Machine learning augmented compact modeling for simultaneous improvement in computational speed and accuracy
K Sheelvardhan, S Guglani… - … on Electron Devices, 2023 - ieeexplore.ieee.org
In this article, we have presented the use of prior physics knowledge-based artificial neural
networks (KBANNs) to improve the simulation speed and accuracy of compact models for …
networks (KBANNs) to improve the simulation speed and accuracy of compact models for …
Modified knowledge-based neural networks using control variates for the fast uncertainty quantification of on-chip MWCNT interconnects
In this article, a modified knowledge-based artificial neural network (KBANN) metamodel is
developed for the efficient uncertainty quantification of on-chip multiwalled carbon nanotube …
developed for the efficient uncertainty quantification of on-chip multiwalled carbon nanotube …
Artificial Neural Networks With Fast Transfer Learning for Statistical Signal Integrity Analysis of MWCNT and MLGNR Interconnect Networks
In this article, artificial neural network (ANN) metamodels have been developed for the fast
statistical signal integrity analysis of multiwalled carbon nanotube and multilayer graphene …
statistical signal integrity analysis of multiwalled carbon nanotube and multilayer graphene …
Prior knowledge accelerated transfer learning (PKI-TL) for machine learning assisted uncertainty quantification of MLGNR interconnect networks
In this paper, an algorithm to combine the distinct advantages of knowledge-based training
and transfer learning has been developed for the fast artificial neural network (ANN) …
and transfer learning has been developed for the fast artificial neural network (ANN) …
Combining Prior Knowledge with Transfer Learning (PKID-TL) for Fast Neural Network Enabled Uncertainty Quantification of Graphene On-Chip Interconnects
In this paper, an artificial neural network (ANN) enabled uncertainty quantification technique
is developed for graphene on-chip interconnects. In the proposed technique, primary ANNs …
is developed for graphene on-chip interconnects. In the proposed technique, primary ANNs …
Fast Multi-ANN Composite Models for Repeater Optimization in Presence of Parametric Uncertainty for on-Chip Hybrid Copper-Graphene Interconnects
In this paper, composite models are developed to predict the statistics of the optimal number
and size of repeaters required to minimize the power delay product (PDP) of on-chip hybrid …
and size of repeaters required to minimize the power delay product (PDP) of on-chip hybrid …
Noise-Aware Uncertainty Quantification of MLGNR Interconnects using Fast Trained Artificial Neural Networks
This paper presents a prior knowledge accelerated transfer learning technique to efficiently
train artificial neural networks (ANNs) to address uncertainty in on-chip multilayer graphene …
train artificial neural networks (ANNs) to address uncertainty in on-chip multilayer graphene …