A brief overview of the black-box behavioural modelling of electronic circuits for transient simulations

Transient simulations of electronic circuits in the time domain can be one of the most complex tasks that engineers encounter in the field of electronics and microelectronics due to two main problems: slow speed of SPICE transistor level circuit simulations and limited accuracy of classic techniques for improving speed of circuit simulations. The black-box approach to behavioural modelling of electronic circuits, although very challenging, is particularly interesting due to possibility of fast and accurate simulations and that is the reason why black-box modelling techniques are gaining more and more attention in industry and academic community. The goal of this professional paper is to give a brief overview of the topic, identify current state-of-the-art and give suggestions for the future research in the area of black-box behavioural modelling of electronic circuits in the time-domain.

[1]  M. Schetzen The Volterra and Wiener Theories of Nonlinear Systems , 1980 .

[2]  V. Ceperic,et al.  Artificial neural network in modelling of voltage controlled oscillators with jitter , 2004, Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference (IEEE Cat. No.04CH37521).

[3]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[4]  Qi-Jun Zhang,et al.  Neural Networks for Microwave Modeling: Model Development Issues and Nonlinear Modeling Techniques , 2001 .

[5]  W. Schilders,et al.  Behavioural modelling using the MOESP algorithm, dynamic neural networks and the Bartels--Stewart algorithm , 2008 .

[6]  O. De Feo,et al.  PWL approximation of nonlinear dynamical systems, part-II: Identification issues , 2004 .

[7]  Robert I. Damper,et al.  ANN Application to Modelling of the D/A and A/D Interface for Mixed-mode Behavioural Simulation , 2004 .

[8]  Qi-Jun Zhang,et al.  A New Training Approach for Robust Recurrent Neural-Network Modeling of Nonlinear Circuits , 2009, IEEE Transactions on Microwave Theory and Techniques.

[9]  Mongia Mhiri,et al.  Neural-Based Models of Semiconductor Devices for SPICE Simulator , 2008 .

[10]  Qi-Jun Zhang,et al.  State-space dynamic neural network technique for high-speed IC applications: modeling and stability analysis , 2006, IEEE Transactions on Microwave Theory and Techniques.

[11]  Georges G. E. Gielen,et al.  Sparse multikernel support vector regression machines trained by active learning , 2012, Expert Syst. Appl..

[12]  G.J. Coram,et al.  How to (and how not to) write a compact model in Verilog-A , 2004, Proceedings of the 2004 IEEE International Behavioral Modeling and Simulation Conference, 2004. BMAS 2004..

[13]  Qi-Jun Zhang,et al.  Statistical Neuro-Space Mapping Technique for Large-Signal Modeling of Nonlinear Devices , 2008, IEEE Transactions on Microwave Theory and Techniques.

[14]  Jerome H. Friedman,et al.  An Overview of Predictive Learning and Function Approximation , 1994 .

[15]  R. Plana,et al.  Nonlinear behavioral modeling of oscillators in VHDL-AMS using Artificial Neural Networks , 2008, 2008 IEEE Radio Frequency Integrated Circuits Symposium.

[16]  Rob A. Rutenbar,et al.  Hierarchical Modeling, Optimization, and Synthesis for System-Level Analog and RF Designs , 2007, Proceedings of the IEEE.

[17]  Bo Yan,et al.  A Support Vector Regression Nonlinear Model for SiC MESFET , 2007, 2007 International Workshop on Electron Devices and Semiconductor Technology (EDST).

[18]  Jianjun Xu,et al.  Neural based dynamic modeling of nonlinear microwave circuits , 2002, IMS 2002.

[19]  Dominique Schreurs,et al.  Constitutive relations for nonlinear modeling of Si/SiGe HBTs using an ANN model , 2005 .

[20]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[21]  Jose C. Pedro,et al.  Modeling MESFETs and HEMTs intermodulation distortion behavior using a generalized radial basis function network , 1999 .

[22]  Qi-Jun Zhang,et al.  Neuro-space mapping technique for semiconductor device modeling , 2008 .

[23]  Juan Martínez-Alajarín,et al.  A behavioral model development methodology for microwave components and integration in VHDL-AMS , 2007, Microelectron. J..

[24]  Dominique Schreurs,et al.  Black box modelling of the op-amp including switching power supply on effect , 2008 .

[25]  Alberto L. Sangiovanni-Vincentelli,et al.  Steady-state methods for simulating analog and microwave circuits , 1990, The Kluwer international series in engineering and computer science.

[26]  Qi-Jun Zhang,et al.  A Broadband and Parametric Model of Differential Via Holes Using Space-Mapping Neural Network , 2009, IEEE Microwave and Wireless Components Letters.

[27]  Zeljko Mrcarica,et al.  MOS transistor modelling using neural network , 1992 .

[28]  E. Ngoya,et al.  Envelop transient analysis: a new method for the transient and steady state analysis of microwave communication circuits and systems , 1996, 1996 IEEE MTT-S International Microwave Symposium Digest.

[29]  Vladimir Ceperic,et al.  Modeling of analog circuits by using support vector regression machines , 2004, Proceedings of the 2004 11th IEEE International Conference on Electronics, Circuits and Systems, 2004. ICECS 2004..