First detection yield real-time monitoring and data analysis method

Abstract

The invention relates to the technical field of data monitoring and analysis, in particular to a first detection yield real-time monitoring and data analysis method. The method comprises the following steps: automatically acquiring batch first workpiece yield data generated by a test machine through an FTP protocol, extracting key indexes such as yield, total test quantity and BIN distribution through an analysis engine, and storing the key indexes into a structured database; the user interaction interface provides time range screening, multi-dimensional sorting and visual chart generation functions, and supports one-key export of standard format reports; and the system integration program log module records the whole operation process, deploys an intelligent alarm mechanism, and automatically triggers an alarm when the yield is lower than a preset threshold value. According to the method, the problems of low manual acquisition efficiency, abnormal positioning lagging, lack of equipment life cycle management and the like are solved, closed-loop automation of test data from acquisition and analysis to decision support is realized, and the maintenance efficiency and the product quality controllability are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of data monitoring and analysis technology, specifically to a method for real-time monitoring and data analysis of first-inspection yield. Background Technology

[0002] In the field of electronic component testing, especially in the final testing stage of integrated circuits, real-time monitoring and data analysis of first-piece yield are crucial for ensuring product quality. Traditional methods rely on manual extraction of first-piece yield data from batches of test units from testing equipment, followed by manual recording, organization, and report generation for basic analysis. This approach has systemic drawbacks: First, data acquisition efficiency is low, with each processing session requiring significant time for data extraction and entry, resulting in severely delayed yield information. Second, anomaly localization capabilities are weak; low-yield test units cannot be identified immediately during testing, requiring manual analysis to pinpoint the anomaly, significantly extending the fault response cycle. Third, a data traceability mechanism is lacking; changes in equipment performance after repair are difficult to quantify and assess, and there is a lack of continuous tracking of the entire lifecycle of the test equipment, resulting in a lack of data support for maintenance strategies.

[0003] Currently, the industry is attempting to address these issues by having testing equipment manufacturers open their data interfaces. However, most manufacturers lack standardized ports or protocol support, making direct integration with external systems difficult. While some existing automation solutions can achieve basic data acquisition, they still have significant limitations: automation scripts only support parsing fixed-format data, failing to adapt to various types of testing equipment; data processing workflows are tightly coupled with business logic, lacking flexibility; and visualization functions are limited to static reports, lacking dynamic monitoring capabilities. These shortcomings collectively create data silos in the testing process, preventing yield fluctuations from being fed back to maintenance in real time, thus impacting overall production line efficiency and product reliability.

[0004] Therefore, existing technologies still need further development. Summary of the Invention

[0005] The purpose of this invention is to overcome the above-mentioned technical deficiencies and provide a method for real-time monitoring and data analysis of first-inspection yield, so as to solve the problems existing in the prior art.

[0006] To achieve the above technical objectives, this invention provides a method for real-time monitoring and data analysis of first-inspection yield, comprising: S100: Automatically collects data on the first-piece yield of the test batch generated by the test machine via FTP protocol; S200. Analyze the collected data to extract key indicators; S300 generates structured databases to support real-time querying and analysis, and provides user interfaces for data visualization and tracking.

[0007] Specifically, the data acquisition includes a manual download mode and an automatic download mode, wherein the automatic download mode captures data in real time, and the manual download mode processes historical data in batches.

[0008] Specifically, the user interface includes a query module that supports filtering test batch yield reports within a custom time range.

[0009] Specifically, the program also uses a log module to record the data processing process, which facilitates troubleshooting.

[0010] Specifically, the query module also supports sorting, filtering, and exporting yield reports.

[0011] Specifically, the query module integrates visualization tools to generate yield trend charts and BIN distribution heatmaps.

[0012] Specifically, the program processing log module stores the logs in a specified directory path and records them synchronously in a background log file.

[0013] Specifically, the key indicators include yield, total number of tests, and BIN distribution, where BIN distribution refers to the classification data of fault types.

[0014] Specifically, the visualization tool is also equipped with an intelligent alarm function, which automatically triggers an alarm and pushes it to the responsible person when the yield is lower than a preset threshold.

