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

## TL;DR

This paper investigates copper grain boundary resistivity using atomistic models and develops a neural network-based compact model, providing insights into orientation effects and resistivity distribution in large structures.

## Contribution

It introduces a methodology combining atomistic tight binding, EAM, and NEGF for resistivity modeling, and constructs a neural network-based predictive model.

## Key findings

- Resistivity distribution in three-grain structures is approximately normal.
- Methodology validated with <5nm grain boundaries showing 6.4% deviation.
- Neural network model effectively predicts grain boundary resistivity.

## Abstract

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.