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The first step to using Atomscale is connecting your data sources. When data arrives, Atomscale’s adapter models automatically process it into structured, analysis-ready representations. This page covers the most common connection methods and walks you through your first integration.

Connection Methods

Atomscale supports multiple ways to bring data into the platform, from zero-setup screen capture to fully automated programmatic integration:
Upload characterization data or process log files manually.Best for: Initial evaluation, ad-hoc usage, historical dataRequires: Accessible data files in a supported format

Your First Connection

Choose the path that best fits your situation:

Option A: Upload a File

The quickest way to get data into Atomscale, useful for initial evaluation or loading historical data.
1

Access Web Upload

In the Atomscale dashboard, click Add Data in the sidebar and select Upload from the connection options.
2

Upload Files

Drag and drop your characterization data or process log files, or click to browse. Atomscale auto-detects most common instrument file formats.
3

Confirm and Upload

Click Upload to start uploading files in the background. The platform will notify you when uploads complete and processing is ready.

Option B: Stream via Screen Capture

The quickest way to get live data flowing, with no agent install or API setup required.
1

Open Screen Capture

In the Atomscale dashboard, click Add Data in the sidebar and selectScreen Capture.
2

Select a Window

Choose the instrument GUI or application window to capture from. Atomscale streams the visual content and extracts quantitative data in real-time.
3

Verify Data Extraction

Confirm that Atomscale is extracting the expected data from the captured window. Derived metrics should begin appearing within seconds.
Screen Capture is particularly useful for instruments where the data isn’t easily accessible as files or API streams, such as RHEED camera feeds or proprietary instrument GUIs.

Going Further

Once you’ve verified that Atomscale processes your data correctly, consider setting up persistent connections for ongoing use:
  • File Watcher: Install the desktop agent to automatically upload files from a monitored directory. In the dashboard, click Add DataFile Watcher to download the agent and configure a watched directory.
  • Python SDK: For full programmatic control, see the SDK documentation to set up automated streaming or bulk upload pipelines.

Supported Instruments

Atomscale includes built-in adapters for common characterization and process data across thin film growth:
CategoryInstruments
DiffractionRHEED (STAIB, kSA, custom), XRD, LEED
SpectroscopyXPS (PHI, Kratos, Thermo), Raman, Ellipsometry, NMR
MicroscopySEM, AFM, TEM, STEM
Process LogsMBE, MOCVD, CVD, ALD, PVD, and sputtering systems
GeneralCSV, HDF5, IMM, TIFF, VMS, and other common formats
Don’t see your instrument or file format? Contact our team at [email protected] to quickly set up a new integration.

Data Organization

As you connect data, Atomscale organizes it around a few core concepts that carry through to analysis and monitoring:
  • Data Items are individual files or data streams: a single RHEED recording, an XPS spectrum, or a process log.
  • Physical Samples group data items associated with a sample. A physical sample might have in-situ RHEED data, ex-situ XPS, and the growth process log, all linked together.
  • Projects group related physical samples by growth system, research campaign, product line, or however fits your workflow. Projects are used for run-to-run analysis and monitoring.

Explore Your Data

Once data is connected, the Data Catalog is where you find it all. Click Data in the sidebar to open a table of all your data items, where you can search, sort, and filter across all fields and metadata types.

Next Steps

With data connected, Atomscale’s adapters extract derived metrics like lattice spacing, intensity profiles, and composition signatures, depending on your data type. You’re ready to start comparing runs, monitoring growths, and exploring your process data.