Comprehensive Physics Based TCAD Model for 2D MX2 Channel Transistors

D. Mahaveer Sathaiya,Terry Y. T. Hung,Edward Chen,Wen-Chia Wu,Aslan Wei,Chih-Piao Chuu,Sheng-Kai Su,Ang Sheng Chou,Cheng-Ting Chung,Chao-Hsin Chien,Han Wang,Jin Cai,Chung-Cheng Wu,Iuliana P. Radu,Jeff Wu

2022 INTERNATIONAL ELECTRON DEVICES MEETING, IEDM（2022）

Taiwan Semicond Mfg Co

Cited 7|Views7

Abstract

For the first time, a comprehensive TCAD model is developed to unambiguously extract key device parameters: contact resistance (R c ), channel mobility (μ CH ), Schottky barrier height (SBH), & D it from experimental data on back-gate (BG) transistors with MX 2 channel. The model is tested and validated against three different data sets with different contact metal, quality of channel, contact, and interfaces. Using model's output, we analyze the accuracy of R c and μ CH extracted by the TLM method and provide guidance on the limits of its applicability. Finally, the model is used to project contact requirements (SBH ~ 0eV, high doping density >2e13cm -2 ) for performant, scaled transistors with 2D material channel in stacked nanosheet configuration.

Translated text

Key words

2D material channel,2D MX2 channel transistors,back-gate transistors,channel mobility,contact metal,contact requirements,contact resistance,high doping density,scaled transistors,Schottky barrier height,TCAD model

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

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

Summary is being generated by the instructions you defined