Adaptive Sequential Test Planning for Multi-Mechanism Reliability Qualification via Bayesian Monte Carlo Tree Search
2026-08-10 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors developed a smart testing method for advanced semiconductor devices that adjusts stress tests based on each device's unique behavior and real-time data. They use a combination of mathematical models and a search algorithm to choose the best sequence of tests that identify damage without causing device failure. Their approach outperforms traditional fixed test plans, improving the chances of accurately characterizing device reliability while keeping damage within safe limits. This work showcases how adaptive decision-making can better manage multiple failure mechanisms during device testing.
reliability qualificationsemiconductor devicesstress testingMonte Carlo tree searchextended Kalman filterbias temperature instabilityelectromigrationtime-dependent dielectric breakdownBayesian planning
Authors
Youssef A. Elhagrasy, Ian Hill, André Ivanov
Abstract
Reliability qualification of advanced semiconductor devices requires sequential stress decisions that balance characterization objectives against multiple competing failure mechanisms. Current practice relies on static test plans derived from population-level acceleration models, which cannot adapt to per-unit variability or real-time degradation observations. This paper presents a closed-loop adaptive test planning framework that formulates reliability qualification as a partially observable sequential decision problem and solves it using Monte Carlo tree search for seed-action simulators (MCTS-SA) coupled with extended Kalman filter (EKF) belief-state estimation. The framework models stochastic, per-device variability in bias temperature instability (BTI), electromigration (EM), and time-dependent dielectric breakdown (TDDB), and treats stress selection as a constrained sequential optimization, i.e., to maximize the probability of successful degradation characterization while respecting catastrophic failure constraints. Under the experimental assumptions used here (discrete stress actions, proxy damage observability, and cumulative degradation without recovery), we believe this to be a novel application of tree-search-based adaptive test planning to multi-mechanism reliability qualification. Across 5,000 planning iterations, the characterization yield (CY) improves from 20% in the first 500 iterations to over 54% in the final 500, with 39% cumulative success, while the best successful test sequence terminates with EM and TDDB damage fractions DEM=0.564 and DTDDB=0.537, well within safety margins. These results demonstrate that sequential Bayesian planning can synthesize damage-aware test policies that significantly outperform non-adaptive strategies for reliability qualification under competing failure modes.