JOURNAL ARTICLE
Novel L‐shaped drain dual‐gate SiGe MOSFET for high‐frequency, low power applications.
Published In: International Journal of Numerical Modelling, 2023, v. 36, n. 1. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Yadav, Shekhar; Negi, Chandra Mohan Singh 3 of 3
Abstract
In this work, vertically trenched double gate architecture has been investigated, in which the gates are implemented inside vertical oxide trenches. Besides, a silicon‐germanium channel dual‐gate MOSFET with unconventional L‐shaped drain architecture is proposed to improve device performance further. We have shown that the proposed device is the first‐of‐its‐kind device architecture that controls short channel effects and markedly improves transistor performance. Here, The Atlas 2D device simulator has been used to analyze the performance of the devices. At nanoscaled device dimensions, the proposed L‐shaped DG MOS device demonstrated superior ON current characteristics, higher values of transconductance, larger unity gain cut off frequencies, greater Ion to Ioff ratios, higher gain, lower parasitic capacitance, greater transconductance to drain conductance ratio, suppressed drain induced barrier lowering (DIBL), and lower subthreshold swing (S.S.), in comparison to the recently presented Double Gate MOS with unconventional side drain architecture. All these intriguing features demonstrated by the proposed transistor structure make it a potential candidate for low‐power, high‐frequency applications. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Numerical Modelling. 2023/01, Vol. 36, Issue 1, p1
- Document Type:Article
- Subject Area:Geology
- Publication Date:2023
- ISSN:0894-3370
- DOI:10.1002/jnm.3039
- Accession Number:160900198
- Copyright Statement:Copyright of International Journal of Numerical Modelling is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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