A Repeater Optimization Methodology for Global Multi-Walled Carbon Nanotube Interconnects

In this paper, the optimal repeater number and size are analyzed for multi-walled carbon nanotube interconnects by using the particle swarm optimization (PSO) algorithm. Genetic algorithm (GA) is also used to verify the corresponding results. Further, the neural network (NN) is trained to facilitate the EDA process. It is found that the computational time can be dramatically reduced with the implementation of NN.