[0015] Specifically, the intelligent alarm function, combined with password protection settings, requires authorization verification for sensitive operations to ensure data security.

[0016] Beneficial effects: This invention achieves a comprehensive improvement in technical performance by constructing an automated and intelligent first-article yield monitoring system. 1. End-to-end efficiency optimization: An automated data acquisition mechanism based on the FTP protocol completely replaces manual operation, eliminates data latency bottlenecks, and reduces single-batch data processing time from hours to seconds, improving maintenance response speed by more than 50%. Dynamic monitoring capabilities enable low-yield units to be exposed in real time during testing, reducing maintenance intervention time by 90%.

[0017] 2. Deeper Data Analysis Capabilities: The structured database supports multi-dimensional, penetrating queries, allowing analysis of yield trends based on any combination of conditions such as batch, time, and equipment type. Combined with a visualization engine, it generates heatmaps and trend curves, intuitively revealing fault distribution patterns. Seamless integration of historical and real-time data enables quantifiable tracking of equipment performance degradation curves, providing a data foundation for predictive maintenance.

[0018] 3. Breakthrough in System Adaptability: The dual-mode data acquisition mechanism (real-time / manual) is compatible with both bursty data analysis and long-term traceability needs. The customizable query interface supports dynamically configured filtering rules, adapting to different testing scenarios without code modification. An open data output format (such as Excel) ensures that analysis results can be directly connected to third-party toolchains, breaking down data silos.

[0019] 4. Enhanced Operational Reliability: The log system comprehensively records the entire data processing chain status, and the authorization verification mechanism ensures operational auditability; the intelligent alarm function dynamically detects abnormal fluctuations through thresholds, realizing a paradigm shift from passive response to proactive intervention. The system operates continuously in an "unattended" mode, reducing labor costs by 70%. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the real-time monitoring and data analysis method for first-inspection yield provided in a specific embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments in this application, other similar embodiments obtained by those skilled in the art without creative effort should all fall within the scope of protection of this application. Furthermore, directional terms mentioned in the following embodiments, such as "up," "down," "left," and "right," are only for reference to the directions in the accompanying drawings; therefore, the directional terms used are for illustrative purposes and not for limiting the invention.

[0022] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments.

[0023] Please see Figure 1 This invention provides a method for real-time monitoring and data analysis of first-inspection yield, including: S100 automatically collects data on the first-piece yield of the test batch generated by the test machine via the FTP protocol.

[0024] Specifically, the data acquisition includes a manual download mode and an automatic download mode, wherein the automatic download mode captures data in real time, and the manual download mode processes historical data in batches.

[0025] It should be further explained that the data acquisition module is designed for dual-mode operation. The manual download mode is used for batch processing of historical data (such as by day or week). After being triggered by the user interface, the system retrieves data files within a specified time range from the FTP server. The automatic download mode monitors changes in the FTP directory in real time. Once a new batch of data is detected (such as a file upload completion event), the capture and parsing process is immediately initiated. Mode switching is achieved through the configuration interface without requiring code modification.

[0026] Understandably, the benefits of the above solution include: dual-mode flexibility to adapt to different scenario needs (such as real-time monitoring or historical analysis), automatic mode to ensure zero-latency data acquisition, manual mode to save storage resources, overall improvement of system adaptability and resource utilization, and the document points out that the method saves more than 30% of labor costs.

[0027] S200. Analyze the collected data to extract key indicators.

[0028] It should be further explained that the core of this method lies in automatically collecting the first-piece yield data of test batches generated by the testing machine via the FTP protocol. A test batch represents a group of test units (DUTs), and there is no need to limit the number of units to broaden its applicability. After collection, the system parses the data to extract key indicators, including yield, total test volume, and BIN distribution (BIN distribution refers to the classification data of fault types). These indicators are converted into a structured format using an algorithm and stored in a database. The database is designed to be queryable in real time and supports SQL or similar query languages. The user interface provides graphical tools for visualizing data trends (such as yield changes) and tracking historical records. The interface is implemented based on a web or desktop application to ensure cross-platform compatibility.

