Comparative Analysis of Prior Knowledge-Based Machine Learning Metamodels for Modeling Hybrid Copper–Graphene On-Chip Interconnects-Reference-Cited by-同舟云学术

Comparative Analysis of Prior Knowledge-Based Machine Learning Metamodels for Modeling Hybrid Copper–Graphene On-Chip Interconnects

Published:2022-12 Issue:6 Volume:64 Page:2249-2260
ISSN:0018-9375
Container-title:IEEE Transactions on Electromagnetic Compatibility
language:
Short-container-title:IEEE Trans. Electromagn. Compat.

Author:

Kushwaha Suyash¹,Soleimani Nastaran²,Treviso Felipe²^ORCID,Kumar Rahul³,Trinchero Riccardo²^ORCID,Canavero Flavio G.²^ORCID,Roy Sourajeet⁴^ORCID,Sharma Rohit¹^ORCID

Affiliation:

1. Department of Electrical Engineering, Indian Institute of Technology Ropar, Rupnagar, India

2. Department of Electronics and Telecommunication, Politecnico de Turino, Torino, Italy

3. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA

4. Department of Electronics and Communication Engineering, Indian Institute of Technology Roorkee, Roorkee, 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/ielx7/15/9985237/09899763.pdf?arnumber=9899763

Reference37 articles.

1. Deep Reinforcement Learning-Based Optimal Decoupling Capacitor Design Method for Silicon Interposer-Based 2.5-D/3-D ICs

2. Causal and Passive Parameterization of S-Parameters Using Neural Networks

3. Machine Learning for the Performance Assessment of High-Speed Links

4. Machine Learning and Uncertainty Quantification for Surrogate Models of Integrated Devices With a Large Number of Parameters

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