Physical parameter‐based data‐driven modeling of small signal parameters of a metal‐semiconductor field‐effect transistor

SATILMIŞ>, Gökhan; Güneş, Filiz; Mahoutı, Peyman

doi:10.1002/jnm.2840

Cited by 8 publications

(7 citation statements)

References 28 publications

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“…This research also includes a comparative study of some algorithms (mainly artificial bee colony (ABC) algorithm and particle swarm optimization (PSO) algorithm). Satılmış et al 13 elaborated that physical parameter‐based modeling of the S ‐parameters for MESFET is done by utilizing device simulator for generation of big dataset to perform a symbolic regression (SR) technique. Jarndal et al 14 performed the modeling of a GaN HEMT with the application of ANN and SVR modeling techniques.…”

Section: Literature Overviewmentioning

confidence: 99%

Auto‐encoder based hybrid machine learning model for microwave scaled GaAs pHEMT devices

Bhargava

Vadalà

Majumdar

et al. 2022

Int J RF Mic Comp-Aid Eng

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In this article, a study of performing machine learning (ML) based modeling for semiconductor devices has been developed using experimental microwave data. Characterization of gallium arsenide (GaAs) pseudomorphic high electron mobility transistors (pHEMTs) with different gate widths is used as the illustrative example to demonstrate the accuracy and effectiveness of the presented modeling procedure. The tested devices are based on the multifinger layout, in which the total gate width (W) is obtained by multiplying the number of fingers (N f ) and their length (W 0 ). Machines are trained with scattering (S-)parameter measurements up to 65 GHz by using the extreme gradient boosting (XGBoost) algorithm with K-fold cross-validation. Then, the output of the trained machine is utilized by the parameters such as N f and W 0 inside the Auto-encoder (AE) model. In particular, the ML model with AE has a maximum of 99.88% prediction accuracy, despite the uncertainty inherent in the microwave measurements and the unavoidable deviations from the ideal behavior of the analyzed devices.

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Section: Literature Overviewmentioning

confidence: 99%

Auto‐encoder based hybrid machine learning model for microwave scaled GaAs pHEMT devices

Bhargava

Vadalà

Majumdar

et al. 2022

Int J RF Mic Comp-Aid Eng

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“…15,16 In recent years, ANN methods have made good progress in modeling microwave device. [17][18][19][20][21][22][23][24] ANN models can be extensively and efficiently modeled for different devices. For the modeling of HBT, neural network modeling methods have been proposed such as multilayer perceptron (MLP), 25 neuro-space mapping (neuro-SM), 26 fuzzy neural network (FNN), 27 and so forth.…”

Section: Introductionmentioning

confidence: 99%

Modeling of heterojunction bipolar transistors based on novel Wiener‐type dynamic neural network

Han

Tan

Liu

et al. 2022

Int J RF Mic Comp-Aid Eng

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A novel Wiener-type dynamic neural network modeling method for heterojunction bipolar transistors (HBTs) is proposed in this article. The model structure of the novel Wiener-type dynamic neural network applicable to HBT modeling is determined by the vector fitting. For the novel Wiener-type dynamic neural network structure, we also proposed a training algorithm applicable to this novel structure to improve its accuracy. We considered the transmission characteristics of the HBT and use the H-parameter to replace the Y-parameter for small-signal analysis. The DC and small-signal model formulas for HBT are also derived. The proposed novel Wiener-type dynamic neural network modeling method does not require detailed parameter information and the internal design structure of the HBT and can model any type of HBT as long as accurate training data are available. In this paper, an application example on modeling a 3 Â 30μm 2 InGaP/GaAs HBT is presented, and a combined DC and small-signal model is established to demonstrate the accuracy and feasibility of the proposed novel Wiener-type dynamic neural network modeling method. The modeling method can also be used for computeraided design, and the established models can be more easily applied to circuit simulation.

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“…A viable and efficient solution for this problem is the usage of data driven surrogate models 19–23 . Application of data driven surrogate models for repetitive optimization process is a well‐known technique which is being studied for many different type of microwave and RF research topics such as; modeling of RF small signal microwave transistors, 24–28 design and optimization of passive microwave stages such as filters, 29–42 Meta surfaces, 43–45 and microwave antenna designs 46–57 …”

Section: Introductionmentioning

confidence: 99%

A novel near field radiation shaping technique by using data driven surrogate based optimization for nondestructive hyperthermia

Ünal

Mahoutı

Türk

2022

Int J Numerical Modelling

Self Cite

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Herein, a novel and computationally efficient near field shaping technique for nondestructive hyperthermia applications is presented. The aim of nondestructive hyperthermia studies is to treat cancer cells in the body without damaging the healthy cells using the focused near‐field characteristic of the antenna array. However, optimal near field shaping requires an optimization process for having accurately focus the energy into the desired coordinates, which is, a computational expensive and inefficient task due to the nature of full‐wave electromagnetic (EM) analysis. To enable computationally efficient, and accurate near field shape optimization, a surrogate model is required. To achieve this, the near field radiation of the designed horn antenna array in free space is obtained by using a fast and reliable technique, analytical regularization method (ARM). After that, by using Artificial Neural Network based regression algorithm, a data driven surrogate model is created using the data samples generated by ARM. By this means, near field shaping optimization for focusing the energy to the region where the tumor is located can be achieved, as a multi‐objective optimization problem to be solved via data driven surrogate model assisted optimization techniques. The performance results of K‐fold validation and hold‐out data sets for proposed data driven surrogate model are calculated using Mean Absolute Error metric which are obtained as 0.9 × 10−3 and 1.4 × 10−3 respectively. By observing the presented study case scenario, after 10 min of illumination with antenna array with optimally selected configurations, the targeted area (position of tumor) had reached to the critical temperature of 43 Celsius. Thus, it can be said that the proposed method is an efficient method, both in terms of (1) destroying tumor cells by heating without damaging the healthy cells, and (2) being a computationally efficient method.

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Physical parameter‐based data‐driven modeling of small signal parameters of a metal‐semiconductor field‐effect transistor

Cited by 8 publications

References 28 publications

Auto‐encoder based hybrid machine learning model for microwave scaled GaAs pHEMT devices

Auto‐encoder based hybrid machine learning model for microwave scaled GaAs pHEMT devices

Modeling of heterojunction bipolar transistors based on novel Wiener‐type dynamic neural network

A novel near field radiation shaping technique by using data driven surrogate based optimization for nondestructive hyperthermia

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