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Conference Papers Year : 2023

Aging-Induced Failure Prognosis via Digital Sensors

Abstract

Aggressive scaling continues to push technology into smaller feature sizes and results in more complex systems in a single chip. With such scaling, various robustness concerns have come into account among which the change of circuits' properties during their lifetime, so-called device aging, has received a lot of attention. Due to aging, the electrical behavior of transistors deviates from its original intended one resulting in degrading the chip's performance, and ultimately the chip fails to provide correct outputs. Thereby, prognosis of circuit performance degradation during the runtime, before the chip actually fails is highly crucial in increasing the reliability of chips. Accordingly in this paper, we develop a machine-learning based framework that, leveraging the outcome of embedded time-to-digital-convertors (so-called "digital sensors''), predicts aging-induced degradation. This information can be used to prevent chip failures via deploying Dynamic Voltage and Frequency Scaling (DVFS)
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Dates and versions

hal-04260871 , version 1 (26-10-2023)

Identifiers

Cite

Md Toufiq Hasan Anik, Hasin Ishraq Reefat, Jean-Luc Danger, Sylvain Guilley, Naghmeh Karimi. Aging-Induced Failure Prognosis via Digital Sensors. GLSVLSI '23: Great Lakes Symposium on VLSI 2023, Jun 2023, Knoxville TN USA, United States. pp.703-708, ⟨10.1145/3583781.3590204⟩. ⟨hal-04260871⟩
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