Machine defect trend and historical action rapid query method and device, medium, program product and terminal

Abstract

According to the machine defect trend and historical action rapid query method and device, the medium, the program product and the terminal, integrated query and analysis functions are provided, and the machine management and fault handling capacity is remarkably improved. A user can easily inquire detailed actions and states of a machine and obtain a closed-loop result of a case so as to evaluate the effect of improvement measures, and all information is presented through a visual chart. The machine state chart and defect data analysis are integrated, a user can comprehensively understand the historical operation condition of the machine, identify abnormal reasons and effective countermeasures, and provide a basis for quick response, so that the wafer influence quantity is reduced, and the yield fluctuation is controlled. And real-time and historical data query is supported, so that the working efficiency is greatly improved, and the problems of low speed and tedious process of manual query are avoided. Meanwhile, the database comprehensively records detailed action information of the machine, data incompleteness is overcome, and a user can analyze defect distribution and directivity problems in a specific time period.

Description

Technical Field

[0001] This application relates to the field of semiconductor device manufacturing, and in particular to a method, apparatus, medium, program product, and terminal for rapidly querying machine defect trends and historical actions. Background Technology

[0002] In modern semiconductor manufacturing, equipment is a core component of the production process, and its operating status and performance directly affect product quality and production efficiency. As production processes become increasingly complex, equipment generates a large amount of data during operation, including detailed action logs and defect trends. This data is crucial for analyzing equipment operating status, identifying potential problems, and optimizing production processes.

[0003] However, current machine status query systems have many shortcomings. The status query results for each machine are often complex, requiring operators to sift through large amounts of information to extract useful data for defect and action analysis. This process is not only complex and time-consuming, but also typically requires manual confirmation via telephone or other means, increasing the uncertainty and risk of errors in information transmission. Furthermore, machine defect trends and action history records are usually stored independently, lacking effective integration and analysis tools, making it impossible to obtain the overall machine status with a single click. Regarding case tracking, existing technologies lack machine action analysis functions for specific cases, and do not summarize the actions taken by the same machine or model in similar cases. This forces operators to perform multiple manual operations and judgments when handling abnormal events, prolonging reaction time and consequently impacting production efficiency and product quality. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of this application is to provide a method, device, medium, program product and terminal for rapid query of machine defect trends and historical actions, in order to solve the problem that machine status query systems are complicated and lack effective integration, which leads to operators having to spend a lot of time on data filtering and manual confirmation, thereby affecting production efficiency and product quality.

[0005] To achieve the above and other related objectives, a first aspect of this application provides a method for quickly querying machine defect trends and historical actions. The method includes: during machine operation, performing defect detection on a wafer placed on the machine to generate defect data, and simultaneously recording the machine's operation actions to generate action data; when the detected defect data exceeds a machine abnormality threshold, performing preprocessing operations on the action data, and generating an action time map based on the preprocessed action data; generating a defect time map based on the defect data; and generating a machine status map based on the action time map and the defect time map.

[0006] In some embodiments of the first aspect of this application, the process of performing preprocessing operations on the motion data includes: generating a corresponding conversion file based on the motion data.

[0007] In some embodiments of the first aspect of this application, the process of generating a corresponding conversion file based on the action data includes: performing data cleaning and time standardization operations on the action data to generate preprocessed action data; assigning a unique label to each different type of action in the preprocessed action data to generate an action label corresponding to each action type; and constructing a conversion data structure based on the one-to-one correspondence of the action label and action type to generate the conversion file.

[0008] In some embodiments of the first aspect of this application, the process of generating a machine state diagram based on the action time map and the defect time map includes: obtaining a defect state and its corresponding timestamp from the defect time map, and obtaining an operation action and its timestamp from the action time map; performing a data merging operation based on the timestamps of the defect time map and the action time map to generate machine state data containing timestamps, defect states, and operation actions; and generating a machine state diagram based on the machine state data to characterize the trend of changes in machine actions and changes in defect states.

[0009] In some embodiments of the first aspect of this application, after generating the machine status diagram based on the action time diagram and the defect time diagram, the following operations are further performed: obtaining the real-time defect status of the machine and the corresponding timestamp; extracting the optimized operation action corresponding to the current defect status in the machine status diagram based on the real-time defect status and the corresponding timestamp; converting the optimized operation action into an executable instruction for the machine and sending it to the machine, while monitoring and analyzing the execution result.

