An SAC-Based Auto Optimization Model for Dynamic Current Balancing of Multichip Paralleled SiC Power Module

Abstract

The dynamic current imbalance between the paralleled SiC mosfets in multichip power modules, which is commonly attributed to the asymmetric module layout, severely limits their current capacity and thermal reliability. Adjusting the connection points of bonding wires is an effective method to mitigate imbalanced dynamic current. However, manual trial-and-error is currently the most common method for optimizing connection points, which is both deficient and inefficient. Existing automated solutions usually rely on a prefitting process based on large datasets, which is time-consuming and impractical for high-dimensional parameter applications. Thus, this article proposes an optimization model to mitigate dynamic current imbalance, which can automatically adjust the connection points of bonding wires without any manual intervention. The reinforcement learning (RL) soft actor-critic algorithm was applied to the power module optimization, eliminating the need for prefitting and enabling high-dimensional parameter optimization. After optimization, nearly complete dynamic current balancing in both high-side and low-side switches in a multichip-paralleled half-bridge power module is achieved, as verified by simulations and experiments. This model achieves true dynamic current balancing automation for the first time, providing an important reference for the application of RL to the automated optimization of multichip power modules.