# Atomscale Documentation

## Docs

- [Authentication](https://docs.atomscale.ai/api-reference/authentication.md): Secure your API requests with API keys
- [Delete Data Entries](https://docs.atomscale.ai/api-reference/data-catalogue/delete.md): Delete one or more data entries from the catalogue
- [Get Processed Data](https://docs.atomscale.ai/api-reference/data-catalogue/get-processed.md): Retrieve processed data for a data entry
- [Get Raw Data](https://docs.atomscale.ai/api-reference/data-catalogue/get-raw.md): Retrieve raw data for a specific data entry
- [List Data Entries](https://docs.atomscale.ai/api-reference/data-catalogue/list.md): Retrieve data catalogue entries with filtering
- [API Reference](https://docs.atomscale.ai/api-reference/index.md): Programmatic access to the Atomscale platform
- [Finalize Metrology Stream](https://docs.atomscale.ai/api-reference/metrology/finalize-stream.md): Finalize a stream, marking it as complete
- [Ingest Chunk](https://docs.atomscale.ai/api-reference/metrology/ingest-chunk.md): Ingest a single chunk of time series data for one channel
- [Ingest Multi-Channel Chunk](https://docs.atomscale.ai/api-reference/metrology/ingest-chunk-multi.md): Ingest a chunk with multiple channels at once
- [Initialize Metrology Stream](https://docs.atomscale.ai/api-reference/metrology/initialize-stream.md): Initialize a new metrology time series stream
- [Get Metrology Timeseries](https://docs.atomscale.ai/api-reference/metrology/timeseries.md): Retrieve metrology measurement timeseries data
- [Get Optical Video Frames](https://docs.atomscale.ai/api-reference/optical/frames.md): Retrieve individual frames from optical video recordings
- [Get Optical Timeseries](https://docs.atomscale.ai/api-reference/optical/timeseries.md): Retrieve optical monitoring timeseries data
- [Create Project](https://docs.atomscale.ai/api-reference/projects/create.md): Create a new project in your workspace
- [Get Project Samples](https://docs.atomscale.ai/api-reference/projects/get-samples.md): List all physical samples associated with a project
- [List Projects](https://docs.atomscale.ai/api-reference/projects/list.md): Retrieve all projects in your organization
- [Update Project Samples](https://docs.atomscale.ai/api-reference/projects/update-samples.md): Update the physical samples associated with a project
- [End RHEED Stream](https://docs.atomscale.ai/api-reference/rheed/end-stream.md): End a RHEED streaming session and finalize processing
- [Get RHEED Fingerprint](https://docs.atomscale.ai/api-reference/rheed/fingerprint.md): Retrieve the RHEED pattern fingerprint as a serialized graph
- [Get RHEED Mask](https://docs.atomscale.ai/api-reference/rheed/mask.md): Retrieve the RLE-encoded mask for a RHEED image
- [Start RHEED Stream](https://docs.atomscale.ai/api-reference/rheed/start-stream.md): Start a new RHEED streaming session
- [Get RHEED Timeseries](https://docs.atomscale.ai/api-reference/rheed/timeseries.md): Retrieve processed RHEED timeseries data
- [Create Physical Sample](https://docs.atomscale.ai/api-reference/samples/create.md): Register a new physical sample
- [Delete Physical Samples](https://docs.atomscale.ai/api-reference/samples/delete.md): Delete one or more physical samples
- [List Physical Samples](https://docs.atomscale.ai/api-reference/samples/list.md): Retrieve physical samples in your organization
- [Get SEM Fingerprint](https://docs.atomscale.ai/api-reference/sem/fingerprint.md): Retrieve the SEM image fingerprint for surface morphology analysis
- [Get Photoluminescence Data](https://docs.atomscale.ai/api-reference/spectroscopy/photoluminescence.md): Retrieve photoluminescence spectroscopy data
- [Get Raman Data](https://docs.atomscale.ai/api-reference/spectroscopy/raman.md): Retrieve Raman spectroscopy data
- [Get XPS Data](https://docs.atomscale.ai/api-reference/spectroscopy/xps.md): Retrieve X-ray photoelectron spectroscopy data and analysis
