Predict Material Properties During Growth
You need to know composition, surface roughness, or other material properties, but feedback only comes after you pull the sample and run ex-situ characterization hours or days later. By then, it’s too late to adjust. Without Atomscale: Deposit the full film blind to material state. Wait for ex-situ XPS, AFM, or other measurements. Discover problems after the fact. Iterate by running another full growth cycle. With Atomscale:Build proxy models from historical data
Atomscale correlates your existing in-situ monitoring data (RHEED, ellipsometry, tool sensors)
with ex-situ characterization results across past runs. Even small labeled datasets can produce
practical predictive relationships.
Get predictions in real time on future runs
Once trained, proxy models estimate material properties (composition, roughness, phase regime)
from in-situ data as layers form. Signal that was previously too noisy or abstract to use
becomes actionable.
Detect and Respond to Process Drift
In production, processes drift. Sources degrade, chamber conditions shift, calibrations slip. The question is whether you catch it before or after it affects your product. Without Atomscale: Discover drift at final characterization, days or weeks after the affected runs. Scrap material. Miss shipments. Investigate retroactively with incomplete data. With Atomscale:Detect Drift in the Material
Atomscale monitors the material itself, beyond tool parameters. Quantitative fingerprints
extracted from in-situ characterization (RHEED, ellipsometry, spectroscopy) surface changes in
crystal structure, surface reconstruction, and growth dynamics at signal-to-noise levels too low
for manual detection.
Maintain Visibility at Speed
At high rotation speeds needed for wafer uniformity, operators lose visual access to the
material state. Atomscale preserves full process visibility by observing material state along
multiple angles simultaneously, unlocking higher uniformity and faster deposition rates without
sacrificing monitoring.
Intervene During the Run
When drift is caught early, you can adjust parameters mid-run or abort before wasting a full
cycle. Atomscale provides in-process adjustments and run-to-run insights fed directly back to
the operator, closing the gap between detection and action.
Diagnose Why a Growth Run Failed
A run didn’t produce the expected results. Was it a parameter drift? Contamination? Equipment issue? Something in the substrate prep? Without Atomscale: Manually export logs from each instrument. Align timestamps in spreadsheets. Compare against previous runs by memory or scattered notes. Spend hours (or days) hunting for the deviation, and even then miss patterns buried in raw characterization data that require expert intuition to interpret. With Atomscale:Pull the run into comparison view
Select the failed run and one or more known-good references. Atomscale automatically aligns all
data streams by timestamp.
Identify deviations in the material beyond parameters
The platform compares quantitative material fingerprints rather than tool parameter values.
Surface transitions, reconstruction changes, and compositional shifts that were previously
invisible without manual analysis of raw metrology data are surfaced automatically. Anomaly
detection flags statistically significant differences.
Correlate across instruments
See if the deviation in one system (e.g., substrate temperature) correlates with observations in
another (e.g., RHEED pattern changes or compositional drift).
Transfer a Process from R&D to Production
A process that works in the research lab needs to move to a manufacturing environment. This transition is where many materials programs fail. Without Atomscale: Transfer a recipe and hope it works. When it doesn’t, troubleshoot from scratch. Struggle to distinguish equipment differences from operator differences from environmental differences. With Atomscale:Document the operating window
Use existing R&D run data to quantify how much each parameter can vary before outcomes degrade.
Know which parameters are critical and which have margin, without dedicated characterization
experiments.
Characterize the target system
Run baseline experiments on the production equipment. Compare its fingerprint to the R&D system
to understand exactly where the systems differ.
Predict necessary adjustments
Based on system differences, identify which recipe parameters need modification before the first
transfer attempt.
Optimize a New Recipe
You have a baseline process that works, but you need to improve a specific property, whether uniformity, growth rate, interface sharpness, or something else. Without Atomscale: Run experiments based on intuition. Track results in notebooks or spreadsheets. Struggle to remember what you already tried. Miss subtle parameter interactions. Spend weeks running characterization to understand the effect of each change. With Atomscale:Discover Process-Property Relationships
Rapidly identify robust relationships between process variables (temperature, flux ratio,
timing) and material quality (roughness, composition, uniformity), even from small labeled
datasets. Atomscale’s information extraction captures signals that manual analysis misses.
Map the Design Space
Build surrogate models that predict outcomes across the parameter space. Understand which
variables matter, which interact, and where the boundaries of your process window lie, without
running every combination.
Execute and Track
Each run is automatically logged with complete process and characterization data. No manual data
entry. No forgotten conditions. Every experiment builds on the last.