Modeling and analysis of leakage power considering within-die process variations
Proceedings of the 2002 international symposium on Low power electronics and design
Circuit Failure Prediction and Its Application to Transistor Aging
VTS '07 Proceedings of the 25th IEEE VLSI Test Symmposium
NBTI-aware synthesis of digital circuits
Proceedings of the 44th annual Design Automation Conference
Self-calibrating Online Wearout Detection
Proceedings of the 40th Annual IEEE/ACM International Symposium on Microarchitecture
System power management support in the IBM POWER6 microprocessor
IBM Journal of Research and Development
Modeling of NBTI-Induced PMOS Degradation under Arbitrary Dynamic Temperature Variation
ISQED '08 Proceedings of the 9th international symposium on Quality Electronic Design
Circuit-level NBTI macro-models for collaborative reliability monitoring
Proceedings of the 20th symposium on Great lakes symposium on VLSI
NBTI-aware DVFS: a new approach to saving energy and increasing processor lifetime
Proceedings of the 16th ACM/IEEE international symposium on Low power electronics and design
Error Rate Estimation for Defective Circuits via Ones Counting
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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In this work we propose an online reliability tracking framework that utilizes a hybrid network of on-chip temperature and delay sensors together with a circuit reliability macromodel. We are concerned specifically with NBTI-induced transistor aging, which manifests itself as the gradual increase of PMOS threshold voltage and increase of circuit delay over time. The key feature of our work is an explicit macromodel which maps operating temperature to circuit degradation. The macromodel allows for cost-effective reliability tracking. The accuracy of the model is improved by online calibration of model parameters via monitoring the delay degradation of ring oscillators. The number of model parameters is relatively small. For example, in ISCAS'85 benchmark circuits, at most 21 parameters are required for the macromodel. The prediction of circuit degradation using our online monitoring strategy can be up to 20% less conservative compared to the worst-case reliability prediction.