基于AI的多尺度热建模

IF 33.7 1区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

Nature Electronics Pub Date : 2024-12-06 DOI:10.1038/s41928-024-01323-9

Matthew Parker

引用次数: 0

摘要

来自DeepSim公司、加州大学戴维斯分校和斯坦福大学的Alexander Gabourie及其同事现在报告了一种人工智能（AI）加速热模型，该模型可以在从原子到厘米尺度的多个长度尺度上有效地工作。该模型首先使用分子动力学来模拟原子结构和界面的热传递。对于这些模拟，使用了机器学习潜力，可以以更低的计算成本实现与第一原理计算相似的精度，并且可以在图形处理单元（GPU）上有效地执行。这些结果然后被用来参数化一个晶体管尺度的有限元模型。研究人员表示，该方法可以在带有商用GPU的台式计算机上，在10分钟内创建英特尔16 FinFET（鳍场效应晶体管）的热模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Multiscale thermal modelling with AI

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来源期刊

Nature Electronics Engineering-Electrical and Electronic Engineering

CiteScore

47.50

自引率

2.30%

发文量

159

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