A novel approach for automated model generation

Automatic approaches for macromodel generation from SPICE-level descriptions have been of great interest over the last few years for the design of large, complex mix- signal SoCs (system-on-chips) and SiPs (system-in-packages). In this paper a novel approach termed multiple model generation system (MMGS) is developed for extracting either single-input single-output (SISO) or multiple-input single-output (MISO) macromodels from a SPICE netlist. This model generation process detects nonlinearity through variations in output error. Examples of the application of MMGS are presented for simple two-input systems incorporating a two-stage CMOS operational amplifier (op amp).

[1]  Jacob K. White,et al.  A trajectory piecewise-linear approach to model order reduction and fast simulation of nonlinear circuits and micromachined devices , 2001, IEEE/ACM International Conference on Computer Aided Design. ICCAD 2001. IEEE/ACM Digest of Technical Papers (Cat. No.01CH37281).

[2]  Graham C. Goodwin,et al.  Digital control and estimation : a unified approach , 1990 .

[3]  Georges G. E. Gielen,et al.  Performance space modeling for hierarchical synthesis of analog integrated circuits , 2005, Proceedings. 42nd Design Automation Conference, 2005..

[4]  J.J. Davila.  Genetic evolution of neural networks that remember , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[5]  Peng Li,et al.  NORM: compact model order reduction of weakly nonlinear systems , 2003, Proceedings 2003. Design Automation Conference (IEEE Cat. No.03CH37451).

[6]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[7]  Ning Dong,et al.  Piecewise polynomial nonlinear model reduction , 2003, Proceedings 2003. Design Automation Conference (IEEE Cat. No.03CH37451).

[8]  Jaijeet Roychowdhury,et al.  Automated macromodel generation for electronic systems , 2003, Proceedings of the 2003 IEEE International Workshop on Behavioral Modeling and Simulation.

[9]  Lawrence T. Pileggi,et al.  Compact reduced-order modeling of weakly nonlinear analog and RF circuits , 2005, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[10]  Qi-Jun Zhang,et al.  Neural Networks for RF and Microwave Design , 2000 .

[11]  Lawrence T. Pileggi,et al.  Asymptotic waveform evaluation for timing analysis , 1990, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[12]  Ron J. Patton,et al.  Neuro‐fuzzy uncertainty de‐coupling: a multiple‐model paradigm for fault detection and isolation , 2005 .

[13]  B. Mutnury,et al.  Macro-modeling of non-linear I/O drivers using spline functions and finite time difference approximation , 2003, Electrical Performance of Electrical Packaging (IEEE Cat. No. 03TH8710).

[14]  Robert J. Boncella,et al.  Fuzzy Logic: An Introduction , 1995 .

[15]  Baolin Yang,et al.  An essentially non-oscillatory (ENO) high-order accurate adaptive table model for device modeling , 2004, Proceedings. 41st Design Automation Conference, 2004..

[16]  J. Phillips,et al.  Model reduction of time-varying linear systems using approximate multipoint Krylov-subspace projectors , 1998, 1998 IEEE/ACM International Conference on Computer-Aided Design. Digest of Technical Papers (IEEE Cat. No.98CB36287).

[17]  Patrick Naïm,et al.  Applications of Neural Networks , 1991 .

[18]  Jaijeet S. Roychowdhury.  Reduced-order modelling of time-varying systems , 1999, Proceedings of the ASP-DAC '99 Asia and South Pacific Design Automation Conference 1999 (Cat. No.99EX198).

[19]  Jaijeet Roychowdhury,et al.  Reduced-order modeling of time-varying systems , 1999 .

[20]  Alex Doboli,et al.  Systematic development of analog circuit structural macromodels through behavioral model decoupling , 2005, Proceedings. 42nd Design Automation Conference, 2005..

[21]  Robert J. Boncella.  Fuzzy Logic: An Introduction , 1995 .

[22]  Ning Dong,et al.  Automated extraction of broadly applicable nonlinear analog macromodels from SPICE-level descriptions , 2004, Proceedings of the IEEE 2004 Custom Integrated Circuits Conference (IEEE Cat. No.04CH37571).

[23]  H. Alan Mantooth,et al.  Modeling nonlinear dynamics in analog circuits via root localization , 2003, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[24]  S.X.-D. Tan,et al.  Efficient DDD-based term generation algorithm for analog circuit behavioral modeling , 2003, Proceedings of the ASP-DAC Asia and South Pacific Design Automation Conference, 2003..

[25]  Qi-Jun Zhang,et al.  Artificial neural networks for RF and microwave design - from theory to practice , 2003 .

[26]  Ning Dong,et al.  Automated nonlinear macromodelling of output buffers for high-speed digital applications , 2005, Proceedings. 42nd Design Automation Conference, 2005..