scispace - formally typeset
Search or ask a question

Why VTFET model is better? 


Best insight from top research papers

The VTFET model is considered better because it offers improved device performance and is suitable for various applications. The L-NC-DT-VTFET design maximizes vertical tunnelling over corner tunnelling, resulting in better performance for high-performance, low-power applications . The Graphene Channel Double Gate Dual Gate Material VTFET shows better results in DC characteristics and is a promising candidate for analog and high-frequency range applications . The VTFET with a V-shaped groove in the top junction region provides improved device performance . The CP-FG-VTFET is a promising device in terms of linearity and sensitivity for biosensor applications . The double gate vertical TFET using hetero-dielectric buried oxide (HDB) improves linearity performance for high-frequency applications .

Answers from top 1 papers

More filters
Papers (1)Insight
The L-NC-DT-VTFET model is better because it maximizes vertical tunnelling over corner tunnelling, making it suitable for high-performance, low-power applications.

Related Questions

How does the channel thickness affect the performance of vtfet?4 answersThe performance of vertical tunneling field-effect transistors (VTFETs) is affected by the thickness of the channel. For amorphous indium-gallium-zinc oxide (a-IGZO) FETs, the mobility and subthreshold swing (SS) values improve as the channel thickness decreases from 20 to 7 nm, but deteriorate when the thickness is less than 7 nm. In FETs with a thinner first region, the threshold voltage (Vth) is larger than in the second region, causing an asymmetric Vth across the channel length. In double-gate FETs with transition metal dichalcogenides (TMDs) channels, optimizing the thickness of the TMD channel and increasing the effective channel area enhances carrier behavior and device performance. In InAs quantum well nMOSFETs, the logic and analog performance is influenced by the channel thickness, with different parameters exhibiting optimal values at different thicknesses. Ultrascaled WS2 FETs show excellent ON-state and OFF-state performance, with the best performance observed for a WS2 body thickness of 2.1 nm.
What are the advantages of the transformer model?5 answersThe transformer model offers several advantages. Firstly, it excels in handling long dependencies between input sequence elements and enables parallel processing, making it suitable for tasks with sequential data. Secondly, it utilizes a self-attention mechanism to comprehend contextual relationships within the data, allowing it to capture complex patterns and dependencies. Thirdly, the transformer model has shown remarkable achievements in various domains, including Natural Language Processing (NLP), computer vision, audio and speech processing, healthcare, and the Internet of Things (IoT). Additionally, it has been proven to be computationally efficient while learning long-term dependencies, thanks to architectures like Transformer-XL. Lastly, the transformer model has been successful in generating meaningful predictions and representations, outperforming previous models in tasks such as ship trajectory prediction and clinical diagnosis.
What are the modeling and simulation approaches for organic FET?5 answersModeling and simulation approaches for organic FETs have been extensively studied in the literature. Comparative analysis between single gate and double gate organic FETs has been performed to evaluate performance parameters such as ON-OFF current ratio, threshold voltage, mobility, transconductance, and output conductance. Multiscale techniques have been developed to study organic solar cells, light emitting diodes, and field effect transistors, with a focus on parametrizing coarse-grained models for morphology and charge transport simulations. A modular framework based on VHDL-AMS has been proposed for Molecular-FET devices, allowing for the comparison of different models in terms of accuracy and computational efforts. Multiscale modeling approaches have been used to understand the relations between the molecular structure of organic materials, their aggregate morphology, and the performance of resulting electronic devices. Additionally, the influence of a photo-induced dipolar field on charge transfer in organic FET-like photoactive devices has been modeled using DFT quantum-chemical calculations.
How does most recent FinFET design differ from a conventional planar MOSFET ?5 answersFinFET design differs from a conventional planar MOSFET in several ways. FinFETs are non-planar devices that have a fin-shaped channel instead of a flat channel like MOSFETs. This allows for better control of the gate over the channel, reducing sub-threshold leakage and improving short channel effects (SCE) control. Additionally, FinFETs have high sub-threshold slope, high current drive strength, low dopant-prompted variations, and decreased power dissipation, making them suitable for low-power and high-performance circuits. In terms of scaling, FinFETs offer better scalability and improved performance metrics in the ultra-nanoscale regime. Furthermore, FinFETs can be enhanced by incorporating air-trench-isolation (ATI) and air-spacer between gate and source/drain (S/D) to further improve DC and RF performance. Overall, FinFETs provide a viable alternative to MOSFETs for future technology nodes, offering improved performance, reduced power consumption, and better control over leakage power.
What are the different sources of variation in finfets?5 answersThe different sources of variation in FinFETs include gate-metal work-function and oxide charge variations, random dopant fluctuation (RDF), work function variation (WFV), oxide thickness variation (OTV), metal gate granularity (MGG), and line-edge roughness (LER). These sources contribute to variability in threshold voltage (Vt) and subthreshold slope (Ssub). In sub-10nm W fin FinFETs, gate-metal work-function and oxide charge variations are shown to be the major contributors to in-wafer variability. RDF is revealed as the most significant source of variability as devices scale down. The impacts of MGG and LER on Vt variation are decomposed based on the differences in the physical mechanisms. Additionally, LER is observed to cause device-to-device variation in FinFETs under heavy ion irradiation. These findings provide insights for understanding and mitigating process-induced variation in FinFET technology.
How does the V-model compare to other software development methodologies?5 answersThe V-model is a software development lifecycle model that has been widely used in the industry. It is known for its rigorous and structured approach, with a focus on verification and validation. The V-model has been modified in some studies to add an intermediate step, which checks the fault diagnosability of each module. This modification provides advantages such as early detection of modeling deficiencies and decreased costs. In comparison to other software development methodologies, the V-model is one of many options available. Other popular methodologies include the waterfall model, agile, and object-oriented models. Each of these methodologies has its own advantages and disadvantages, and the choice of methodology depends on the specific requirements of the project. Overall, the V-model offers a structured and systematic approach to software development, but it is important to consider other methodologies and their suitability for different types of projects.