An Automatic Optimization Method for Electrical and Thermal Performance of Stacked DBC Power Module

To meet the stringent performance of electric vehicle controllers, it is highly needed to optimize the electrical and thermal performance of power semiconductor modules. As stacked direct bonded copper (DBC) power modules have higher power density and enable the parallel of more chips, the demand for optimization is increasingly imperative. In this article, an automated optimization method based on genetic algorithm (GA) and lattice Boltzmann method (LBM) was proposed for stacked DBC power modules with water-cooled PinFin heat sink. With the proposed automatic generation method for electrical layout and heat-sink structures, GA is used to select candidates. Because of the expanded optimization space, there is a growing requirement for faster and more precise evaluation methods. The proposed evaluation method based on LBM can assess layout parasitic inductance and the flow velocity and temperature distribution of power module with PinFin heat sink, which verifies an advantage in balancing speed and accuracy compared to traditional evaluation methods. According to the optimization results, a 1200-V/1400-A, 16 chips in parallel, silicon carbide (SiC) MOSFET power module was fabricated in a standard EconoDUAL packaging size. Dynamic and static parameter testing as well as thermal testing were conducted, and the experiment results demonstrate the effectiveness of the proposed automatic optimization method.