Equipment Intelligence

Use AI to optimize semiconductor equipment performance and predictive maintenance

Business impact

• Equipment uptime — AI predicts failures to schedule maintenance, minimizing unexpected downtime
• Process yield — Optimized equipment parameters improve manufacturing consistency and reduce defects
• Operational costs — Reduced unplanned repairs and optimized maintenance lower overall fab expenses

Data requirements

• Equipment sensor telemetry (Numeric) — Monitors real-time machine status and performance metrics for anomaly detection
• Maintenance logs (Structured) — Historical repair and service records inform predictive maintenance models
• Process control data (Numeric) — Parameters from manufacturing steps used to correlate equipment behavior with yield

AI methods and techniques

• Predictive AI — Forecasts equipment failures and maintenance needs from historical and real-time data
• Agentic AI — Automates decision-making for maintenance scheduling and process adjustments

AI models and model families

GPT-4, Llama 2, Proprietary predictive maintenance models