The Problem
Advanced materials are designed at the atomic scale but manufactured with little visibility into what’s actually happening during processing. Feedback on critical material properties typically arrives hours or days later after ex-situ characterization, long after it’s possible to correct issues in-process or abort failing runs early. This gap is growing. As materials become increasingly complex, process windows narrow and the design space for experimentation explodes. Traditional process development relying on parametric statistical models scale poorly with the amount of interacting variables in modern tools and recipes. The result: most useful process data collected during growth isn’t used because it isn’t actionable in real time and standard operating procedures rely on only a fraction of available signal.What Atomscale Does
Atomscale extracts 1000x more information from existing process data and makes it usable in real time. Continuous monitoring provides active process control to improve yield and throughput for complex materials, and a quantitative foundation for predictive anomaly and process drift detection.Key Capabilities
Real-Time Feedback
Stream data from growth systems and metrology tools to get automated analysis and actionable
insights as layers form with 100% of your data signal.
Virtual Process Characterization
Physics-aware AI models predict process state in real time and generate actionable in-situ
proxies for costly ex-situ measurements.
Anomaly Detection
Automatically detect process drift, defects, and flag out-of-distribution runs before they
impact yield with better consistency than manual monitoring.
Adaptive Process Control
Close the loop from monitoring to intervention with process adjustments informed by direct
materials feedback, compounding value as your process history grows.
How Atomscale Works
Atomscale transforms raw process data into actionable insights in three stages:Connect
Tool-specific adapter models ingest and unify data across make and measure tools in real-time,
transforming raw signals into comprehensive fingerprints that unlock large-scale pattern
recognition, analysis, and automation.
Analyze
Timeseries foundation models embed adapter outputs to answer: How does the current run compare
to previous runs? How uniform is the process from segment to segment, down to individual atomic
layers and up to whole-recipe sequences?
What Differentiates Atomscale
- Works with raw data: Eliminates rigid statistical assumptions by working directly with raw signals instead of point-solution outputs from individual analysis tools.
- Real-time, short-loop feedback: Scalable feedback that replaces homegrown scripts, brittle manual analysis, and heavy MES add-ons.
- Flexible across tools and materials: Tool-specific adapters handle integration complexity so the platform works across deposition methods and characterization techniques without requiring you to build custom pipelines.
- Compounds over time: Every run, successful or not, enriches the models. The intelligence layer adapts and grows as fast as your process development does.