speed sensor for monitoring the cooling fan speed. The preset ratio is 90%, and the preset value is 3℃ / min.

[0046] Optical component contamination fault: If the light intensity data in the real-time operating parameters decreases by ≥3% within five minutes, and a stray peak with an intensity exceeding 3% of the main peak appears in the spectral data, the optical component contamination fault indicator is triggered; correspondingly, the sensor group includes a spectral sensor. That is, the light intensity suddenly decreases by ≥3% without a significant light source attenuation trend and stray peaks appear in the spectral distribution, with the stray peak intensity exceeding 3% of the main peak.

[0047] Drive circuit fault: If the current data fluctuation in the real-time operating parameters exceeds ±3% and the light intensity data fluctuation is ≥1%, a drive circuit fault indicator will be triggered; correspondingly, the sensor group includes a current sensor. That is, the current sensor detects a current fluctuation exceeding ±3% and unstable light intensity output (fluctuation ≥1%).

[0048] Among these methods, a rule-based model is used for fault detection and assessment. By pre-setting specific and quantifiable rules, the model can automatically analyze and accurately locate the cause of the fault. For example, it can accurately distinguish between "light source aging" and "optical component contamination." It also outputs maintenance suggestions, improving maintenance efficiency and accuracy, and avoiding the time-consuming problem of relying on manual experience for troubleshooting.

[0049] In some embodiments, the dynamic maintenance plan module 4 is configured to generate an adjustment instruction based on at least two of the following device status parameters: light intensity attenuation rate, color temperature fluctuation frequency, cumulative usage time, and number of uses, and compare them with the corresponding preset thresholds.

[0050] Among them, dynamic decision-making based on the actual performance degradation and usage intensity of the equipment enables on-demand maintenance. Compared with the traditional "fixed periodic maintenance", it can effectively avoid over-maintenance or under-maintenance while ensuring the equipment's condition, thus extending the equipment's service life and reducing maintenance costs.

[0051] Furthermore, the dynamic maintenance plan module 4 executes the following quantitative adjustment logic: The monthly light intensity decay rate calculated based on device status parameters is compared with a preset threshold of 0.5%. The hourly color temperature fluctuation frequency calculated based on device status parameters is compared with a preset threshold of 2 times. Compare the cumulative usage time of the light source in the equipment status parameters with the preset threshold of 5000 hours; Compare the number of uses in the device status parameters with a preset threshold of 10,000 times; If the light intensity decay rate is greater than 0.8% per month, or the color temperature fluctuation frequency is greater than 3 times per hour, or the cumulative usage time is greater than 6000 hours, or the number of uses is greater than 12000 times, then an adjustment command will be output to shorten the maintenance cycle to 70% of the original cycle. If the light intensity decay rate is less than 0.3% per month, the color temperature fluctuation frequency is less than once per hour, the cumulative usage time is less than 4000 hours, and the number of uses is less than 8000 times, then an adjustment command will be output to extend the maintenance cycle to 1.5 times the original cycle.

[0052] The dynamic maintenance plan module also outputs maintenance content adjustment instructions: When an adjustment command to shorten the maintenance cycle is issued, the performance testing and maintenance of the light source components are added simultaneously. When an adjustment command to extend the maintenance cycle is issued, the maintenance content is simultaneously optimized to optical lens cleaning and light intensity calibration.

[0053] Specifically, the dynamic maintenance plan module executes quantitative adjustment logic: it compares the monthly light intensity decay rate calculated based on equipment status parameters with a 0.5% threshold; it compares the calculated hourly color temperature fluctuation frequency with a threshold of 2 times; and it compares the number of uses with a threshold of 10,000 times. It generates corresponding adjustment instructions: if the light intensity decay rate is >0.8% per month, or the color temperature fluctuation frequency is ≥3 times / hour, or the cumulative usage time is ≥6000 hours, or the number of uses is ≥12000 times, then it outputs an adjustment instruction to shorten the maintenance cycle to 70% of the original cycle, and simultaneously adds maintenance content including performance testing of the light source components. Conversely, if the light intensity decay rate is <0.3% per month, and the color temperature fluctuation frequency is <1 time / hour, and the cumulative usage time is <4000 hours, and the number of uses is <8000 times, then it outputs an adjustment instruction to extend the maintenance cycle to 1.5 times the original cycle, and simultaneously optimizes the maintenance content to optical lens cleaning and light intensity calibration.