[0029] It is understood that the beneficial effects of this invention include: this solution eliminates manual intervention, achieves second-level data processing, and improves efficiency by over 50% compared to traditional methods (the documentation mentions a 50% improvement in maintenance efficiency). Furthermore, automation reduces human error, resulting in a yield rate accuracy of over 99%. Real-time monitoring capabilities enable rapid location of abnormal units, supporting data-driven decision-making and optimizing in-plant maintenance strategies and customer response.

[0030] S300 generates structured databases to support real-time querying and analysis, and provides user interfaces for data visualization and tracking.

[0031] Specifically, the user interface includes a query module that supports filtering test batch yield reports within a custom time range.

[0032] It should be further noted that the query module in the user interface allows users to customize time ranges (such as start and end dates) to filter yield reports for test batches. Report generation is based on a database query engine and supports dynamic filtering conditions (such as by batch ID or DUT number). The module is provided in API or GUI form, with input parameters including timestamps and filter keys, and the output is a formatted report.

[0033] Understandably, the benefits of the above solution include: the customizable filtering function makes yield analysis more targeted, quickly identifies low-yield batches, and the documentation emphasizes that this method reduces anomaly response time to within 10 minutes, improving maintenance efficiency and data traceability.

[0034] Specifically, it also includes a program processing log module, which records the data processing process to facilitate troubleshooting.

[0035] It should be further noted that the program's logging module is integrated into the system backend, automatically recording events throughout the entire data processing process, including data acquisition time, parsing status, and error codes. Log entries are stored in a local directory (e.g., D:) and synchronously written to the database for easy real-time retrieval via a log viewer. The log format uses standard text (e.g., JSON) and supports timestamp sorting and keyword search.

[0036] Understandably, the benefits of the above solution include: the logging module provides a complete audit trail, simplifying troubleshooting (such as locating the source of the error when parsing fails), and the documentation indicates that this method enhances system reliability and reduces downtime by more than 20%.

[0037] Specifically, the query module also supports sorting, filtering, and exporting yield reports.

[0038] It should be further explained that the report operations in the query module include sorting (such as ascending or descending by yield), filtering (such as based on BIN type or DUT status), and export functions. The export operation is triggered by a one-click button, converting the report into Excel format. Open source libraries (such as Apache POI) are used to ensure format compatibility and that the data can be imported into external tools (such as in-plant analysis software).

[0039] Understandably, the beneficial effects of the above solution include: sorting and filtering accelerate data insights, export functionality improves data sharing efficiency, documentation explains the method to facilitate tracking of maintenance effectiveness, and quantitatively evaluate maintenance strategies.

[0040] Specifically, the query module integrates visualization tools to generate yield trend charts and BIN distribution heatmaps.

[0041] It should be further noted that the query module incorporates visualization tools, generating dynamic yield trend charts (showing yield changes over time) and BIN distribution heatmaps (using color gradients to represent the frequency of different failure types) through a charting engine (such as ECharts). The tools support interactive zooming and annotation, and the data source is updated in real time.

[0042] Understandably, the beneficial effects of the above solution include: visualization makes yield trends clear at a glance, heatmaps help to quickly locate high-frequency failure points, documentation emphasizes that the method improves decision-making accuracy, and supports customer demand response.

[0043] Specifically, the program processing log module stores the logs in a specified directory path and records them synchronously in a background log file.

[0044] It should be further noted that the storage path for the program's log processing module is configurable (default D:). Log files are automatically rotated to limit their size (e.g., a new file is generated daily), and are synchronously written to the table structure of the backend database to ensure log persistence and high availability. Path settings are managed through configuration files, avoiding hardcoding.

[0045] Understandably, the benefits of the above approach include: enhanced system flexibility through path configuration, prevention of data loss through synchronized storage, and, as the documentation indicates, optimization of lifetime management and support for tracking device usage time.

[0046] Specifically, the key indicators include yield, total number of tests, and BIN distribution, where BIN distribution refers to the classification data of fault types.

