DC model for SiC MOSFETs using artificial neural network optimized by artificial bee colony algorithm

A DC model for silicon carbide (SiC) metal–oxide–semiconductor field effect transistors (MOSFETs) is proposed in this paper using a hybrid modeling method based on the artificial neural network and artificial bee colony (ABC) algorithm. A multi-layer perceptron neural network using the Levenberg–Mar...

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Autores principales:	Yuan Liu, Wanqin Zhang, Zeqi Zhu, Xiao Dong, Wanling Deng
Formato:	article
Lenguaje:	EN
Publicado:	AIP Publishing LLC 2021
Materias:	Physics QC1-999
Acceso en línea:	https://doaj.org/article/5ecf265b3d00439ba3c0354692805523
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spelling	oai:doaj.org-article:5ecf265b3d00439ba3c03546928055232021-12-01T18:52:06ZDC model for SiC MOSFETs using artificial neural network optimized by artificial bee colony algorithm2158-322610.1063/5.0072302https://doaj.org/article/5ecf265b3d00439ba3c03546928055232021-11-01T00:00:00Zhttp://dx.doi.org/10.1063/5.0072302https://doaj.org/toc/2158-3226A DC model for silicon carbide (SiC) metal–oxide–semiconductor field effect transistors (MOSFETs) is proposed in this paper using a hybrid modeling method based on the artificial neural network and artificial bee colony (ABC) algorithm. A multi-layer perceptron neural network using the Levenberg–Marquardt (LM) method is applied to model SiC MOSFETs based on the data provided by the datasheet. The search strategy of artificial bees is improved based on the standard ABC, which enhances the search ability of the standard ABC. In view of the sensitivity of the LM method to the initial value, the improved ABC algorithm is adopted to help the neural network find initial weights and biases, which improves the accuracy of the modeling results. Comparing the modeling results with the I–V curves in the datasheet, the accuracy of the DC model is verified under different temperatures. In addition, the small signal parameters gm and gd that are not exposed in the training process also fit well with the datasheet, which fully demonstrates the feasibility of this hybrid modeling method.Yuan LiuWanqin ZhangZeqi ZhuXiao DongWanling DengAIP Publishing LLCarticlePhysicsQC1-999ENAIP Advances, Vol 11, Iss 11, Pp 115219-115219-5 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999
spellingShingle	Physics QC1-999 Yuan Liu Wanqin Zhang Zeqi Zhu Xiao Dong Wanling Deng DC model for SiC MOSFETs using artificial neural network optimized by artificial bee colony algorithm
description	A DC model for silicon carbide (SiC) metal–oxide–semiconductor field effect transistors (MOSFETs) is proposed in this paper using a hybrid modeling method based on the artificial neural network and artificial bee colony (ABC) algorithm. A multi-layer perceptron neural network using the Levenberg–Marquardt (LM) method is applied to model SiC MOSFETs based on the data provided by the datasheet. The search strategy of artificial bees is improved based on the standard ABC, which enhances the search ability of the standard ABC. In view of the sensitivity of the LM method to the initial value, the improved ABC algorithm is adopted to help the neural network find initial weights and biases, which improves the accuracy of the modeling results. Comparing the modeling results with the I–V curves in the datasheet, the accuracy of the DC model is verified under different temperatures. In addition, the small signal parameters gm and gd that are not exposed in the training process also fit well with the datasheet, which fully demonstrates the feasibility of this hybrid modeling method.
format	article
author	Yuan Liu Wanqin Zhang Zeqi Zhu Xiao Dong Wanling Deng
author_facet	Yuan Liu Wanqin Zhang Zeqi Zhu Xiao Dong Wanling Deng
author_sort	Yuan Liu
title	DC model for SiC MOSFETs using artificial neural network optimized by artificial bee colony algorithm
title_short	DC model for SiC MOSFETs using artificial neural network optimized by artificial bee colony algorithm
title_full	DC model for SiC MOSFETs using artificial neural network optimized by artificial bee colony algorithm
title_fullStr	DC model for SiC MOSFETs using artificial neural network optimized by artificial bee colony algorithm
title_full_unstemmed	DC model for SiC MOSFETs using artificial neural network optimized by artificial bee colony algorithm
title_sort	dc model for sic mosfets using artificial neural network optimized by artificial bee colony algorithm
publisher	AIP Publishing LLC
publishDate	2021
url	https://doaj.org/article/5ecf265b3d00439ba3c0354692805523
work_keys_str_mv	AT yuanliu dcmodelforsicmosfetsusingartificialneuralnetworkoptimizedbyartificialbeecolonyalgorithm AT wanqinzhang dcmodelforsicmosfetsusingartificialneuralnetworkoptimizedbyartificialbeecolonyalgorithm AT zeqizhu dcmodelforsicmosfetsusingartificialneuralnetworkoptimizedbyartificialbeecolonyalgorithm AT xiaodong dcmodelforsicmosfetsusingartificialneuralnetworkoptimizedbyartificialbeecolonyalgorithm AT wanlingdeng dcmodelforsicmosfetsusingartificialneuralnetworkoptimizedbyartificialbeecolonyalgorithm
_version_	1718404693451866112

DC model for SiC MOSFETs using artificial neural network optimized by artificial bee colony algorithm

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