Experimental Study of High Performance 4H-SiC Floating Junction JBS Diodes

Yuan, Hao; Wang, Chengsen; Tang, Xiaoyan; Song, Qi; He, Yanjing; Zhang, Yimen; Zhang, Yuming; Xiao, Li; Wang, Liangyong; Yong, Wu

doi:10.1109/access.2020.2994625

Cited by 8 publications

(2 citation statements)

References 33 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 2 shows the influence of input parameters on Ron and BV based on TCAD Sentaurus simulation. Through simulation results, the reasonable range of Epidop, Impdop, L, and Impthickness should be 3 × 10 15 –1 × 10 16 cm −3 , 3 × 10 17 –1 × 10 18 cm −3 , 0.2–0.6 µm, 0.15–0.4 µm [ 8 ]. Table 2 lists the specific values of each input parameter.…”

Section: Gan Jbs Diode Tcad Modeling and Simulationmentioning

confidence: 99%

GaN JBS Diode Device Performance Prediction Method Based on Neural Network

Duan

Wang

et al. 2023

Micromachines

View full text Add to dashboard Cite

GaN JBS diodes exhibit excellent performance in power electronics. However, device performance is affected by multiple parameters of the P+ region, and the traditional TCAD simulation method is complex and time-consuming. In this study, we used a neural network machine learning method to predict the performance of a GaN JBS diode. First, 3018 groups of sample data composed of device structure and performance parameters were obtained using TCAD tools. The data were then input into the established neural network for training, which could quickly predict the device performance. The final prediction results show that the mean relative errors of the on-state resistance and reverse breakdown voltage are 0.048 and 0.028, respectively. The predicted value has an excellent fitting effect. This method can quickly design GaN JBS diodes with target performance and accelerate research on GaN JBS diode performance prediction.

show abstract

Section: Gan Jbs Diode Tcad Modeling and Simulationmentioning

confidence: 99%

GaN JBS Diode Device Performance Prediction Method Based on Neural Network

Duan

Wang

et al. 2023

Micromachines

View full text Add to dashboard Cite

show abstract

“…Schottky barrier lowing model and tunnelling models are also involved in the simulations to characterise the SBD performances [17]. Related model parameters have been optimised and applied in previous simulation studies [3,18,19].…”

Section: Device Structure and Simulation Setupmentioning

confidence: 99%

A novel SiC trench MOSFET with integrated Schottky barrier diode for improved reverse recovery charge and switching loss

Liu

Tang

Song

et al. 2021

IET Power Electronics

Self Cite

View full text Add to dashboard Cite

In this paper, a novel silicon carbide (SiC) trench metal oxide semiconductor field effect transistor (MOSFET) with improved reverse recovery charge and switching energy loss is proposed and investigated utilising ISE-TCAD simulations. The proposed structure features an integrated Schottky barrier diode on one side-wall of the trench below the P-base region, and an inserted thicker oxide layer between the polysilicon gate and source metal on one side-wall of the trench. The simulation results show that compared with Infineon's CoolSiC™ Trench MOSFET, the proposed device decreases the gate charge Q g by 29.2%, leading to significantly improved figures of merit (R on,sp × Q g ). The reverse recovery charge Q rr and peak reverse recovery current (I RRM ) are reduced by 64.1% and 66.0%, respectively. Meanwhile, the total switching energy loss decreases 36.7% for the dynamic performance. How to cite this article: Liu S., et al.: A novel SiC trench MOSFET with integrated Schottky barrier diode for improved reverse recovery charge and switching loss.

show abstract