用于瞬态发热芯片电路板布局热设计优化的机器学习热路网络模型
Machine learning thermal circuit network model for thermal design optimization of electronic circuit board layout with transient heating chips
摘要：
本文介绍了一种方法结合贝叶斯优化（BO）和一个灯电容热电路网络模型，是有效的，以加快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和复杂，热设计优化以确保散热性能的重要性已经增加。然而，这样的热设计优化是困难的，因为必须考虑与发热部件的封装和瞬态温度变化相关联的各种权衡。本研究旨在借由人工智能来改善热设计最佳化的效能。采用高斯过程的BO与灯容热路网络模型相结合，并通过实例验证了其性能。结果，BO成功地找到了理想的电路板布局以及粒子群优化（PSO）和遗传算法（GA）可以。BO的CPU时间分别是PSO和GA的1/5和1/4。此外，BO在大约7分钟内从1000万个布局模式中找到了一个非直观的最佳解决方案。据估计，这是分析所有布局图案所需CPU时间的1/1000。
英文摘要：
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.
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。