[0054] In some embodiments, the light source machine maintenance management system 100 further includes a maintenance verification module 5, which is connected to the real-time monitoring module 1 and the optical parameter adaptation module 2 respectively. After maintenance is completed, the module 5 acquires the light intensity and color temperature data after maintenance and compares the data with the current test model standard value called by the optical parameter adaptation module 2 to determine whether the maintenance is qualified.

[0055] Specifically, after maintenance is completed, the maintenance verification module 5 compares and analyzes the post-maintenance light intensity data and color temperature data obtained by the real-time monitoring module 1 with the standard values ​​of the current test model called by the optical parameter adaptation module 2.

[0056] The execution logic of maintenance verification module 5 includes: If the deviation between the light intensity data after maintenance and the standard value of light intensity for the current test model is ≤ ±3%, then the light intensity verification is deemed to have passed. If the color temperature data after maintenance deviates from the standard color temperature value of the current test model by ≤±50K, the color temperature verification is deemed to have passed. If the current and temperature operating parameters of the light source machine are within the preset normal range and the color sensitivity test data error of the CMOS image sensor wafer test is ≤±2%, then the performance verification is deemed to have passed. When the light intensity verification, color temperature verification, and performance verification all pass, a verification pass signal is output and synchronized to the maintenance record management module. If any verification fails, a verification failure signal is generated and the dynamic maintenance plan module is triggered to re-optimize the maintenance plan.

[0057] After maintenance is completed, the maintenance verification module executes the post-maintenance verification standards, which include: Light intensity verification: If the deviation between the light intensity data after maintenance and the standard value of the light intensity of the current test model is ≤±3%, then it passes.

[0058] Color temperature verification: If the deviation between the color temperature data after maintenance and the standard value of the color temperature of the current test model is ≤ ±50K, then it passes.

[0059] Comprehensive performance verification: If the current and temperature operating parameters of the light source are within the preset normal range, and the error of the color sensitivity test data of the CIS wafer is ≤±2%, then it passes.

[0060] When all three verifications above pass, the module outputs a verification pass signal.

[0061] By directly linking the verification standards to the CIS testing accuracy, it ensures that the performance of the light source machine can be restored to a precise state that meets the testing requirements after each maintenance. This guarantees the stability of the light source machine's performance and the accuracy of its light output during CIS product wafer testing, ensuring the accuracy of test results. A quantitative maintenance quality closed loop has been established, avoiding the risk that the performance cannot be quantitatively confirmed after traditional maintenance.

[0062] In some embodiments, the light source machine maintenance management system 100 further includes a maintenance record management module 6.

[0063] Specifically, after maintenance is completed, maintenance personnel upload the maintenance information to the maintenance record management module via a terminal device. The maintenance information includes the maintenance time, the specific maintenance performed, the replaced parts, and key performance parameters such as light intensity, color temperature, and spectral density collected before and after the maintenance. Simultaneously, the system supports linking this maintenance record with subsequent CIS test batch information.

[0064] The maintenance record management module 6 stores the above information in a cloud database and uses data visualization technology to automatically generate and display the maintenance history curve of the light source machine, the trend chart of core performance parameter changes, and the correlation analysis chart between maintenance effect and CIS test results (such as color sensitivity).

[0065] Among them, the maintenance record management module realizes the digital and traceable management of maintenance history data, equipment performance, and test results, overcoming the problems of "inconvenient search, easy loss, and unfavorable data analysis" caused by traditional "paper or simple electronic spreadsheet records", and providing intuitive and reliable data support for evaluating maintenance effectiveness and optimizing maintenance strategies.

