Modified Knowledge-Based Neural Networks Using Control Variates for the Fast Uncertainty Quantification of On-Chip MWCNT Interconnects
Author:
Affiliation:
1. Electronics and Communications Engineering Department, Indian Institute of Technology Roorkee, Roorkee, India
2. Department of Electrical Engineering, Indian Institute of Technology Ropar, Rupnagar, India
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Condensed Matter Physics,Atomic and Molecular Physics, and Optics
Link
http://xplorestaging.ieee.org/ielx7/15/10219031/10153925.pdf?arnumber=10153925
Reference28 articles.
1. Two-Level Multifidelity Algorithm With Dimension Reduction for Efficient Uncertainty Quantification of MWCNT Interconnects
2. A Predictor–Corrector Algorithm for Fast Polynomial Chaos-Based Uncertainty Quantification of Multi-Walled Carbon Nanotube Interconnects
3. Exploring the Impact of Parametric Variability on Eye Diagram of On-Chip Multi-walled Carbon Nanotube Interconnects using Fast Machine Learning Techniques
4. A Bilevel Multi-Fidelity Polynomial Chaos Approach for the Uncertainty Quantification of MWCNT Interconnect Networks With Variable Imperfect Contact Resistances
5. Reduced dimensional polynomial chaos approach for efficient uncertainty analysis of multi-walled carbon nanotube interconnects
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