Surrogate modeling and optimization of inductor performances using Kriging functions

Integrated inductors are one of the most important passive elements in RF circuits. However, time-consuming simulations, such as electromagnetic simulations, have to be used to evaluate their performances with high accuracy. In order to overcome this problem, analytical models can be used. In this paper, a surrogate model based on Kriging functions is presented that accurately predicts the performance parameters of integrated inductors. The different approaches followed to obtain the model are presented. Finally, the model is linked to an evolutionary algorithm to optimize inductor performances.

[1]  Sushanta K. Mandal,et al.  ANN- and PSO-Based Synthesis of On-Chip Spiral Inductors for RF ICs , 2008, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[2]  S. Wong,et al.  Physical modeling of spiral inductors on silicon , 2000 .

[3]  E. Roca,et al.  Lumped element model for arbitrarily shaped integrated inductors — A statistical analysis , 2013, 2013 IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS 2013).

[4]  Georges G. E. Gielen,et al.  Performance modeling of analog integrated circuits using least-squares support vector machines , 2004, Proceedings Design, Automation and Test in Europe Conference and Exhibition.

[5]  Nan Jiang,et al.  Frequency-Independent Asymmetric Double-$pi $Equivalent Circuit for On-Chip Spiral Inductors: Physics-Based Modeling and Parameter Extraction , 2006, IEEE Journal of Solid-State Circuits.

[6]  Chuan Wang,et al.  A Wideband Predictive “Double-$\pi$ ” Equivalent-Circuit Model for On-Chip Spiral Inductors , 2009, IEEE Transactions on Electron Devices.

[7]  Francisco V. Fernández,et al.  Automated Generation of the Optimal Performance Trade-Offs of Integrated Inductors , 2014, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[8]  Yu Cao,et al.  Frequency-independent equivalent-circuit model for on-chip spiral inductors , 2003 .

[9]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[10]  Andy J. Keane,et al.  Engineering Design via Surrogate Modelling - A Practical Guide , 2008 .

[11]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[13]  Piet Demeester,et al.  A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design , 2010, J. Mach. Learn. Res..

[14]  Georges G. E. Gielen,et al.  Synthesis of Integrated Passive Components for High-Frequency RF ICs Based on Evolutionary Computation and Machine Learning Techniques , 2011, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[15]  Ali M. Niknejad.  Electromagnetics for High-Speed Analog and Digital Communication Circuits: Distributed circuits , 2007 .

[16]  Fábio Moreira de Passos,et al.  Parametric macromodeling of integrated inductors for RF circuit design , 2014 .

[17]  Yannis Papananos,et al.  Systematic analysis and modeling of integrated inductors and transformers in RF IC design , 2000 .

[18]  K.J. Chen,et al.  A Physical Model for On-Chip Spiral Inductors With Accurate Substrate Modeling , 2006, IEEE Transactions on Electron Devices.