[0010] In some embodiments of the first aspect of this application, the process of performing defect detection on a wafer placed on a machine to generate defect data includes: acquiring images of the wafer placed on the machine to generate a wafer image file; performing recognition in the wafer image file based on an image recognition algorithm to generate images labeled with multiple different types of defects; judging each recognized defect image based on a preset defect threshold parameter to determine whether the current defect image belongs to an abnormal situation, and generating defect data based on defect images belonging to an abnormal situation.

[0011] To achieve the above and other related objectives, a second aspect of this application provides a device for rapid querying of machine defect trends and historical actions, comprising: a data acquisition and preprocessing module: during machine operation, performing defect detection on wafers placed on the machine to generate defect data, and simultaneously recording machine operation actions to generate action data; a machine status visualization query module: when the detected defect data exceeds the machine abnormality threshold, performing preprocessing operations on the action data, and generating an action time map based on the action data after preprocessing; generating a defect time map based on the defect data; and generating a machine status map based on the action time map and the defect time map.

[0012] To achieve the above and other related objectives, a third aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for quickly querying machine defect trends and historical actions.

[0013] To achieve the above and other related objectives, a fourth aspect of this application provides a computer program product, which includes computer program code. When the computer program code is run on a computer, the computer implements the method for quickly querying machine defect trends and historical actions.

[0014] To achieve the above and other related objectives, a fifth aspect of this application provides an electronic terminal, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program to implement the method for quickly querying machine defect trends and historical actions.

[0015] As described above, the method, apparatus, medium, program product, and terminal for rapid querying of machine defect trends and historical actions in this application have the following beneficial effects: This application provides integrated query and analysis functions, significantly improving machine management and fault response capabilities. Users can easily query detailed machine actions and status, and obtain closed-loop results for individual cases to evaluate the effectiveness of improvement measures, all presented through intuitive charts. By integrating detailed machine action charts with defect data analysis charts, users can comprehensively understand the historical operating status of the machine, identify abnormal causes and effective countermeasures, providing a basis for rapid response to similar situations in the future, thereby reducing the number of wafer impacts and controlling yield fluctuations. This application also supports real-time and historical data queries, greatly improving work efficiency and avoiding the slow speed and cumbersome process of manual queries. The database also comprehensively records detailed machine action information, overcoming the problem of data incompleteness, allowing users to perform defect distribution and directional problem analysis for specific time periods, as well as summarize and evaluate the effects of actions, thus providing a solid data foundation for continuous improvement. Attached Figure Description

[0016] Figure 1 This document presents a flowchart illustrating an embodiment of the method for quickly querying machine defect trends and historical actions according to this application.

[0017] Figure 2 This document illustrates a flowchart of a method for quickly querying machine defect trends and historical actions according to an embodiment of the present application, showing a one-stop query process for machine actions.

[0018] Figure 3 This document illustrates a flowchart of a one-stop query process for a single case in one embodiment of the method for quickly querying machine defect trends and historical actions according to this application.

[0019] Figure 4 This document illustrates a flowchart of a one-stop query process for machine status in one embodiment of the method for quickly querying machine defect trends and historical actions according to this application.

[0020] Figure 5 This is a flowchart illustrating another embodiment of the method for quickly querying machine defect trends and historical actions according to this application.

[0021] Figure 6 This diagram shows a structural schematic of an embodiment of the machine defect trend and historical action quick query device of this application.

[0022] Figure 7 This diagram illustrates the structure of an embodiment of the terminal for quickly querying machine defect trends and historical actions according to this application. Detailed Implementation

[0023] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0024] Before providing a further detailed description of the present invention, the nouns and terms used in the embodiments of the present invention are explained, and the nouns and terms used in the embodiments of the present invention are subject to the following interpretations:

[0025] <1> Equipment: Devices or apparatus used in wafer manufacturing to perform specific processing or testing operations, such as etching machines, coating machines, centrifuges, and testing instruments. These equipment typically have specific functions and process parameters, playing a crucial role in the entire production line.

[0026] <2> Wafer defects: Various physical or chemical defects generated during wafer manufacturing, such as dimensional deviations, surface scratches, particle contamination, and structural defects. These defects affect the performance and yield of integrated circuits and are key indicators that need to be monitored and controlled during the manufacturing process.

[0027] <3> Machine operation: The specific processing, testing, or auxiliary operations performed by the machine during the production process, such as etching, deposition, coating, centrifugation, and inspection. These sequences of operations constitute the entire manufacturing process.

