SiC MOSFET with Integrated SBD Device Performance Prediction Method Based on Neural Network
The SiC MOSFET with an integrated SBD (SBD-MOSFET) exhibits excellent performance in power electronics. However, the static and dynamic characteristics of this device are influenced by a multitude of parameters, and traditional TCAD simulation methods are often characterized by their complexity. Due...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Micromachines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-666X/16/1/55 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587939919429632 |
|---|---|
| author | Xiping Niu Ling Sang Xiaoling Duan Shijie Gu Peng Zhao Tao Zhu Kaixuan Xu Yawei He Zheyang Li Jincheng Zhang Rui Jin |
| author_facet | Xiping Niu Ling Sang Xiaoling Duan Shijie Gu Peng Zhao Tao Zhu Kaixuan Xu Yawei He Zheyang Li Jincheng Zhang Rui Jin |
| author_sort | Xiping Niu |
| collection | DOAJ |
| description | The SiC MOSFET with an integrated SBD (SBD-MOSFET) exhibits excellent performance in power electronics. However, the static and dynamic characteristics of this device are influenced by a multitude of parameters, and traditional TCAD simulation methods are often characterized by their complexity. Due to the increasing research on neural networks in recent years, such as the application of neural networks to the prediction of GaN JBS and Finfet devices, this paper considers the application of neural networks to the performance prediction of SiC MOSFET devices with an integrated SBD. This study introduces a novel approach utilizing neural network machine learning to predict the static and dynamic characteristics of the SBD-MOSFET. In this research, SBD-MOSFET devices are modeled and simulated using Sentaurus TCAD(2017) software, resulting in the generation of 625 sets of device structure and sample data, which serve as the sample set for the neural network. These input variables are then fed into the neural network for prediction. The findings indicate that the mean square error (MSE) values for the threshold voltage (Vth), breakdown voltage (BV), specific on-resistance (R<sub>on</sub>), and total switching power dissipation (E) are 0.0051, 0.0031, 0.0065, and 0.0220, respectively, demonstrating a high degree of accuracy in the predicted values. Meanwhile, in the comparison of convolutional neural networks and machine learning, the CNN accuracy is much higher than the machine learning methods. This method of predicting device performance via neural networks offers a rapid means of designing SBD-MOSFETs with specified performance targets, thereby presenting significant advantages in accelerating research on SBD-MOSFET performance prediction. |
| format | Article |
| id | doaj-art-a3a5d8e4b9444564a65de2a27862fb15 |
| institution | Kabale University |
| issn | 2072-666X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj-art-a3a5d8e4b9444564a65de2a27862fb152025-01-24T13:41:59ZengMDPI AGMicromachines2072-666X2024-12-011615510.3390/mi16010055SiC MOSFET with Integrated SBD Device Performance Prediction Method Based on Neural NetworkXiping Niu0Ling Sang1Xiaoling Duan2Shijie Gu3Peng Zhao4Tao Zhu5Kaixuan Xu6Yawei He7Zheyang Li8Jincheng Zhang9Rui Jin10Beijing Institute of Smart Energy, Beijing 102209, ChinaBeijing Institute of Smart Energy, Beijing 102209, ChinaState Key Laboratory of Wide-Bandgap Semiconductor Devices and Integrated Technology, Xidian University, Xi’an 710071, ChinaState Key Laboratory of Wide-Bandgap Semiconductor Devices and Integrated Technology, Xidian University, Xi’an 710071, ChinaState Key Laboratory of Wide-Bandgap Semiconductor Devices and Integrated Technology, Xidian University, Xi’an 710071, ChinaBeijing Institute of Smart Energy, Beijing 102209, ChinaBeijing Institute of Smart Energy, Beijing 102209, ChinaBeijing Institute of Smart Energy, Beijing 102209, ChinaBeijing Institute of Smart Energy, Beijing 102209, ChinaState Key Laboratory of Wide-Bandgap Semiconductor Devices and Integrated