> ## Documentation Index
> Fetch the complete documentation index at: https://docs.atomscale.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Use Cases

> Specific tasks and workflows that Atomscale enables

These are the jobs Atomscale helps you do: concrete tasks that researchers and engineers face regularly in thin film development and manufacturing.

***

## 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**:

<Steps>
  <Step title="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.
  </Step>

  <Step title="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.
  </Step>

  <Step title="Act on predictions mid-run">
    Adjust process parameters in response to predicted material state. Correct drift toward an
    undesired composition or terminate a run heading for failure before wasting a full cycle.
  </Step>
</Steps>

**Example**: Correlating diffraction features with ex-situ composition measurements enables real-time composition predictions on future runs, replacing a measurement that previously required pulling the sample and running XPS.

***

## 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**:

<Steps>
  <Step title="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.
  </Step>

  <Step title="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.
  </Step>

  <Step title="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.
  </Step>

  <Step title="Diagnose the Source">
    After stabilizing, use correlation analysis across all connected instruments to identify what
    caused the drift. Fix the root cause rather than the symptom.
  </Step>
</Steps>

**Typical outcome**: Organizations catch drift events before they impact final product, versus discovering them days later at characterization.

***

## 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**:

<Steps>
  <Step title="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.
  </Step>

  <Step title="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.
  </Step>

  <Step title="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).
  </Step>

  <Step title="Document the finding">
    Annotate the run with your diagnosis. The insight becomes searchable for the next time something
    similar happens.
  </Step>
</Steps>

**Typical time savings**: What previously took 4-8 hours now takes 15-30 minutes.

***

## 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**:

<Steps>
  <Step title="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.
  </Step>

  <Step title="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.
  </Step>

  <Step title="Predict necessary adjustments">
    Based on system differences, identify which recipe parameters need modification before the first
    transfer attempt.
  </Step>

  <Step title="Validate systematically">
    Run confirmation experiments with real-time monitoring. Verify that production results fall
    within the established operating window.
  </Step>
</Steps>

**Typical outcome**: Process transfers that previously took 6-12 months complete in 2-4 months with higher first-pass success rates.

***

## 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**:

<Steps>
  <Step title="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.
  </Step>

  <Step title="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.
  </Step>

  <Step title="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.
  </Step>

  <Step title="Converge on Optimal">
    Use process-property models to guide your next experiments toward the target. Reach your goal in
    fewer runs by letting the data direct exploration rather than intuition alone.
  </Step>
</Steps>

**Typical outcome**: Teams report reaching optimization targets in 40-60% fewer experimental runs compared to traditional approaches.

***

## Next Steps

<CardGroup cols={2}>
  <Card title="Case Studies" icon="book-open" href="/platform/case-studies">
    See detailed examples of these workflows in practice.
  </Card>

  <Card title="Get Started" icon="rocket" href="/platform/get-started">
    Start using Atomscale with your own data.
  </Card>
</CardGroup>