APA
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Kumar, R., Kumar, A., Guglani, S., Kumar, S., Roy, S., Kaushik, B. K., … Achar, R. (2020). A Temperature and Dielectric Roughness-Aware Matrix Rational Approximation Model for the Reliability Assessment of Copper– Graphene Hybrid On-Chip Interconnects. IEEE Transactions on Components, Packaging, and Manufacturing Technology.
Chicago/Turabian
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Kumar, Rahul, Amit Kumar, Surila Guglani, Somesh Kumar, Sourajeet Roy, Brajesh Kumar Kaushik, Rohit Sharma, and R. Achar. “A Temperature and Dielectric Roughness-Aware Matrix Rational Approximation Model for the Reliability Assessment of Copper– Graphene Hybrid On-Chip Interconnects.” IEEE Transactions on Components, Packaging, and Manufacturing Technology (2020).
MLA
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Kumar, Rahul, et al. “A Temperature and Dielectric Roughness-Aware Matrix Rational Approximation Model for the Reliability Assessment of Copper– Graphene Hybrid On-Chip Interconnects.” IEEE Transactions on Components, Packaging, and Manufacturing Technology, 2020.
BibTeX Click to copy
@article{rahul2020a,
title = {A Temperature and Dielectric Roughness-Aware Matrix Rational Approximation Model for the Reliability Assessment of Copper– Graphene Hybrid On-Chip Interconnects},
year = {2020},
journal = {IEEE Transactions on Components, Packaging, and Manufacturing Technology},
author = {Kumar, Rahul and Kumar, Amit and Guglani, Surila and Kumar, Somesh and Roy, Sourajeet and Kaushik, Brajesh Kumar and Sharma, Rohit and Achar, R.}
}
In this article, a closed-form matrix rational approximation (MRA) model is presented for the reliability assessment of copper–graphene hybrid on-chip interconnect networks. The key feature of this MRA model is its capacity to predict how the different values of temperature and dielectric roughness affect the signal integrity performance of the hybrid interconnect networks. As a result, the proposed MRA model is well suited for very fast parametric sweeps and worst case analysis of the hybrid interconnect networks, which has not been possible using existing closed-form models or even SPICE simulations. Numerical examples show that the proposed model is significantly more efficient than conventional models while exhibiting error less than 5%.