Technology, Xidian University, Xi’an 710071, ChinaBeijing Institute of Smart Energy, Beijing 102209, ChinaThe SiC MOSFET with an integrated SBD (SBD-MOSFET) exhibits excellent performance in power electronics. However, the static and dynamic characteristics of this device are influenced by a multitude of parameters, and traditional TCAD simulation methods are often characterized by their complexity. Due to the increasing research on neural networks in recent years, such as the application of neural networks to the prediction of GaN JBS and Finfet devices, this paper considers the application of neural networks to the performance prediction of SiC MOSFET devices with an integrated SBD. This study introduces a novel approach utilizing neural network machine learning to predict the static and dynamic characteristics of the SBD-MOSFET. In this research, SBD-MOSFET devices are modeled and simulated using Sentaurus TCAD(2017) software, resulting in the generation of 625 sets of device structure and sample data, which serve as the sample set for the neural network. These input variables are then fed into the neural network for prediction. The findings indicate that the mean square error (MSE) values for the threshold voltage (Vth), breakdown voltage (BV), specific on-resistance (R<sub>on</sub>), and total switching power dissipation (E) are 0.0051, 0.0031, 0.0065, and 0.0220, respectively, demonstrating a high degree of accuracy in the predicted values. Meanwhile, in the comparison of convolutional neural networks and machine learning, the CNN accuracy is much higher than the machine learning methods. This method of predicting device performance via neural networks offers a rapid means of designing SBD-MOSFETs with specified performance targets, thereby presenting significant advantages in accelerating research on SBD-MOSFET performance prediction.https://www.mdpi.com/2072-666X/16/1/55SiC MOSFETSBDneural networks |
| spellingShingle | Xiping Niu Ling Sang Xiaoling Duan Shijie Gu Peng Zhao Tao Zhu Kaixuan Xu Yawei He Zheyang Li Jincheng Zhang Rui Jin SiC MOSFET with Integrated SBD Device Performance Prediction Method Based on Neural Network Micromachines SiC MOSFET SBD neural networks |
| title | SiC MOSFET with Integrated SBD Device Performance Prediction Method Based on Neural Network |
| title_full | SiC MOSFET with Integrated SBD Device Performance Prediction Method Based on Neural Network |
| title_fullStr | SiC MOSFET with Integrated SBD Device Performance Prediction Method Based on Neural Network |
| title_full_unstemmed | SiC MOSFET with Integrated SBD Device Performance Prediction Method Based on Neural Network |
| title_short | SiC MOSFET with Integrated SBD Device Performance Prediction Method Based on Neural Network |
| title_sort | sic mosfet with integrated sbd device performance prediction method based on neural network |
| topic | SiC MOSFET SBD neural networks |
| url | https://www.mdpi.com/2072-666X/16/1/55 |
| work_keys_str_mv | AT xipingniu sicmosfetwithintegratedsbddeviceperformancepredictionmethodbasedonneuralnetwork AT lingsang sicmosfetwithintegratedsbddeviceperformancepredictionmethodbasedonneuralnetwork AT xiaolingduan sicmosfetwithintegratedsbddeviceperformancepredictionmethodbasedonneuralnetwork AT shijiegu sicmosfetwithintegratedsbddeviceperformancepredictionmethodbasedonneuralnetwork AT pengzhao sicmosfetwithintegratedsbddeviceperformancepredictionmethodbasedonneuralnetwork AT taozhu sicmosfetwithintegratedsbddeviceperformancepredictionmethodbasedonneuralnetwork AT kaixuanxu sicmosfetwithintegratedsbddeviceperformancepredictionmethodbasedonneuralnetwork AT yaweihe sicmosfetwithintegratedsbddeviceperformancepredictionmethodbasedonneuralnetwork AT zheyangli sicmosfetwithintegratedsbddeviceperformancepredictionmethodbasedonneuralnetwork AT jinchengzhang sicmosfetwithintegratedsbddeviceperformancepredictionmethodbasedonneuralnetwork AT ruijin sicmosfetwithintegratedsbddeviceperformancepredictionmethodbasedonneuralnetwork |