Name
Abstract | ||
|---|---|---|
Existing industry-practice statistical static timing analysis (SSTA) engines use black-box gate-level models for standard cells, which have accuracy problems as well as require massive amounts of CPU time in Monte-Carlo (MC) simulation. In this paper we present a new transistor-level non-Monte Carlo statistical analysis method based on solving random differential equations (RDE) computed from modified nodal analysis (MNA). In order to maintain both high accuracy and efficiency, we introduce a simplified statistical transistor model for 45nm technology and below. The model is combined with our new simulation-like engine which can do both implicit non-MC statistical simulation and deterministic simulation fast and accurately. The statistics of delay and slew are calculated by means of the proposed analysis method. Experiments show the proposed method is both run time efficient and very accurate. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1837274.1837473 | DAC |
Keywords | Field | DocType |
proposed analysis method,integrated circuit testing,ssta engine,statistical static timing analysis,size 45 nm,statistical analysis method,statistical analysis,deterministic simulation,non-monte carlo,rde-based transistor-level gate simulation,black-box gate-level model,random differential equation,implicit non-mc statistical simulation,integrated circuit design,delay statistics,modified nodal analysis,cpu time,statistical transistor model,transistor-level non-monte carlo statistical analysis,differential equations,transistors,transistor-level modeling,accuracy problem,industry-practice statistical static timing,circuit simulation,computational modeling,differential equation,capacitance,central processing unit,mathematical model,voltage,engines,monte carlo | Central processing unit,Transistor model,Statistical static timing analysis,CPU time,Computer science,Deterministic simulation,Electronic engineering,Real-time computing,Integrated circuit design,Modified nodal analysis,Transistor | Conference |
ISSN | ISBN | Citations |
0738-100X | 978-1-4244-6677-1 | 5 |
PageRank | References | Authors |
0.46 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qin Tang | 1 | 17 | 3.78 |
| Amir Zjajo | 2 | 57 | 20.08 |
| Michel Berkelaar | 3 | 19 | 4.92 |
| Nick van der Meijs | 4 | 30 | 7.49 |