Space Mapping Technique Using Decomposed Mappings for GaN HEMT Modeling

Zhi-hao, Zhao; Zhang, Lei; Feng, Feng; Zhang, Wei; Zhang, Qi‐Jun

doi:10.1109/tmtt.2020.3004622

Cited by 33 publications

(12 citation statements)

References 38 publications

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“…Two major categories of applications for automated modeling techniques are parametric EM modeling for passive components/circuits, e.g., [4]- [6], and nonlinear modeling for active components/circuits, e.g., [38]- [41].…”

Section: Applications Of Automated Modeling Techniquesmentioning

confidence: 99%

“…Such an approach, called Neuro-Space Mapping (Neuro-SM), has been used for device modeling as well as statistical modeling [39], [40]. A further advance to Neuro-SM is the use of decomposed mapping for GaN HEMTs with trapping effects [41]. By moving the mapping space into the internal branches of the knowledge model (equivalent circuit), separate mappings for individual branches can be developed based on their vastly different behaviors.…”

Section: B Automated Modeling For Nonlinear Devicesmentioning

confidence: 99%

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Simulation and Automated Modeling of Microwave Circuits: State-of-the-Art and Emerging Trends

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Section: Applications Of Automated Modeling Techniquesmentioning

confidence: 99%

Section: B Automated Modeling For Nonlinear Devicesmentioning

confidence: 99%

Simulation and Automated Modeling of Microwave Circuits: State-of-the-Art and Emerging Trends

et al. 2021

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“…11,12 After training, it is widely recognized that fast, accurate, and reliable neural network models can be established from measured or simulated microwave data. 13 In recent years, many ANNs have been proposed, including multilayer perceptron (MLP), radial basis function (RBF), [13][14][15][16][17][18][19] neuro-space mapping (neuro-SM) [20][21][22] and knowledge-based neural network (KBNN). [23][24][25] In Refs.…”

Section: Introductionmentioning

confidence: 99%

Small‐signal modeling of microwave transistors using radial basis function artificial neural network‐comparison of different methods for spread constant determined

Yan

et al. 2022

Int J RF Mic Comp-Aid Eng

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This article presents a high‐precision modeling method to build a small signal model of GaAs pseudomorphic high electron mobility transistor (pHEMT) by using a radial basis function artificial neural network (RBF ANN). Both the RBF ANN Method 1 that uniformly distributed spread constant (SC) in the given range and Method 2 that increasingly changed SC with a chosen step have been modeled in this work. Compared with the l RBF ANN Method 1, the RBF ANN method 2 can automatically obtain the optimal ANN corresponding to the SC. The RBF ANN method 2 is developed for S‐parameters model and equivalent circuit parameters (ECPs) model of HEMTs. S‐parameters model establishes S‐parameters versus bias, temperature and frequency, while the ECPs model establishes ECPs versus bias and temperature. To validate the capability of the RBF ANN in small signal modeling of GaAs pHEMTs, measured and modeled data of S‐parameters model and ECPs model of a 4 × 75 μm gate width, 0.15 μm gate length GaAs pHEMT are compared, and very good agreement is achieved up to 50 GHz. For the S‐parameters model, the average error of the method 2 is improved by about 20% at the temperature of −20°C, 25°C, and 85°C. For the ECPs model, the average error of the method 2 is 80% higher than that of the method 1.

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“…Artificial neural network (ANN) is a recognized tool for modeling and design optimization in RF and microwave computer-aided design (CAD) [1][2][3][4][5][6][7][8][9]. This technique has been successfully used in parametric modeling of microwave components [10][11][12], electromagnetic (EM) optimization [13,14], parasitic modeling [15], nonlinear device modeling [16][17][18], nonlinear microwave circuit optimization [19][20][21][22], power amplifier modeling [23][24][25], and more.…”

Section: Introductionmentioning

confidence: 99%

A Time Delay Neural Network Based Technique for Nonlinear Microwave Device Modeling

et al. 2020

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This paper presents a nonlinear microwave device modeling technique that is based on time delay neural network (TDNN). The proposed technique can accurately model the nonlinear microwave devices when compared to static neural network modeling method. A new formulation is developed to allow for the proposed TDNN model to be trained with DC, small-signal, and large signal data, which can enhance the generalization of the device model. An algorithm is formulated to train the proposed TDNN model efficiently. This proposed technique is verified by GaAs metal-semiconductor-field-effect transistor (MESFET), and GaAs high-electron mobility transistor (HEMT) examples. These two examples demonstrate that the proposed TDNN is an efficient and valid approach for modeling various types of nonlinear microwave devices.

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Space Mapping Technique Using Decomposed Mappings for GaN HEMT Modeling

Cited by 33 publications

References 38 publications

Simulation and Automated Modeling of Microwave Circuits: State-of-the-Art and Emerging Trends

Simulation and Automated Modeling of Microwave Circuits: State-of-the-Art and Emerging Trends

Small‐signal modeling of microwave transistors using radial basis function artificial neural network‐comparison of different methods for spread constant determined

A Time Delay Neural Network Based Technique for Nonlinear Microwave Device Modeling

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