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[2602.20177] Enhancing Heat Sink Efficiency in MOSFETs using Physics Informed Neural Networks: A Systematic Study on Coolant Velocity Estimation
Summary
This study explores the use of Physics Informed Neural Networks (PINNs) to optimize coolant velocity for enhancing heat sink efficiency in MOSFETs, addressing a complex thermal management challenge.
Why It Matters
Effective cooling of MOSFETs is crucial for the reliability of power electronic systems. This research presents a novel approach using PINNs, which could significantly improve thermal management strategies in electronic devices, thereby enhancing performance and longevity.
Key Takeaways
- PINNs can effectively estimate coolant velocity for MOSFET cooling.
- The proposed sequential training method simplifies optimization in multilayered structures.
- Results show good agreement with experimental data, validating the approach.
Computer Science > Neural and Evolutionary Computing arXiv:2602.20177 (cs) [Submitted on 13 Feb 2026] Title:Enhancing Heat Sink Efficiency in MOSFETs using Physics Informed Neural Networks: A Systematic Study on Coolant Velocity Estimation Authors:Aniruddha Bora, Isabel K. Alvarez, Julie Chalfant, Chryssostomos Chryssostomidis View a PDF of the paper titled Enhancing Heat Sink Efficiency in MOSFETs using Physics Informed Neural Networks: A Systematic Study on Coolant Velocity Estimation, by Aniruddha Bora and 3 other authors View PDF HTML (experimental) Abstract:In this work, we present a methodology using Physics Informed Neural Networks (PINNs) to determine the required velocity of a coolant, given inlet and outlet temperatures for a given heat flux in a multilayered metal-oxide-semiconductor field-effect transistor (MOSFET). MOSFETs are integral components of Power Electronic Building Blocks (PEBBs) and experiences the majority of the thermal load. Effective cooling of MOSFETs is therefore essential to prevent overheating and potential burnout. Determining the required velocity for the purpose of effective cooling is of importance but is an ill-posed inverse problem and difficult to solve using traditional methods. MOSFET consists of multiple layers with different thermal conductivities, including aluminum, pyrolytic graphite sheets (PGS), and stainless steel pipes containing flowing water. We propose an algorithm that employs sequential training of the MOSFET layers in...
