02

Case File 02 · Semiconductor

Lifting yield with inline defect analysis

Representative — advanced-node fab

▸ ENGAGEMENT DETAILS

Representative scenario · benchmark-based

Specialized AgentChipSense
IndustrySemiconductor
Duration16 weeks
01Background

The Challenge

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.

02ChipSense · Multi-Agent Architecture

Our Agent Solution

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.

01 / 04

Inspection Agent

Analyzes wafer images at multiple magnification levels for defect detection

Agent · Live
02 / 04

Analysis Agent

Classifies defects by type and severity, correlates with process parameters

Agent · Live
03 / 04

Optimization Agent

Suggests process adjustments based on defect pattern analysis

Agent · Live
04 / 04

Reporting Agent

Generates quality reports, alerts, and trend analysis for engineering teams

Agent · Live
03Timeline

Implementation Timeline

A representative 16 weeks delivery path, from discovery to deployment.

01

Data Audit & Assessment

Audited existing defect library, assessed imaging equipment, mapped inspection workflow

Weeks 1-3

02

Model Architecture

Designed multi-resolution inspection pipeline, defined defect taxonomy

Weeks 4-6

03

Training & Validation

Trained on 2M labeled images, validated against expert inspectors

Weeks 7-12

04

Integration & Calibration

Integrated with production line cameras, calibrated thresholds per product line

Weeks 13-15

05

Production Deployment

Full deployment with real-time monitoring and feedback loop

Weeks 16

04Impact

Results

Representative outcomes for this scenario, aligned to published industry benchmarks.

▸ OUTCOME

Less yield detraction

30%

▸ OUTCOME

Defect-classification accuracy

99%

▸ OUTCOME

Lower cost at scale

17%

▸ OUTCOME

Of fab cost is yield loss and test

25%

05Representative

A representative perspective

Inline inspection and root-cause analysis surfaced patterns our manual process missed, and the suggestions moved yield in the right direction.

VoM
VP of Manufacturing
Advanced-node fab, Representative composite

▸ NEXT ENGAGEMENT

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