Impact of fermi level engineering on the conductive behavior of MLGNR for nano-scale interconnects

The Moore law for scaling of nodes of technology nodes has led to the major advancement in chip design and functionality. With the downscaling of technological nodes, the efficiency of gate becomes insignificant compared to the interconnect performance. Graphene nanoribbons have demonstrated remarkable potential and outstanding performance as on-chip interconnects. To analyze the parasitic behavior of multi-layer graphene nanoribbons (MLGNRs), an ESC model representing an electrically equivalent single conductor is proposed, incorporating an analytical approach. This model is used to investigate the impact of intercalation doping on the performance of MLGNRs across 16 nm, 22 nm, and 32 nm technology nodes. The analysis demonstrates that intercalation doping raises the Fermi energy of MLGNR layers, resulting in improved conductivity. Further, the study examines how variations in Fermi energy affect the parasitic parameters of MLGNR interconnects over global interconnect lengths ranging from 500 to 2000μm. The performance of MLGNRs is evaluated using the SPICE simulation tool, focusing on delay and power-delay product (PDP). The outcomes indicate that increased Fermi energy enhances MLGNR interconnect performance by reducing both delay and PDP across all three nodes of technology. • Fermi level tuning enables ultra-fast, low-delay MLGNR interconnects. • MLGNR exceeds copper in conductivity and energy efficiency at nanoscales. • Intercalation doping markedly boosts MLGNR’s electrical performance. • MLGNR offers strong promise for next-generation VLSI interconnects.