For Organizational Leaders
De-risk development timelines and accelerate the path from lab to production. The path from lab-scale demonstration to production is where most materials programs stall. For executives and program managers, the value of Atomscale is strategic: faster time-to-market with reduced development costs and lower risk of late-stage surprises.- Commercialize Faster
- Reduce Organizational Risk
- Accelerate Time to Insight
Atomscale compresses the R&D-to-manufacturing timeline by making process transfer
systematic rather than ad hoc:
- Quantify operating windows so transfer targets are explicit, not inferred from tribal knowledge
- Identify scale-up risks early through cross-reactor comparison and equipment fingerprinting
- Preserve development knowledge so teams don’t re-learn lessons when personnel change
For Manufacturing Teams
Catch problems during growth, not weeks later at characterization. Process engineers and module owners are accountable for yield, uptime, and product variance. The IPE gives them real-time visibility into the material itself, beyond tool controller state, so they can detect drift and intervene before defects occur.- Real-Time Process Visibility
- Faster Root Cause Analysis
- Yield Improvement
Most process data collected during growth goes unused because it isn’t
actionable in real time. Atomscale changes this:
- Live dashboards with synchronized data from all connected instruments
- Quantitative process fingerprints that surface what’s happening in the material, beyond what the tool reports
- Predictive quality models that forecast final film properties from in-situ measurements, creating real-time proxies for expensive ex-situ characterization
For Research Teams
Accelerate discovery and maximize the value of every experiment. Research in thin film materials is inherently exploratory, but that doesn’t mean it has to be inefficient. Atomscale helps research teams move faster by using 100% of their data, surfacing patterns across experiments, and preserving hard-won knowledge.- Reduce Experimental Cycles
- Accelerate Knowledge Transfer
Identify optimal growth conditions faster through systematic parameter exploration
and ML-assisted optimization.
- Rapidly identify robust relationships between process variables and material quality, even from small labeled datasets
- Predict likelihood of trial success from initial conditions, avoiding doomed experiments before they waste a full run
- Teams typically reduce the experiments needed to hit targets by 40-60%
Platform Capabilities
All solutions are powered by the IPE, a unified platform with deep integration into thin film workflows.| Capability | What It Enables |
|---|---|
| Instrument Integration | Connect MBE, MOCVD, ALD, PVD, and sputtering systems, plus RHEED, XPS, XRD, AFM, ellipsometry, Raman, and more |
| Real-Time Data Ingestion | Sub-second acquisition via visual streaming, file watcher, or programmatic client |
| Information Extraction | Tool-specific adapter models transform raw signals into quantitative fingerprints, unlocking large-scale pattern recognition from data that was previously too abstract to use |
| Process Intelligence | Anomaly detection, predictive modeling, and relationship discovery using generalized and custom models |
| Access Methods | Web UX, Python API, and on-premises deployment |