基于神经网络的纳米MOSFET载流子密度量子更正

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.

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

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.