Equipment Fault Detection and Classification System - Intelligent Error Detection Classification - Defect Classification Control - FDC System - Semiconductor FDC - GETECH

Fault Detection and Classification (FDC)

Uses variable control and statistical techniques to detect and analyze abnormalities in equipment during the process, implement real-time countermeasures, and improve equipment OEE and product yield.

Process Modeling | Data Collection | Summary Calculation | Detection Modeling | Data Analysis | Detection Simulation | Linkage Control

Pain Points

1. Old Technical Architecture

Monolithic application, does not support microservices architecture, does not support HA, does not support dynamic scaling, poor performance, low stability;

2. Incomplete Functionality

Weak data computation and processing capability, low flexibility, poor alarm real-time performance, does not support custom control rules;

3. High Implementation Difficulty

Complex system deployment, does not support containerized operation, inconvenient initial configuration, requires a lot of custom logic development;

4. Poor Usability

C/S architecture, inconvenient to use; requires managing a large number of models; too many invalid alarms; incomplete data interfaces;

Core Advantages

  • New Architecture with Excellent Performance
    Adopts high-performance big data technology architecture; supports high-throughput, low-latency real-time computation; supports highly available distributed system deployment; supports microservices and containerization, resources can be elastically scaled;
  • Comprehensive and Complete Functionality
    Supports various forms of data collection channels and collection strategy management; provides window-based summary calculation and custom scenario calculation; fully supports all West Electric control rules, supports custom development; provides real-time data, historical, and simulated data analysis reports;
  • Good Product Usability
    Adopts B/S architecture, supports browser access; supports various forms of deployment, can be localized; supports process-type-based modeling, can manage tens of thousands of instances with one click; opens all system interfaces, allows free custom report creation;
  • Convenient and Fast Operation and Implementation
    Fool-proof system deployment guidance, simple and convenient configuration; supports virtualized containerized deployment, supports HA; initial configuration supports batch import/export, convenient implementation; system logic is configurable, changing logic requires no coding ability;

Application Scenarios

Panel Manufacturing

Control of process parameters for coating, etching, exposure, grinding, etc., to improve product yield;

Silicon Wafer Manufacturing

Control of process parameters for crystal pulling, wire cutting, grinding, polishing, etc., to reduce process accidents;

Wafer Manufacturing

Control of process parameters for grinding, polishing, epitaxy, etc., to reduce equipment failures;

Chip Manufacturing

Control of process parameters for coating, etching, exposure, grinding, etc., to reduce scrap rate;

Cases

Case: FDC Project for a Panel Enterprise Module Factory

Project Background: The module factory's new plant is about to mass produce, urgently needs to build an FDC system; the original self-developed FDC system has weak functionality and cannot meet the control needs of the new plant's new equipment. The project manages over 3000 equipment, over 100,000 data points, over 200,000 detection instances simultaneously, supports simultaneous access for over 1000 people;

Project Features: Enhanced fool-proof monitoring for parameter registration, Sensor monitoring miss management, improved usability of registration and data query functions, increased operational efficiency; through adaptive rules for control threshold AutoRange, significantly reduced invalid alarms; adopted Group management for some equipment based on production needs, more accurate Sensor monitoring miss;

Case: FDC System for a 12-inch Chip Manufacturing Enterprise

Project Background: Building a new plant, the original FDC system vendor cannot continue service due to policy reasons, urgently needs to find a domestic FDC system replacement vendor; the project manages over 1000 equipment, over 1 million data points, over 2 million detection instances simultaneously, supports simultaneous access for over 1000 people;

Project Features: FDC system collects data directly from equipment through virtual port software, needs to support Interface A protocol, supports up to 10Hz collection frequency; has many heterogeneous data collection requirements, data parsing logic is configurable, no coding needed; summary calculation algorithms and control rule requirements are comprehensive, control rules need to support customer customization and import into the system application; massive data query requires extremely high timeliness, 30 days of data results in 1 minute and draws Chart;

Case: Smart Manufacturing Integration Project for a Semiconductor Silicon Wafer Manufacturing Enterprise

Project Background: The original production process has weak process control, poor product parameters, self-damage, and significant waste of man-hours, urgently needs improvement through FDC system. The project mainly manages process equipment such as sticking, wire cutting, degumming, cleaning, etc., totaling over 200 equipment, over 10,000 control data points, running over 30,000 detection instances;

Project Features: Integrated three major modules: fault detection and classification, quality intelligent analysis, and process adjustment, built a unified data platform, achieved data sharing and integration; classified management of key problem-solving experiences and methods, built a knowledge base for reuse in other factories; after three months of system operation, improvement effects are significant, product yield increased by 5%, man-hours saved by 10%, production collaboration efficiency greatly improved;

Functional Modules

Detection modeling of parameters, defining detection dimensions, data filtering, detection rules, setting detection thresholds;

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