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SemiconductorsChip-MakingEcosystemCrystalBand TheoryPN Junctions
Shankar Pandala
Curriculum
What is Virtual Metrology?VM Model BuildingDeploying VM in ProductionML-Enhanced Run-to-Run ControlAdaptive Recipe Optimization
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Phase 5: AI/ML in SemiconductorsSubject 15

Virtual Metrology & Process Control

Learn how ML models predict wafer measurements from equipment sensor data, enabling 100% wafer coverage and real-time process control without physical metrology delays.

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Chapters

1

What is Virtual Metrology?

Predicting metrology from sensor data — why it matters for cost, speed, and 100% coverage

4 sections
2

VM Model Building

Feature engineering from sensor traces, regression models, and rigorous validation for fab-grade predictions

4 sections
3

Deploying VM in Production

Handling model drift, building adaptive models, and estimating confidence for every prediction

3 sections
4

ML-Enhanced Run-to-Run Control

Augmenting traditional R2R control with machine learning for tighter process windows

3 sections
5

Adaptive Recipe Optimization

Bayesian optimization, DoE + ML, and multi-objective tuning for semiconductor recipes

3 sections
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A comprehensive learning path for data scientists and ML engineers entering the semiconductor industry.

Links

  • GitHub Repository
  • MIT License
  • Math4AI

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  • React 19
  • Next.js
  • Tailwind CSS
  • Lucide Icons

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