Performance optimization of high-K pocket hetero-dielectric TFET using improved geometry design

电介质 几何学 材料科学 光电子学 数学
作者
Abdelrahman Elshamy,Ahmed Shaker,Yasmine Elogail,Marwa S. Salem,Mona El Sabbagh
出处
期刊:alexandria engineering journal [Elsevier BV]
卷期号:91: 30-38 被引量:6
标识
DOI:10.1016/j.aej.2024.01.072
摘要

This study explores the optimization of a hetero-dielectric tunnel field-effect transistor (HDTFET) structure to improve device performance. By incorporating a high-k oxide pocket in a portion of the source-side gate insulator, a local minimum in the conduction band edge is induced at the source-channel interface. This technique leads to improved tunneling rates and increased current handling capability. The simulation analysis focuses on optimizing the position and dimension of the high-k dielectric pocket to enhance key device characterization metrics such as ON-state current (ION), ON-to-OFF-state current ratio (ION/IOFF), subthreshold swing (SS), and cutoff frequency (fT). The resulting optimized design for a 30 nm-channel length involves a pocket shift of 1 nm and a pocket length of 12 nm. This configuration achieves a remarkable ON current of 55 µA/µm, which is 30 times higher than that of a conventional TFET. Importantly, other analog performance parameters remain unaffected, with fT surpassing 175 GHz for the 30 nm-channel. Additionally, transient analysis is conducted by applying a resistive load inverter circuit to a pulse input. The fall propagation delay (tphl) exhibits a greater than two orders of magnitude enhancement, along with improved overshoot voltage (VP) compared to a TFET without a pocket. The study further explores the impact of supply scaling on transient parameters. Optimal pocket scalability concerning channel length is found to be 40% for pocket length and approximately 2.5% for pocket shift relative to the source-channel interface. The proposed design significantly enhances DC and analog as well as circuit-level metrics compared to the traditional uniform gate oxide TFET.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
笑不出来发布了新的文献求助10
刚刚
星辰大海应助vv采纳,获得10
1秒前
NilinjunFJTCM完成签到,获得积分10
1秒前
蜘蛛道理完成签到 ,获得积分10
2秒前
3秒前
4秒前
4秒前
负责啤酒完成签到,获得积分10
5秒前
汉堡包应助稳重问蕊采纳,获得30
5秒前
醉意拥桃枝完成签到 ,获得积分10
6秒前
yizhi猫完成签到,获得积分10
8秒前
8秒前
笑点低硬币完成签到,获得积分10
9秒前
siyisan发布了新的文献求助10
9秒前
刘梦男完成签到 ,获得积分10
9秒前
PanY完成签到 ,获得积分10
10秒前
向聿发布了新的文献求助10
10秒前
和气生财君完成签到 ,获得积分0
10秒前
11秒前
Jasper应助兴奋灵采纳,获得10
11秒前
Java完成签到,获得积分0
11秒前
13秒前
14秒前
Singularity发布了新的文献求助10
15秒前
16秒前
元煜祺完成签到,获得积分10
16秒前
你好明天完成签到 ,获得积分10
18秒前
18秒前
Sissi完成签到,获得积分10
19秒前
贝博拉完成签到,获得积分10
19秒前
Hh发布了新的文献求助10
20秒前
财路通八方完成签到 ,获得积分10
20秒前
21秒前
开朗的猕猴桃完成签到,获得积分10
23秒前
NexusExplorer应助棋士采纳,获得10
23秒前
飘逸过客完成签到 ,获得积分10
23秒前
兴奋灵发布了新的文献求助10
24秒前
25秒前
yoqalux发布了新的文献求助10
26秒前
26秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Health Psychology 800
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Electric machines: theory, operating applications, and controls 500
The Analytical and Numerical Solution of Electric and Magnetic Fields 500
When Is Two-Stage Sample Robust Optimization Asymptotically Optimal? 500
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7593030
求助须知:如何正确求助?哪些是违规求助? 9170261
关于积分的说明 19627864
捐赠科研通 7170885
什么是DOI,文献DOI怎么找? 3267554
关于科研通互助平台的介绍 2432418
邀请新用户注册赠送积分活动 2260128