Two Approaches to Uniformity
The same Growth Monitoring and Global Similarity tools used to diagnose why runs differ also work with segments of a single run. Instead of comparing entire runs, you compare phases, layers, or time windows within one run.- Growth Monitoring (Detailed)
- Global Similarity (Broad)
Compare specific segments against reference segments with full granularity.Use Growth Monitoring when you want to track how a particular segment (a layer, recipe phase, or deposition cycle) compares to a reference segment from the same or a previous run. This gives you time-resolved detail: the similarity trajectory within the segment, derived metrics over time, and correlated tool state.What you see:
- Similarity trajectory showing how each segment’s fingerprint evolves relative to the reference segment over time
- Growth metrics compared between segments as time series, revealing exactly which properties vary
- Tool state providing context for why a particular segment diverged
Setting Up
Add reference segments
Select segments that represent your target behavior, such as a known-good layer from mid-run or
the first cycle after stabilization. Assign labels or values to each reference.
Add segments to compare
Add the segments you want to evaluate as tracked samples. For periodic structures, this could be
every cycle in the sequence. For phased recipes, this could be the same phase across different
regions of the run.
What to Look For
Drift Over Time
Segments that gradually shift away from early-run behavior indicate drift. In Growth Monitoring, this appears as a similarity trajectory that steadily diverges from the reference. In Global 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 Global 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. Growth Monitoring makes this visible through the metrics comparison, showing which derived properties vary most during the problematic phase.Periodic Structure Consistency
For multilayers and superlattices, compare each cycle against a reference cycle using Growth Monitoring. Consistent cycles produce nearly identical similarity trajectories. 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.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 |
|---|---|---|
| Comparing each cycle in a periodic structure to a reference | Growth Monitoring | Time-resolved detail shows where within each cycle variation occurs |
| Checking whether all segments in a run are consistent | Global Similarity | Immediately reveals outliers and clustering without pre-selecting references |
| Identifying which recipe phase has the most variation | Global Similarity | Segments from different phases naturally separate on the map if they behave differently |
| Understanding why a specific layer is different | Growth Monitoring | Detailed metrics and tool state pinpoint what changed during that layer |
| Tracking uniformity improvement across iterations | Global Similarity | Compare segment clustering tightness across runs to see if uniformity is improving |