Chrome Extension
WeChat Mini Program
Use on ChatGLM

Impact of Source Height on the Characteristic of U-shaped Channel Tunnel Field-Effect Transistor

Superlattices and Microstructures(2017)SCI 3区

Xian Univ Technol

Cited 8|Views5
Abstract
Tunnel field-effect transistor (TFET) is very attractive in replacing a MOSFET, particularly for low-power nanoelectronic circuits. The U-shaped channel TFET (U-TFET) was proposed to improve the drain-source current with a reduced footprint. In this work, the impact of the source height (Hs) on the characteristic of the U-shaped channel tunnel field-effect transistor (U-TFET) is investigated by using TCAD simulation. It is found that with a fixed gate height (HG) the drain-source current has a negative correlation with Hs. This is because when the gate region is deeper than the source region, the electric field near the corner of the tunneling junction can be enhanced and the tunneling rate is increased. When Hs becomes very thin, the drain-source current is limited by the source region volume. The U-TFET with an n+ pocket is also studied and the same trend is observed. (C) 2017 Elsevier Ltd. All rights reserved.
More
Translated text
Key words
Band-to-band tunneling (BTBT),Electric field,Tunnel field effect transistor (TFET),U-shaped channel TFET (U-TFET)
PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest