Computer-Aided Optimization of Nonlinear Microwave Circuits With the Aid of Electromagnetic Simulation

Rizzoli, Vittorio; Costanzo, Alessandra; Masotti, Diego; Lipparini, Andrea; Mastri, Franco

doi:10.1109/tmtt.2003.820898

Cited by 103 publications

(79 citation statements)

References 69 publications

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“…Neuronų tinklo modulio įvertinimas taip pat labai greitas. Dėl šių priežasčių neuronų tinklų metodai taikomi įvairiems mikrobangų įtaisams modeliuoti: bangolaidžių komponentams (Plonis et al 2010); filtrams (Yahia et al 2010;Michalski 2010); antenoms (Malathi et al 2009); netiesiniams įtaisams modeliuoti (Schreurs et al 2002); netiesiniams mikrobangų grandynams optimizuoti (Rizzoli et al 2004).…”

Section: Neuronų Tinklų Taikymo Pavyzdžiaiunclassified

Modelling Microwave Devices Using Artificial Neural Networks / Mikrobangų Įtaisų Modeliavimas, Taikant Dirbtinių Neuronų Tinklus

Katkevičius

2012

Mokslas - Lietuvos Ateitis

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Mikrobangų įtaisų ModeliaviMas, taikant dirbtinių neuronų tinklus andrius katkevičius Vilniaus Gedimino technikos universitetasEl. paštas andrius.katkevicius@vgtu.lt santrauka. Nagrinėjamos dirbtinių neuronų tinklų taikymo galimybės mikrobangų įtaisams tirti. Apžvelgiami eksperimentiniai ir teoriniai darbai. Pateikiama apibendrinta informacija apie uždavinius, sprendžiamus taikant neuronų tinklus, problemas, kylančias dirbtinius neuronų tinklus taikant mikrobangų uždaviniams spręsti, ir problemų sprendimo būdus. Pateikiama bendra neuronų tinklų struktūra ir konkretūs neuronų tinklų naudojimo pavyzdžiai.reikšminiai žodžiai: mikrobangų įtaisai, neuronų tinklai, lėtinimo sistemos, iteraciniai skaičiavimai.

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Section: Neuronų Tinklų Taikymo Pavyzdžiaiunclassified

Modelling Microwave Devices Using Artificial Neural Networks / Mikrobangų Įtaisų Modeliavimas, Taikant Dirbtinių Neuronų Tinklus

Katkevičius

2012

Mokslas - Lietuvos Ateitis

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“…Note that (9) is added as a phase shift. As well, resonances in the spectrum that previously required knowledgebased techniques to model [7] are now handled directly using only RNN.…”

Section: B Incorporation Of Rnn Macromodel Into Circuit Simulatormentioning

confidence: 99%

“…K 1 and K 2 are the sub-functions between the applied input Inphase (I in ) and Quadrature-phase (Q in ) signals and I out and Q out , respectively. The dynamic AM/AM and AM/PM distortions of the PA are indicated by [7] AM=AM distortion ! 10 log 10…”

Section: Rnn Envelope Modeling Of Power Amplifiersmentioning

confidence: 99%

“…Fast and accurate NN-based macromodels of frequency-domain electromagnetic (EM) behavior have been developed to avoid the high computational burden associated with traditional EM solvers while maintaining a high degree of accuracy [4][5][6][7]. As well, entire nonlinear circuit time-domain behaviors have been modeled using dynamic NN-based techniques [8,9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated time domain modeling of linear and nonlinear microwave circuits using recurrent neural networks

Sharma

Zhang

2008

Int J RF and Microwave Comp Aid Eng

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In this article, a recurrent neural network (RNN) method is employed for dynamic time-domain modeling of both linear and nonlinear microwave circuits. An automated RNN modeling technique is proposed to efficiently determine the training waveform distribution and internal RNN structure during the offline training process. This technique extends a recent automatic model generation (AMG) algorithm from frequency-domain model generation to dynamic time-domain model generation. Two types of applications of the algorithm are presented, transient electromagnetic (EM) behavior modeling of microwave structures, and time-domain envelope modeling of power amplifiers (PA). For transient EM modeling, we consider EM structures with varying material and geometrical parameters. AMG automatically varies the EM structural parameters during training and drives time-domain EM simulators to generate necessary amount of data for RNN to learn. AMG aims to model the transient behavior with minimum RNN order while satisfying accuracy requirements. In modeling PA behavior, an envelope formulation is used to specifically learn the AM/AM and AM/PM distortions due to third-generation (3G) digital modulation input. The RNN PA model is able to model these time domain distortions after training and can accurately model the amplifier behavior in both time (AM/AM, AM/PM) and frequency (spectral re-growth).

