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Grain-Boundary Resistance in Copper Interconnects: From an Atomistic Model to a Neural Network

Daniel Valencia* and Evan Wilson

Zhengping Jiang

Gustavo A. Valencia-Zapata and Kuang-Chung Wang

Gerhard Klimeck

Michael Povolotskyi

  • School of Electrical and Computer Engineering, Network for Computational Nanotechnology, Purdue University, West Lafayette, Indiana 47907, USA

  • Samsung Semiconductor Inc., San Jose, California 95134, USA

  • School of Electrical and Computer Engineering, Network for Computational Nanotechnology, Purdue University, West Lafayette, Indiana 47906, USA

  • School of Electrical and Computer Engineering, Network for Computational Nanotechnology, Purdue University, West Lafayette, Indiana 47906, USA and Birck Nanotechnology Center, Network for Computational Technology, Purdue University, West Lafayette, Indiana 47907, USA

  • Birck Nanotechnology Center, Network for Computational Technology, Purdue University, West Lafayette, Indiana 47907, USA

  • *valencid@purdue.edu
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Phys. Rev. Applied 9, 044005 – Published 4 April, 2018

DOI: https://doi.org/10.1103/PhysRevApplied.9.044005

Abstract

Orientation effects on the specific resistance of copper grain boundaries are studied systematically with two different atomistic tight-binding methods. A methodology is developed to model the specific resistance of grain boundaries in the ballistic limit using the embedded atom model, tight- binding methods, and nonequilibrium Green’s functions. The methodology is validated against first-principles calculations for thin films with a single coincident grain boundary, with 6.4% deviation in the specific resistance. A statistical ensemble of 600 large, random structures with grains is studied. For structures with three grains, it is found that the distribution of specific resistances is close to normal. Finally, a compact model for grain-boundary-specific resistance is constructed based on a neural network.

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Outline

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Phys. Rev. Applied 9, 044005– Published 4 April, 2018
  • Received 20 January 2017
  • Revised 28 November 2017
Article part of CHORUS
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