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• W2912805047 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." @default.
• W2912805047 created "2019-02-21" @default.
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• W2912805047 date "2019-01-01" @default.
• W2912805047 modified "2023-10-17" @default.
• W2912805047 title "Repeater Insertion to Reduce Delay and Power in Copper and Carbon Nanotube-Based Nanointerconnects" @default.
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• W2912805047 doi "https://doi.org/10.1109/access.2019.2893960" @default.
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