Microwave Design Optimization Exploiting Adjoint Sensitivity

Adjustment of geometry and material parameters is an important step in the design of microwave devices and circuits. Nowadays, it is typically performed using high-fidelity electromagnetic (EM) simulations, which might be a challenging and time consuming process because accurate EM simulations are computationally expensive. In particular, design automation by employing an EM solver in an numerical optimization algorithm may be prohibitive. Recently, adjoint sensitivity techniques become available in commercial EM simulation software packages. This makes it possible to speed up the EM-driven design optimization process either by utilizing the sensitivity information in conventional, gradient-based algorithms or by combining it with surrogate-based approaches. In this paper, we review several recent methods and algorithms for microwave design optimization using adjoint sensitivity. We discuss advantages and disadvantages of these techniques and illustrate them through numerical examples.

[1]  Natalia K. Nikolova,et al.  Sensitivity analysis of the scattering parameters of microwave filters using the adjoint network method , 2006 .

[2]  Jen-Tsai Kuo,et al.  Parallel-coupled microstrip filters with over-coupled end stages for suppression of spurious responses , 2003, IEEE Microwave and Wireless Components Letters.

[3]  P. W. Hemker,et al.  Space Mapping and Defect Correction , 2005 .

[4]  J.W. Bandler,et al.  Space mapping: the state of the art , 2004, IEEE Transactions on Microwave Theory and Techniques.

[5]  S. Amari,et al.  Space-mapping optimization of planar coupled-resonator microwave filters , 2006, IEEE Transactions on Microwave Theory and Techniques.

[6]  Jon P. Webb,et al.  Optimization of microwave devices using 3-D finite elements and the design sensitivity of the frequency response , 2003 .

[7]  Andy J. Keane,et al.  Recent advances in surrogate-based optimization , 2009 .

[8]  G. Macchiarella,et al.  Microwave filter design by synthesis and optimization , 2007, IEEE Microwave Magazine.

[9]  K. Izui,et al.  Simultaneous shape and topology optimization for the design of patch antennas , 2009, 2009 3rd European Conference on Antennas and Propagation.

[10]  Dimitris Psychoudakis,et al.  Topology design optimization of dielectric substrates for bandwidth improvement of a patch antenna , 2003 .

[11]  Slawomir Koziel,et al.  Robust microwave design optimization using adjoint sensitivity and trust regions , 2012 .

[12]  John W. Bandler,et al.  Quality assessment of coarse models and surrogates for space mapping optimization , 2008 .

[13]  R. Lewis,et al.  An Overview of First-Order Model Management for Engineering Optimization , 2001 .

[14]  Mohamed H. Bakr,et al.  Bandwidth enhancement of narrow band antennas exploiting adjoint-based geometry evolution , 2011, 2011 IEEE International Symposium on Antennas and Propagation (APSURSI).

[15]  John W. Bandler,et al.  Robust space mapping optimization exploiting EM-based models with adjoint sensitivities , 2012, 2012 IEEE/MTT-S International Microwave Symposium Digest.

[16]  Slawomir Koziel,et al.  Computational Optimization, Methods and Algorithms , 2016, Computational Optimization, Methods and Algorithms.

[17]  S Koziel,et al.  Shape-Preserving Response Prediction for Microwave Design Optimization , 2010, IEEE Transactions on Microwave Theory and Techniques.

[18]  S. Koziel,et al.  Space mapping , 2008, IEEE Microwave Magazine.

[19]  James C. Rautio.  Perfectly calibrated internal ports in EM analysis of planar circuits , 2008, 2008 IEEE MTT-S International Microwave Symposium Digest.

[20]  Stephen J. Wright,et al.  Numerical Optimization (Springer Series in Operations Research and Financial Engineering) , 2000 .

[21]  N. M. Alexandrov,et al.  A trust-region framework for managing the use of approximation models in optimization , 1997 .