Title
Statistical static timing analysis flow for transistor level macros in a microprocessor
Abstract
Process variations are of great concern in modern technologies. Early prediction of their effects on the circuit performance and parametric yield is extremely useful. In today's microprocessors, custom designed transistor level macros and memory array macros, like caches, occupy a significant fraction of the total core area. While block-based statistical static timing analysis (SSTA) techniques are fast and can be used for analyzing cell based designs, they cannot be used for transistor level macros. Currently, such macros are either abstracted with statistical timing models which are less accurate or are analyzed using statistical Monte-Carlo circuit simulations which are time consuming. In this paper, we develop a fast and accurate flow that can be used to perform SSTA on large transistor and memory array macros. The delay distributions of paths obtained using our flow for a large, industrial, 45 nm, transistor level macro have error of less than 6% compared to those obtained after rigorous Monte-Carlo SPICE simulations. The resulting flow enables full-chip SSTA, provides visibility into the macro even at the chip level, and eliminates the need to abstract the macros with statistical timing models.
Year
DOI
Venue
2010
10.1109/ISQED.2010.5450412
Quality Electronic Design
Keywords
Field
DocType
Monte Carlo methods,circuit simulation,microprocessor chips,transistor circuits,block-based statistical static timing analysis,cell based design,circuit performance,custom designed transistor level macros,memory array macros,microprocessor,parametric yield,statistical Monte-Carlo circuit simulations,statistical static timing analysis flow,statistical timing model,Monte-Carlo simulations,Statistical Static Timing Analysis (SSTA),transistor level macros
Logic gate,Statistical static timing analysis,Computer science,Microprocessor,Electronic engineering,Real-time computing,Chip,Parametric statistics,Macro,Transistor,Multi-core processor
Conference
ISSN
ISBN
Citations 
1948-3287
978-1-4244-6454-8
2
PageRank 
References 
Authors
0.36
4
4
Name
Order
Citations
PageRank
Vivek S. Nandakumar120.36
David Newmark220.36
Yaping Zhan31718.85
Malgorzata Marek-Sadowska420.36