> ## 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.

# Atomscale Documentation

> Real-time process intelligence for advanced materials manufacturing

Atomscale is an ***integrated process environment*** for advanced materials manufacturing.
We connect and unify data across tools, transform raw signals into quantitative process
fingerprints, and integrate those into a real-time representation of your material in
process.

This operationalized data drives a process intelligence and agentic framework for **real-time monitoring and closed-loop process control.**

## The Problem

Advanced materials are designed at the atomic scale but **manufactured with
little visibility** into what's actually happening during processing. Feedback on critical
material properties typically arrives hours or days later after ex-situ characterization,
long after it's possible to correct issues in-process or abort failing runs early.

This gap is growing. As materials become increasingly complex, process windows narrow and
the design space for experimentation explodes. Traditional process development relying on
parametric statistical models scale poorly with the amount of interacting variables in modern tools and
recipes.

The result: **most useful process data collected during growth isn't used** because it
isn't actionable in real time and standard operating procedures rely on only a
fraction of available signal.

## What Atomscale Does

Atomscale extracts **1000x more information from existing process data and makes it usable
in real time.** Continuous monitoring provides active process control to improve
yield and throughput for complex materials, and a quantitative foundation for predictive
anomaly and process drift detection.

### Key Capabilities

<CardGroup cols={2}>
  <Card title="Real-Time Feedback" icon="gauge">
    Stream data from growth systems and metrology tools to get automated analysis and actionable
    insights as layers form with 100% of your data signal.
  </Card>

  <Card title="Virtual Process Characterization" icon="eye">
    Physics-aware AI models predict process state in real time and generate actionable in-situ
    proxies for costly ex-situ measurements.
  </Card>

  <Card title="Changepoint Detection" icon="triangle-exclamation">
    Automatically detect process drift, defects, and flag out-of-distribution runs before they
    impact yield with better consistency than manual monitoring.
  </Card>

  <Card title="Adaptive Process Control" icon="rocket">
    Close the loop from monitoring to intervention with process adjustments informed by direct
    materials feedback, compounding value as your process history grows.
  </Card>
</CardGroup>

## How Atomscale Works

Atomscale transforms raw process data into actionable insights in three stages:

<Steps>
  <Step title="Connect" icon="link">
    Tool-specific adapter models ingest and unify data across make and measure tools in real-time,
    transforming raw signals into comprehensive fingerprints that unlock large-scale pattern
    recognition, analysis, and automation.
  </Step>

  <Step title="Analyze" icon="chart-mixed">
    Timeseries foundation models embed adapter outputs to answer: How does the current run compare
    to previous runs? How uniform is the process from segment to segment, down to individual atomic
    layers and up to whole-recipe sequences?
  </Step>

  <Step title="Act" icon="bolt">
    Generate specific process interventions: flag anomalies, identify drift, and recipe adjustments
    in real time to improve yield and achieve target outcomes, creating a path towards a adaptive
    control and a self-driving process of record.
  </Step>
</Steps>

## What Differentiates Atomscale

* **Works with raw data**: Eliminates rigid statistical assumptions by working directly with raw signals instead of point-solution outputs from individual analysis tools.
* **Real-time, short-loop feedback**: Scalable feedback that replaces homegrown scripts, brittle manual analysis, and heavy MES add-ons.
* **Flexible across tools and materials**: Tool-specific adapters handle integration complexity so the platform works across deposition methods and characterization techniques without requiring you to build custom pipelines.
* **Compounds over time**: Every run, successful or not, enriches the models. The intelligence layer adapts and grows as fast as your process development does.

## Next Steps

<CardGroup cols={2}>
  <Card title="Case Studies" icon="play" href="/platform/case-studies">
    See how teams use Atomscale for defect detection, composition control, and process optimization.
  </Card>

  <Card title="Get Started" icon="rocket" href="/platform/get-started">
    Connect your first data source and explore the platform.
  </Card>
</CardGroup>