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arXiv 1701.04897cond-mat.mtrl-sci
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铜互连线晶界电阻的原子模型到神经网络模型

Grain Boundary Resistance in Copper Interconnects from an Atomistic Model to a Neural Network

Daniel Valencia, Evan Wilson, Zhengping Jiang, Gustavo A. Valencia-Zapata, Gerhard Klimeck, Michael Povolotskyi

摘要:

采用两种不同的原子紧束缚方法,系统地研究了铜晶界电阻率的取向效应。采用嵌入原子模型、紧束缚方法和非平衡格林函数(NEGF)建立了晶界电阻率模型。针对电阻率偏差为6.4%的小的超薄体晶界(<5nm)的第一性原理计算,验证了该方法。研究了600个大的随机结构的统计系综。对于具有三个晶粒的结构,发现折射率的分布接近正态分布。最后,基于神经网络建立了一个简洁的晶界电阻率模型。

英文摘要:

Orientation effects on the resistivity of copper grain boundaries are studied systematically with two different atomistic tight binding methods. A methodology is developed to model the resistivity of grain boundaries using the Embedded Atom Model, tight binding methods and non-equilibrum Green's functions (NEGF). The methodology is validated against first principles calculations for small, ultra-thin body grain boundaries (<5nm) with 6.4% deviation in the resistivity. A statistical ensemble of 600 large, random structures with grains is studied. For structures with three grains, it is found that the distribution of resistivities is close to normal. Finally, a compact model for grain boundary resistivity is constructed based on a neural network.

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。

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