Journal
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
Volume 28, Issue 8, Pages 1176-1189Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCAD.2009.2020721
Keywords
Design automation; extreme values; memories; Monte Carlo methods; simulation; statistics; yield estimation
Categories
Funding
- Semiconductor Research Corporation (SRC)
- Focus Center for Circuit and System Solutions
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Circuit reliability under random parametric variation is an area of growing concern. For highly replicated circuits, e. g., static random access memories (SRAMs), a rare statistical event for one circuit may induce a not-so-rare system failure. Existing techniques perform poorly when tasked to generate both efficient sampling and sound statistics for these rare events. Statistical blockade is a novel Monte Carlo technique that allows us to efficiently filter-to block-unwanted samples that are insufficiently rare in the tail distributions we seek. The method synthesizes ideas from data mining and extreme value theory and, for the challenging application of SRAM yield analysis, shows speedups of 10-100 times over standard Monte Carlo.
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