[0066] In some embodiments, the light source machine maintenance management system 100 further includes a remote interaction module 7. The remote interaction module 7 is connected to the diagnostic module 3, the dynamic maintenance plan module 4, and the maintenance verification module 5, respectively, and is used to automatically generate and send alarm or reminder information based on the fault identification of the diagnostic module 3, the adjustment instructions of the dynamic maintenance plan module 4, or the verification failure signal of the maintenance verification module 5.

[0067] Specifically, the remote interaction module 7 is automatically triggered when the diagnostic module 3 outputs a fault indicator and maintenance suggestions (such as light source aging or optical component contamination), the dynamic maintenance plan module 4 determines that the maintenance time is approaching, or the maintenance verification module 5 outputs a verification failure signal. The remote interaction module 7 generates an alarm message or maintenance reminder containing the specific fault cause, maintenance suggestions, maintenance plan details, or verification results, and sends it to the preset remote terminal of the maintenance personnel via SMS, email, or terminal push through the integrated communication interface. The reminder message will clearly inform the CIS testing process that the anomaly may affect, such as "The current color temperature is abnormal, which may cause color sensitivity test distortion."

[0068] Among them, the remote interaction module 7 establishes a bridge from "internal intelligent judgment" to "external proactive response", ensuring that potential faults and maintenance needs can be promptly known and handled by relevant personnel, improving the response speed and processing efficiency of the entire maintenance management process, and avoiding the processing delays caused by information transmission delays in the traditional process.

[0069] Based on the same inventive concept, embodiments of this application also provide a light source machine maintenance management method. This light source machine maintenance management method is applied to the light source machine maintenance management system 100 of any of the foregoing embodiments.

[0070] refer to Figure 4 The maintenance and management methods for light source machines include the following steps: Step S10: Collect the operating parameters of the light source machine in real time through the sensor, and obtain the equipment status parameters of the light source machine through the status record; Step S20: Based on the CMOS image sensor model currently being tested, retrieve the corresponding optical parameter threshold from the database containing the mapping relationship between different CMOS image sensor models and optical parameter thresholds. Step S30: Analyze the operating parameters based on the pre-stored fault judgment logic, and generate a fault identifier if the preset conditions are met. Step S40: Based on equipment status parameters and / or operating parameters, dynamically generate adjustment instructions for adjusting the maintenance cycle.

[0071] Specifically, the light source machine maintenance and management method uses a data acquisition step (step S10) to collect the operating parameters of the light source machine in real time through sensors, obtaining real-time operating data including temperature, light intensity, color temperature, spectrum and current, avoiding the problem of "lack of real-time status monitoring" and providing a data foundation for subsequent analysis.

[0072] The automatic optical parameter recall step (step S20) retrieves the corresponding optical parameter thresholds from the pre-stored model-parameter mapping database based on the current CMOS image sensor model being tested. The optical parameter thresholds include light intensity thresholds, color temperature thresholds, and spectral parameter thresholds. This achieves accurate and automatic matching between the test light source parameters and the CIS model, overcoming the shortcomings of inaccurate matching between optical parameter configuration and test requirements and reliance on human experience.

[0073] Then, through the intelligent fault diagnosis step (step S30), the real-time collected operating parameters are compared and analyzed with the fault judgment logic preset in the rule base. If the preset logical conditions are met, the corresponding fault identifier is generated, thereby transforming fault handling from manual investigation to automatic diagnosis based on preset rules, realizing early detection and accurate location of potential faults.

[0074] Finally, through the dynamic maintenance decision-making step (step S40), based on the real-time collected operating parameters and historical data, the light intensity decay rate, color temperature fluctuation frequency, cumulative usage time and usage frequency of the light source are monitored, and the monitored data are compared and analyzed with preset thresholds. Based on the comparison and analysis results, adjustment instructions for shortening or extending the maintenance cycle are output, thereby generating maintenance instructions based on the actual status data of the equipment, changing the "fixed cycle maintenance" mode and realizing precise "on-demand maintenance".

