积极学习用于发现二元金属间化合物作为先进的相互连接
Guoxiang Cui1, Zikang Guo2, Xiangyu Ren1
1School of Material Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
The journal of physical chemistry letters
|April 2, 2025
概括
机器学习加速了对先进集成电路的新互连材料的发现. 这种方法确定了有前途的二元金属间化合物,与传统的铜互连相比,它们具有优越的性能.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 半导体设备物理 半导体设备物理
背景情况:
- 传统的铜互连在先进的集成电路中面临着挑战,包括电阻的增加和电迁移寿命的缩短.
- 寻找具有高凝聚能和低电阻-密度-寿命产物 (ρ0 × λ) 的新兴材料作为替代品.
研究的目的:
- 加速新型二元金属间化合物的发现,用于下一代互连材料.
- 为了识别比传统的铜互连带有更好的性能材料.
主要方法:
- 积极学习与密度函数理论 (DFT) 计算相结合,用于加速材料选.
- 可解释机器学习,特别是沙普利增量解释 (SHAP),用于物理洞察.
主要成果:
- 在5次积极学习代后对100个二元金属间化合物的选.
- 实现了76%的有前途材料比例,显著超过随机查 (4.9%).
- 确定了关键材料描述符:小细胞体积和类似的孟德列夫数与低的r0 × λ值相关.
结论:
- 机器学习技术显示出发现高性能互连材料的巨大潜力.
- 像VMo,IrRh3,PtRh3,NbRu和CrIr3这样的有希望的金属间候选物被确定为Cu互连的潜在替代品.
- 这项研究证明了主动学习和可解释的人工智能在材料发现中的有效性.
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