Case File 02 · Semiconductor
Representative — advanced-node fab
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Representative scenario · benchmark-based
A representative advanced-node fab produces leading-edge chips for consumer electronics.
Their manual visual inspection process struggled to catch micro-cracks and sub-surface anomalies, and undetected defects could carry significant downstream cost. With increasing demand for chip miniaturization, their quality control methods couldn't keep pace with production volumes.
We implemented ChipSense, our semiconductor AI agent system, trained on the fab's proprietary defect library of millions of labeled wafer images. The multi-agent system performs real-time inspection at multiple magnification levels, classifying defects by type, severity, and likely root cause. An optimization agent then correlates defect patterns with manufacturing parameters to suggest process adjustments.
Analyzes wafer images at multiple magnification levels for defect detection
Classifies defects by type and severity, correlates with process parameters
Suggests process adjustments based on defect pattern analysis
Generates quality reports, alerts, and trend analysis for engineering teams
A representative 16 weeks delivery path, from discovery to deployment.
Audited existing defect library, assessed imaging equipment, mapped inspection workflow
Weeks 1-3
Designed multi-resolution inspection pipeline, defined defect taxonomy
Weeks 4-6
Trained on 2M labeled images, validated against expert inspectors
Weeks 7-12
Integrated with production line cameras, calibrated thresholds per product line
Weeks 13-15
Full deployment with real-time monitoring and feedback loop
Weeks 16
Representative outcomes for this scenario, aligned to published industry benchmarks.
Less yield detraction
30%
Defect-classification accuracy
99%
Lower cost at scale
17%
Of fab cost is yield loss and test
25%
“Inline inspection and root-cause analysis surfaced patterns our manual process missed, and the suggestions moved yield in the right direction.”
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