Abstract:
Aging-induced degradation imposes a major challenge to the designer when estimating timing guardbands. This problem increases as traditional worst-case corners bring over...Show MoreMetadata
Abstract:
Aging-induced degradation imposes a major challenge to the designer when estimating timing guardbands. This problem increases as traditional worst-case corners bring over-pessimism to designers, exacerbating competitive and close-to-the-edge designs. In this work, we present an accurate machine learning approach for aging-aware cell library characterization, enabling the designer to evaluate their circuit under the impact of precisely selected degradation. Unlike state of the art, we bring cell library characterization to the designer, empowering their capability in exploring the impact of aging while protecting confidential information from the foundry at the same time. Furthermore, the fast inference of cell libraries makes it feasible, for the first time, to examine aging-induced variability analysis in a Monte-Carlo fashion. Finally, we show that the designer is able to select a less pessimistic timing guardband by choosing adequate delta threshold voltage ( ΔVth) for their design and their needs. Our machine learning approach reaches an R2 score of >99\% for almost all data stored in the cell library. Only timing constraints show slightly less accuracy with an R2 score around 95%. When using ML-characterized libraries in static timing analysis, we achieve errors smaller than ±0.5% and ±0.1% for path delay and dynamic power, respectively. Errors in leakage power are negligible and even smaller by orders of magnitude. Our machine learning implementation for standard cell library characterization is publicly available. Download: https://opensource.mlcad.org
Published in: IEEE Transactions on Circuits and Systems I: Regular Papers ( Volume: 68, Issue: 6, June 2021)