[0028] <4> File conversion: Converting raw machine operation data (such as PLC logs, SCADA data, etc.) into readable and easily analyzable standard data formats (such as CSV, Excel, etc.). This conversion process is a crucial prerequisite for subsequent data analysis.

[0029] " <5> Timestamp: Records the precise point in time when a machine operation occurs, typically in the format of "year-month-day hour:minute:second". Timestamps provide crucial time-dimensional information for analyzing machine behavior trajectories and technological processes.

[0030] To facilitate understanding of the embodiments of this application, firstly, in conjunction with Figure 1 Detailed explanation. Figure 1 This document illustrates a flowchart of a method for quickly querying machine defect trends and historical actions according to an embodiment of the present invention. The method for quickly querying machine defect trends and historical actions in this embodiment mainly includes the following steps:

[0031] Step S11: During the machine operation, defect detection is performed on the wafer placed on the machine to generate defect data, and the machine operation actions are recorded to generate action data.

[0032] In one embodiment of the present invention, the process of performing defect detection on a wafer placed on a machine to generate defect data includes: acquiring images of the wafer placed on the machine to generate a wafer image file; performing recognition on the wafer image file based on an image recognition algorithm to generate images labeled with multiple different types of defects; judging each recognized defect image based on a preset defect threshold parameter to determine whether the current defect image belongs to an abnormal situation, and generating defect data based on defect images belonging to an abnormal situation.

[0033] In this embodiment, an optical microscope mounted on the equipment system images the wafer placed on the equipment to generate a wafer image file containing imaging parameters (e.g., illumination conditions, magnification, focal length). An image recognition algorithm extracts features representing defects from the image to identify different defects. These defect features include, but are not limited to, color variations, texture variations, intensity variations, and shape. The image recognition algorithm includes, but is not limited to, convolutional neural networks (CNN), support vector machines (SVM), and random forests to classify the extracted features and categorize defects into different types, including, but not limited to, scratches, dents, and particles.

[0034] In particular, various specific parameters and industry standards were comprehensively considered in the process of setting defect thresholds to ensure that the system can effectively identify and handle potential defects. First, the size threshold needs to set the maximum permissible size of specific defects (such as scratches, holes, particles, etc.). For example, the maximum width of a scratch can be set between 1 and 5 micrometers, the diameter threshold for particles is set to be less than 2 micrometers as normal, and the area threshold for defects affecting function should be set to 10 micrometers. 2 The above are considered abnormal.

[0035] In one embodiment of the invention, the status and operation of each piece of equipment on the production line are systematically collected and recorded in a large database for subsequent performance analysis. After completing a specific process, wafers on the production line undergo defect sampling, and all defect data is stored in the same database. Furthermore, when a wafer passes through a machine, if that machine malfunctions, the wafer will also be subject to defect detection, and the relevant data will also be recorded in the database. This systematic data collection and recording provides a reliable foundation for equipment performance evaluation and defect management.

[0036] Step S12: When the defect data is detected to exceed the machine abnormality threshold, perform a preprocessing operation on the motion data and generate an motion time map based on the motion data after the preprocessing operation; generate a defect time map based on the defect data; generate a machine status map based on the motion time map and the defect time map.

[0037] In one embodiment of the present invention, specifically in a semiconductor wafer manufacturing process, multiple dedicated machines within a factory continuously process wafers. Each batch of wafers is monitored in real time using defect detection, and defect information is recorded after sampling and scanning. The machine anomaly thresholds include thresholds for various defect types to assess the severity and impact of each defect in the wafer. When the number of detected defects exceeds a preset threshold, an anomaly detection mechanism is activated for further analysis.

[0038] Preferably, the presence of significant abnormal fluctuations in the current machine number is identified by comparing it with historical data. For example, if a machine has consistently maintained a low defect count in past production cycles, but suddenly experiences a surge in defects, the machine is deemed potentially abnormal. Therefore, although defect data collection is continuous, the generation of machine status diagrams and subsequent manual shutdown inspections are only triggered when an abnormality is identified. This logic ensures efficient and targeted inspections during machine malfunctions, thereby improving overall production efficiency and reducing unnecessary downtime.

[0039] In one embodiment of the present invention, the process of performing preprocessing operations on the action data includes: generating a corresponding conversion file based on the action data.

