An Artificial Neural Network Surrogate Model for Repeater Optimization in the Presence of Parametric Uncertainty for Hybrid Copper-Graphene Interconnect Networks-Reference-Cited by-同舟云学术

An Artificial Neural Network Surrogate Model for Repeater Optimization in the Presence of Parametric Uncertainty for Hybrid Copper-Graphene Interconnect Networks

Published:2022-07-06 Issue: Volume: Page:
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Container-title:2022 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO)
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Author:

Sharif Adeeba¹,Pathania Sunil²,Kushwaha Suyash²,Roy Sourajeet¹,Sharma Rohit²,Kaushik Brajesh Kumar¹

Affiliation:

1. Indian Institute of Technology Roorkee,Department of Electronics and Communications Engineering,Roorkee,Uttarakhand,India,247667

2. Indian Institute of Technology Ropar,Department of Electrical Engineering,Rupnagar,Punjab,India,140001

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10038779/10038515/10038956.pdf?arnumber=10038956

Reference11 articles.

1. Repeater Insertion to Reduce Delay and Power in Copper and Carbon Nanotube-Based Nanointerconnects

2. Analyzing Crosstalk-Induced Effects in Rough On-Chip Copper Interconnects

3. Knowledge-Based Neural Networks for Fast Design Space Exploration of Hybrid Copper-Graphene On-Chip Interconnect Networks

4. Enhanced Electrical and Thermal Conduction in Graphene-Encapsulated Copper Nanowires

5. Electrical Modeling of On-Chip Cu-Graphene Heterogeneous Interconnects

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1. Fast Multi-ANN Composite Models for Repeater Optimization in Presence of Parametric Uncertainty for on-Chip Hybrid Copper-Graphene Interconnects;IEEE Access;2023