Skip to main content
Atomscale provides automated analysis workflows for common thin film characterization techniques. Each workflow processes raw instrument data to extract metrics, identify features, and generate actionable insights.

In-Situ Techniques

Analysis workflows for instruments operating during thin film growth.

RHEED

Surface structure analysis from diffraction patterns and intensity oscillations.

Ellipsometry

Timeseries extraction from spectroscopic ellipsometry measurements.

Optical Images

Video segmentation and morphology tracking from optical microscopy.

Ex-Situ Techniques

Analysis workflows for post-growth characterization instruments.

XPS

Elemental composition analysis from X-ray photoelectron spectroscopy survey spectra.

SEM

Object segmentation and morphological analysis from electron micrographs.

Photoluminescence

Spectral analysis of photoluminescence emission measurements.

Raman

Vibrational spectroscopy for material composition and crystal quality.

SIMS

Depth profiling of elemental composition from secondary ion mass spectrometry.

Common Capabilities

All characterization workflows share these features:
FeatureDescription
Automated processingWorkflows run automatically when data arrives from connected instruments
Metric extractionKey measurements extracted and stored for trending and comparison
Batch processingProcess historical datasets through the same analysis pipelines
SDK accessRetrieve results programmatically via the Python SDK
ExportDownload results in CSV, JSON, or custom formats

Getting Started

1

Connect your instrument

Add your instrument as a data source under Settings > Data Sources. See Connect for details.
2

Enable the workflow

Navigate to the relevant technique page and follow the Setup tab instructions to enable and configure the workflow.
3

Configure alerts

Set up alerts for metrics that matter to your process. Each technique page documents available alert options.
4

Verify outputs

Run a test analysis and confirm outputs match expectations before relying on the workflow in production.

Detect Anomalies During a Run

Use characterization alerts as part of real-time monitoring.

Python SDK

Access analysis results programmatically and build custom pipelines.