How VM Works in Practice

The workflow is straightforward: Collect trace data from every sensor channel during the process run Extract features — statistical summaries (mean, std, slope, endpoint values) from each channel and each recipe step Feed into a trained ML model that maps sensor features → metrology result (CD, thickness, etch depth, sidewall angle, overlay, etc.) Output a prediction within 50ms of process completion — before the wafer even leaves the chamber

What "Good" Looks Like

Production-grade VM models typically achieve: R² > 0.95 — the model explains 95%+ of the variation in actual measurements MAPE < 3% — mean absolute percentage error under 3% of the target value Prediction latency < 50ms — fast enough to feed into Run-to-Run (R2R) control loops False alarm rate < 1% — predictions flagged as out-of-spec that aren't

How MST Deploys Virtual Metrology with NeuroBox E3200

Theory is useful. Deployment is what matters. Here's how MST's NeuroBox E3200 brings VM into a production fab — without disrupting your existing workflow.

What Stays in the Fab, Stays in the Fab

The E3200 runs entirely at the edge. All data collection, model training, and inference happen on the local device. No data leaves the fab. No cloud dependency. No internet connection required. For fabs with strict IP protection requirements — which is every fab — this is non-negotiable. Your process data, your sensor traces, your recipes: they never touch an external server. Model updates are delivered via secure, air-gapped transfer when needed.

Is VM Right for Your Process?

VM works best when: Your process has rich sensor data (etch, CVD, PVD, diffusion — yes; wet bench — limited) There's a measurable correlation between in-situ sensor readings and the metrology result Your metrology bottleneck is real — queue times >2 hours, sampling rate <10% You care about wafer-level granularity, not just lot-level trends It's less effective for processes with minimal in-situ instrumentation or where the metrology target is domin

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Q: How MST Deploys Virtual Metrology with NeuroBox E3200

Theory is useful. Deployment is what matters. Here's how MST's NeuroBox E3200 brings VM into a production fab — without disrupting your existing workflow.

Q: What Stays in the Fab, Stays in the Fab

The E3200 runs entirely at the edge. All data collection, model training, and inference happen on the local device. No data leaves the fab. No cloud dependency. No internet connection required. For fabs with strict IP protection requirements — which is every fab — this is non-negotiable. Your process data, your sensor traces, your recipes: they never touch an external server. Model updates are delivered via secure, air-gapped transfer when needed.

Q: Is VM Right for Your Process?

VM works best when: Your process has rich sensor data (etch, CVD, PVD, diffusion — yes; wet bench — limited) There's a measurable correlation between in-situ sensor readings and the metrology result Your metrology bottleneck is real — queue times >2 hours, sampling rate <10% You care about wafer-level granularity, not just lot-level trends It's less effective for processes with minimal in-situ instrumentation or where the metrology target is domin

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