Understanding the Impact of Inductance in Carbon Nanotube Bundles for VLSI Interconnect Using Scalable Modeling Techniques

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 very large scale integration (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 excellent accuracy when compared to modeling each nanotube individually and provides orders of magnitude improvement in CPU time as the bundle size increases. Leveraging the magnetic inductance modeling techniques, we determine the relative impact of magnetic and kinetic inductance. Based on our results, the relative value of magnetic and kinetic inductance on single-walled carbon nanotube (SWCNT) bundles is highly dependent on the bundle geometry and the per unit length kinetic inductance