Semiconductor equipment maintenance period prediction method, equipment, medium and product

Abstract

The embodiment of the invention relates to the field of equipment maintenance, and discloses a semiconductor equipment maintenance period prediction method, equipment, a medium and a product. The method comprises the following steps: collecting samples of equipment operation parameters when the semiconductor equipment triggers maintenance conditions; the sample comprises collection time and a trigger value; calculating the trigger rate of each sample, wherein the trigger rate is the change rate of the trigger value of the sample relative to the previous sample; performing correction compensation on an average trigger rate according to the trigger rate, wherein the average trigger rate is an average change trend of a plurality of samples; and predicting the next maintenance time based on the corrected and compensated average trigger rate, namely the predicted time when the equipment triggers the maintenance condition. The technical problem of inaccurate prediction of the maintenance period of the semiconductor equipment can be at least solved.

Description

Technical Field

[0001] The present application relates to the field of equipment maintenance, and in particular to a method, equipment, medium and product for predicting the maintenance cycle of semiconductor equipment. Background Art

[0002] In the semiconductor manufacturing process, the stable operation of equipment is crucial. In order to ensure the efficient operation of equipment and production quality, the Preventive Maintenance System (PMS) is widely used in semiconductor manufacturing companies. The core goal of the PMS system is to regularly maintain and service equipment to prevent equipment failures and ensure the continuity and stability of the production process.

[0003] In the PMS system, the Condition Based Maintenance Plan (CBM) is an important maintenance method. The CBM plan monitors the operating parameters of the equipment and performs corresponding maintenance operations when these parameters meet the preset trigger conditions. This method can effectively avoid unnecessary maintenance and perform preventive maintenance before the equipment fails, thereby reducing the equipment failure rate and improving equipment utilization.

[0004] At present, in the mainstream CBM plan, the algorithm for predicting the next maintenance time (Due Date) is mainly based on the trigger rate between the previous sample and the current sample. However, when the sample data is not ideal, this algorithm will cause large fluctuations in the trigger rate, making the predicted Due Date inaccurate and unstable. For example, when the sample data is greatly affected by factors such as environmental changes and equipment status fluctuations, the prediction method based on a single sample trigger rate is difficult to provide a reliable maintenance cycle reference. The drawback of the existing technology is that it cannot fully utilize historical sample data and lacks an effective correction and compensation mechanism for the trigger rate. Summary of the invention

[0005] One purpose of the present application is to provide a semiconductor equipment maintenance cycle prediction method, equipment, medium and product, at least to solve the problem of inaccurate semiconductor equipment maintenance cycle prediction.

[0006] To achieve the above objectives, some embodiments of the present application provide the following aspects:

[0007] In the first aspect, some embodiments of the present application also provide a method for predicting the maintenance cycle of semiconductor equipment, including collecting samples of equipment operating parameters when the semiconductor equipment triggers a maintenance condition; the samples include a collection time and a trigger value; calculating a trigger rate for each of the samples, the trigger rate being the rate of change of the trigger value of the sample relative to the previous sample; correcting and compensating an average trigger rate according to the trigger rate, the average trigger rate being the average change trend of multiple samples; and predicting the time of the next maintenance based on the corrected and compensated average trigger rate, that is, the expected time when the equipment triggers the maintenance condition.

[0008] In a second aspect, some embodiments of the present application further provide an electronic device, comprising: one or more processors; and a memory storing computer program instructions, wherein the computer program instructions, when executed, cause the processor to perform the steps of the method described above.

[0009] In a third aspect, some embodiments of the present application further provide a computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method as described above.

[0010] In a fourth aspect, some embodiments of the present application further provide a computer program product, comprising a computer program / instruction, which implements the steps of the method described above when executed by a processor.

[0011] Compared with the related art, the solution provided in the embodiment of the present application corrects and compensates the trigger rate by integrating multiple algorithms, thereby achieving accurate prediction of the maintenance cycle of semiconductor equipment, thereby improving the stability and accuracy of Due Date prediction, reducing unnecessary maintenance operations, reducing equipment failure rate, and significantly improving equipment utilization and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0013] Figure 1 A schematic diagram of a process flow of a semiconductor equipment maintenance cycle prediction method provided according to an embodiment of the present application;

[0014] Figure 2 A schematic diagram of the effect of trigger rate prediction provided according to an embodiment of the present application;

[0015] Figure 3A schematic diagram of the effect of a semiconductor equipment maintenance cycle prediction method provided according to an embodiment of the present application;

[0016] Figure 4 The present invention is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] The following terms are used in this article.