- [Complete Upload](https://docs.atomscale.ai/api-reference/upload/complete-upload.md): Finalize a multi-part upload
- [Get Staged Timeseries](https://docs.atomscale.ai/api-reference/upload/get-timeseries.md): Retrieve timeseries data from a staged upload
- [Get Upload URLs](https://docs.atomscale.ai/api-reference/upload/get-upload-urls.md): Generate pre-signed URLs for multi-part file uploads
- [Case Studies](https://docs.atomscale.ai/platform/case-studies.md): Validated demonstrations of Atomscale capabilities across deposition processes and characterization methods
- [Ellipsometry](https://docs.atomscale.ai/platform/characterization/ellipsometry.md): Timeseries extraction from spectroscopic ellipsometry measurements
- [Characterization Overview](https://docs.atomscale.ai/platform/characterization/index.md): Automated analysis workflows for thin film characterization techniques
- [Optical Images](https://docs.atomscale.ai/platform/characterization/optical-images.md): Video segmentation and morphology tracking from optical microscopy
- [Photoluminescence](https://docs.atomscale.ai/platform/characterization/photoluminescence.md): Spectral analysis of photoluminescence emission measurements
- [Raman](https://docs.atomscale.ai/platform/characterization/raman.md): Vibrational spectroscopy for material composition and crystal quality
- [RHEED](https://docs.atomscale.ai/platform/characterization/rheed.md): Surface structure analysis from diffraction patterns and intensity oscillations
- [SEM](https://docs.atomscale.ai/platform/characterization/sem.md): Object segmentation and morphological analysis from electron micrographs
- [SIMS](https://docs.atomscale.ai/platform/characterization/sims.md): Depth profiling of elemental composition from secondary ion mass spectrometry
- [XPS](https://docs.atomscale.ai/platform/characterization/xps.md): Elemental composition analysis from X-ray photoelectron spectroscopy survey spectra
- [Act](https://docs.atomscale.ai/platform/get-started/act.md): Turn real-time process intelligence into decisions, interventions, and control
- [Analyze](https://docs.atomscale.ai/platform/get-started/analyze.md): Compare runs, track active growths, and extract process understanding from your data
- [Connect](https://docs.atomscale.ai/platform/get-started/connect.md): Integrate your growth and characterization data with Atomscale
- [Overview](https://docs.atomscale.ai/platform/get-started/index.md): Get up and running with Atomscale in three steps
- [Detect and Respond to Anomalies](https://docs.atomscale.ai/platform/guides/detect-anomalies.md): Detect process anomalies across instruments, predict quality outcomes, and take corrective action
- [Diagnose Why Runs Differ](https://docs.atomscale.ai/platform/guides/diagnose-run-differences.md): Understand what changed when a run doesn't match expectations using growth monitoring and global similarity
- [Identify Uniformity Issues](https://docs.atomscale.ai/platform/guides/identify-uniformity-issues.md): Find consistency problems within a single run by comparing segments using growth monitoring and global similarity
- [Guides Overview](https://docs.atomscale.ai/platform/guides/index.md): Goal-oriented workflows for process monitoring, analysis, and optimization
- [Atomscale Documentation](https://docs.atomscale.ai/platform/index.md): Real-time process intelligence for advanced materials manufacturing
- [Accessibility](https://docs.atomscale.ai/platform/reference/accessibility.md): Atomscale's commitment to accessible design
- [File Watcher](https://docs.atomscale.ai/platform/reference/connecting-data/file-watcher.md): Automatically upload files from a local directory
- [Manual Upload](https://docs.atomscale.ai/platform/reference/connecting-data/manual-upload.md): Upload files to Atomscale through the web interface
