CAJ | 학술논문

为了处理纳米MOSFET载流子分布的量子效应,提出了基于Levenberg-Marquardt BP神经网络的量子更正模型,通过载流子的经典密度计算其量子密度,并对拥有不同隐层数和隐层神经元数的神经网络的训练速度和精度进行了研究.结果表明:含有2个隐层的神经网络具有高的训练速度和精度,但隐层神经元数对速度和精度的影响并不明显;对于单栅和双栅纳米MOSFET,其载流子量子密度可以通过神经网络进行快速计算,其结果与Schrodinger-Poisson方程的吻合程度很高.
위료처리납미MOSFET재류자분포적양자효응,제출료기우Levenberg-Marquardt BP신경망락적양자경정모형,통과재류자적경전밀도계산기양자밀도,병대옹유불동은층수화은층신경원수적신경망락적훈련속도화정도진행료연구.결과표명:함유2개은층적신경망락구유고적훈련속도화정도,단은층신경원수대속도화정도적영향병불명현;대우단책화쌍책납미MOSFET,기재류자양자밀도가이통과신경망락진행쾌속계산,기결과여Schrodinger-Poisson방정적문합정도흔고.
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.