[0019] PMS (Preventive Maintenance System): (Equipment) preventive maintenance system.

[0020] PM Plan: Maintenance plan.

[0021] CBM (Condition Based Maintenance) Plan: A maintenance plan triggered by equipment operating conditions.

[0022] Trigger Condition: The condition that triggers maintenance. A CBM Plan can be configured with multiple trigger conditions. When any condition is met, maintenance needs to be performed on the corresponding equipment (execution of PM Plan).

[0023] Trigger Value: The trigger value corresponding to the trigger condition, also referred to as a sample below. The PMS system will continuously collect samples from MES (Manufacturing Execution System) / EAP (Equipment Automation Program) according to user configuration.

[0024] Read Time: The time when the sample was collected.

[0025] Trigger Spec: Trigger threshold. When the trigger value reaches the trigger threshold, the corresponding trigger condition is met.

[0026] Trigger Rate: Trigger rate, that is, the rate of change of the trigger value.

[0027] Due Date: The time when the sample reaches the bottom threshold, which is also the next execution time of the maintenance plan.

[0028] Real Rate: The actual trigger rate.

[0029] Sample Rate: Sample trigger rate, that is, the trigger rate between the previous sample and the current sample.

[0030] AVG Rate (Average Rate): Average trigger rate, which is the trigger rate calculated by integrating the trigger rates of multiple samples in the past.

[0031] PMS is the equipment preventive maintenance system, which aims to ensure that the equipment can produce with continuous, stable and efficient quality, and to automate the entire process of equipment maintenance. CBM Plan is a major type of maintenance plan in the PMS system, which triggers maintenance operations based on equipment operating conditions. Accurately predicting the next trigger time (Due Date), that is, the next execution time of CBM Plan, is of great reference significance for equipment maintenance.

[0032] Due Date prediction is based on samples collected from EAP / MES, that is, the Due Date needs to be recalculated (predicted) every time a new sample is collected. The basic calculation formula is:

[0033]

[0034] Among them, except TRIGGER_RATE, all are known. In other words, the core of Due Date prediction is the prediction of trigger rate, that is, a certain algorithm is used to calculate (predict) the future trigger rate based on the past trigger rate. The algorithm of the current mainstream PMS system is to calculate the trigger rate between the previous sample and the current sample (that is, the sample rate of the current sample). However, when the sample is not ideal, the trigger rate calculated by this algorithm fluctuates greatly, resulting in the predicted DueDate being unstable and of little reference value to users. The present application uses a combination of multiple algorithms to correct the predicted trigger rate according to different scenarios, so as to accurately predict the Due Date of the CBM Plan.

[0035] First embodiment

[0036] The first embodiment of the present application relates to a method for predicting the maintenance cycle of semiconductor equipment. Figure 1 As shown, the method may include the following steps:

[0037] S101, collecting samples of equipment operating parameters when a maintenance condition is triggered for a semiconductor device; the samples include a collection time and a trigger value;

[0038] S102, calculating a trigger rate of each sample, where the trigger rate is a rate of change of a trigger value of the sample relative to a previous sample;

[0039] S103, correcting and compensating an average trigger rate according to the trigger rate, where the average trigger rate is an average change trend of multiple samples;

[0040] S104, predicting the time of the next maintenance based on the corrected and compensated average trigger rate, that is, the estimated time when the equipment triggers the maintenance condition.

[0041] The following is a detailed description of each of the above steps.

[0042] For step S101, the collection time refers to the specific time point when the equipment operating parameters are recorded, and the trigger value refers to the specific parameter value that reflects the equipment operating status, which is an important basis for judging whether equipment maintenance is needed. In order to ensure the real-time and accuracy of the data, these sample data can be obtained through the manufacturing execution system (MES) or the equipment automation program (EAP) system. The MES system is an important management system in the production process, which can monitor and record the operating status, production data and other relevant information of the equipment in real time. Through the MES system, the operating parameters of the equipment at different time points can be obtained to ensure the continuity and reliability of the data. The EAP system can automatically control and monitor the operation of the equipment. The operating parameters and status information of the equipment can be collected in real time through the EAP system to provide timely sample data.

[0043] Sample collection process: monitor the operating status of the equipment in real time to ensure that relevant parameters can be obtained immediately when maintenance conditions are triggered; when the equipment operating parameters reach or exceed the preset trigger conditions, immediately record the current collection time and trigger value; store the collected sample data in the database for subsequent calculation and analysis; regularly check and verify the collected data to ensure its accuracy and completeness and avoid data loss or errors. Continuously collect samples to provide complete equipment operation history data to support subsequent trigger rate calculation and correction.

