ACM Home Page
Please provide us with feedback. Feedback
Digital Library logoTake a look at the new version of this page: [ beta version ]. Tell us what you think.
A taylor series methodology for analyzing the effects of process variation on circuit operation
Full text PdfPdf (591 KB)
Source
Great Lakes Symposium on VLSI archive
Proceedings of the 19th ACM Great Lakes symposium on VLSI table of contents
Boston Area, MA, USA
SESSION: Physical level optimization table of contents
Pages: 203-208  
Year of Publication: 2009
ISBN:978-1-60558-522-2
Authors
Raghuram Srinivasan  University of Cincinnati, Cincinnati, OH, USA
Harold W. Carter  University of Cincinnati, Cincinnati, OH, USA
Sponsors
ACM: Association for Computing Machinery
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 9,   Downloads (12 Months): 62,   Citation Count: 0
Additional Information:

abstract   references   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/10.1145/1531542.1531593
What is a DOI?

ABSTRACT

We present a methodology that can analyze the effect of process variations without requiring the repeated simulations of a Monte Carlo type method. A graph theoretic procedure is described to obtain an explicit differential equation from the differential algebraic equations modeling a circuit netlist. With this explicit form, Taylor series polynomials are used to represent the system variables. The non-constant process parameters are represented as intervals, the Taylor series expansion is used to perform interval computations to generate bounds for the system variables. Methods are discussed to prevent blow-up of intervals during the time marching method.


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
M. Berz and J. Hoefkens. Verified High-Order Inversion of Functional Dependencies and Interval Newton Methods. Reliable Computing, 7:379--398, 2001.
 
2
K. E. Brenan, S. L. Campbell, and L. R. Petzold. Numerical Solution of Initial Value Problems in Differential-Algebraic Equations. SIAM, 1995.
 
3
 
4
 
5
D. S. Boning et al. Variation. IEEE Transactions on Semiconductor Manufacturing, 21(1):63--71, February 2008.
 
6
A. J. Encinas and R. Riaza. Index Characterization in DAE Circuit Models without Passivity Assumptions. Mathematics in Industry, 12:923--927, 2007.
 
7
 
8
9
 
10
O. Knuppel. PROFIL/BIAS - A fast interval library. Computing, 53(3-4):277--287, September 1994.
 
11
 
12
 
13
K. Makino and M. Berz. Efficient Control of the Dependency Problem Based on Taylor Model Methods. Reliable Computing, 5:3--12, 1999.
 
14
A. A. Mutlu and M. Rahman. Statistical Methods for the Estimation of Process Variation Effects on Circuit Operation. IEEE Transactions on Electronics Packaging Manufacturing, 28(4):364--375, October 2005.
15
 
16
N. S. Nedialkov, K. R. Jackson, and G. F. Corliss. Validated solutions of initial value problems in ordinary differential equations. Applied Mathematics and Computation, 105:21--68, 1997.
 
17
N. S. Nedialkov and J. D. Pryce. Solving Differential-Algebraic Equations by Taylor Series(I): COmputing Taylor Coefficients. BIT Numerical Mathematics, 45(3):561--591, September 2005.
 
18
 
19
A. Neumaier. Taylor Forms - Use and Limits. Reliable Computing, 9(1):43--79, February 2003.
 
20
 
21
M. Rencher. Analog Statistical Simulation. In CICC, pages 29.2/1--29.2/4, 1991.
 
22
 
23
 
24

Collaborative Colleagues:
Raghuram Srinivasan: colleagues
Harold W. Carter: colleagues