发表机构
University of British Columbia（英属哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对多机制半导体可靠性鉴定，提出结合MCTS-SA与EKF的闭环自适应序贯测试规划框架，实验显示其表征良率较非自适应策略大幅提升。
AI 中文摘要
先进半导体器件的可靠性鉴定需要序贯应力决策，以平衡表征目标与多种相互竞争的失效机制。当前实践依赖于由总体级加速模型推导的静态测试计划，该计划无法适应单器件变异性或实时退化观测结果。本文提出一种闭环自适应测试规划框架，将可靠性鉴定表述为部分可观测序贯决策问题，并采用种子动作模拟器的蒙特卡洛树搜索（MCTS-SA）结合扩展卡尔曼滤波（EKF）置信态估计来求解。该框架对偏置温度不稳定性（BTI）、电迁移（EM）和时变介质击穿（TDDB）的随机单器件变异性进行建模，将应力选择视为约束序贯优化，即最大化成功退化表征的概率，同时遵守灾难性失效约束。在本文采用的实验假设（离散应力动作、代理损伤可观测性、无恢复的累积退化）下，这是树搜索自适应测试规划在多机制可靠性鉴定中的新应用。在5000次规划迭代中，表征良率（CY）从最初500次迭代的20%提升至最后500次迭代的54%以上，累计成功率为39%，而最佳成功测试序列终止时的EM和TDDB损伤分数分别为DEM=0.564和DTDDB=0.537，处于安全裕度内。这些结果表明，序贯贝叶斯规划可综合感知损伤的测试策略，在存在相互竞争失效模式的可靠性鉴定中，显著优于非自适应策略。
英文摘要
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.
CommentsAccepted to IEEE Transactions on Reliability
