Tunnel Field-Effect Transistor Triggered Silicon-Controlled Rectifier

Zhaonian Yang,Jinghao Xu,Shi Pu, Panqi Gao,Yue Zhang,Yuan Yang,Yan Zhang

IEEE TRANSACTIONS ON ELECTRON DEVICES（2023）

Xian Univ Technol | Xian Xiangteng Microelect Co Ltd | Xidian Univ

Cited 0|Views13

Abstract

In this article, a tunnel field-effect transistor (TFET) triggered silicon-controlled rectifier (SCR) device is proposed. A TFET is embedded into the SCR enabling an early carrier’s transportation through the n-well/p-well reverse-biased junction. This accelerates the junction breakdown and helps to establish the SCR’s positive feedback regeneration. The applications of the TFET-triggered SCR (TTSCR) in electrostatic discharge (ESD) protection are explored and the electrical characteristics are investigated. Moreover, the impact of the device structure on the device performance is discussed. The principle of the proposed device is verified using technology computer-aided design (TCAD) simulation. The simulation results show that compared to the prevalent diode-triggered SCR, the proposed device has a small layout area, a low leakage current, and a low overshoot voltage.

Translated text

Key words

TFETs,Junctions,Logic gates,Anodes,Integrated circuit modeling,Equivalent circuits,Rectifiers,Band-to-band tunneling (BTBT),electrostatic discharge (ESD),silicon-controlled rectifier (SCR),tunnel field-effect transistor (TFET)

求助PDF

上传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

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper