Key Takeaways
NeuroBox is MST’s on-premise process-AI layer for semiconductor equipment — Smart DOE, Virtual Metrology, Run-to-Run control and FDC running at the edge, with no cloud dependency and no data leaving the fab. These capabilities are validated on offline and historical fab datasets (Measured Offline-Lab); production results are confirmed per tool during a co-validation pilot on your own equipment. Validation targets, each measured against your current baseline: Smart DOE try-wafer reduction of 70–80% to the same Cpk; Virtual Metrology toward 100% wafer coverage inferred from equipment sensor data; Run-to-Run per-wafer auto-tuning. ROI is modeled per site from your wafer cost, tool count and current scrap rate (Modeled), then confirmed in the pilot.
Why Semiconductor Fabs Need AI
Modern semiconductor manufacturing generates terabytes of sensor data per tool per day, yet most fabs only measure 4% of wafers physically. AI bridges this gap — predicting quality, optimizing processes, and reducing waste without slowing production.
Validation target · offline-lab
Validation target
Measured offline-lab
Smart DOE — Equipment Commissioning AI
Traditional DOE burns 50-100 test wafers per tool commissioning. Smart DOE uses Bayesian optimization and transfer learning to reach the same Cpk targets with far fewer wafers: on offline and historical datasets (Measured Offline-Lab) it reached comparable Cpk with 10–15 wafers, and a pilot’s goal is to reproduce this on your own tool.
- Stop Burning Test Wafers: How Smart DOE Cuts Costs by 80%
- Smart DOE vs Traditional DOE: Why 80% Fewer Wafers
- The Hidden Cost of Manual DOE: $5,000+ Per Wafer
- Complete Smart DOE Deployment Workflow
- Transfer Learning: From 15 Wafers to 2
- Cost model (modeled): what try-wafer reduction could save a 50k-WSPM fab
Virtual Metrology — Predict Without Measuring
VM uses equipment sensor data to predict wafer quality in real time, turning 4% physical measurement coverage into 100% virtual coverage.
- Virtual Metrology Explained
- From 4% to 100% Wafer Coverage
- VM Approaches: Linear vs Deep Learning vs Hybrid
- VM for CMP: Post-Polish Thickness Prediction
Run-to-Run Control
Fault Detection & Classification (FDC)
Process-Specific AI
- AI for Etch: CD Control & Endpoint Detection
- AI for CVD/PVD: Thin Film Prediction
- AI for Lithography: Overlay Control
- AI for Ion Implant: Sheet Resistance Prediction
- AI for Wet Cleaning
- AI for Diffusion & Oxidation
- AI for Advanced Packaging: CoWoS & HBM
Platform & Strategy
- NeuroBox E3200 vs E5200: Which Fits Your Needs?
- NeuroBox vs Applied Materials AIx
- How to Choose AI for Your Fab
- Why Edge AI Beats Cloud AI
- On-Premise vs Cloud: Security Comparison
- OEE Optimization Beyond 90%
- 5 Proven AI Strategies for Yield
SECS/GEM & Factory Integration
- Complete SECS/GEM Protocol Guide
- GEM300 Standard Guide
- SECS/GEM vs OPC UA
- Our Open-Source SECS/GEM Driver
Validate NeuroBox on your own tool data
On-premise. No cloud. No data leaving your fab. A scoped pilot on one tool returns a first validated result on your own historical data in about two weeks.