A Global Semiconductor Manufacturing Leader
Transforms a Core Manufacturing
Process with Real-Time AI Dispositioning
3%
Cycle Time Reduction
2.5 hrs/day
Productivity Gain
Higher
Throughput & Efficiency
Semiconductors
Company Profile
- ✓Global leader in high-precision device manufacturing
- ✓Multi-site fabrication network serving worldwide demand
- ✓High-volume production held to nanoscale tolerances
The Challenge
The Solution
The Full Context
Holds are how a fab protects yield. A wafer trips a measurement limit, it stops, and someone decides what happens next. The cost sits in that decision, not in the hold itself. An engineer had to pull historical hold records, cross-reference tool data, and form a judgment for every lot that stopped. Wafers waited while that happened.
Because the judgment depended on who was reviewing it, similar anomalies did not always get similar answers. That variability showed up downstream as unnecessary rework, avoidable scrap risk, and yield that was harder to explain after the fact.
AuxoAI worked with the manufacturing team to build Wafer Ops AI, a prediction layer that reads the same signals an engineer would and returns a recommended disposition as soon as the hold is raised. The engineer stays in the loop and keeps the decision. What changes is that the analysis is already done.
The Approach
AuxoAI sequenced three phases so that each one had to earn engineering trust before the next began.
Unified the Data the Decision Needs
Metrology, inspection and process tool sources were integrated alongside historical hold outcomes, so every prediction draws on the same evidence an engineer would gather manually.
Trained the Disposition Model
A machine learning model learned from historical hold and tool data to predict the right disposition for a wafer: rework, re-measure, or continue to the next step.
Put It Where the Work Happens
Predictions surfaced in real time at the point of hold. Manual classification gave way to AI-assisted dispositioning, with engineers reviewing and confirming rather than starting from raw data.
The Impact
Wafer Ops AI delivered a 3% reduction in total cycle time and returned 2.5 hours per day of engineering productivity, with increased throughput and efficiency across the line.The hours came back first. Engineers stopped rebuilding the same analysis for every hold and moved to reviewing a recommendation, which freed time for the process work that only they can do.
Cycle time followed. Wafers spent less time waiting for a decision, and downstream steps stopped absorbing the delay from upstream holds.
The quieter gain is consistency. Dispositioning now starts from the same evidence and the same logic every time, which makes rework accuracy easier to hold steady and yield outcomes easier to trace back to a cause. The same pattern applies to any decision point in the fab where a trained judgment is being repeated against tool data.
Project Highlights
Solution
Real-time wafer disposition prediction
ML model on historical hold and tool data
Integrated metrology, inspection and process sources
AI-assisted dispositioning in the engineer workflow