利用物理信息神经网络提升MOSFET散热效率：关于冷却剂速度估计的系统研究
Enhancing Heat Sink Efficiency in MOSFETs using Physics Informed Neural Networks: A Systematic Study on Coolant Velocity Estimation
AI总结：
本文利用物理信息神经网络研究MOSFET散热效率提升，提出基于冷却剂速度估计的系统方法，通过分层训练优化热传导参数，提高冷却效果。
摘要：
本文提出了一种利用物理信息神经网络（PINNs）的方法，用于确定给定热流的入口和出口温度下的冷却剂所需速度。MOSFET是电力电子基本模块（PEBBs）的核心组件，承受大部分热负荷。因此，有效冷却MOSFET对于防止过热和潜在烧毁至关重要。确定所需速度对于有效冷却很重要，但这是一个病态的逆问题，传统方法难以解决。MOSFET由多个具有不同热导率的层组成，包括铝、石墨片（PGS）和含有流动水的不锈钢管。我们提出了一种算法，该算法在PINNs中依次训练MOSFET的各层。数学上，顺序训练方法通过将其他层的参数视为常数来解耦每层的优化。这减少了优化景观的维度，使更容易找到每层参数的全局最小值并避免较差的局部最小值。PINNs解的收敛性到解析解的理论分析。最后，我们展示了所提出方法的预测结果与实验结果良好一致。
英文摘要：
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 PINNs. Mathematically, the sequential training method decouples the optimization of each layer by treating the parameters of other layers as constants during its training phase. This reduces the dimensionality of the optimization landscape, making it easier to find the global minimum for each layer's parameters and avoid poor local minima. Convergence of the PINNs solution to the analytical solution is theoretically analyzed. Finally we show the prediction of our proposed methodology to be in good agreement with experimental results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
