论文标题
用于热设计优化电子电路板布局的机器学习热电路网络模型，并使用瞬态加热芯片
Machine learning thermal circuit network model for thermal design optimization of electronic circuit board layout with transient heating chips
论文作者
论文摘要
本文介绍了一种结合贝叶斯优化（BO）和灯泡热电路网络模型的方法，该模型有效地加速了具有瞬态加热芯片的电子电路板布局的热设计优化。随着电子设备变得越来越小，越来越复杂，热设计优化的重要性以确保耗散性能提高。但是，这种热设计优化很困难，因为有必要考虑与加热成分的包装和瞬态温度变化相关的各种权衡。这项研究旨在提高人工智能的热设计优化的性能。 BO使用高斯工艺与灯泡热电路网络模型相结合，并通过案例研究验证其性能。结果，BO成功地发现了理想的电路板布局以及粒子群优化（PSO）和遗传算法（GA）。 BO的CPU时间分别为PSO和GA的1/5和1/4。此外，BO在大约7分钟内从1000万个布局模式中发现了一个非直觉的最佳解决方案。据估计，这是分析所有布局模式所需的CPU时间的1/1000。
This paper describes a method combining Bayesian optimization (BO) and a lamped-capacitance thermal circuit network model that is effective for speeding up the thermal design optimization of an electronic circuit board layout with transient heating chips. As electronic devices have become smaller and more complex, the importance of thermal design optimization to ensure heat dissipation performance has increased. However, such thermal design optimization is difficult because it is necessary to consider various trade-offs associated with packaging and transient temperature changes of heat-generating components. This study aims to improve the performance of thermal design optimization by artificial intelligence. BO using a Gaussian process was combined with the lamped-capacitance thermal circuit network model, and its performance was verified by case studies. As a result, BO successfully found the ideal circuit board layout as well as particle swarm optimization (PSO) and genetic algorithm (GA) could. The CPU time for BO was 1/5 and 1/4 of that for PSO and GA, respectively. In addition, BO found a non-intuitive optimal solution in approximately 7 minutes from 10 million layout patterns. It was estimated that this was 1/1000 of the CPU time required for analyzing all layout patterns.
