一个新的机器学习工作流程,以优化基于固态物理的冷却设备
Julian G Fernandez1,2, Guéric Etesse3, Natalia Seoane4
1Centro Singular de Investigación en Tecnoloxías Intelixentes, USC, 15782, Santiago de Compostela, Spain. popingarcia@gmail.com.
Scientific reports
|November 18, 2024
概括
研究人员开发了一种机器学习 (ML) 工作流程,以优化集成电路的固态冷却设备. 这种方法显著减少了设计高效纳米冷却解决方案的计算时间.
科学领域:
- 固态物理 固态物理
- 纳米技术纳米技术
- 计算材料科学 计算材料科学
背景情况:
- 固态冷却设备对于集成芯片的热管理至关重要.
- 准确的建模需要将电子的量子不平衡格林函数与热方程 (NEGF+H) 的合.
- NEGF+H模拟是计算密集型的,阻碍了快速的设计优化.
研究的目的:
- 开发一种机器学习 (ML) 工作流程,以加快固态冷却设备的设计优化.
- 为了减少与NEGF+H模拟相关的计算负担.
- 确定最佳的异构结构设计,平衡冷却功率和电子温度.
主要方法:
- 提出了一个新的机器学习 (ML) 工作流,并使用NEGF+H模拟数据进行训练.
- 机器学习工作流探索了[公式:查看文本]设备配置的广设计空间.
- 该方法优化了异构结构,以便在冷却功率 (CP) 和电子温度 (Te) 之间取得最佳的权衡.
主要成果:
- 机器学习工作流实现了以下的预测相对错误: CP 的 [公式:查看文本] 和 Te 的 [公式:查看文本].
- 在一个很大的搜索空间内确定了最佳的设备设计.
- 计算时间从每次模拟的两天大幅缩短到设计优化的10秒.
结论:
- 拟议的ML工作流显著加快了固态纳米冷却设备的设计过程.
- 这种方法减轻了传统NEGF+H方法的高计算成本.
- ML工作流程可以有效地发现高性能冷却装置设计.
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