[0047] It should be further explained that the export operation is specifically implemented as a one-click export to Excel. The export process includes data formatting, header generation, and file compression, offering strong compatibility and seamless integration with external analysis tools (such as in-plant statistical software). The exported file naming convention is based on time batches (e.g., "Yield Report_YYYYMMDD.xlsx").

[0048] Understandably, the benefits of the above solution include: Excel compatibility simplifies the data analysis process, one-click operation saves users time, documentation explains the method to improve traceability, and facilitates historical data auditing.

[0049] Specifically, the visualization tool is also equipped with an intelligent alarm function, which automatically triggers an alarm and pushes it to the responsible person when the yield is lower than a preset threshold.

[0050] It should be further explained that the visualization tool integrates an intelligent alarm function, allowing users to set a yield threshold (the preferred value is 95%, chosen because, based on industry standards and historical data analysis, when the yield is below 95%, the probability of anomalies increases significantly to over 80%, which balances the false alarm rate and detection sensitivity). Once the threshold is triggered, the system automatically sends an alarm (such as an email or push notification) to a pre-defined list of responsible personnel. The alarm logic includes delayed detection (e.g., triggering only after three consecutive times the yield falls below the threshold) to reduce noise.

[0051] Understandably, the benefits of the above solution include: intelligent alarms enable proactive monitoring and rapid response to anomalies. The document indicates that this method reduces the location time of low-yield units by 90% and improves maintenance efficiency.

[0052] Specifically, the intelligent alarm function, combined with password protection settings, requires authorization verification for sensitive operations to ensure data security.

[0053] It should be further explained that the intelligent alarm function is combined with a password protection mechanism. Sensitive operations (such as threshold modification or data export) require authorization verification (username and password). The verification module integrates OAuth or similar protocols, and the access control list (ACL) defines different user roles (such as administrators and operators).

[0054] Understandably, the benefits of the above approach include: password protection ensures data security and integrity, and the documentation emphasizes that the method meets customer security needs and prevents unauthorized access.

[0055] The working principle of this invention will be further explained below: This invention relates to a method for real-time monitoring and data analysis of first-piece yield. In implementation, the method first automatically collects the first-piece yield data of test batches generated by the testing machine via the FTP protocol. Each test batch represents a group of test units (DUTs). The data collection process is designed with dual modes to adapt to different scenarios. The automatic download mode monitors directory changes on the FTP server in real time, and immediately starts capturing data once a new data file is detected. The manual download mode allows users to select historical time ranges for batch processing via an interface. Both modes can be switched through a configuration interface to ensure flexibility. In implementation, the FTP connection uses a standard library (such as Python's ftplib) to set parameters including IP address, port, and authentication information. The collected raw data is in text format (such as CSV or TXT). The system then parses the data to extract key indicators, including yield, total test volume, and BIN distribution. The parsing algorithm uses regular expressions or parser libraries (such as Pandas) to segment data fields and calculate statistical values ​​(e.g., the yield formula is number of good products / total test volume). The processed data generates a structured database (such as a MySQL table). The table structure includes fields such as batch ID, timestamp, and DUT yield, and supports real-time SQL queries to optimize performance. The user interface is built as a web application, employing a front-end / back-end separation architecture (the front-end uses the React framework, and the back-end uses Spring Boot). The query module supports custom time range filtering of yield reports. After the user inputs the start and end dates, the module calls the database via API to return dynamic reports. Report operations include sorting (e.g., by yield or batch ID), filtering (e.g., based on BIN type), and exporting. Exporting the report to Excel format is triggered with a single click. The Apache POI library is used for compatibility with external tools. The query module also integrates visualization tools, generating yield trend charts and BIN distribution heatmaps using the ECharts engine. The trend chart displays yield change curves, while the heatmap uses color gradients to indicate fault distribution, enhancing data readability. The program's logging module is integrated into the system backend, automatically recording all processing events (e.g., data acquisition, parsing status, and error details). Log entries are stored in a configurable directory path (default D:) and synchronously written to the database log table. The path can be customized via a YAML file, and a log file rotation strategy limits its size to prevent overflow. The visualization tool is also equipped with an intelligent alarm function. Users can set a yield threshold (the preferred value is 95%, based on historical data analysis and experimental verification in the IC testing industry. When the yield is below 95%, the probability of anomalies increases significantly to over 80%. This value can effectively balance detection sensitivity and false alarm rate, ensuring alarm reliability). After the threshold is triggered, the system automatically sends an alarm (such as via SMTP email or Slack message) to the responsible person. The alarm logic includes conditional detection (such as triggering only when three consecutive batches are below the threshold) to reduce false positives.Furthermore, intelligent alerts are combined with password protection mechanisms. Sensitive operations (such as threshold adjustments or data exports) require authorization verification. The implementation uses the OAuth 2.0 protocol to manage user authentication, and access control is role-based (e.g., administrators can modify settings while operators can only view them), ensuring data security. The entire system runs automatically in the background without manual intervention. Through the above steps, efficient real-time monitoring is achieved. Domain professionals can use this description to select an open-source toolchain for complete deployment.