[0075] The light source machine maintenance management method provided in this application embodiment transforms the traditional "passive, manual, and fixed" maintenance management of light source machines into a "proactive, intelligent, and dynamic" mode in a systematic and integrated manner.

[0076] Furthermore, step S20, which involves retrieving the corresponding optical parameter threshold from the pre-stored model-parameter mapping relationship, includes: If the current test model is a high-pixel CMOS image sensor, then the optical parameter thresholds of light intensity accuracy control within ±2%, color temperature fluctuation range control within ±150K, and spectral half-width ≤20nm are applied. If the current test model is a low-power CMOS image sensor, then the optical parameter thresholds with light intensity accuracy controlled within ±3% and color temperature fluctuation range controlled within ±200K are applied. If the current test model is an infrared-sensitive CMOS image sensor, then the optical parameter threshold with a spectral center wavelength of 850nm, a half-width of 30nm, and a light intensity dynamically adjusted within the range of 500-1200 lux will be used.

[0077] Specifically, by transforming the differentiated adaptation rules for optical parameter calls into executable method steps, the method ensures that its implementation meets the stringent lighting requirements of different types of CIS products, such as high-pixel counts, low-power consumption, and infrared-sensitive products. For example, by applying a ±2% light intensity accuracy and a ±150K color temperature fluctuation range to high-pixel CIS, the color reproduction and detail capture capabilities of the pixels are guaranteed; while applying a relatively lenient threshold (±3%, ±200K) to low-power CIS prioritizes keeping the light source's power consumption at a low level. In this way, specific, quantified technical knowledge is solidified into the process, ensuring the consistency and accuracy of testing conditions.

[0078] The fault determination logic in step S30 includes at least one of the following: The light intensity fluctuates by more than ±3% for 3 consecutive minutes and the temperature is above 60℃; The cooling fan speed remains below 90% of the rated speed and the temperature rise rate exceeds 3℃ / min; The light intensity decreased by ≥3% within five minutes, and the spectral data showed extraneous peaks with an intensity exceeding 3% of the main peak; Current fluctuations exceed ±3% and light intensity output fluctuations are ≥1%.

[0079] Specifically, validated and quantified core fault diagnosis rules are transformed into specific logical conditions within the methodological steps. These rules (e.g., light intensity fluctuations of ±3% for 3 minutes coupled with a temperature >60℃ are considered light source aging) ensure that the fault diagnosis process is based on objective and repeatable data comparison. This allows for precise differentiation of faults with similar symptoms; for example, it effectively distinguishes between slow performance degradation caused by "light source aging" and sudden performance decline caused by "optical component contamination," thus providing maintenance personnel with accurate repair guidance. This avoids the problems of low efficiency in fault diagnosis and repair and reliance on manual experience for troubleshooting.

[0080] Step S40 dynamically generates adjustment instructions for adjusting the maintenance cycle based on equipment status parameters and / or operating parameters, including: The monthly light intensity decay rate calculated based on monitoring data is compared with a preset threshold of 0.5%. The hourly color temperature fluctuation frequency calculated based on monitoring data is compared with a preset threshold of 2 times. Compare the cumulative usage time of the light source machine with the preset threshold of 5000 hours; The number of uses is compared with a preset threshold of 10,000; the deviation between real-time operating parameters and the current CIS test standard value is continuously monitored. If the light intensity decay rate is greater than 0.8% per month, or the color temperature fluctuation frequency is greater than 3 times per hour, or the cumulative usage time is greater than 6000 hours, or the number of uses is greater than 12000 times, or the deviation between the real-time light intensity / color temperature data and the standard value continues to exceed ±4%, then an adjustment command will be output to shorten the maintenance cycle to 70% of the original cycle. If the light intensity decay rate is less than 0.3% per month, the color temperature fluctuation frequency is less than 1 time per hour, the cumulative usage time is less than 4000 hours, and the number of uses is less than 8000 times, then an instruction will be generated to extend the maintenance cycle to 1.5 times the original cycle. Step S40, which dynamically generates adjustment instructions for adjusting maintenance cycles based on equipment status parameters and / or operating parameters, also includes: When an adjustment instruction to shorten the maintenance cycle is generated, an instruction to add performance testing and maintenance content for the light source components is generated simultaneously. When an adjustment command to extend the maintenance cycle is generated, a command to optimize the maintenance content to optical lens cleaning and light intensity calibration is generated simultaneously.

