Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Sneh Saurabh"'
Autor:
Abhinav Gupta, Sneh Saurabh
Publikováno v:
IEEE Journal of the Electron Devices Society, Vol 12, Pp 211-220 (2024)
This paper proposes a novel implementation of a ternary Spiking Neural Network (SNN) and investigates it using a hierarchical simulation framework. The proposed ternary SNN is trained in an unsupervised manner using the Spike Timing Dependent Plastic
Externí odkaz:
https://doaj.org/article/42eef74052764910930097845619891f
Publikováno v:
IEEE Access, Vol 9, Pp 141321-141328 (2021)
In this paper, with the help of calibrated 2-D simulations, we report a detailed study on the effect of drain induced barrier enhancement on the subthreshold swing and OFF-state current of a short channel MOSFET. We demonstrate that the presence of g
Externí odkaz:
https://doaj.org/article/bacb1fb917a049f2b78cf38122655665
Publikováno v:
IEEE Access, Vol 9, Pp 150366-150372 (2021)
In this paper, using calibrated TCAD simulations, we demonstrate how the performance of a Tunneling FET (TFET) can be improved by using a new phenomenon called drain induced barrier widening (DIBW) at the source-channel junction. Our results indicate
Externí odkaz:
https://doaj.org/article/fd1719d153a84db3884bfc9a7ef41dde
Autor:
Shelly Garg, Sneh Saurabh
Publikováno v:
IEEE Journal of the Electron Devices Society, Vol 8, Pp 1001-1009 (2020)
Recently, a few compact logic function realizations such as AND, OR, NAND and NOR have been proposed using double-gate tunnel field-effect transistor (DGTFET) with independent gate-control. In this article, using two-dimensional device simulations, w
Externí odkaz:
https://doaj.org/article/e3a71e5c68b14f029c430ba075df5fe2
Autor:
S. Garg, Sneh Saurabh
Publikováno v:
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol 6, Iss 2, Pp 146-154 (2020)
Tunnel field-effect transistors (TFETs) are being examined as a possible replacement of MOSFETs for digital applications. However, TFETs have small ON-state current and, typically, exhibit reduced speed compared with conventional MOSFETs. Nevertheles
Externí odkaz:
https://doaj.org/article/50d0c84061164c3cb8958d3805a267dd
Autor:
Shelly Garg, Sneh Saurabh
Publikováno v:
IEEE Open Journal of Nanotechnology, Vol 1, Pp 100-108 (2020)
In this paper, using device simulations, we investigate electrical characteristics of a tunnel field-effect transistor (TFET) in which band-to-band tunneling (BTBT) occurs dominantly within the channel, rather than at source-channel junction. The wit
Externí odkaz:
https://doaj.org/article/a58e99c4ad0f4c0d8bb69532c82a052a
Autor:
Shelly Garg, Sneh Saurabh
Publikováno v:
IEEE Journal of the Electron Devices Society, Vol 7, Pp 435-443 (2019)
Tunnel field-effect transistors (TFETs) are known to exhibit degraded electrical characteristics at smaller channel lengths, primarily due to direct source-to-drain band-to-band tunneling (BTBT). In this paper, we propose a technique to suppress dire
Externí odkaz:
https://doaj.org/article/857298fc7a82416886a10818e6311219
Autor:
Akhil James, Sneh Saurabh
Publikováno v:
IEEE Access, Vol 7, Pp 88960-88969 (2019)
In this paper, we have proposed a dopingless 1T DRAM (DL-DRAM) that utilizes the charge plasma concept. The proposed device employs a misaligned double-gate architecture to store holes and differentiates between the two logic states. The source, drai
Externí odkaz:
https://doaj.org/article/5f6335138bcb444093571b9319e5465b
Autor:
Shelly Garg, Sneh Saurabh
Publikováno v:
IEEE Access, Vol 7, Pp 117591-117599 (2019)
Recently, a compact realization of logic gates using double-gate tunnel field effect transistors (DGTFETs) with independently-controlled gate has been proposed. The key elements in the proposed implementation are the suppression of the tunneling at t
Externí odkaz:
https://doaj.org/article/6bbaeb0ba8014885bab930559c3375bc
Autor:
Syed Asrar Ul Haq, Abdul Karim Gizzini, Shakti Shrey, Sumit J. Darak, Sneh Saurabh, Marwa Chafii
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems. :1-13
Reliable and fast channel estimation is crucial for next-generation wireless networks supporting a wide range of vehicular and low-latency services. Recently, deep learning (DL) based channel estimation has been explored as an efficient alternative t