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.