[0081] Specifically, by defining a quantitative method for dynamic maintenance decisions, performance trend parameters such as light intensity decay rate and color temperature fluctuation frequency are combined with usage intensity parameters such as cumulative usage time and usage frequency. These parameters are then compared with multiple precise thresholds to generate quantified maintenance cycle adjustment instructions (shortening to 70% or extending to 1.5 times). Compared to single time or frequency indicators, this achieves multi-dimensional and more refined maintenance decisions. Furthermore, the adjustment of maintenance cycles is linked to specific maintenance content (e.g., adding performance testing when the condition is poor, and optimizing cleaning and calibration when the condition is good), making maintenance work more targeted and achieving the effects of reducing maintenance costs and extending service life.

[0082] In some implementations, such as Figure 5 As shown, the maintenance and management method for the light source machine also includes the following steps: Step S50: After maintenance is completed, obtain the performance parameters after maintenance and compare them with the standard values ​​of the current test model to determine whether the maintenance is qualified. Specifically, this includes: The sensor acquires light intensity and color temperature monitoring data after maintenance. The light intensity monitoring data and color temperature monitoring data collected after maintenance were compared and verified with the standard values ​​of the current CMOS image sensor model being tested. The comparison and verification includes: Verify that the deviation between the light intensity data after maintenance and the standard light intensity value of the current test model is ≤ ±3%; Verify that the deviation between the color temperature data after maintenance and the standard color temperature value of the current test model is ≤ ±50K; Verify whether the current and temperature operating parameters of the light source machine are within the preset normal range, and whether the error of the color sensitivity test data for the CMOS image sensor wafer test is ≤ ±2%; If the light intensity verification, color temperature verification, and performance verification all pass, it is determined that the maintenance is qualified; if any one of the verifications fails, it is determined that the maintenance is unqualified.

[0083] Step S60: If a fault flag, adjustment instruction, or unqualified maintenance is generated, an alarm message or maintenance reminder corresponding thereto is automatically generated and sent to the remote terminal.

[0084] Among them, step S50 is the maintenance verification step, and the light intensity monitoring data and color temperature monitoring data after maintenance are collected again through the sensor. Subsequently, the collected data is strictly compared and verified with the light intensity and color temperature standard values corresponding to the current tested CMOS image sensor model. Three specific and quantitative checks are performed during the verification process: Verify whether the deviation of the light intensity data after maintenance from the standard value is ≤ ±3%. Verify whether the deviation of the color temperature data after maintenance from the standard value is ≤ ±50K. Verify whether the current and temperature parameters of the light source machine are normal, and whether the error of the color sensitivity test data for the CIS wafer test is ≤ ±2%. If all three verifications pass, it is determined that the maintenance is qualified; otherwise, it is determined to be unqualified. Thus, a quantitative quality closed-loop is established for the entire maintenance process. Directly link the performance recovery status of the light source machine after maintenance with the final CIS test accuracy, and ensure that the light source machine can stably output light signals that meet the test requirements through objective data, thereby guaranteeing the accuracy and reliability of the CIS product wafer test results from the process, and avoiding the inability to quantitatively confirm the performance after maintenance.