[0044] For step S102, the trigger rate is an indicator of the speed at which the device parameters change. When calculating, it is necessary to consider the trigger value of the current sample, the operating parameter value of the device at the current acquisition time point and the time when it is recorded; the trigger value of the previous sample, the operating parameter value of the device at the previous acquisition time point and the time when it is recorded. This process continues, and the trigger rate is recalculated every time a new sample is collected.

[0045] For step S103, the current average trigger rate is corrected using the newly calculated trigger rate. The average trigger rate is the average change trend of the trigger rate based on multiple samples. The correction and compensation process includes correction factor compensation. In the initial stage, when there is not enough historical sample data, the correction factor is compensated based on the initial maintenance time estimated by the user, and the actual average trigger rate is quickly converged. Trigger rate compensation, in the actual production process, the trigger rate may change at any time. In order to reflect this change, the average trigger rate is compensated to ensure that it is consistent with the change trend of the actual trigger rate.

[0046] For step S104, based on the average trigger rate after correction and compensation, the time of the next maintenance of the semiconductor equipment is predicted. When the trigger condition is met, the estimated maintenance time of the equipment is calculated. This time point provides an accurate reference for the user to ensure that the equipment is maintained at the best time, thereby ensuring stable and efficient operation of the equipment.

[0047] It is not difficult to find that compared with the related art, the solution provided by the embodiment of the present application obtains a more accurate average trigger rate by calibrating and compensating the average trigger rate. The implementation of correction factor compensation and trigger rate compensation can significantly improve the accuracy of semiconductor equipment maintenance cycle prediction. The rapid convergence in the initial stage and the real-time adjustment during the production process ensure that the predicted Due Date is stable and reliable, providing a maintenance time prediction with high reference value.

[0048] Second embodiment

[0049] The second embodiment of the present application relates to a method for predicting the maintenance cycle of semiconductor equipment. The second embodiment is an improvement made on the basis of the first embodiment, and the specific improvements are:

[0050] For each trigger condition of the CBM Plan, an average trigger rate is recorded. When a new sample is collected, the sample trigger rate of the sample is first calculated, and then the sample trigger rate is used to correct the average trigger rate. Basic correction formula:

[0051] AVG_RATE current =(1-FACTOR)*AVG_RATE last +FACTOR*SAMPLE_RATE;

[0052] Among them, AVG_RATE current is the average trigger rate after correction and compensation, FACTOR(∈[0,1]) is the correction factor, AVG_RATE lastis the average trigger rate before correction compensation, and SAMPLE_RATE is the trigger rate. The smaller the correction factor, the weaker the correction effect, the more stable and accurate the average trigger rate, but the disadvantage is that the convergence speed to the actual trigger rate is slower ( Figure 2 :AVG_RATE(FACTOR=0.1)). In order to maintain the stability and accuracy of the average rate (using a smaller correction factor) and obtain a faster convergence speed, different correction compensations are introduced according to different scenarios.

[0053] When a user creates a maintenance plan, since there are no historical samples as a basis for prediction, the user needs to estimate the next Due Date by himself. Based on this Due Date and the basic calculation formula for Due Date mentioned above, its trigger rate can be inferred and used as the initial value of the average trigger rate. However, since the Due Date provided by the user is only a subjective estimate, there may be a large error between it and the actual value, so there may also be a large error between the initial average trigger rate and the actual trigger rate. In this case, the average trigger rate will have a process of converging to the actual trigger rate. In order to speed up this convergence process, compensation for the correction factor is introduced. The correction factor is compensated according to the following formula:

[0054]

[0055] Among them, ΔFACTOR is the correction factor compensation index, SAMPLE_COUNT is the number of samples currently collected; SAMPLE_COUNT_SPEC is the number of credible samples; when the number of samples currently collected reaches or exceeds the number of credible samples, compensation stops. Supports user configuration. Obviously, when the number of samples is zero, the compensation is the largest, the correction factor reaches the maximum value, and the convergence speed is the fastest at this time. As the number of samples increases, the compensation decreases. When the number of samples reaches the number of credible samples, the compensation drops to zero. At this time, the convergence ends and the average trigger rate reaches a relatively stable state. The effect after applying compensation is as follows Figure 2 shown.

