WeChat Mini Program
Old Version Features

An Analytic Surface-Field-Based Quasi-Atomistic Model for Nanowire Mosfets with Random Dopant Fluctuations

IEEE transactions on electron devices(2015)SCI 2区SCI 3区

Peking Univ

Cited 7|Views19
Abstract
For the first time, an analytic surface-field-based model for nanowire MOSFETs with random dopant fluctuations (RDF) is reported. In this model, the depletion charge due to the discrete dopant distribution is described by the Dirac δ functions, while the mobile charge keeps its continuous form. By introducing two new variables, the discrete 1-D Poisson's equation is transformed into a simple algebraic equation to correlate the surface potential with the field (due to the inversion charge). Without solving the potential distribution, the drain current can be calculated from the Pao-Sah integral using the oxide-interface boundary condition. This model is shown to be more accurate in predicting the RDF effects than the continuous TCAD simulations for all the operating regions. We also discuss the RDF-incorporated short-channel effects by solving the discrete 2-D Poisson's equation in the subthreshold regime.
More
Translated text
Key words
Dirac delta function,discrete Poisson's equation,random dopant fluctuations (RDFs),surface-field-based model
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
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

要点】:本文首次提出了一种基于表面场分析模型的纳米线MOSFETs随机掺杂波动(RDF)准原子级模型,该模型在预测RDF效果方面比连续的TCAD模拟更准确。

方法】:通过将离散的掺杂电荷描述为Dirac δ函数,同时保持可动电荷的连续形式,并引入两个新变量,将一维泊松方程转化为简单的代数方程,以关联表面势与由反相电荷产生的场。

实验】:通过求解二维泊松方程在亚阈值区讨论了RDF影响下的短沟道效应,未具体提及实验数据集名称,但该模型在所有操作区域均显示出比连续TCAD模拟更高的准确性。