Meshless analysis of bilayer graphene nanoribbon for radio frequency interconnects

Kanthamani, S.; Gayathiri, G.; Rohini, S.

doi:10.1049/mnl.2015.0155

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…One of the major parameters observed in the majority of MEMS/NEMS structures is the pull-down voltage instability and copious research have been done in this area using different numerical approaches [13]. Numerical analysis for different MEMS/NEMS structures in the determination of pull-down voltage was analysed [14,15].…”

Section: Introductionmentioning

confidence: 99%

Pull in Analysis of Nano sized RF switches using Artificial Neural Networks

Sethuraman¹,

Chaitanya²,

Kanthamani³

et al. 2023

Preprint

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An Artificial Neural Network (ANN) model for the pull-down voltage analysis of Radio Frequency (RF) NEMS (Nano Electro-Mechanical) switch is presented in this paper. The fixed-fixed beam and cantilever beam structure have been chosen and analyzed in terms of pull-down voltage with the variation of its physical parameter values. An ANN model was trained with several training algorithms to achieve higher performance and computational efficiency. The pull-down voltage predicted from the Levenberg-Marquardt backpropagation ANN model has been compared with previous results presented in experimental and theoretical studies available in the literature. The proposed ANN model was also used to investigate the geometric and physical influence of the nano-switch on the pull-in voltage, and results were reported. The proposed ANN model performs better, with less Mean Square Error (MSE) of 0.000045 and 0.000092 for cantilever and fixed-fixed beam structures respectively.

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Section: Introductionmentioning

confidence: 99%

Pull in Analysis of Nano sized RF switches using Artificial Neural Networks

Sethuraman¹,

Chaitanya²,

Kanthamani³

et al. 2023

Preprint

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“…Various prototypes that are made of CNT-based NEMS switcheslike nano tweezers [10,11]and their switching characteristics have been reported [12][13][14][15].Dynamic and static analysis of pulldown voltage for CNT switches such as xed-xed and cantileverswitches that are suspended on a ground plane made of graphene has also been reported for radiofrequency applications [16][17][18][19] paving the pathway for developingrapid and recon gurable microwave circuitry.One of the major parameterobserved in the majority of MEMS/NEMS structures is the pull-down voltage instability and copious research have been done in this area using different numerical approaches [20][21][22][23]. Experimentalanalysis and FEM analysis using CAD tools for different MEMS/NEMS structures in the determination of pull-down voltage were analyzed [24][25][26][27][28]. In recent years, notable studies have been performed on the instability analysis of MEMS/NEMS structures by considering the couple stress theory and stress-driven nonlocal integral models [29][30][31][32][33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Pull in Analysis of Nano sized RF switches using Artificial Neural Networks

Sethuraman¹,

Chaitanya²,

Kanthamani³

et al. 2022

Preprint

View full text Add to dashboard Cite

An Artificial Neural Network (ANN) model for the pull-down voltage analysis of Radio Frequency (RF) NEMS(Nano Electro-Mechanical) switch is presented in this paper. The fixed-fixed beam and cantilever beam structure have been chosen and analyzed in terms of pull-down voltage with the variation of its physical parameter values. An ANN model was trained with several training algorithms to achieve higher performance and computational efficiency.The pull-down voltage predicted from theLevenberg-Marquardt backpropagationANN modelhas been compared with previous results presented in experimental and theoretical studies available in the literature.The proposed ANN model was also used to investigate the geometric and physical influence of the nano-switch on the pull-in voltage, and results were reported.The proposed ANN model performs better, with less Mean Square Error (MSE) of 0.000045 and 0.000092 for cantilever and fixed-fixed beam structures respectively.

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“…Graphene nanoribbon (GNR) is a narrow strip of graphene sheet, which can be classified into multilayer GNR (MLGNR) and single-layer GNR (SLGNR) depending on its stacked number of layers. SLGNR is not suitable for on-chip interconnects owing to its larger intrinsic resistance [7][8][9]. Based on the types of connection with other devices or interconnects, MLGNR can be further categorized into top contact MLGNR (TC-MLGNR) and side contact MLGNR (SC-MLGNR) [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

The Ultra-Low-k Dielectric Materials for Performance Improvement in Coupled Multilayer Graphene Nanoribbon Interconnects

Zhang

Tang

2019

Electronics

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The ultra-low-k dielectric material replacing the conventional SiO2 dielectric medium in coupled multilayer graphene nanoribbon (MLGNR) interconnects is presented. An equivalent distributed transmission line model of coupled MLGNR interconnects is established to derive the analytical expressions of crosstalk delay, transfer gain, and noise output for 7.5 nm technology node at global level, which take the in-phase and out-of-phase crosstalk into account. The results show that by replacing the SiO2 dielectric mediums with the nanoglass, the maximum reduction of delay time and peak noise voltage are 25.202 ns and 0.102 V for an interconnect length of 3000 µm, respectively. It is demonstrated that the ultra-low-k dielectric materials can significantly reduce delay time and crosstalk noise and increase transfer gain compared with the conventional SiO2 dielectric medium. Moreover, it is found that the coupled MLGNR interconnect under out-of-phase mode has a larger crosstalk delay and a lesser transfer gain than that under in-phase mode, and the peak noise voltage increases with the increase of the coupled MLGNR interconnect length. The results presented in this paper would be useful to aid in the enhancement of performance of on-chip interconnects and provide guidelines for signal characteristic analysis of MLGNR interconnects.

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Meshless analysis of bilayer graphene nanoribbon for radio frequency interconnects

Cited by 10 publications

References 21 publications

Pull in Analysis of Nano sized RF switches using Artificial Neural Networks

Pull in Analysis of Nano sized RF switches using Artificial Neural Networks

Pull in Analysis of Nano sized RF switches using Artificial Neural Networks

The Ultra-Low-k Dielectric Materials for Performance Improvement in Coupled Multilayer Graphene Nanoribbon Interconnects

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