Two Approaches to Diagnosis
- Monitor Session View (Detailed)
- Explore Similarity (Broad)
Inspect a single run over time to see exactly what happened.The Monitor page replays a run with its growth metrics, tool state, and detected events on one synchronized timeline. It shows time-resolved detail on how the process evolved, which metrics shifted and when, and what the tool was doing at the moment of a change.Use this when you need to understand exactly when and how a run departs from expected behavior.What you see:
- Growth metrics charts of derived RHEED metrics across the course of the run
- Tool state instrument parameter logs, synchronized with the metrics so you can line up a change with the conditions that produced it
- Activity timeline of detected events and changepoints, which you can filter, relabel, and annotate
Setting Up
Open the run on the Monitor page
Select the session from the Sessions sidebar on the
Monitor page. An active run appears under Ongoing; a
finished run appears under Completed.
Replay or follow the run
For a completed run, use the playback bar to replay it and adjust the speed. For an active run,
the view follows the live edge as data streams in. See
Session View.
Line up metrics with tool state
Watch the growth metrics and tool state together. Where a metric shifts, check the tool state at
the same moment to see whether an instrument event coincides with it.
What to Look For
Both approaches follow the same logic: identify where the run diverges, assess how much, and connect that to what it means for your outcome.Identifying Divergence
In the Monitor session view, watch the growth metrics for the point where they depart from the run’s earlier behavior or from what a healthy run looks like. The synchronized tool state shows whether a specific instrument event coincides with the shift, and the activity timeline flags changepoints automatically so you can jump straight to them. In Explore Similarity, look at where the problem run sits relative to runs with known outcomes. A run that clusters with failures but used a “good” recipe suggests the issue is in execution, not design. A run between clusters may indicate a borderline process condition.Assessing Significance
Not every difference matters. The Monitor session view shows the magnitude of a shift over time: a brief excursion that self-corrects is different from a sustained drift. Explore Similarity scores quantify how far a run sits from its nearest neighbors, which you can compare against the natural spread of your process. Cross-reference with anomaly detection results: if a run looks anomalous in similarity analysis and also triggered drift or point anomalies, that strengthens the case that the deviation is real.Connecting to Outcomes
The diagnostic value comes from linking process divergence to outcome differences. When the Monitor session view shows a run shifting during a specific phase, check whether that phase historically correlates with the outcome property that degraded. When Explore Similarity shows a run clustering with failures, examine what those failures have in common. Runs that are “close” in embedding space tend to produce similar outcomes, making the distance itself a meaningful diagnostic signal.Choosing the Right Approach
| Scenario | Approach | Why |
|---|---|---|
| You need to know exactly when and how a single run changed | Monitor session view | Time-resolved metrics, tool state, and changepoints on one synchronized timeline |
| You want to find what a problem run is most similar to | Explore Similarity | Searches your full dataset without pre-selecting references |
| An active run looks like it’s drifting | Monitor session view | Follows the live edge so you can decide whether to intervene before the run completes |
| Process transfer to a new tool | Explore Similarity | Shows how new-tool behavior compares to original-tool signatures across your history |
| Recurring intermittent failures | Both | Explore Similarity identifies what failing runs share; the session view shows the full detail of each |
Documenting Findings
When you identify the source of divergence, record it:- Tag the run with descriptive labels (e.g., “temperature-drift”, “flux-excursion”) so it’s findable in future similarity analyses.
- Add notes in the session’s Metadata documenting which parameters diverged, your hypothesis, and any corrective action taken.
- Build up labeled examples of good and bad runs over time so your similarity comparisons grow more informative as your dataset matures.
Next Steps
Identify Uniformity Issues
Diagnose consistency problems within a single run that similarity analysis alone may not
surface.
Detect Anomalies
Set up automated detection to catch divergence patterns before they require manual diagnosis.