> ## 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.

# Identify Uniformity Issues

> Find consistency problems within a single run by inspecting it over time and comparing its segments across your dataset

A run can look acceptable in aggregate but contain internal variation that affects device performance: early-stage drift, mid-run excursions, or layer-to-layer inconsistency.

Atomscale catches these by inspecting a run over time and by comparing segments of it against the rest of your dataset.

## Two Approaches to Uniformity

The same tools used to [diagnose why runs differ](/platform/guides/diagnose-run-differences) apply within a single run. Instead of comparing entire runs, you look at how the run behaves across its own phases, layers, or time windows.

<Tabs>
  <Tab title="Monitor Session View (Detailed)">
    **Inspect a single run over time to see where its behavior changes.**

    The [Monitor](/platform/reference/monitoring/index) session view replays a run with its growth metrics, tool state, and detected events on one timeline. Watching how the metrics evolve across the run reveals drift, excursions, and phases that behave differently from the rest.

    **What you see:**

    * **Growth metrics** charts of derived RHEED metrics across the full run, where drift and excursions show up as the values move
    * **Tool state** instrument parameters synchronized with the metrics, giving context for why a particular stretch of the run varied
    * **Activity timeline** of detected changepoints, marking the moments where the run's behavior shifted

    ### Setting Up

    <Steps>
      <Step title="Open the run on the Monitor page">
        Select the session from the **Sessions** sidebar on the
        [Monitor](/platform/reference/monitoring/index) page.
      </Step>

      <Step title="Replay the run end to end">
        Use the playback bar to scrub through the run, or follow the live edge for an active one. See
        [Session View](/platform/reference/monitoring/session-view).
      </Step>

      <Step title="Watch the metrics across phases">
        Look for stretches where a metric drifts away from the run's earlier behavior, or where one
        phase, layer, or cycle spreads more than the others.
      </Step>

      <Step title="Use the activity timeline">
        Open the **Activity** tab to see detected changepoints. These mark transitions between phases and
        the moments where uniformity breaks down.
      </Step>
    </Steps>
  </Tab>

  <Tab title="Explore Similarity (Broad)">
    **See how all segments within a run relate to each other across your full dataset.**

    Use Explore Similarity for a broad view of internal consistency without pre-selecting references. Every segment is positioned by its process fingerprint, so you can immediately see whether segments cluster tightly (good uniformity) or spread out (variation present).

    **What you see:**

    * **Similarity map** where segments cluster by process signature (tight clustering = high uniformity, spread = variation)
    * **Ranked matches** with similarity scores for the most and least similar segments
    * **Filtering** by project, sample, or tags to compare segments against previous runs

    ### Setting Up

    <Steps>
      <Step title="Select a workflow and metric">
        In the <Badge stroke color="blue">Explore Similarity</Badge>
        page, choose a metric, transformation, and time scale window appropriate for your segment
        granularity (e.g., per-cycle for ALD, per-phase for recipes).
      </Step>

      <Step title="Explore the similarity map">
        Segments from the same run appear alongside segments from other runs. Look at how tightly the
        current run's segments cluster and whether any sit apart.
      </Step>

      <Step title="Investigate outlier segments">
        Click any outlier segment and select <Badge stroke color="blue">View Similar Data</Badge>
        to see ranked matches. If an outlier is more similar to segments from a *different* run than to
        its own neighbors, that's a strong signal of a uniformity break.
      </Step>
    </Steps>
  </Tab>
</Tabs>

## What to Look For

### Drift Over Time

Segments that gradually shift away from early-run behavior indicate drift. In the **Monitor session view**, this appears as growth metrics that steadily trend away from where they started. In **Explore Similarity**, drifting segments form a gradient on the map rather than a tight cluster.

### Outlier Segments

A single segment that diverges sharply from the rest points to a transient disturbance, easiest to spot in **Explore Similarity** where the outlier sits visibly apart.

### Phase-Specific Variation

Some recipe phases may be consistently less uniform than others. If segments from one phase always spread more widely, that phase likely needs tighter process control. The **Monitor session view** makes this visible in the growth metrics, showing which derived properties vary most during the problematic phase.

### Periodic Structure Consistency

For multilayers and superlattices, watch how each cycle behaves in the growth metrics on the **Monitor session view**. Consistent cycles repeat nearly identical metric patterns. If early cycles differ from late cycles, or if specific cycles stand out, the time-resolved view shows exactly where within the cycle the variation occurs. **Explore Similarity** complements this: plot the cycles on the map and consistent ones cluster tightly while outliers separate.

## Connecting Uniformity to Outcomes

Within-run uniformity metrics become most valuable when connected to final device or material properties. Runs with tight internal clustering tend to produce more consistent outcomes. If loosely clustered runs correlate with degraded performance, that gives you a quantitative uniformity threshold to monitor against.

## Choosing the Right Approach

| Scenario                                              | Approach             | Why                                                                                     |
| ----------------------------------------------------- | -------------------- | --------------------------------------------------------------------------------------- |
| Seeing where within a run a cycle or phase varies     | Monitor session view | Time-resolved metrics and changepoints show exactly when the run's behavior shifts      |
| Checking whether all segments in a run are consistent | Explore Similarity   | Immediately reveals outliers and clustering without pre-selecting references            |
| Identifying which recipe phase has the most variation | Explore Similarity   | Segments from different phases naturally separate on the map if they behave differently |
| Understanding why a specific layer is different       | Monitor session view | Detailed metrics and tool state pinpoint what changed during that layer                 |
| Tracking uniformity improvement across iterations     | Explore Similarity   | Compare segment clustering tightness across runs to see if uniformity is improving      |

## Next Steps

<CardGroup cols={2}>
  <Card title="Diagnose Why Runs Differ" icon="code-compare" href="/platform/guides/diagnose-run-differences">
    Compare entire runs against each other when the issue is between runs rather than within one.
  </Card>

  <Card title="Detect Anomalies" icon="bell" href="/platform/guides/detect-anomalies">
    Set up automated detection to catch uniformity problems as they develop during active runs.
  </Card>
</CardGroup>