[0056] Affected by various factors, in the actual production environment, the actual trigger rate may change at any time, and the average trigger rate will also have a convergence process to the actual trigger rate. In order to speed up this convergence process, compensation for the average trigger rate is introduced:

[0057] The average trigger rate is compensated according to the following formula:

[0058] ΔAVG_RATE=OFFSET*TREND;

[0059] AVG_RATE current =AVG_RATE current +ΔAVG_RATE;

[0060] Among them, ΔAVG_RATE is the average trigger rate compensation value, OFFSET is the offset indicator; TREND is the change trend indicator, and the compensation value of the average trigger rate is calculated and applied.

[0061] TREND trend indicator calculation formula includes:

[0062]

[0063] Among them, TREND current TREND is the trend indicator of the current sample trigger rate. last It is the change trend indicator of the previous sample trigger rate, and updates the table change trend indicator.

[0064] The OFFSET offset indicator calculation formula includes:

[0065]

[0066]

[0067] Among them, RATE current Updates the offset indicator for the current sample trigger rate.

[0068] Execute the above formulas in sequence to calculate the trigger rate compensation. OFFSET is an indicator to measure the degree and direction of deviation between the sample trigger rate curve and the average sample trigger rate curve. TREND is an indicator to measure the convergence trend of the sample trigger rate. The more times the sample trigger rate curve changes in the same direction (continuously increasing / decreasing), the more obvious the convergence trend is. Combining these two indicators, the credibility and amount of change in the actual trigger rate can be evaluated, and the compensation size can be determined. Based on the average trigger rate after correction and compensation, the time of the next maintenance is predicted, that is, the estimated time when the equipment triggers the maintenance condition, such as Figure 3 As shown, compared with the traditional algorithm, the Due Date predicted by this application is more stable, closer to the actual Due Date (simulated hypothetical value), and has a higher reference value for users.

[0069] Samples of equipment operating parameters are collected from EAP or MES; the maintenance cycle of semiconductor equipment is predicted through the PMS system. The operating parameters of semiconductor equipment are collected in real time through the equipment automation program (EAP) or manufacturing execution system (MES). These parameters can include temperature, pressure, usage time, current, voltage, etc. The collected data samples include the collection time and trigger value. The collected parameter data is transmitted to the preventive maintenance system (PMS) for recording and storage. The PMS system continuously records all collected sample data to form a time series data set for subsequent analysis and prediction.

[0070] The trigger rate calculation includes: calculating the trigger rate of each sample, obtaining the latest sample data and the previous sample data from the PMS system, and updating the sample data record each time a new sample is collected; calculating the rate of change of the current sample trigger value relative to the previous sample trigger value, and the trigger rate is expressed as the current sample trigger value minus the previous sample trigger value, divided by the time difference; correcting and compensating the trigger rate. In the initial stage, there is a lack of historical sample data, and the user needs to estimate an initial maintenance time (Due Date). Based on this initial maintenance time, the initial average trigger rate is inferred, and a correction factor compensation is introduced to accelerate the convergence of the average trigger rate; in the actual production process, the trigger rate of the equipment may change at any time, so the average trigger rate needs to be compensated to reflect the actual changes, and the next maintenance time (Due Date) is predicted based on the corrected and compensated average trigger rate; when the trigger condition is met, the estimated maintenance time of the equipment is calculated, and the predicted maintenance time is provided to the user to ensure that the equipment is maintained at the best time, thereby ensuring stable and efficient operation of the equipment.

[0071] It is not difficult to find that compared with the related art, in the solution provided in the embodiment of the present application, samples of equipment operating parameters are collected in real time through the EAP or MES system to ensure the timeliness and accuracy of the data; each time new sample data is collected, the trigger rate of the current sample relative to the previous sample is immediately calculated to ensure that the calculation result of the trigger rate reflects the actual operation of the equipment in real time; correction factor compensation is introduced to quickly adjust the average trigger rate according to the user's estimated maintenance time in the initial stage to ensure the accuracy of the initial prediction; it not only improves the accuracy and stability of the maintenance cycle prediction, but also provides users with a higher reference value to ensure efficient and stable operation of the equipment.

[0072] The step division of the above methods is only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0073] In addition, some embodiments of the present application also provide an electronic device. The electronic device may be a digital computer in various forms, such as a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, etc. The electronic device may also be a mobile device in various forms, such as a personal digital processing, a cellular phone, a smart phone, a wearable device, and other similar computing devices.

[0074] The electronic device includes: one or more processors; and a memory storing computer program instructions, wherein when the computer program instructions are executed, the processor executes the steps of the method provided in any one or more of the above embodiments. Figure 4 An exemplary structural diagram of the electronic device is disclosed. Figure 4 As shown, the electronic device includes: one or more processors 1101, memory 1102, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Among them, the components shown in this article, their connections and relationships, and their functions are only used as examples, and are not intended to limit the implementation of the present application described and / or required herein.

