DC model for SiC MOSFETs using artificial neural network optimized by artificial bee colony algorithm Article Swipe
Yuan Liu
,
Wanqin Zhang
,
Zeqi Zhu
,
Xiao Dong
,
Wanling Deng
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1063/5.0072302
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1063/5.0072302
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.
Related Topics
Concepts
Datasheet
Artificial bee colony algorithm
Artificial neural network
Computer science
Silicon carbide
Perceptron
Multilayer perceptron
Algorithm
Hybrid algorithm (constraint satisfaction)
Artificial intelligence
Materials science
Probabilistic logic
Constraint logic programming
Operating system
Constraint satisfaction
Metallurgy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1063/5.0072302
- https://aip.scitation.org/doi/pdf/10.1063/5.0072302
- OA Status
- gold
- Cited By
- 2
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3212082729
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3212082729Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1063/5.0072302Digital Object Identifier
- Title
-
DC model for SiC MOSFETs using artificial neural network optimized by artificial bee colony algorithmWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2021Year of publication
- Publication date
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2021-11-01Full publication date if available
- Authors
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Yuan Liu, Wanqin Zhang, Zeqi Zhu, Xiao Dong, Wanling DengList of authors in order
- Landing page
-
https://doi.org/10.1063/5.0072302Publisher landing page
- PDF URL
-
https://aip.scitation.org/doi/pdf/10.1063/5.0072302Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://aip.scitation.org/doi/pdf/10.1063/5.0072302Direct OA link when available
- Concepts
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Datasheet, Artificial bee colony algorithm, Artificial neural network, Computer science, Silicon carbide, Perceptron, Multilayer perceptron, Algorithm, Hybrid algorithm (constraint satisfaction), Artificial intelligence, Materials science, Probabilistic logic, Constraint logic programming, Operating system, Constraint satisfaction, MetallurgyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2Per-year citation counts (last 5 years)
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12Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_date | 2021-11-01 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2029699094, https://openalex.org/W2012804470, https://openalex.org/W2936688174, https://openalex.org/W2463374179, https://openalex.org/W2166315761, https://openalex.org/W2604938527, https://openalex.org/W2018519785, https://openalex.org/W2064314320, https://openalex.org/W2137983211, https://openalex.org/W2236623899, https://openalex.org/W3005368022, https://openalex.org/W2160574494 |
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