Enhancing Heat Sink Efficiency in MOSFETs using Physics Informed Neural Networks: A Systematic Study on Coolant Velocity Estimation

A research team has published a breakthrough paper demonstrating how Physics-Informed Neural Networks (PINNs) can solve complex thermal management problems in power electronics. The study focuses on optimizing coolant flow in multi-layered MOSFETs (metal-oxide-semiconductor field-effect transistors), which are critical components in Power Electronic Building Blocks that experience significant thermal loads. Traditional methods struggle with this inverse problem—determining required coolant velocity from temperature measurements—but the team's PINN approach provides accurate predictions that match experimental results, potentially preventing component burnout and improving system reliability.

The technical innovation lies in a sequential training algorithm that treats each material layer (aluminum, pyrolytic graphite sheets, stainless steel pipes with flowing water) separately during optimization. This decoupling reduces the dimensionality of the optimization landscape, making it easier to find global minima and avoid poor local solutions. The methodology represents a significant advancement in applying AI to physical engineering problems, bridging the gap between neural networks and fundamental physics equations. As power electronics become more compact and powerful, this approach could enable more efficient cooling designs without extensive physical prototyping.