As semiconductor products grow more complex—with chiplets, 3D packaging, wide bandgap materials, and multi-die systems—test workflows are under pressure to be faster, smarter, and more collaborative. Yet, test teams often find themselves overwhelmed, not by a lack of data, but by too much of it—scattered across logs, dashboards, and silos that don’t talk to each other. This disconnect slows down decision-making, bottlenecks debug cycles, and prevents cross-functional teams from working as one.
This poster presents a future-facing vision: an Autonomous Test Decision Center—a centralized, intelligent layer that turns scattered test data into real-time, role-specific, and context-aware insights. The concept is simple but powerful: instead of engineers chasing information, the system curates and delivers the most critical, actionable signals directly to them.
At its core is a semantic data pipeline that brings together structured and unstructured data from across the test lifecycle—automatic test equipment (ATE) logs, wafer sort data, failure analysis reports, inline fab metrics, and even field quality feedback. This data is ingested into a cloud-based platform, harmonized using metadata tagging and schema unification, and modeled into a lightweight knowledge graph that links test events to known defect types, risk bins, and design domains.
Layered on top are machine learning (ML) models trained to detect subtle patterns and alert teams before issues escalate. These include anomaly clustering, signature recognition, time-series trend shifts, and bin-predictive classification. But most importantly, the insights are delivered differently depending on who’s looking:
A debug engineer sees a timeline of test steps where failure signatures converge.
A quality engineer is alerted to reliability risks based on correlated power stress trends.
A program manager views issue closure timelines and risk scores tied to lot movement and NPI milestones.
This vision enables engineers to stop digging through gigabytes of logs and start acting on what matters. Alert fatigue is reduced. Actionability goes up. And so does team alignment.
The abstract explores:
The architecture of the data ingestion and modeling pipeline
Role-specific dashboard mockups and prioritization logic
A use case where memory instability, usually buried in logs, was flagged, clustered, and escalated within hours
A concept roadmap for extending the system to enable closed-loop feedback between design-for-test (DFT), manufacturing, and reliability
By enabling a truly connected test ecosystem, this Autonomous Test Decision Center doesn’t just support engineers—it elevates them. It shifts test from reactive measurement to strategic foresight, helping semiconductor teams deliver faster debug, smarter decisions, and better products.
In alignment with the theme “Advancing Together with Innovation,” this concept envisions a smarter future for test—one that’s built on clean data, actionable intelligence, and collaboration by design.