- [Programmatic Integration](https://docs.atomscale.ai/platform/reference/connecting-data/programmatic.md): Upload data and stream frames using the Python SDK
- [Screen Capture](https://docs.atomscale.ai/platform/reference/connecting-data/screen-capture.md): Stream instrument data by recording your screen
- [FAQ](https://docs.atomscale.ai/platform/reference/faq.md): Frequently asked questions about Atomscale
- [IP and Data](https://docs.atomscale.ai/platform/reference/ip-and-data.md): Data ownership, privacy controls, and IP protection
- [Data Items](https://docs.atomscale.ai/platform/reference/models/data-items.md): Individual instrument and characterization files
- [Overview](https://docs.atomscale.ai/platform/reference/models/index.md): Understanding Atomscale's data model and core concepts
- [Projects](https://docs.atomscale.ai/platform/reference/models/projects.md): Grouping physical samples for comparison and analysis
- [Physical Samples](https://docs.atomscale.ai/platform/reference/models/samples.md): Representing growth runs and grouping related data
- [Onboarding](https://docs.atomscale.ai/platform/reference/onboarding.md): Account creation, organization setup, and user management
- [Security](https://docs.atomscale.ai/platform/reference/security.md): Data isolation, encryption, and organizational safeguards
- [Anomaly Detection](https://docs.atomscale.ai/platform/reference/workflows/anomaly-detection.md): Embedding-based anomaly detection, cross-source correlation, and predictive forecasting for timeseries data
- [Workflows](https://docs.atomscale.ai/platform/reference/workflows/index.md): Composable analysis pipelines that process your data
- [Similarity](https://docs.atomscale.ai/platform/reference/workflows/similarity.md): Embed characterization timeseries for visual comparison and track similarity to reference items over time
- [Tool State](https://docs.atomscale.ai/platform/reference/workflows/tool-state.md): Parse, clean, and analyze instrument log files from synthesis tools
- [Solutions](https://docs.atomscale.ai/platform/solutions.md): Atomscale delivers value for organizational leadership, manufacturing operations, and research teams
- [Use Cases](https://docs.atomscale.ai/platform/use-cases.md): Specific tasks and workflows that Atomscale enables
- [Python SDK](https://docs.atomscale.ai/sdk/index.md): Programmatic access to Atomscale for automation and custom analysis
- [Inspect Results](https://docs.atomscale.ai/sdk/inspect-results.md): Work with analysis outputs, time series data, and extracted frames
- [Poll Time Series Updates](https://docs.atomscale.ai/sdk/poll-timeseries.md): Monitor streaming RHEED analysis with sync and async polling
- [Poll Similarity Trajectory](https://docs.atomscale.ai/sdk/poll-trajectory.md): Monitor similarity trajectory updates from streaming analysis
- [Quickstart](https://docs.atomscale.ai/sdk/quickstart.md): Install the SDK and create your first client
- [Client](https://docs.atomscale.ai/sdk/reference/client.md): Complete API reference for the atomscale Client class
- [Streaming](https://docs.atomscale.ai/sdk/reference/streaming.md): Complete API reference for RHEEDStreamer and TimeseriesStreamer
- [Samples and Projects](https://docs.atomscale.ai/sdk/samples-projects.md): Work with physical samples, projects, and aligned timeseries
- [Search the Catalogue](https://docs.atomscale.ai/sdk/search-data.md): Find uploaded data with filters and keywords
- [Stream Metrology Data](https://docs.atomscale.ai/sdk/stream-metrology.md): Push instrument readings for real-time analysis
- [Stream RHEED Video](https://docs.atomscale.ai/sdk/stream-rheed.md): Push frames from your instrument for real-time analysis
- [Upload Data](https://docs.atomscale.ai/sdk/upload-data.md): Send RHEED videos, images, and XPS files for automated analysis