[0040] In this embodiment, file conversion is the process of transforming raw machine motion data into a format that is easy to analyze and visualize. This conversion simplifies complex machine motion records into structured data, making subsequent data analysis and decision-making more efficient. The converted data typically uses specific numerical codes to visually represent different machine states in charts, such as using 0 to represent the IDLE state and 1 to represent the RUN state. This standardized format not only improves data accuracy and consistency but also provides users with convenient query and analysis functions, helping them better understand machine performance and make timely adjustments and optimizations.

[0041] In one embodiment of the present invention, the process of generating a corresponding conversion file based on the action data includes: performing data cleaning and time standardization operations on the action data to generate preprocessed action data; assigning a unique label to each different type of action in the preprocessed action data to generate an action label corresponding to each action type; and constructing a conversion data structure based on the one-to-one correspondence of action labels and action types to generate the conversion file.

[0042] In this embodiment, as Figure 2As shown, after a machine performs a specific action, the EAP (Enterprise Application Platform) automatically collects information such as the module, the machine performing the action, the action time, and the specific action, and stores it in a large database. Simultaneously, this embodiment also collects a large amount of machine action data from the production environment, including IDLE, RUN, DOWN, WAITMG (awaiting manufacturing), WAITREP (awaiting maintenance), MONITOR, EQ (equipment), PM (preventive maintenance), and Action (machine action), to generate result files. The types of Action machine actions include etching, coating, centrifugation, and other machine operations.

[0043] Furthermore, the collected machine motion data undergoes data filtering and optimization to ensure data quality and consistency. Invalid or erroneous data is removed through data filtering to generate optimized machine motions and times, which are then stored in a results file. The process of generating corresponding conversion files based on the motion data includes: converting the motion records in the optimized file into text, and assigning mathematical meaning to the corresponding actions using numbers to facilitate chart generation in subsequent steps. For example, the converted text includes: 0 representing the specific time point of machine IDLE, 1 representing the time point of machine RUN, -1 representing the time point of machine DOWN, -2 representing the notification downtime, 2 representing the actual downtime of PM, -3 representing the actual downtime of Module, 3 representing the WAITMG time, -4 representing the WAITREP time, and 4 representing Action.

[0044] The subsequent generation of the action timeline includes: using time as the X-axis and the transformed machine actions as the Y-axis to ultimately generate an action chart. The action timeline includes machine actions performed at different times and actions performed at different times. Specifically, a complete action chart is constructed in the visualization software, and a one-click query function is implemented via a webpage. Furthermore, a table is created to statistically analyze the machine's historical actions and perform categorized analysis. Users can quickly click on each action through an interactive interface to obtain all actions performed by the machine over a past period, facilitating the summarization of effective response measures under different abnormal conditions.

[0045] In one embodiment of the present invention, such as Figure 3As shown, by recording the machine's actions, using action time as the horizontal axis and the transformed machine actions as the vertical axis, a "one-stop query result chart by case" is created. All complete action records are stored in the visualization software, supporting one-click queries via a webpage, thus enabling rapid data access. Specifically, during the machine's recovery from an anomaly to normal operation, its actions include one or multiple actions, which may be the same or different. All actions performed during the anomaly period are compiled into a data table, including case number, module, machine number, action time, action category, and detailed action.

[0046] Furthermore, or adopting, such as Figure 4 The process illustrated involves recording wafer scanning defect data in a large database, extracting and organizing this data according to actual needs, storing it in a summary file, and generating corresponding defect charts for relevant machines based on time. Simultaneously, by combining machine action data from the large database with the defect chart data, a new machine status chart is generated. This chart not only includes defect trends but also records detailed historical machine actions. Finally, these charts are stored in visualization software for one-click querying via a webpage, allowing users to easily obtain the information they need.

[0047] In one embodiment of the present invention, the process of generating a machine state diagram based on the action time diagram and the defect time diagram includes: obtaining the defect state and corresponding timestamp from the defect time diagram, and obtaining the operation action and timestamp from the action time diagram; performing a data merging operation based on the timestamp of the defect time diagram and the timestamp of the action time diagram to generate machine state data containing timestamps, defect states and operation actions; and generating a machine state diagram based on the machine state data to characterize the trend of changes in machine actions and changes in defect states.

[0048] In this embodiment, as Figure 5 As shown, the process of merging data based on the timestamps of the defect timemap and the action timemap includes: sorting the extracted timestamps; matching the defect timestamps and action timestamps according to a set time window (e.g., ±1 second or ±5 seconds) to identify defect states and operational actions occurring within similar timeframes; integrating the results into a new data structure where each record contains a timestamp, defect state, and operational action; and using a computer to check and remove duplicate and unnecessary records to ensure the accuracy of the dataset. Finally, a machine status dataset is generated based on the merged data to support subsequent analysis and visualization. The data structures include, but are not limited to, lists (ordered sets for storing multiple records), dictionaries (representing the specific content of each record, such as timestamp, defect state, and operational action), data frames (such as structures in the Pandas library for easy data analysis), or database tables (storing data in tabular form).

