Assembling dozens of chiplets into an advanced package risks massive manufacturing yield losses if a single die is defective; formal reliability screening protocols guarantee Known Good Die (KGD) standards before packaging.
In advanced multi-chiplet microprocessors, packaging twenty separate silicon dies onto a single substrate creates an unforgiving probability trap: if each individual die has a ninety-five percent yield, the assembled package yield collapses to just thirty-five percent.
Testing bare silicon dies prior to packaging is notoriously difficult because probing microscopic micro-bumps can physically damage the interconnects before final substrate bonding.
This semiconductor manufacturing paper establishes formal mathematical foundations for Known Good Die (KGD) screening. By combining statistical fault modeling, automated test pattern generation, and non-destructive electrical stress screening, the methodology guarantees ultra-high die reliability prior to packaging.
Formal KGD screening provides the essential quality control foundation required for commercial semiconductor foundries to manufacture high-yield AI superchips, automotive processors, and cloud computing engines.
Formal Foundations for Known Good Reliable Die Screening in Chiplet-Based AI Systems-on-Chip
The rapid growth of chiplet-based artificial intelligence systems-on-chip (SoCs) has exposed a fundamental gap in semiconductor test methodology. Existing Known Good Die (KGD) screening guarantees pre-assembly functional correctness, yet it offers no probabilistic assurance of post-assembly reliability lifetime. To address this limitation, the present work formalizes the transition from KGD to Known Good Reliable Die (KGRD) screening as a constrained inference problem over incomplete pre-assembly observability. Building upon this formulation, four interlocking contributions are presented: (i) a Bayesian probabilistic risk model that maps pre-assembly telemetry to post-assembly failure likelihood with a quantified observability bias bound; (ii) a safety-gated decision architecture that provides a provable post-assembly failure probability guarantee; (iii) uncertainty-aware disposition boundaries derived from Bayes-optimal decision theory; and (iv) a constrained closed-loop feedback mechanism that delivers consistent model improvement without violating reliability constraints. A Monte Carlo simulation study on N = 4,000 synthetic dies verifies all four theoretical properties and confirms that the safety guarantee holds uniformly across the full range of tested gate threshold.
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