Fast Multi-ANN Composite Models for Repeater Optimization in Presence of Parametric Uncertainty for on-Chip Hybrid Copper-Graphene Interconnects

Fast Multi-ANN Composite Models for Repeater Optimization in Presence of Parametric Uncertainty for on-Chip Hybrid Copper-Graphene Interconnects

In this paper, composite models are developed to predict the statistics of the optimal number and size of repeaters required to minimize the power delay product (PDP) of on-chip hybrid copper-graphene interconnects when subject to parametric uncertainty. Specifically, two distinct artificial neural...

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Bibliographic Details
Main Authors: Suyash Kushwaha, Avirup Dasgupta, Sourajeet Roy, Rohit Sharma
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Artificial neural networks (ANNs)
copper-graphene interconnects
fin field effect transistors (FinFETs)
parametric uncertainty
repeaters
statistical analysis
Online Access:https://ieeexplore.ieee.org/document/10323398/

Internet

https://ieeexplore.ieee.org/document/10323398/

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