Fast Multi-ANN Composite Models for Repeater Optimization in Presence of Parametric Uncertainty for on-Chip Hybrid Copper-Graphene Interconnects
Author:
Affiliation:
1. Department of Electrical Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India
2. Department of Electronics and Communication Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Funder
Semiconductor Research Corporation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10323398.pdf?arnumber=10323398
Reference20 articles.
1. Optimum wire sizing of RLC interconnect with repeaters
2. An Artificial Neural Network Surrogate Model for Repeater Optimization in the Presence of Parametric Uncertainty for Hybrid Copper-Graphene Interconnect Networks
3. Comparative Analysis of Prior Knowledge-Based Machine Learning Metamodels for Modeling Hybrid Copper–Graphene On-Chip Interconnects
4. Knowledge-Based Neural Networks for Fast Design Space Exploration of Hybrid Copper-Graphene On-Chip Interconnect Networks
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