GOS dual-metal split-gate step channel dielectrically modulated DG-TFET biosensor: A label-free non-ideal case study and analysis

Abstract This article performs an extensive analysis of the ideal and non-ideal case of hybridization of biomolecules in the nano-gap cavity present in a biosensor device. A gate-on-source (GOS) dual metal, split-gate, step channel dielectrically modulated (DM) double gate TFET-based label-free biosensor is proposed in this paper. The commercial TCAD simulation software (Silvaco TCAD) is used to simulate the proposed device. The paper focusses on both the performance improvement of the TFET device used to implement the biosensor and the sensitivity of the biosensor device. This proposed label-free bio-sensing device exhibits a high drain current sensitivity ∼ 10 8 (for dielectric constant k in the range of 15) irrespective of the presence/absence of charge on the biomolecules. A high I ON /I OFF ratio of the range ∼ 10 8 is obtained for the proposed device. A low sub-threshold swing (less than 60 mV dec −1 ) is seen to be given by the proposed device. There are several non-ideal cases investigated in this work to allow the comprehension of the viability of this biosensor in practical applications. The non-ideal cases with fill factors ranging from 30% to 80% are analysed alongwith steric hindrance effects n the device sensitivity. The integration of electrical FET device with the biological sensing module to develop a biosensor is successfully achieved in this work to allow the attainment of a feasible solution for everyday needs.

Authors

Publication Details

Journal
Advances in Natural Sciences Nanoscience and Nanotechnology
Published
2024-12-30
DOI
https://doi.org/10.1088/2043-6262/ad9f4a
Citations
1
Primary Topic
Advancements in Semiconductor Devices and Circuit Design
Type
article
Field-Weighted Citation Impact
0.21
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

GOS dual-metal split-gate step channel dielectrically modulated DG-TFET biosensor: A label-free non-ideal case study and analysis

Namrata Shaw, Bratati Mukhopadhyay
1 citations
Advances in Natural Sciences Nanoscience and Nanotechnology
Advancements in Semiconductor Devices and Circuit Design
0.21
article

GOS dual-metal split-gate step channel dielectrically modulated DG-TFET biosensor: A label-free non-ideal case study and analysis

Namrata Shaw, Bratati Mukhopadhyay
article en
1 citations

Abstract

Abstract This article performs an extensive analysis of the ideal and non-ideal case of hybridization of biomolecules in the nano-gap cavity present in a biosensor device. A gate-on-source (GOS) dual metal, split-gate, step channel dielectrically modulated (DM) double gate TFET-based label-free biosensor is proposed in this paper. The commercial TCAD simulation software (Silvaco TCAD) is used to simulate the proposed device. The paper focusses on both the performance improvement of the TFET device used to implement the biosensor and the sensitivity of the biosensor device. This proposed label-free bio-sensing device exhibits a high drain current sensitivity ∼ 10 8 (for dielectric constant k in the range of 15) irrespective of the presence/absence of charge on the biomolecules. A high I ON /I OFF ratio of the range ∼ 10 8 is obtained for the proposed device. A low sub-threshold swing (less than 60 mV dec −1 ) is seen to be given by the proposed device. There are several non-ideal cases investigated in this work to allow the comprehension of the viability of this biosensor in practical applications. The non-ideal cases with fill factors ranging from 30% to 80% are analysed alongwith steric hindrance effects n the device sensitivity. The integration of electrical FET device with the biological sensing module to develop a biosensor is successfully achieved in this work to allow the attainment of a feasible solution for everyday needs.

Advances in Natural Sciences Nanoscience and NanotechnologyVol. 16(1)
Affordable and clean energy
Openalex Percentile: Top 44%
Advancements in Semiconductor Devices and Circuit Design
0.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.