ACM Home Page
Please provide us with feedback. Feedback
Mapping statistical process variations toward circuit performance variability: an analytical modeling approach
Full text PdfPdf (292 KB)
Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 42nd annual Design Automation Conference table of contents
Anaheim, California, USA
SESSION: Circuit performance under parameter variation table of contents
Pages: 658 - 663  
Year of Publication: 2005
ISBN:1-59593-058-2
Authors
Yu Cao  ASU, Tempe, AZ
Lawrence T. Clark  ASU, Tempe, AZ
Sponsors
ACM: Association for Computing Machinery
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 21,   Downloads (12 Months): 69,   Citation Count: 17
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1065579.1065752
What is a DOI?

ABSTRACT

A physical yet compact gate delay model is developed integrating short-channel effects and the Alpha-power law based timing model. This analytical approach accurately predicts both nominal delay and delay variability over a wide range of bias conditions, including sub-threshold. Excellent model scalability enables efficient mapping between process variations and delay variability at the circuit level. Based on this model, relative importance of physical effects on delay variability has been identified. While effective channel length variation is the leading source for variability at current 90nm node, performance variability is actually more sensitive to threshold variation at the sub-threshold region. Furthermore, this model is applied to investigate the limitation of low power design techniques in the presence of process variations, particularly dual Vth and L biasing. Due to excessive variability under low VDD, these techniques become ineffective.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
International Technology Roadmap for Semiconductors, 2003.
 
2
K. A. Bowman, S. G. Duvall, J. D. Meindl, "Impact of die-to-die and within-die parameter fluctuations on the maximum clock frequency distribution for gigascale integration," JSSC, vol. 37, no. 2, pp. 183--190, Feb. 2002.
 
3
D. Boning and S. Nassif, "Models of process variations in device and interconnect," Design of High-Performance Microprocessor Circuits, Chapter 6, pp. 98--115, IEEE Press, 2000.
 
4
C. Viswesvariah, "Statistical timing of digital integrated circuits," ISSCC, 2004.
5
 
6
 
7
 
8
 
9
M. Orshansky, J. C. Chen, C. Hu, "Direct sampling methodology for statistical analysis of scaled CMOS technologies," TSM, vol. 12, no. 4, pp. 403--408, Nov. 1999.
 
10
T. Sakurai and A. R. Newton, "Alpha-power law MOSFET model and its applications to CMOS inverter delay and other formulas," JSSC, vol. 25, no. 2, pp. 584--594, Apr. 1990.
 
11
 
12
BSIM4.2.1 MOSFET Model - User's Manual, 2001.
13
 
14
 
15
P. A. Stolk, F. P. Widdershoven, and D. B. M. Klaassen, "Modeling statistical dopant fluctuations in MOS transistors," TED, vo. 45, no. 9, pp. 1960--1971, Sep. 1998.
 
16
J. Tschanz et al., "Adaptive body bias for reducing impacts of die-to-die and within-die parameter variations on microprocessor frequency and leakage," JSSC, vol. 1, pp. 422--478, Feb. 2002.

CITED BY  17

Collaborative Colleagues:
Yu Cao: colleagues
Lawrence T. Clark: colleagues