[0056] In a preferred embodiment, this application also provides an electronic device, the electronic device comprising: The computer device includes a memory and a processor, wherein the memory stores computer-readable instructions that, when executed by the processor, implement the real-time monitoring and data analysis method for first-inspection yield. The computer device can be broadly categorized as a server, terminal, or any other electronic device with the necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, memory, network interface, communication interface, etc., connected via a system bus. The processor of the computer device can be used to provide the necessary computing, processing, and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system, computer programs, etc. The internal memory can provide an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface and communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, it performs the steps of the method of the present invention.

[0057] This invention can be implemented as a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the steps of the methods of embodiments of the invention to be performed. In one embodiment, the computer program is distributed across multiple network-coupled computer devices or processors, such that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be executed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be executed by one or more computer devices or processors, and one or more other method steps / operations may be executed by one or more other computer devices or processors. One or more computer devices or processors may execute a single method step / operation, or execute two or more method steps / operations.

[0058] Those skilled in the art will understand that the method steps of this invention can be performed by a computer program instructing related hardware, such as a computer device or processor, to perform the steps of this invention when executed. Depending on the context, any references herein to memory, storage, databases, or other media may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0059] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0060] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for real-time monitoring and data analysis of first-inspection yield, characterized in that, include: S100: Automatically collects data on the first-piece yield of the test batch generated by the test machine via FTP protocol; S200. Analyze the collected data to extract key indicators; S300 generates structured databases to support real-time querying and analysis, and provides user interfaces for data visualization and tracking; The key metrics include yield, total number of tests, and BIN distribution, where BIN distribution refers to the classification data of fault types.

2. The method for real-time monitoring and data analysis of first-inspection yield according to claim 1, characterized in that, The data acquisition includes a manual download mode and an automatic download mode. The automatic download mode captures data in real time, while the manual download mode processes historical data in batches.

3. The method for real-time monitoring and data analysis of first-inspection yield according to claim 1, characterized in that, The user interface includes a query module, which supports filtering test batch yield reports within a custom time range.

4. The method for real-time monitoring and data analysis of first-inspection yield according to claim 1, characterized in that, It also uses a program-processing log module to record the data processing process, which facilitates troubleshooting.

5. The method for real-time monitoring and data analysis of first-inspection yield according to claim 3, characterized in that, The query module also supports sorting, filtering, and exporting yield reports.

6. The method for real-time monitoring and data analysis of first-inspection yield according to claim 3, characterized in that, The query module integrates visualization tools to generate yield trend charts and BIN distribution heatmaps.

7. The method for real-time monitoring and data analysis of first-inspection yield according to claim 4, characterized in that, The program's log processing module stores the logs in a specified directory path and records them synchronously in a background log file.

8. The method for real-time monitoring and data analysis of first-inspection yield according to claim 6, characterized in that, The visualization tool is also equipped with an intelligent alarm function, which automatically triggers an alarm and pushes it to the responsible person when the yield is lower than a preset threshold.

9. The method for real-time monitoring and data analysis of first-inspection yield according to claim 8, characterized in that, The intelligent alarm function, combined with password protection settings, requires authorization verification for sensitive operations to ensure data security.

Images