Why Scan Diagnosis Should Be Part Of Every Fabless Company’s Yield Playbook

A fabless semiconductor company’s world spins around two things, pushing design differentiation and getting those designs to market quickly and profitably.

Yield isn’t just a manufacturing KPI. It’s a business lever. And one of the most under-used levers in modern fabs is scan diagnosis, the practice of turning deterministic test infrastructure and failing test data into precise and actionable insight.

When combined with AI/ML analytics, scan diagnosis changes yield from a reactive firefight into a predictable, design-driven optimization loop. What Tessent Diagnosis’ scan diagnosis gives you (that you probably don’t get today):

• High-resolution defect localization: By leveraging your existing DFT (scan) infrastructure and ATPG-based simulation, scan diagnosis pinpoints defects in both scan chains and internal logic. That’s far more informative than a simple FAIL on a tester log.
• Faster failure analysis (FA): Accurate diagnosis reduces fault isolation requirements cutting the mean FA time to root cause a defect. It is important when each FA run can be expensive and time-consuming.
• Design-feedback that scales: Diagnosing failures at die-level and aggregating results lets teams identify recurring defect modes tied to design choices, allowing you to harden designs for future product generations.
• Fuel for AI/ML: Volume diagnosis data becomes a rich input for machine learning models that can find complex multivariate correlations across test, process, and design datasets — enabling earlier detection of excursions or even predictive corrections.

How it fits into a fabless workflow
Scan DFT and Tessent ATPG are used to test the digital device in production. They log failing vectors on the ATE. From there, Tessent Diagnosis can be used to convert failing cycles from ATE into local defect callouts using the DFT collateral and patterns. Aggregate diagnosis reports across volumes are fed into yield analytics (RCD/ML), alongside WAT, PCM, parametrics, and metrology, and the analytics outputs can be used to prioritize FA, guide process adjustments, and feed design changes back into tape-out decisions.

Expected outcomes

• Faster yield ramp: Early detection of systematic defects that accelerates the ramp and reduces time-to-volume.
• Lower cost per good device: Even modest yield percentage improvements translate to meaningful cost savings across production volumes.
• Smarter FA spend: Prioritization based on diagnosis and aggregated analytics increases FA hit rates and reduces wasted effort.
• Design-for-yield improvements: Diagnosis-driven feedback helps designers eliminate defect-prone structures to mitigate systematic failure modes across future products.

Why aggregation + ML matters
A single diagnosis report is helpful, but thousands of them are transformational. Aggregation techniques (e.g., Root-Ccause Deconvolution in Tessent YieldInsight) extract statistical patterns and Pareto distributions across many failing dies. That aggregated view reveals weak signals and recurring modes that are invisible at single-die scale.

When analytics platforms apply unsupervised or supervised ML to these aggregated diagnosis sets along with process/test metadata, they can:

• Detect drift or excursions earlier;
• Suggest corrective actions based on historical outcomes;
• Predict yield-impacting trends before they impact your P&L;
• Provide practical KPIs to monitor, including FA hit rate (percent of FAs that find root cause) and time to address yield excursions. The yield delta is attributable to diagnosis-driven fixes, and cost-per-FA and cost-per-good-device improvements.

Fabless companies have a unique advantage. They control design and design intent. Turning test failure data into design-level lessons via scan diagnosis and analytics lets you extend that advantage into manufacturing outcomes. Think of scan diagnosis as turning test time from a cost center into a feedback engine. It helps you make smarter design choices, accelerate ramps, and protect margins. In a market where every percentage point of yield and every week of time-to-market matter, that’s a competitive edge worth building into your playbook.