Yield Optimization Through AI-Driven Process Control

In semiconductor fabrication, yield is the primary economic lever. A 1% improvement in wafer yield at scale translates to significant revenue recovery. AI-driven process control enables fabs to achieve yield improvements that manual SPC approaches cannot sustain.

The Challenge

Advanced semiconductor fabs generate terabytes of in-line metrology data per day — far more than process engineers can manually analyse. Subtle correlations between process parameters and yield outcomes are buried in noise. By the time a yield excursion is detected through traditional SPC, hundreds of wafers may already be affected.

Our Approach

• Real-time process correlation models — ML models continuously analyse in-line metrology data to identify parameter combinations that predict yield degradation before excursions occur.
• Automated SPC augmentation — AI overlays on existing SPC systems that detect subtle patterns below traditional control limits, triggering early corrective actions.
• Defect root cause attribution — Automated analysis of wafer defect maps against process history to attribute defects to specific tools, layers, or process windows.

The Outcome

Process engineers gain leverage — AI handles the continuous monitoring and pattern recognition work while engineers focus on process optimisation decisions that require domain expertise.