Semiconductor & advanced packaging
Isolate the tool that moved, not the lot that failed
Parametric drift rarely announces itself in a single measurement. The platform monitors tool-level parametric behaviour, chamber-to-chamber matching and yield binning together, so engineering time goes into fixing the tool rather than proving which one caused the excursion.
- excursion response time
- -34%
- parametric readings per day
- 1.6 M
- query on hot data
- < 1 s
- earlier drift detection
- 42 min
Standards supported
- ISO 9001
- SEMI practices
- Customer PCN requirements
Typical monitored characteristics
- Film thickness
- Critical dimension
- Overlay
- Etch depth
- Sheet resistance
- Bin yield
Where the cost sits
The problems we start from
These are the recurring findings from discovery workshops in this sector. If none of them describe your operation, a pilot is probably premature — and we will say so.
Operational problems
Excursions are confirmed at electrical test
By the time wafer sort reveals the problem, several lots have passed through the same tool and the suspect population is large.
Chamber mismatch hides in aggregate data
Averaged tool statistics look stable while one chamber drifts, so the signal is masked precisely where it matters.
Yield analysis is a specialist activity
Correlating parametric, defect and bin data requires bespoke scripts owned by a handful of engineers, which does not scale across fabs.
Customer PCN and audit evidence is manual
Change notification and qualification evidence is assembled from multiple systems for each customer request.
Engineering constraints
Extremely high dimensionality
Thousands of parameters per tool and step mean univariate charting alone produces more noise than signal.
Long, multi-step routes
A wafer visits hundreds of operations, so cause and effect can be separated by weeks of processing.
Strict data confidentiality
Recipe and process data is core IP, which frequently rules out any multi-tenant model training or external egress.
Legacy fab systems
Older MES and equipment interfaces expose data in inconsistent, poorly documented formats.
Platform capabilities
How the platform is configured for semiconductor
Same architecture, same modules — configured against the characteristics, sampling logic and evidence expectations of this sector.
Tool and chamber matching
Per-chamber capability comparison against the tool fleet, so a single drifting chamber is isolated instead of averaged away.
Multivariate excursion detection
Models learn the normal operating envelope per recipe and step, catching combinations of parameters that individually look acceptable.
Route-aware genealogy
Wafer and lot history across every operation, so a bin signature can be traced back to the operations and tools it passed through.
Bin and parametric correlation
Links sort bins and parametric results to upstream process data without a bespoke analysis script per investigation.
Air-gapped deployment
Full on-premise deployment including model serving, with offline update bundles when no egress is permitted.
- OPC UA
- Legacy MES via SQL
- Ignition
- On-premise model serving
- Okta
- Databricks
Business outcomes
What changed for operations like yours
Customer-reported figures measured against documented pre-deployment baselines. Ranges, not single numbers, because process maturity dominates the result.
- Excursion response time
- -34%
- Earlier detection
- 42 min
- Wafers at risk
- -70%
- Data egress required
- 0
Excursion response time
Detection to containment decision
Earlier detection
Median across monitored tool sets
Wafers at risk
Smaller suspect population per excursion
Data egress required
Air-gapped option available
| KPI | Typical movement | Measurement note |
|---|---|---|
| Excursion detection lag | -30% to -50% | Versus electrical-test discovery |
| Wafers held per excursion | -50% to -70% | Route-aware scoping |
| Line yield | +0.5 to +1.5 pt | Highly process dependent |
| Analysis cycle time | Hours → minutes | No bespoke script per investigation |
| Tool matching variance | -25% | Chamber-level capability convergence |
Expected ROI
A value case your controller can interrogate
Every line below is an assumption, not a promise. During a pilot each one is replaced with a measured figure from your own baseline, which is what makes the business case defensible in a capital review.
| Value driver | Assumption | Annual |
|---|---|---|
| Yield improvement | +0.8 pt line yield | €2.1 M |
| Reduced material at risk | Smaller holds per excursion | €640k |
| Engineering productivity | 4 yield engineers, 20% time recovered | €210k |
| Faster qualification | Shorter tool and recipe qualification cycles | €300k |
| Indicative total | Before platform and integration cost | €3.25 M |
Payback
6–12 months
From first connector to cumulative break-even
How we validate it
- Baseline recorded before any change
- Success criteria written into the pilot scope
- Measured comparison in the pilot report
- Exit conditions agreed up front
Illustrative model. Semiconductor value cases are validated per tool set during a scoped pilot.
Chamber-to-chamber comparison isolated the tool responsible for a parametric shift that had previously been attributed to material variation, cutting excursion response time by a third.
Related sectors: Automotive, Battery manufacturing, Electronics & EMS
Start the evaluation
Find out where quality drift is hiding in your plant.
Bring one line, one defect family or one audit workflow. We will map the available data sources, quantify the cost of the current detection delay, and show the fastest route to measurable control.
45-minute technical walkthrough
With a solution architect who knows manufacturing data, not a scripted demo.
NDA before any data review
We can assess feasibility from sample exports without production access.
Written pilot scope
Baseline metrics, success criteria and exit conditions agreed up front.