Abstract
Optimal repeater designs are performed for Cu and carbon nanotube (CNT)-based nanointerconnects to reduce the delay and power dissipation. The effects of inductance and metal-CNT contact resistance are treated appropriately. In this paper, the circuit parameters are calculated analytically, while they can be extracted experimentally for a specific foundry at a specific technology node. The particle swarm optimization (PSO) technique is employed to numerically calculate the optimal repeater size and the optimal number of repeaters in the Cu and CNT-based nanointerconnects. The results are verified against the analytical and genetic algorithm results. To facilitate CAD design, the machine-learning neural network (NN) is adopted. The data obtained using the PSO algorithm are used to train the NN and the feasibility of the NN is investigated and validated.
Original language | English (US) |
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Article number | 8620350 |
Pages (from-to) | 13622-13633 |
Number of pages | 12 |
Journal | IEEE Access |
Volume | 7 |
DOIs | |
State | Published - 2019 |
All Science Journal Classification (ASJC) codes
- General Computer Science
- General Materials Science
- General Engineering