Neural network processing for adaptive array antennas

Christodoulou, C.G.; Zooghby, A.H. El; Georgiopoulos, Michael

doi:10.1109/aps.1999.789337

Cited by 9 publications

(5 citation statements)

References 3 publications

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“…Neural network can learn and represent the general input-output behavior of a de- Researchers have investigated a variety of important applications utilizing the ability of artificial neural networks to perform parametric modeling and optimization of microwave components and circuits, such as high-speed VLSI interconnects [37], [38], bends [17], [39], vias [40], spiral inductors [36], [41], microwave FETs [35], [42], HBTs [43], [44], HEMTs [45], waveguides [46], laser diodes [47], filters [48]− [50], amplifiers [4], [51], mixers [51], antennas [52], coplanar waveguide (CPW) components [27], [53], embedded passives [26], [54], multilayer circuit packages [55].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Parametric Modeling of EM Behaviors of Microwave Components Using Combined Neural Networks and Pole-Residue Transfer Functions

Feng¹

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Parametric modeling of electromagnetic (EM) behaviors has become important for EM design optimizations of microwave components. The EM based design, such as design optimization, what if analysis and yield-driven design, can be time consuming because it usually requires repetitive EM simulations with varying values of geometrical parameters as design variables. Parametric models can be developed from the information of EM responses as functions of geometrical parameters. The developed parametric models allow faster simulations and optimizations with varying values of geometrical parameters and subsequently can be implemented in high-level circuit and system design optimizations. This thesis proposes a novel technique to develop combined neural network and pole-residue-based transfer function models for parametric modeling of EM behaviors of microwave components. In this technique, neural networks are trained to learn the relationships between pole/residues of the transfer functions and geometrical parameters. The orders of the pole-residue transfer functions may vary over different regions of geometrical parameters. We develop a pole-residue tracking technique to solve this order-changing problem. After the proposed modeling process, the trained model can be used to provide accurate and fast predictions of the EM behavior of microwave components with geometrical parameters as variables. An advanced pole-residue tracking technique is proposed to exploit sensitivity information to solve the challenges of pole-residue tracking especially when the amount of training data are reduced and/or the geometrical step sizes between the data samples are enlarged. The proposed technique takes advantages of sensitivity i information to split one pole into two separate new poles to achieve the increase of the orders of the transfer functions and ultimately form transfer functions of a constant order over the entire region of geometrical parameters. The proposed technique addresses the challenges of pole-residue tracking when training data are limited. As a further advancement, we introduce EM sensitivity analysis into the poleresidue-based neuro-transfer function modeling technique. The purpose is to increase the model accuracy by utilizing EM sensitivity information and to speedup the model development process by reducing the number of training data required for developing the model. The proposed parametric model consists of the original and adjoint pole-residue based neuro-TF models. New formulations are derived for calculating the second order derivatives for training the adjoint pole-residue based neuro-TF model. By exploiting the sensitivity information, the proposed technique can further speed up the model development process over the existing pole-residue parametric modeling method without using sensitivity analysis. The proposed parametric modeling techniques in this thesis are demonstrated by several microwave examples. KEY WORDS: Electromagnetic, parametric modeling, neural network, transfer function, sensitivity analys...

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Section: Artificial Neural Networkmentioning

confidence: 99%

Parametric Modeling of EM Behaviors of Microwave Components Using Combined Neural Networks and Pole-Residue Transfer Functions

Feng¹

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“…During the CAD process, the most critical step is the accurate and efficient modeling of the RF and microwave components. It has been well-recognized [35] that the accuracy of modeling is the main bottleneck in implementing CAD tools for the first pass design of - [31], vias [18], spiral inductors [1], FET devices [31,11], HBT devices [40], HEMT devices [41], filters [42], amplifiers [39,43], coplanar waveguide (CPW) circuit components [21], mixers [43], antennas [44], embedded resistors [44][45][46][47], packaging and interconnects [16], and etc. Neural networks have also been used in circuit simulation and optimization [39,48], signal integrity analysis and optimization of VLSI interconnects [16,49], microstrip circuit design [9], process design [50], synthesis [51], and microwave impedance matching [8].…”

Section: Roles Of Anns In Rf/microwave Circuit Designmentioning

confidence: 99%

Neural network based techniques for microwave passive components modeling and design

Zhang¹

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“…The fast learning ability of neural networks is very useful because often it is not easy to create the analytical model for a new invented device. ANN has been successfully used in a variety of applications such as modeling and optimization of high-speed VLSI interconnects [15], microstrip interconnects [16], vias [17], spiral inductors [18], coplanar waveguide (CPW) circuit components [19], filter [20], mixers [21], antennas [22], embedded resistors [23] [24], microwave FETs and amplifiers [9][25], CMOS and HBTs [26] [27], HEMT devices [28], EM-optimization [29], yield optimization [9] and circuit synthesis [30] [31], etc. These achievements have set up the foundation of neural modeling technique in both device and circuit level of microwave applications.…”

Section: Neural Network Applications In Microwave/rf Circuit Designmentioning

confidence: 99%

Linear dynamic space mapping approach for large-signal statistical modeling of microwave devices

Bo¹

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This thesis presents a novel technique for large-signal statistical modeling of nonlinear microwave devices. A new statistical space mapping concept is introduced that can expand a large-signal nominal model into a large-signal statistical model. The nominal model is an accurate large-signal model developed from one complete large-signal measurement and it describes the nominal performance of the device population. The mapping contains the statistical parameters estimated by fitting the DC and biasdependent S-parameter data of the device population. In this way, the nominal model mainly represents the large-signal nonlinear behavior of the device population while the random variations around the nominal model are represented by the space mapping functions. It helps to efficiently develop large-signal statistical models while reducing the expense of otherwise massive large-signal measurements for many devices. Examples of MESFET and HEMT statistical modeling demonstrate that the technique can approximate the large-signal statistical characteristics using only one set of large-signal data. The use of such statistical model in amplifier yield design further demonstrates the capability of the technique in capturing the large-signal statistical properties.Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

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Neural network processing for adaptive array antennas

Cited by 9 publications

References 3 publications

Parametric Modeling of EM Behaviors of Microwave Components Using Combined Neural Networks and Pole-Residue Transfer Functions

Parametric Modeling of EM Behaviors of Microwave Components Using Combined Neural Networks and Pole-Residue Transfer Functions

Neural network based techniques for microwave passive components modeling and design

Linear dynamic space mapping approach for large-signal statistical modeling of microwave devices

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