Predictive Maintenance for Semiconductor CMP Equipment: A Complete Practice from Sensor Data to Intelligent Decision-Making

In semiconductor manufacturing, chemical mechanical planarization (CMP) equipment is a key link in wafer processing. The traditional regular maintenance mode has problems such as low efficiency, waste, or unexpected downtime. This project presents a complete predictive maintenance solution that combines rule engines and random forest models to enable equipment health monitoring, fault prediction, and maintenance recommendation generation. It supports decision-making through a Streamlit visual dashboard to address industry pain points.

CMP equipment achieves nanoscale planarization of wafers through chemical corrosion and mechanical grinding, involving multiple precision subsystems. Traditional maintenance faces core issues: passive response (unplanned downtime due to post-failure repairs), over-maintenance (waste from fixed-cycle component replacement), experience dependence (difficulty standardizing decisions based on technicians' personal experience), and data silos (underutilization of sensor data). Abnormalities in any link may affect product yield.

The system builds an end-to-end workflow covering data collection, feature engineering, model training, and other links. Multi-dimensional sensors monitor mechanical systems (grinding plate/carrier head motor current, vibration), process parameters (slurry flow rate, downforce, removal rate), consumable status (grinding pad/retainer ring duration), and environmental indicators (temperature, number of alarms). The dual-layer early warning mechanism: the rule layer generates easily interpretable warnings based on expert knowledge (e.g., increased current accompanied by abnormal vibration); the model layer uses a random forest classifier with a test set accuracy of 99.9%, and key features include sensor threshold counts, process drift trends, etc.

The interactive dashboard provides three main functions: equipment health overview (real-time risk status, priority), technician workflow mode (probability of failure causes, inspection steps, business impact), and simulation and training functions (synthetic data demonstration, personnel training), helping frontline teams quickly identify work priorities.

The data processing workflow includes: 1. Data generation (3240 records, 3 devices, 6 simulated maintenance events); 2. Feature engineering (cleaning feature tables, calculating sliding window statistics); 3. Rule application (generating 586 warning records); 4. Model training (random forest classification); 5. Result output (prediction results, feature importance). Automated via PowerShell scripts, one-click execution of the complete pipeline and dashboard startup.

The system brings multi-dimensional value to enterprises: operational efficiency (reducing unplanned downtime), cost control (optimizing consumable replacement cycles), knowledge precipitation (converting technicians' experience into rules and models), personnel training (simulation environment), and decision support (data-driven maintenance plan formulation).

The project's success lies in pragmatic engineering thinking (choosing interpretable rules + random forests instead of complex deep learning) and emphasis on user experience (designing interfaces from technicians' perspectives, clear maintenance recommendations). It provides a reference for industrial predictive maintenance systems; the modular architecture, clear data flow, and visualization functions can serve as a starting point for similar projects.