TY - GEN
T1 - LVFGen
T2 - 34th ACM International Symposium on Physical Design, ISPD 2025
AU - Zhou, Junzhuo
AU - Xia, Haoxuan
AU - Xing, Wei
AU - Lin, Ting Jung
AU - Huang, Li
AU - He, Lei
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/3/16
Y1 - 2025/3/16
N2 - As transistor dimensions shrink, process variations significantly impact circuit performance, signifying the need for accurate statistical circuit analysis. In digital circuit timing analysis, the Liberty Variation Format (LVF) has emerged as an industrial leading representation of timing distributions in cell libraries at 22 nm and below. However, LVF characterization relies on the Monte Carlo (MC) method, which requires excessive SPICE simulations of cells with process variations. Similar challenges also exist for uncertainty propagation and quantification in chip manufacturing and the broader scientific communities. To resolve this foundational challenge, this paper presents LVFGen, a novel method that reduces the simulation costs of MC while generate high-accuracy LVF library. LVFGen utilizes an active learning strategy based on variational analysis to identify process variation samples that impact timing distributions more significantly. Compared to the state-of-the-art Quasi-MC method, LVFGen demonstrates an overall 2.27× speedup in LVF library generation within an accuracy level of 5k-sample MC and a 4.06× speedup within a 100k-sample MC accuracy.
AB - As transistor dimensions shrink, process variations significantly impact circuit performance, signifying the need for accurate statistical circuit analysis. In digital circuit timing analysis, the Liberty Variation Format (LVF) has emerged as an industrial leading representation of timing distributions in cell libraries at 22 nm and below. However, LVF characterization relies on the Monte Carlo (MC) method, which requires excessive SPICE simulations of cells with process variations. Similar challenges also exist for uncertainty propagation and quantification in chip manufacturing and the broader scientific communities. To resolve this foundational challenge, this paper presents LVFGen, a novel method that reduces the simulation costs of MC while generate high-accuracy LVF library. LVFGen utilizes an active learning strategy based on variational analysis to identify process variation samples that impact timing distributions more significantly. Compared to the state-of-the-art Quasi-MC method, LVFGen demonstrates an overall 2.27× speedup in LVF library generation within an accuracy level of 5k-sample MC and a 4.06× speedup within a 100k-sample MC accuracy.
KW - Active learning
KW - LVF
KW - Statistical library generation
KW - Uncertainty quantification
KW - Yield
UR - https://www.scopus.com/pages/publications/105001098026
U2 - 10.1145/3698364.3705359
DO - 10.1145/3698364.3705359
M3 - Conference contribution
AN - SCOPUS:105001098026
T3 - Proceedings of the International Symposium on Physical Design
SP - 182
EP - 190
BT - Proceedings of the 2025 International Symposium on Physical Design, ISPD 2025
PB - Association for Computing Machinery
Y2 - 16 March 2025 through 19 March 2025
ER -