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In precision industries such as semiconductors and high-end electronics manufacturing, stable equipment operation directly affects production output and product yield. However, most factories still rely on traditional maintenance models, with various pain points continuously restricting production efficiency: maintenance heavily depends on the personal experience of senior staff, faults are always detected late, and minor hidden issues eventually escalate into sudden shutdowns; massive equipment data is not effectively utilized, maintenance decisions lack data support, leading to frequent over-maintenance and under-maintenance; maintenance plans often conflict with production schedules, wasting resources such as labor and spare parts while disrupting normal production pace.

Leveraging years of industry experience and practical implementation in intelligent manufacturing, Gtrontec has launched the Equipment Intelligent Agent solution. Using a multi-agent collaborative architecture, it redefines the maintenance model, helping factories gradually reduce reliance on manual labor and achieve intelligent upgrades in equipment maintenance.

Solution Architecture: Covering Multiple Scenarios, Integrating AI Technology to Build an Intelligent Maintenance System

This solution deeply integrates cutting-edge AI technology with industrial mechanisms, covering core business scenarios such as equipment health assessment, predictive maintenance, and process anomaly detection. It connects the entire workflow of data sensing, analysis diagnosis, and decision execution, creating a lightweight, high-efficiency intelligent equipment maintenance hub for factories. To address key challenges in equipment maintenance, the solution offers three specialized capabilities:

AI-based Fault Detection and Classification (AI-FDC): Traditional FDC requires engineers to manually select monitoring parameters, which is inefficient and leads to delayed anomaly detection and difficult alarm tracing. AI-FDC integrates natural language data querying, intelligent parameter selection, and explainable intelligent specifications to achieve real-time, reliable process monitoring. It traces problem origins from multiple dimensions, effectively improving equipment OEE and reducing costs from ineffective maintenance.

Predictive Health Management for Process Equipment (PHM): Previously, equipment maintenance was mostly reactive, fixing faults after they occurred. PHM focuses on key components of core process equipment, accurately predicting remaining spare part life, assessing overall equipment health, automatically identifying root causes of faults, and recommending optimal maintenance plans. At the same time, the system can coordinate maintenance timing with production plans to avoid unplanned downtime and improve effective equipment utilization.

Equipment Health Management for Auxiliary Equipment (EHM): Factories have numerous and scattered auxiliary equipment, making manual inspection difficult. Hidden anomalies are hard to detect in time, and overall maintenance costs remain high. Relying on data collection and visualization capabilities, combined with real-time monitoring and fault diagnosis modules, EHM effectively reduces maintenance pressure on auxiliary equipment.

The above solution relies on the collaborative operation of four intelligent agents to achieve full-process intelligent operation for semiconductor equipment maintenance. The Equipment Health Assessment Agent comprehensively analyzes equipment operation status and outputs a quantitative health score. The Predictive Maintenance Agent continuously tracks equipment degradation trends, predicting component life and potential faults in advance. The Maintenance Strategy Recommendation Agent considers maintenance costs and on-site risks to output optimal maintenance plans. The Execution Optimization Agent coordinates production plans and real-time resources, dynamically adjusting maintenance schedules to achieve efficient coordination between maintenance and production.

AI-FDC: Intelligent Process Anomaly Detection, Abandoning Dependency on Manual Experience

In semiconductor wafer manufacturing, traditional manual sampling and post-event inspection often detect quality issues only after large-scale outbreaks, when losses are irreversible. Traditional FDC also has obvious shortcomings, such as parameter setting relying on engineer manual modeling and selection, making the solution difficult to replicate. It cannot correlate equipment anomalies with product defects, and static thresholds can easily lead to missed detections and false alarms, further increasing maintenance burdens. To address these pain points, Gtrontec AI-FDC relies on three core capabilities to promote full intelligent process quality inspection.

Intelligent Parameter Selection Engine: Combining LLM, knowledge graphs, and multimodal feature selection technology, it accumulates massive expert experience and project implementation data. The system automatically recommends optimal monitoring parameter combinations across processes and machines, facilitating rapid model training and solving issues of low efficiency and excessive reliance on experience in manual parameter selection.

Intelligent Specification and Anomaly Tracing: Using representation learning and dual-mode machine learning algorithms, the system automatically classifies anomaly levels and accurately establishes correlations between equipment anomalies and product defects. Leveraging small-sample ensemble learning, it outputs system-level anomaly scores, supports problem tracing, and quickly locates fault sources.

Online Model Active Evolution: The system builds an online framework for human-machine collaboration feedback and autonomous iterative learning. Facing on-site changes such as production data fluctuations, new process launches, and parameter adjustments, the model can adapt and optimize in real-time, maintaining high-precision detection capability over the long term and stably ensuring production quality.

Currently, AI-FDC has been successfully deployed in multiple leading semiconductor companies, with quantifiable results and significant value.

Core Value

Production Increase and Efficiency Improvement: Combining generative models with industrial-specific machine learning algorithms, it effectively improves equipment OEE and helps factories save overall costs. Quality Traceability: Through anomaly labels and full-chain tracing capabilities, it establishes correlations between equipment anomalies and product defects, providing a clear basis for maintenance optimization and yield improvement. Low Barrier and Scalability: It can adapt to mass production lines as well as R&D small-batch trial scenarios, requiring no massive data for rapid deployment, with low implementation barriers and strong scalability.

Next Issue Preview: We will focus on Equipment Health Intelligent Diagnosis (AI-EHM), exploring how intelligent agents deepen auxiliary equipment management, achieve real-time anomaly monitoring, precise fault diagnosis, and closed-loop maintenance optimization, further reducing abnormal downtime and maintenance costs. Stay tuned.