Title: Grain Boundary Resistance in Copper Interconnects from an Atomistic Model to a Neural Network,
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.