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Design and Simulation of a High-Performance Tunneling Field Effect Transistor-Based Biosensor Using a Heterojunction Electron-Hole Bilayer
被引:2
|作者:
Bahrami, Hossein
[1
]
Vadizadeh, Mahdi
[2
]
Borjlu, Shaban Rezaei
[1
]
机构:
[1] Islamic Azad Univ, Dept Elect Engn, Ashtian Branch, Ashtian, Iran
[2] Islamic Azad Univ, Dept Elect Engn, Karaj Branch, Karaj, Iran
关键词:
electron-hole bilayer;
dielectrically modulated tunnel FET;
high-switching speed;
low-power;
FET-BASED BIOSENSOR;
VERTICAL TFET;
GATE;
D O I:
10.1149/1945-7111/ad7bf1
中图分类号:
O646 [电化学、电解、磁化学];
学科分类号:
081704 ;
摘要:
This study introduces a novel dielectrically-modulated heterojunction electron-hole bilayer tunnel field-effect transistor (DM-HEHBTFET) for bio-sensing applications. The device features a Ga0.85Sb0.15As/Ga0.8In0.2As heterojunction and a p-type pocket in the channel, achieving a remarkably low threshold voltage (VT) of 20 mV, an average subthreshold slope (SS) of 5.7 mV/dec, and a leakage current (IOFF) as low as 5 x 10(-11) A/mu m. The staggered bandgap in the heterostructures enhances electric field control, enabling lower gate voltage operation. Furthermore, the strategically positioned nanogap cavities in non-overlapping regions of the top and bottom gates effectively mitigate gate control issues over the channel, ensuring improved device performance. A modified design, the modified DM-HEHBTFET, is also proposed, featuring source and drain regions engineered with Ga0.85Sb0.15As/Ga0.8In0.2As heterojunctions. This design mitigates leakage current and improves the average subthreshold slope (SS). For biomolecules with a dielectric constant of 12, the modified biosensor exhibits a drain current sensitivity (S-current) of 2.6e4, average SS = 2.7 mV/dec, and I-OFF = 1e-12 A/mu m. The device's performance is assessed by examining steric hindrance and band tailing effects. The modified biosensor outperforms recent DM-TFET biosensors, making it a promising candidate for low-power, high-switching speed bio-sensing. (c) 2024 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
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页数:13
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