Technology-Independent Non-Quasi-Static Table-Based Nonlinear Model Generation

Homayouni, Seyed Majid; Schreurs, Dominique; Crupi, Giovanni; Nauwelaers, Bart

doi:10.1109/tmtt.2009.2033840

Cited by 32 publications

(18 citation statements)

References 18 publications

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“…The training of the neural structures is done using LM algorithm optimization owing to its stability and fast convergence. All the three networks will be implemented using the same three set of inputs-V GS (V), V DS (V), and frequency ( GHz) and eight set of outputs-jS 11 j, jS 22 j, jS 12 j, jS 21 j, ∠S 11 ,∠S 22 , ∠S 12 , and ∠S 21 .…”

Section: Neural Network Structuresmentioning

confidence: 99%

“…[11][12][13][14][15][16][17][18][19] It is imperative to note that an accurate and reliable small signal equivalent model will ensure the development of reliable large signal and noise models. [20][21][22][23][24][25][26] The design of circuits generally requires behavior of the transistor and in this context, the analytical techniques have been found to be too involved due to technology dependency, and complex parameter extraction procedure. The optimization-based approach addresses these concerns to some extent.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Small signal behavioral modeling technique of GaN high electron mobility transistor using artificial neural network: An accurate, fast, and reliable approach

Khusro

Husain

Hashmi

et al. 2019

Int J RF Microw Comput Aided Eng

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This article reports a comparative study of two artificial neural network structures and associated variants used to describe and predict the behavior of 2 × 200 μm2 GaN high electron mobility transistors (HEMTs), utilizing radiofrequency characterization. Two architectures namely multilayer perceptron and cascade feedforward, have been investigated in this work to develop the behavioral model. A study is conducted utilizing the two architectures, all trained using Levenberg‐Marquardt, in terms of accuracy, convergence rate, and generalization capability to develop the behavioral model of GaN HEMT. However, to ensure the robustness of the model, accuracy, convergence rate, time elapsed, and generalization capability of the proposed model is also tested under couple of training algorithms, activation functions, number of hidden layers and neuron embedded inside it, methods for initialization of weights and bias and certain other vital parameters playing vital role in influencing the model accuracy and effectiveness. An excellent agreement found between measured S‐parameters and the proposed model proves the effectiveness of the proposed approach and excellent prediction ability for a sweeping multibias set and broad frequency range of 1 to 18 GHz. Moreover, a very good generalization capability is also recorded under variation of crucial parameters of GaN HEMT‐based neural model.

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Section: Neural Network Structuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Small signal behavioral modeling technique of GaN high electron mobility transistor using artificial neural network: An accurate, fast, and reliable approach

Khusro

Husain

Hashmi

et al. 2019

Int J RF Microw Comput Aided Eng

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show abstract

“…The complete non-quasi-static formulation with higher-order currents and charges were first presented in [5]. In this paper, we propose to use a simplified version for LDMOS modeling.…”

Section: B Non-quasi-static Model Formulationmentioning

confidence: 99%

“…The large-signal non-quasi-static drain current model can be formulated in addition to the existing quasi-static model as [5] [6]:…”

Section: B Non-quasi-static Model Formulationmentioning

confidence: 99%

“…In this paper, we present a new non-quasi-static large-signal model for LDMOS devices, with higher-order current and charge sources. This work is based on [5] and is the first time to be applied to LDMOS device modeling. These higher-order current and charge sources are used to compensate the nonquasi-static effect, and are directly extracted from the pulsed S- Circuit representation of (a) the small-signal non-quasi-static model and (b) the proposed large-signal non-quasi-static model, composed of the quasi-static approximation and higher-order current and charge sources.…”

Section: Introductionmentioning

confidence: 99%

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Non-Quasi-Static Large-Signal Model for RF LDMOS Power Transistors

Zhang¹,

Rueda²,

Kim³

et al. 2018

2018 IEEE/MTT-S International Microwave Symposium - IMS

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In this paper, we propose a non-quasi-static large-signal model to capture the high-frequency dispersion exhibited by laterally diffused metal-oxide semiconductor (LDMOS) devices. We show that industry-standard nonlinear large-signal models for LDMOS based on quasi-static assumptions are not sufficient for high-efficiency designs at frequencies higher than 2 GHz. This dispersive behavior results from the combination of high-frequency operation and the lengthened drain extension region that is needed to support high-voltage operation. To improve the model accuracy, higher-order current and charge components, which are directly integrated from bias-dependent S-parameter data, are included in the model. The non-quasi-static large-signal model improves the efficiency and gain predictions by 10% and 0.5 dB at 3.5 GHz. These improvements in accuracy are essential for power amplifier designers to achieve the performance targets necessary for 4G and upcoming 5G designs.

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A novel large‐signal FET model considering trapping‐induced dispersions

Yuan

Zheng

Guo

et al. 2019

Int J Numerical Modelling

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A novel large-signal model construction technique is proposed in this paper.High-order current and charge sources with additional dimensions derived from pulsed small-signal measurement data are used to describe the dispersive behaviour of FETs. Added dimensions can effectively account for trappinginduced dispersions. Instead of look-up tables, empirical functions and artificial neural network are adopted to implement the model into simulators, which can reduce the volume of the model and the possible oscillation caused by interpolation and provide better prediction performance beyond the measurement data range. The validity of proposed modelling technique has been verified by a 2 × 75um GaAs pHEMT. This proposed technique can be easily extended to GaN devices with the similar procedure.

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Technology-Independent Non-Quasi-Static Table-Based Nonlinear Model Generation

Cited by 32 publications

References 18 publications

Small signal behavioral modeling technique of GaN high electron mobility transistor using artificial neural network: An accurate, fast, and reliable approach

Small signal behavioral modeling technique of GaN high electron mobility transistor using artificial neural network: An accurate, fast, and reliable approach

Non-Quasi-Static Large-Signal Model for RF LDMOS Power Transistors

A novel large‐signal FET model considering trapping‐induced dispersions

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