[0049] In one embodiment of the present invention, after generating the machine status diagram based on the action time diagram and the defect time diagram, the following operations are performed: obtaining the real-time defect status of the machine and the corresponding timestamp; extracting the optimized operation action corresponding to the current defect status from the machine status diagram based on the real-time defect status and the corresponding timestamp; converting the optimized operation action into an executable instruction for the machine and sending it to the machine, while monitoring and analyzing the execution result.

[0050] In this embodiment, the process of monitoring and analyzing the execution results includes: continuously adjusting and optimizing the operation strategy based on the monitoring results to form a closed-loop feedback mechanism, thereby continuously improving machine performance and reducing the defect rate. Specifically, a comprehensive machine state diagram is constructed by collecting various machine state and action data. The machine state diagram is then analyzed. If improvements in machine performance and defect rate are found, subsequent machine operations continue. If no improvements are found in machine performance and defect rate, a corresponding action strategy is selected based on the defect trend in the machine state diagram.

[0051] It should be noted that, in the embodiments of this application, the words "exemplary" or "for example" indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0052] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0053] Figure 6 This is a schematic block diagram of the machine defect trend and historical action rapid query device provided in the embodiments of this application. Figure 6 As shown, the device includes a data acquisition and preprocessing module 601 and a machine status visualization query module 602.

[0054] Data acquisition and preprocessing module 601: During the machine's operation, it performs defect detection on the wafer placed on the machine to generate defect data, and records the machine's operation actions to generate action data.

[0055] Machine status visualization query module 602: When the defect data is detected to exceed the machine abnormality threshold, a preprocessing operation is performed on the action data, and an action time map is generated based on the action data after the preprocessing operation; a defect time map is generated based on the defect data; and a machine status map is generated based on the action time map and the defect time map.

[0056] It should be understood that the specific process of each module performing the above-mentioned corresponding steps has been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0057] It should also be understood that the module division in the embodiments of this application is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. Furthermore, the functional modules in the various embodiments of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0058] Figure 7 This is a schematic block diagram of the electronic terminal provided in an embodiment of this application. Figure 7 As shown, the electronic terminal includes at least one processor 701, a memory 702, at least one network interface 703, and a user interface 705. The various components in the device are coupled together via a bus system 704. It is understood that the bus system 704 is used to implement communication between these components. In addition to a data bus, the bus system 704 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 7 The general will label all buses as bus systems.

[0059] The user interface 705 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0060] It is understood that memory 702 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0061] In this embodiment of the invention, the memory 702 is used to store various types of data to support the operation of the electronic terminal 700. Examples of this data include: any executable program for operation on the electronic terminal 700, such as the operating system 7021 and application program 7022; the operating system 7021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 7022 may contain various applications, such as a media player, browser, etc., for implementing various application services. The method for quickly querying machine defect trends and historical actions provided in this embodiment of the invention can be included in the application program 7022.

[0062] The methods disclosed in the above embodiments of the present invention can be applied to or implemented by processor 701. Processor 701 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 701 or by instructions in software form. The processor 701 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 701 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. General-purpose processor 701 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0063] In an exemplary embodiment, the electronic terminal 700 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to execute the aforementioned method.

[0064] According to the method provided in the embodiments of this application, this application also provides a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to execute the machine defect trend and historical action quick query method as described in any of the embodiments above.

[0065] According to the method provided in the embodiments of this application, this application also provides a computer-readable storage medium storing program code, which, when run on a computer, causes the computer to execute the machine defect trend and historical action quick query method as described in any of the embodiments above.

[0066] As used in this specification, the terms "component," "module," "system," etc., are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in a process and / or an execution thread, and components may be located on a single computer and / or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0067] Those skilled in the art will recognize that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0068] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0069] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0070] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0071] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0072] In the above embodiments, the functions of each functional unit can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer instructions (programs). When the computer program instructions (programs) are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state drives (SSDs)).

