A python implementation based lattice Boltzmann method for thermal behavior analysis in silicon carbide MOSFET

In the new global industrial sector, electronics systems efficiency has become a central issue should be improved. Recently, there has been renewed interest for addressing this challenge by integrating Silicon Carbide (SiC) technology in industrial systems due to its outstanding materials properties compared to other used material. Meanwhile due to continue minimization of the scale of electronic devices, SiC MOSFETs are highly affected by thermal behavior, a major issue that requires further scientific investigation. In this paper, a Python code-based Lattice Boltzmann Method is implemented to characterize the thermal behavior inside a SiC MOSFETs . The aim of this essay is to explore the relationship between the hotspot and the specularity parameter which is the probability of phonons scattering in the considered nano structure . What emerges from the results reported in this work is that there is an association between the reduction of hotspots inside Silicon Carbide MOSFET and the augmentation of specularity parameter. For R th −1 = 10 8 W/(m 2 K) and specularity parameter of 0.7 and 0.3, the temperature difference was 1.74 K and 1.95 K, respectively while it was 1.21 K and 1.47 K for R th −1 = 10 10 W/(m 2 K) at the same specularity parameters.

• SiC MOSFET
• thermal effects
• lattice Boltzmann method
• phonon scattering
• hot spots
• thermal resistance
• Python implementation

• Lattice Boltzmann Simulation Studies
• Silicon Carbide Semiconductor Technologies
• Thermal properties of materials
• SiC MOSFET
• thermal effects
• lattice Boltzmann method
• phonon scattering
• hot spots
• thermal resistance
• Python implementation
• Computational Mechanics
• Electrical and Electronic Engineering
• Materials Chemistry
• Engineering
• Materials Science