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“…The evaluation from input to output of a neural network model is also very fast. For these reasons, neural network techniques have been utilized in various microwave design applications [1]- [2], [4]- [5] such as vias and interconnects [6], embedded passives [7]- [8], coplanar wave-guide components [9]- [11], parasitic modeling [12], antenna applications [13]- [15], nonlinear microwave circuit optimization [16]- [18], nonlinear device modeling [19]- [22], power amplifier modeling [23]- [26], waveguide filter [27]- [29], enhanced EM computation [30], etc. In this article, we present an overview of neural networkbased modeling techniques and their applications in microwave modeling and design.…”

mentioning

confidence: 99%

Smart Modeling of Microwave Devices

Kabir

Zhang²,

et al. 2010

IEEE Microwave

205

118

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Modeling and computer-aided design (CAD) techniques are essential for microwave design, especially with our drive towards first-pass design success. In the past few decades, tremendous progress in microwave CAD has led to a large variety of microwave models for passive and active devices and circuit components. The high quality and the availability of these models have enabled us to design circuits efficiently. These models have also allowed us to design larger and more complicated circuits than ever before.At the same time, new technologies and materials, emerging and non-traditional devices continue to evolve. Although the existing models are good for modeling mature technologies and existing devices, they are often inadequate or unsuitable when new devices are needed in system design. Conventional approaches to create or modify models are heavily based on slow trial-and-error processes.As new technologies and devices continue to evolve, we need not only new models, but also computeraided modeling algorithms such that model development becomes fast and systematic.At high frequencies, equivalent circuit models often lack fidelity. Detailed electromagnetic (EM) based simulations become essential to achieve design accuracy. However, EM simulations are computationally expensive especially when physical or geometrical parameters have to be repeatedly adjusted during design cycle. With the increasing design complexities, coupled with tighter component tolerances and shorter design cycles, there is a demand for design methodologies that are both accurate and fast at the same time. These are contradictory requirements and difficult to satisfy with conventional CAD techniques. The problem becomes even more severe in yield optimization and statistical validation where process variations and manufacturing tolerances of components are required to be taken into account. In addition, accurate parametric modeling techniques have become increasingly necessary, where we strive to describe not only the behavior of the microwave device, but also the change of the behavior against physical or geometrical parameters of the device.In recent years, neural network (NN) or artificial neural network (ANN) techniques have been recognized as a useful alternative to conventional approaches in microwave modeling [1]- [2]. Artificial neural networks can be used to develop new models or to enhance the accuracy of existing models. Neural networks learn device data through an automated training process, and the trained neural networks are then used as fast and accurate models for efficient high-level circuit and system design. These models have the ability to capture multi-dimensional arbitrary nonlinear relationships. The theoretical basis of neural network is based on the universal approximation theory [3], which states that a neural network with at least one hidden layer can approximate any nonlinear continuous multidimensional function to any desired accuracy. This makes neural networks a useful choice for device modeling where a mathemati...

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Computer-Aided Optimization of Nonlinear Microwave Circuits With the Aid of Electromagnetic Simulation

Cited by 103 publications

References 69 publications

Modelling Microwave Devices Using Artificial Neural Networks / Mikrobangų Įtaisų Modeliavimas, Taikant Dirbtinių Neuronų Tinklus

Modelling Microwave Devices Using Artificial Neural Networks / Mikrobangų Įtaisų Modeliavimas, Taikant Dirbtinių Neuronų Tinklus

Automated time domain modeling of linear and nonlinear microwave circuits using recurrent neural networks

Smart Modeling of Microwave Devices

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