Yield and test cost dominate
Small improvements in yield prediction or test time compound into material margin across a production run.
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Semiconductor & hi-tech
Where a percentage point
of yield is the
entire business case.
Semiconductor and hardware engineering produce dense, high-quality process data and operate on margins where small analytical gains are worth a great deal.
The pressure
Good data, hard physics, unforgiving economics.
This is one of the few sectors where the data is genuinely good. The difficulty moves to domain grounding and to earning the trust of process engineers.
Engineering time is the constraint
Verification, characterisation and debug consume senior engineering capacity that is difficult to expand by hiring.
Process engineers are demanding reviewers
A model that cannot explain itself in process terms will be dismissed, correctly, by the people who own the tool.
Use cases
Where we do the work
Six places we are usually brought in. Each one is scoped to a measurable surface rather than a theme.
Yield analytics and excursion detection
Detect process excursions earlier and localise probable cause across tools and steps.
Test optimisation
Reduce test time and cost while holding escape rates inside agreed limits.
Design and verification productivity
Assist with RTL review, testbench generation and regression triage under engineer supervision.
Equipment health and predictive maintenance
Anticipate tool drift and schedule intervention around production windows.
Technical knowledge retrieval
Make specifications, characterisation reports and prior debug history searchable in engineering terms.
Supply and demand forecasting
Improve planning signal across long, constrained lead times.
The honest part
What makes this domain genuinely hard
We would rather set expectations here than discover these in month three. Every one of them has changed a delivery plan at some point.
- Process data is highly correlated across steps, so naive feature importance produces confident conclusions that a process engineer will immediately disprove.
- Excursions are rare by definition, which makes class imbalance and evaluation design the central technical problem rather than an afterthought.
- Fab and design data is among the most sensitive intellectual property a company holds, so deployment topology and access control drive the architecture.
- Domain grounding is not optional: a model that cannot express findings in the vocabulary of the process will not be adopted, whatever its accuracy.
Engagement shape
How engagements usually start
Engagements here almost always begin with a scoped analytical question owned by a named process or test engineer, because their judgement is the acceptance criterion. We spend the first phase on data access, evaluation design and imbalance handling. If a deployment has to sit entirely inside your boundary for IP reasons, that constraint shapes the architecture from the outset rather than being retrofitted.
Other industries
Ready when you are
Bring us your hardest workflow in this sector.
We will tell you whether it is ready for AI, what has to change first, and what a realistic first release looks like.