Scalable Modeling of Magnetic Inductance in Carbon Nanotube Bundles for VLSI Interconnect

In this paper, we develop accurate and scalable models for the magnetic inductance in bundles of single-walled carbon nanotubes, which have been proposed as a means to alleviate the increasingly critical resistance problems associated with traditional copper interconnect in VLSI applications. The models consider the density and statistical distribution of both metallic and semiconducting nanotubes within the bundle. We evaluate the speed, accuracy and scalability of our magnetic inductance modeling techniques and previously proposed inductance models. The inductance model with the best performance evaluates the magnetic inductance of nanotube bundles with typical errors of less than 0.8 percent when compared with modeling each nanotube individually and provides orders of magnitude improvement in CPU time as the bundle size increases.