Power modules with excellent inductance and temperature metrics are significant to meet the rising sophistication of energy demand in new technologies. In this paper, we use a surrogate-based approach to render optimal layouts of power modules with feasible and attractive inductance-temperature ratios at low computational budget. In particular, we use the class of feedforward networks to estimate the surrogate relationships between power module layout-design variables and inductance-temperature factors rendered from simulations; and Differential Evolution algorithms to optimize and locate feasible layout configurations of power module substrates minimizing inductance and temperature ratios. Our findings suggest the desirable classes of feedforward networks and gradient-free optimization algorithms being able to estimate and optimize power module layouts efficiently and effectively.

翻译：具备优异电感与温度特性的功率模块对于满足新技术中日益复杂的能源需求至关重要。本文采用代理模型方法，在低计算成本条件下实现了功率模块布局的优化设计，获得了兼具可行性与理想电感-温度比值的方案。具体而言，我们利用前馈神经网络类模型，通过仿真数据建立功率模块布局设计变量与电感-温度因子之间的代理关系；采用差分进化算法进行优化求解，定位能够最小化电感-温度比值的功率模块基板可行布局构型。研究结果表明，特定类别的前馈神经网络与无梯度优化算法能够高效且有效地实现功率模块布局的预估与优化。