Traditional semiconductor test operations rely heavily on static key performance indicators (KPIs) such as bin yield, retest rates, time-to-root-cause, and overall test coverage. While useful, these KPIs are typically backward-looking and siloed—detached from early design constraints, supply chain conditions, or field quality outcomes. As the industry transitions toward increasingly complex silicon architectures—chiplets, 2.5D/3D integration, and power-hungry applications—test teams require more than dashboards. They need intelligent KPIs that predict, adapt, and guide engineering effort toward high-impact issues in real time.
This poster presents a visionary, yet practical framework for building Dynamic KPI Intelligence into test operations—redefining success not just by pass/fail, but by how well test aligns with design intent, product performance, and cost objectives.
The vision is operationalized through a federated data pipeline that ingests key data sets across the semiconductor product lifecycle:
Automatic Test Equipment (ATE) logs: raw measurement results, test time per vector, binning distributions
Manufacturing traceability: lot lineage, wafer maps, and die-to-package associations
Field returns and quality reports: issue recurrence, time-to-failure, repair disposition
Operational data: retest volume, tool availability, intervention time
All of this is unified using a metadata-rich schema, allowing each data point to be tagged by product, revision, lot, or location. Once contextualized, the system applies machine learning (ML) models—including time-series analysis, anomaly detection (e.g., Isolation Forests), and clustering—to track when a traditionally “healthy” KPI begins to deviate in a meaningful, non-obvious way.
Rather than flagging a yield drop only after it crosses a set threshold, the system analyzes historical variability and detects trend shifts relative to context: a slight but sustained yield decline for a specific scan depth range in a product with known routing sensitivity. This shift—still within spec—becomes meaningful when linked to early silicon validation and customer use-case behavior.
These insights are grouped into KPI clusters, dynamically defined collections of interrelated indicators aligned to critical product or business outcomes. For instance, a cluster may combine:
Yield by bin vs. design margin distribution
Scan coverage vs. retest count
Package stress correlation vs. long-term failure risk
This cluster model enables each stakeholder to engage differently. A test engineer can explore drift in scan yield over temperature; a product lead can review cost-per-unit increases from rising retest volume; a quality owner can assess the predictive accuracy of test metrics for field failure risk.
The poster explores:
A reference architecture for building this federated data pipeline
Simulations of KPI drift detection using synthetic data sets
Role-based dashboards that highlight not just data, but its relevance to decisions
Vision roadmap for linking KPI intelligence to adaptive test program generation
Aligned with the theme “Advancing Together with Innovation,” this work challenges the status quo of test metrics and offers a data-driven path forward—where KPIs become not just measurements, but catalysts for innovation, alignment, and differentiation.
