Mostafa A. Ismail

Neuromodeling of microwave circuits exploiting space mapping technology

Space mapping (SM) technology based neuromodels decrease the cost of training, improve generalization ability and reduce the complexity of the ANN topology w.r.t. classical neuromodeling. Three novel techniques are proposed to generate SM based neuromodels: space-mapped neuromodeling (SMN), frequency dependent space-mapped neuromodeling (FDSMN), and frequency-space-mapped neuromodeling (FSMN). Huber optimization is proposed to train the neuro-space-mapping (NSM). The techniques are illustrated by a microstrip right angle bend.

Neuromodeling of microwave circuits exploiting space-mapping technology

Space mapping (SM) technology based neuromodels decrease the cost of training, improve generalization ability and reduce the complexity of the ANN topology w.r.t. classical neuromodeling. Three novel techniques are proposed to generate SM based neuromodels: space-mapped neuromodeling (SMN), frequency dependent space-mapped neuromodeling (FDSMN), and frequency-space-mapped neuromodeling (FSMN). Huber optimization is proposed to train the neuro-space-mapping (NSM). The techniques are illustrated by a microstrip right angle bend.

EM-based design of large-scale dielectric-resonator filters and multiplexers by space mapping

A novel design methodology for filter and multiplexer design is presented. For the first time, finite-element electromagnetic (EM)-based simulators and space-mapping optimization are combined to produce an accurate design for manifold-coupled output multiplexers with dielectric resonator (DR) loaded raters. Finite-element EM-based simulators are used as a fine model of each multiplexer channel, and a coupling matrix representation is used as a coarse model. Fine details such as tuning screws are included in the fine model. The DR filter and multiplexer design parameters are kept bounded during optimization. The sparsity of the mapping between the design parameters and the coupling elements has been exploited. Our approach has been used to design large-scale output multiplexers and it has significantly reduced the overall tuning time compared to traditional techniques. The technique is illustrated through design of a five-pole DR filter and a ten-channel output multiplexer.

Neural space-mapping optimization for EM-based design

We propose, for the first time, neural space-mapping (NSM) optimization for electromagnetic based design. NSM optimization exploits our space-mapping (SM)-based neuromodeling techniques to efficiently approximate the mapping. A novel procedure that does not require troublesome parameter extraction to predict the next point is proposed. The initial mapping is established by performing upfront fine-model analyses at a reduced number of base points. Coarse-model sensitivities are exploited to select those base points. Huber optimization is used to train, without testing points, simple SM-based neuromodels at each NSM iteration. The technique is illustrated by a high-temperature superconducting quarter-wave parallel coupled-line microstrip filter and a bandstop microstrip filter with quarter-wave resonant open stubs.

A generalized space-mapping tableau approach to device modeling

A comprehensive framework to engineering device modeling, which we call generalized space mapping (GSM) is introduced in this paper. GSM permits many different practical implementations. As a result, the accuracy of available empirical models of microwave devices can be significantly enhanced. We present three fundamental illustrations: a basic space-mapping super model (SMSM), frequency-space-mapping super model (FSMSM) and multiple space mapping (MSM). Two variations of MSM are presented: MSM for device responses and MSM for frequency intervals. We also present novel criteria to discriminate between coarse models of the same device. The SMSM, FSMSM, and MSM concepts have been verified on several modeling problems, typically utilizing a few relevant full-wave electromagnetic simulations. This paper presents four examples: a microstrip line, a microstrip right-angle bend, a microstrip step junction, and a microstrip shaped T-junction, yielding remarkable improvement within regions of interest.

A Generalized Space Mapping Tableau Approach to Device Modeling

A novel, comprehensive framework to engineering device modeling called Generalized Space Mapping (GSM) is introduced. The accuracy of available empirical models of microwave devices can be significantly enhanced by exploiting GSM. We present three fundamental illustrations: a basic Space Mapping Super Model (SMSM), a basic Frequency-Space Mapping Super Model (FSMSM) and Multiple Space Mapping (MSM). The new concept is verified on several device modeling problems, typically utilizing very few full-wave EM simulations, yielding remarkable improvement in accuracy.

Expanded space-mapping EM-based design framework exploiting preassigned parameters

We present a novel design framework for microwave circuits. We calibrate coarse models (circuit based models) to align with fine models (full-wave electromagnetic simulations) by allowing some preassigned pa- rameters (which are not used in optimization) to change in some compo- nents of the coarse model. Our expanded space-mapping design-frame- work (ESMDF) algorithm establishes a sparse mapping from optimizable to preassigned parameters. We illustrate our approach through two mi- crostrip design examples.

New directions in model development for RF/microwave components utilizing artificial neural networks and space mapping

This paper presents advances in model development for RF/microwave components exploiting two powerful technologies: artificial neural networks (ANN) and space mapping (SM). We survey the fundamental issues on classical neuromodeling. We review some state-of-the-art neuromodeling techniques, emphasizing SM based neuromodeling techniques. We show how SM based neuromodels decrease the cost of training, improve generalization ability and reduce the complexity of the ANN topology w.r.t. the classical neuromodeling approach. We illustrate these novel approaches through a practical microwave modeling problem. We conclude by proposing some possible exciting future applications of ANN and SM in microwave CAD.

Neural inverse space mapping EM-optimization

For the first time, we present Neural Inverse Space Mapping (NISM) optimization for EM-based design of microwave structures. The inverse of the mapping from the fine to the coarse model parameter spaces is exploited for the first time in a Space Mapping algorithm. NISM optimization does not require: up-front EM simulations, multipoint parameter extraction or frequency mapping. The inverse of the mapping is approximated by a neural network whose generalization performance is controlled through a network growing strategy. We contrast our new algorithm with Neural Space Mapping (NSM) optimization.

Half-wave dielectric rod resonator filter

A new half-wave dielectric rod resonator and its filter realization are proposed for satellite input multiplexer applications. Single resonator and a 10-pole filter with 8 real and complex transmission zeros are modeled and designed to desired specification by using efficient optimization technique and a finite element method solver. Excellent filter responses are obtained.