Carbon Nanotube Based Delay Model For High Speed Energy Efficient on Chip Data Transmission Using: Current Mode Technique
Sunil Jadav, Munish Vashistah, Rajeevan Chandel

TL;DR
This paper presents a novel delay model for high-speed, energy-efficient on-chip data transmission using current mode signaling with carbon nanotube interconnects, demonstrating significant improvements over voltage mode methods.
Contribution
The paper introduces a new current mode delay model for CNT-based interconnects, incorporating inductive effects and validated at 45nm technology, showing enhanced performance and energy efficiency.
Findings
Current mode signaling reduces energy consumption by 66.66% compared to voltage mode.
The proposed model accurately predicts delay considering inductive effects at 45nm.
CNT interconnects improve system response and energy efficiency in high-speed VLSI networks.
Abstract
Speed is a major concern for high density VLSI networks. In this paper the closed form delay model for current mode signalling in VLSI interconnects has been proposed with resistive load termination. RLC interconnect line is modelled using characteristic impedance of transmission line and inductive effect. The inductive effect is dominant at lower technology node is modelled into an equivalent resistance. In this model first order transfer function is designed using finite difference equation, and by applying the boundary conditions at the source and load termination. It has been observed that the dominant pole determines system response and delay in the proposed model. Using CNIA tool (carbon nanotube interconnect analyzer) the interconnect line parameters has been estimated at 45nm technology node. The novel proposed current mode model superiority has been validated for CNT type of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