[0085] Step S60 is the reminder and remote interaction step, which is an actively triggered communication process. When other modules within the system generate key outputs, including the fault flag generated by the diagnostic module, the maintenance cycle adjustment instruction generated by the dynamic maintenance plan module, or the verification failure signal output by the maintenance verification module, this reminder and remote interaction step will be automatically triggered. Immediately, an alarm message or maintenance reminder containing specific fault reasons, repair suggestions, maintenance plan details, or verification results is generated, and is sent to the preset maintenance personnel's remote terminal via the integrated communication interface in the form of text messages, emails, or terminal push. Thus, a bridge is built between the system's intelligent decision-making and personnel execution, enabling proactive maintenance management, ensuring that potential faults, maintenance requirements, and abnormal results can be promptly and accurately known to relevant personnel, improving the response speed and processing efficiency of the entire maintenance process, and avoiding processing delays and production interruptions caused by information transmission delays or omissions from the mechanism, and further transforming the management mode from "passive response" to "active warning".

[0086] In some implementations, after the maintenance is completed, the light source machine maintenance management method further includes: The system receives and stores maintenance information after maintenance operations are completed, including maintenance time, maintenance content, replaced parts, and performance parameters before and after maintenance. It also associates and stores the maintenance information with the corresponding CMOS image sensor test batch information. Based on the associated stored data, it generates visual analysis charts of maintenance history and performance trends.

[0087] By setting up maintenance record management steps, the system is responsible for receiving and storing comprehensive maintenance information after maintenance is completed. This information includes the maintenance time, specific maintenance content performed, replaced parts, and key performance parameters before and after maintenance. More importantly, the system supports linking this maintenance record with subsequent CIS test batch information. After all information is stored, the system uses data visualization technology to automatically generate and display maintenance history curves, performance parameter change trend graphs, and correlation analysis charts between maintenance effects and CIS test results. This achieves digital and traceable management of maintenance work, overcoming the problems of inconvenience in searching, easy loss, and difficulty in data analysis caused by traditional paper or simple spreadsheet records. Through visualized historical data and correlation analysis, it provides intuitive and reliable data support for evaluating the effect of a single maintenance, understanding the long-term degradation pattern of equipment performance, and optimizing future maintenance strategies.

[0088] The light source machine maintenance management system and method provided in this application achieve closed-loop intelligent management of the entire maintenance process of the light source machine through the collaborative work of a real-time monitoring module, an optical parameter adaptation module, a diagnostic module, a dynamic maintenance plan module, a maintenance record management module, and a remote interaction module. Specifically, real-time monitoring and multi-source data acquisition provide a data foundation for accurate decision-making; automatic adaptation and retrieval of optical parameters ensure the accuracy of test conditions; intelligent diagnosis based on pre-stored fault judgment logic enables early detection and accurate location of faults; dynamic maintenance planning based on the actual state of the equipment realizes the transformation from fixed-cycle to on-demand maintenance; digital maintenance record management and visualization analysis enable traceability of maintenance history and sustainable optimization of maintenance strategies; and finally, proactive remote reminders and interaction mechanisms ensure timely response and handling of abnormal situations and maintenance tasks. From data acquisition, parameter adaptation, fault diagnosis, dynamic planning, maintenance execution, effect verification, and record feedback, the maintenance management of light source machines has been systematically and integratedly upgraded from the traditional "passive response, manual experience, fixed cycle, and lack of verification" model to a "data-driven, intelligent diagnosis, dynamic planning, traceable records, and proactive interaction" management model. This effectively improves equipment reliability, testing accuracy, and maintenance efficiency, while reducing operation and maintenance costs.

