Neural-Network-Based Charge Density Quantum Correction of Nanoscale MOSFETs

作     者：李尊朝 蒋耀林 张瑞智 Li Zunchao;Jiang Yaolin;Zhang Ruizhi

作者机构：西安交通大学电子与信息工程学院西安710049 西安交通大学理学院西安710049 

出 版 物：《Journal of Semiconductors》 (半导体学报（英文版）)

年 卷 期：2006年第27卷第3期

页      面：438-442页

核心收录：

学科分类：080903[工学-微电子学与固体电子学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080501[工学-材料物理与化学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 

基　　金：国家自然科学基金(批准号:60472003) 国家重点基础研究发展计划(批准号:2005CB321701)资助项目~~ 

主　　题：neural network quantum correction nanoscale MOSFET charge density 

摘      要：For the treatment of the quantum effect of charge distribution in nanoscale MOSFETs,a quantum correction model using Levenberg-Marquardt back-propagation neural networks is presented that can predict the quantum density from the classical density. The training speed and accuracy of neural networks with different hidden layers and numbers of neurons are studied. We conclude that high training speed and accuracy can be obtained using neural networks with two hidden layers,but the number of neurons in the hidden layers does not have a noticeable effect, For single and double-gate nanoscale MOSFETs, our model can easily predict the quantum charge density in the silicon layer,and it agrees closely with the Schrodinger-Poisson approach.