Crosstalk Delay and Reliability Analysis of Carbon Nanotube On-Chip Interconnects: Modelling and Optimization using Metaheuristic Algorithm

In the field of VLSI technology, the miniaturization of ICs poses significant challenges to overall interconnect performance, particularly due to the rising concerns of crosstalk-induced reliability and durability issues. This work explores graphene-based randomly mixed carbon nanotube bundle (RMCB) interconnects as a viable solution for advanced reliable VLSI applications. Employing metaheuristic algorithms (Maximum Hole Degree, Particle Swarm Optimization, Stoyan and Yaskov Algorithm), this study seeks to optimize RMCB structures, maximizing carbon nanotube density within a fixed area. Notably, this study explores the effectiveness of algorithms' performance in optimizing RMCB structures at a nano-technology node. Extensive signal integrity and reliability assessments, considering both rugged and pristine substrates, reveal that Stoyan and Yaskov (SY)-based optimization excels over PSO and MHD-based counterparts in terms of on-chip interconnect performance and reliability. The SY structure significantly dominates MHD-based (and PSO) counterparts by reducing crosstalk delay by 50% (30%), enhancing the average failure rate by 40% (37%), and improving electromigration reliability by 170% (63%).