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Faster, parametric trajectory-based macromodels via localized linear reductions
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Source International Conference on Computer Aided Design archive
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design table of contents
San Jose, California
SESSION: Model order reduction and parametric analysis table of contents
Pages: 876 - 883  
Year of Publication: 2006
ISBN ~ ISSN:1092-3152 , 1-59593-389-1
Authors
Saurabh K Tiwary  Cadence Berkeley Labs, Berkeley, CA
Rob A Rutenbar  Carnegie Mellon University, Pittsburgh, PA
Sponsors
IEEE-CS : Computer Society
IEEE-CAS : Circuits & Systems
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 2,   Downloads (12 Months): 26,   Citation Count: 3
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ABSTRACT

Trajectory-based methods offer an attractive methodology for automated, on-demand generation of macromodels for custom circuits. These models are generated by sampling the state trajectory of a circuit as it simulates in the time domain, and building macromodels by reducing and interpolating among the linearizations created at a suitably spaced subset of the time points visited during training simulations. However, a weak point in conventional trajectory models is the reliance on a single, global reduction matrix for the state space. We develop a new, faster method that generates and weaves together a larger set of smaller localized linearizations for the trajectory samples. The method not only improves speedups to 30X over SPICE, but as a side benefit also provides a platform for parametric small-signal simulation of circuits with variational device/process parameters, at a speedup of roughly 200X over SPICE.


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
BSIM3 transistor models. http://www-device.eecs.berkeley.edu/bsim3.
 
2
A. P. Dempster, N. Laird, and D. Rubin. Maximum-likelihood from incomplete data via EM algorithm. In J. Royal Statistics Society B39, 1977.
 
3
4
 
5
N. Dong and J. Roychowdhury. Automated extraction of broadly applicable nonlinear analog macromodels from SPICE-level descriptions. In CICC, 2004.
6
 
7
D. Ramaswamy. Automatic generation of macromodels for microelectromechanical systems (MEMS). In Ph.D. dissertation, Massachusetts Inst. Technol., Cambridge, MA., 2001.
8
 
9
E. Grimme. Krylov projection methods for model reduction. In PhD Thesis, UIUC, 1997.
 
10
F. Wang and J. White. Automatic model order reduction of a microdevice using the arnoldi approach. In Proc. Int. Mechanical Engineering Congr. and Exposition, pages 527--530, 1998.
 
11
K. Gallivan, E. Grimme, and P. V. Dooren. Asymptotic waveform evaluation via a lanczos method. In Applied Mathematics Letters, pages 75--80, 1994.
 
12
L. T. Pillage and R. A. Rohrer. Asymptotic waveform evaluation. In TCAD, pages 352--366, 1990.
 
13
A. Odabasioglu, M. Celik, and L. Pileggi. PRIMA: Passive reduced-order interconnect macromodeling algorithm. In TCAD, Vol 17, No 8, pages 645--654, 1998.
 
14
M. Rewienski and J. White. A trajectory piecewise-linear approach to model order reduction and fast simulation of nonlinear circuits and micromachined devices. In TCAD, pages 155--170, 2003.
 
15
M. J. Rewienski. A trajectory piecewise-linear approach to model order reduction of nonlinear dynamical systems. PhD Dissertation, MIT, 2003.
 
16
17
 
18
S. K. Tiwary. Scalable trajectory methods for on demand analog macromodel extraction. In Phd Thesis (in preparation), CMU, 2006.
 
19


Collaborative Colleagues:
Saurabh K Tiwary: colleagues
Rob A Rutenbar: colleagues