[0073] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0074] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0075] In summary, this application provides a method, device, medium, program product, and terminal for rapid querying of machine defect trends and historical actions. It offers integrated query and analysis functions, significantly improving machine management and fault response capabilities. Users can easily query detailed machine actions and status, and obtain closed-loop results for individual cases to evaluate the effectiveness of improvement measures, all presented through intuitive charts. By integrating detailed machine action charts with defect data analysis charts, users can comprehensively understand the historical operating status of the machine, identify the causes of anomalies and effective countermeasures, providing a basis for rapid response to similar situations in the future, thereby reducing the number of wafer impacts and controlling yield fluctuations. This application also supports real-time and historical data queries, greatly improving work efficiency and avoiding the slow speed and cumbersome process of manual queries. The database also comprehensively records detailed machine action information, overcoming the problem of data incompleteness, allowing users to perform defect distribution and directional problem analysis over specific time periods, as well as summarize and evaluate the effects of actions, thus providing a solid data foundation for continuous improvement. Therefore, this application effectively overcomes the various shortcomings of existing technologies and has high industrial application value.

[0076] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for rapid querying of machine defect trends and historical actions, characterized in that, The method includes: During the machine's operation, defects are detected on the wafers placed on the machine to generate defect data, and the machine's operation actions are recorded to generate action data. When the defect data is detected to exceed the machine abnormality threshold, a preprocessing operation is performed on the motion data, and an motion time map is generated based on the motion data after the preprocessing operation; a defect time map is generated based on the defect data; and a machine status map is generated based on the motion time map and the defect time map.

2. The method for rapid querying of machine defect trends and historical actions according to claim 1, characterized in that, The process of performing preprocessing operations on the action data includes: generating a corresponding conversion file based on the action data.

3. The method for rapid querying of machine defect trends and historical actions according to claim 2, characterized in that, The process of generating the corresponding conversion file based on the action data includes: Perform data cleaning and time standardization operations on the motion data to generate preprocessed motion data; Assign a unique label to each different type of action in the preprocessed action data to generate an action label corresponding to each action type; Based on the one-to-one correspondence of action numbers and action types, a conversion data structure is constructed to generate the conversion file.

4. The method for rapid querying of machine defect trends and historical actions according to claim 2, characterized in that, The process of generating a machine status diagram based on the action time diagram and the defect time diagram includes: Obtain the defect status and corresponding timestamp from the defect time graph, and obtain the operation action and timestamp from the action time graph; A data merging operation is performed based on the timestamps of the defect time map and the timestamps of the action time map to generate machine status data containing timestamps, defect status, and operation actions. Based on the machine status data, a machine status diagram is generated to characterize the trends of machine action changes and defect status changes.

5. The method for rapid querying of machine defect trends and historical actions according to claim 4, characterized in that, After generating the machine status diagram based on the action time diagram and the defect time diagram, the following operations are performed: Obtain the real-time defect status and corresponding timestamp of the machine; Based on the real-time defect status and the corresponding timestamp, the optimized operation action corresponding to the current defect status is extracted from the machine status diagram. The optimized operation actions are converted into executable instructions for the machine and sent to the machine. At the same time, the execution results are monitored and analyzed.

6. The method for rapid querying of machine defect trends and historical actions according to claim 1, characterized in that, The process of performing defect detection on wafers placed on a testing machine to generate defect data includes: Images are acquired from the wafers placed on the machine to generate wafer image files; Based on image recognition algorithms, the wafer image file is used to identify defects and generate images labeled with various different types of defects. Based on the preset defect threshold parameters, each identified defect image is judged to determine whether the current defect image belongs to an abnormal situation, and defect data is generated based on the defect images that belong to an abnormal situation.

7. A device for rapidly querying machine defect trends and historical actions, characterized in that, include: Data acquisition and preprocessing module: During the machine's operation, it performs defect detection on the wafers placed on the machine to generate defect data, and records the machine's operation actions to generate action data; Machine status visualization query module: When the defect data is detected to exceed the machine abnormality threshold, a preprocessing operation is performed on the action data, and an action time map is generated based on the action data after the preprocessing operation; a defect time map is generated based on the defect data; and a machine status map is generated based on the action time map and the defect time map.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for quickly querying machine defect trends and historical actions as described in any one of claims 1 to 6.

9. A computer program product, characterized in that, The computer program product includes computer program code, which, when run on a computer, enables the computer to implement the method for quickly querying machine defect trends and historical actions as described in any one of claims 1 to 6.

10. An electronic terminal, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method for quickly querying machine defect trends and historical actions as described in any one of claims 1 to 6.

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