[0075] The electronic device may further include: an input device 1103 and an output device 1104. The processor 1101, the memory 1102, the input device 1103 and the output device 1104 may be connected via a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0076] The input device 1103 can receive input digital or character information, and generate key signal input related to the user settings and function control of the electronic device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick and other input devices. The output device 1104 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display and a plasma display. In some embodiments, the display device may be a touch screen.

[0077] To provide interaction with a user, the electronic device may be a computer. The computer has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0078] In the embodiments of the present application, a computer program / instruction is stored on a computer-readable medium, and when the computer program / instruction is executed by a processor, the steps of the method provided by any one or more of the above embodiments are implemented. The computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more computer-readable instructions.

[0079] The memory 1102 can be used as a non-transient computer-readable storage medium, which can be used to store non-transient software programs, non-transient computer executable programs and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 1102, so as to implement the program instructions / modules corresponding to the method provided by any one or more embodiments in the embodiments of the present application.

[0080] The memory 1102 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 1102 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 1102 may optionally include a memory remotely arranged relative to the processor 1101, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0081] It should be noted that the computer-readable medium described in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0082] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0083] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0084] In the above-described embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. For example, it can be implemented by using an application specific integrated circuit (ASIC), a general-purpose computer or any other similar hardware device. In certain embodiments, the software program of the present application can be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including relevant data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and similar devices. In addition, some steps or functions of the present application can be implemented by hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0085] The computer program product provided in the embodiment of the present application includes one or more computer programs / instructions, and when the computer program / instructions are executed by the processor, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from a computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0086] The flow chart or block diagram in the accompanying drawings shows the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0087] The scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim may also be implemented by one unit or device through software or hardware. The words "first", "second", etc. are only used to distinguish the description, and do not indicate any particular order, nor can they be understood as indicating or implying relative importance.

[0088] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily mention changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims, and the above embodiments should be regarded as exemplary and non-restrictive.

Claims

1. A method for predicting the maintenance cycle of semiconductor equipment, characterized in that: The method comprises: Collecting samples of equipment operating parameters when the semiconductor equipment triggers maintenance conditions; the samples include collection time and trigger value; Calculating a trigger rate of each sample, where the trigger rate is a rate of change of a trigger value of the sample relative to a previous sample; Correcting and compensating an average trigger rate according to the trigger rate, wherein the average trigger rate is an average change trend of multiple samples; The time of the next maintenance is predicted based on the corrected and compensated average trigger rate, that is, the estimated time when the equipment triggers the maintenance condition.

2. The method according to claim 1, characterized in that The correcting and compensating the average trigger rate according to the trigger rate comprises: AVG_RATE current = (1-FACTOR)*AVG_RATE last +FACTOR*SAMPLE_RATE; Among them, AVG_RATE current is the average trigger rate after correction and compensation, FACTOR(∈[0,1]) is the correction factor, AVG_RATE last is the average trigger rate before correction compensation, and SAMPLE_RATE is the trigger rate.

3. The method according to claim 2, characterized in that The correction factor is compensated according to the following formula: FACTOR=FACTOR+ΔFACTOR; Among them, ΔFACTOR is the correction factor compensation index, SAMPLE_COUNT is the number of samples currently collected; SAMPLE_COUNT_SPEC is the number of credible samples; when the number of samples currently collected reaches or exceeds the number of credible samples, compensation stops.

4. The method according to claim 3, characterized in that The average trigger rate is compensated according to the following formula: ΔAVG_RATE=OFFSET*TREND; AVG_RATE current =AVG_RATE current +ΔAVG_RATE; Among them, ΔAVG_RATE is the average trigger rate compensation value, OFFSET is the offset indicator; TREND is the change trend indicator.

5. The method according to claim 4, characterized in that The TREND trend indicator calculation formula includes: Among them, TREND current TREND is the trend indicator of the current sample trigger rate. last It is the changing trend indicator of the trigger rate of the previous sample.

6. The method according to claim 4, characterized in that The OFFSET offset indicator calculation formula includes: Among them, RATE current is the current sample trigger rate.

7. The method according to any one of claims 1 to 6, characterized in that: The samples of the equipment operating parameters are collected from EAP or MES; the maintenance cycle of semiconductor equipment is predicted through the PMS system.

8. An electronic device, characterized in that: The electronic device comprises: one or more processors; and A memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method as claimed in any one of claims 1 to 7.

9. A computer readable medium having a computer program / instructions stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images