[0089] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0090] The above embodiments merely illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A light source machine maintenance management system, applied to wafer testing scenarios of CMOS image sensors, characterized in that, include: The real-time monitoring module includes a sensor group connected to the light source machine for collecting the real-time operating parameters of the light source machine. The real-time monitoring module also includes a status recording unit for acquiring and recording the equipment status parameters of the light source machine, including the cumulative usage time and the number of times it has been used. The optical parameter adaptation module is used to call the corresponding optical parameter threshold from the pre-stored model-parameter mapping relationship according to the model of the CMOS image sensor currently being tested. The diagnostic module, connected to the real-time monitoring module, is used to analyze the real-time operating parameters based on pre-stored fault judgment logic and output fault identifiers and maintenance suggestions. The dynamic maintenance plan module, connected to the real-time monitoring module, is used to dynamically generate adjustment instructions for adjusting the maintenance cycle based on the equipment status parameters and / or the real-time operating parameters.

2. The light source machine maintenance management system according to claim 1, characterized in that, The sensor group includes at least one of a light intensity sensor, a temperature sensor, a color temperature sensor, a spectral sensor, and a current sensor.

3. The light source machine maintenance management system according to claim 1, characterized in that, The optical parameter adaptation module pre-stores the mapping relationship between different CMOS image sensor models and optical parameter thresholds, including light intensity threshold, color temperature threshold and spectral parameter threshold.

4. The light source machine maintenance management system according to claim 1, characterized in that, The pre-stored fault judgment logic includes at least one of the following: When the light intensity data continues to fluctuate beyond the first preset range and the temperature data is higher than the preset temperature, the light source aging fault indicator is triggered. When the cooling fan speed is continuously lower than a preset percentage of the rated speed and the temperature rise rate exceeds a preset value, the cooling system fault indicator is triggered.

5. The light source machine maintenance management system according to claim 1, characterized in that, The dynamic maintenance plan module is configured to generate the adjustment instruction by comparing at least two of the following parameters in the device status parameters: light intensity attenuation rate, color temperature fluctuation frequency, cumulative usage time, and number of uses, with the corresponding preset thresholds.

6. The light source machine maintenance management system according to claim 1, characterized in that, The light source machine maintenance management system also includes a maintenance verification module, which is connected to the real-time monitoring module and the optical parameter adaptation module respectively. After maintenance is completed, the maintenance verification module acquires the light intensity and color temperature data after maintenance and compares them with the standard values ​​of the current test model called by the optical parameter adaptation module to determine whether the maintenance is qualified.

7. The light source machine maintenance management system according to claim 6, characterized in that, The light source machine maintenance management system also includes a remote interaction module, which is connected to the diagnostic module, the dynamic maintenance plan module, and the maintenance verification module, respectively. The remote interaction module is used to automatically generate and send alarm or reminder information based on the fault identifier, adjustment instruction, or maintenance verification result.

8. A method for maintaining and managing a light source, applied to wafer testing scenarios for CMOS image sensors, characterized in that, The method includes: The operating parameters of the light source machine are collected in real time by sensors, and the equipment status parameters of the light source machine are obtained through status recording. Based on the CMOS image sensor model currently being tested, the corresponding optical parameter threshold is retrieved from the pre-stored model-parameter mapping relationship; The operating parameters are analyzed based on the pre-stored fault judgment logic, and a fault identifier is generated if the preset conditions are met. Based on the equipment status parameters and / or the operating parameters, adjustment instructions for adjusting the maintenance cycle are dynamically generated.

9. The maintenance and management method for a light source machine according to claim 8, characterized in that, The method further includes: Receive and store maintenance information, which includes maintenance time, maintenance content, replaced parts, and performance parameters before and after maintenance; The maintenance information is associated with and stored in conjunction with the corresponding CMOS image sensor test batch information; Based on the data stored in the association, visualized analysis charts are generated to show maintenance history and performance trends.

10. The maintenance and management method for a light source machine according to claim 8, characterized in that, The method further includes: After maintenance is completed, the performance parameters after maintenance are obtained and compared with the standard values ​​of the current test model to determine whether the maintenance is qualified. If the fault identifier, adjustment instruction, or maintenance is not up to standard, the corresponding alarm information or maintenance reminder will be automatically generated and sent to the remote terminal.

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