We present a windowing technique of waveform relaxation for dynamic systems. An effective estimation on window length is derived by an iterative error expression provided here. Relaxation processes can be speeded up if one takes the windowing technique in advance. Numerical experiments are given to further illustrate the theoretical analysis.
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.
In order to suppress drain-induced barrier lowering in dual material gate SOI MOSFETs,halo doping is used in the channel near the source. Two-dimensional analytical models of surface potential and threshold voltage for the novel SOI MOSFET are developed based on the explicit solution of the two-dimensional Poisson＇s equation. Its characteristic improvement is investigated. It is concluded that the novel structure exhibits better suppression of drain-induced barrier lowering and higher carrier transport efficiency than conventional dual material gate SOI MOSFETs. Its drain-induced barrier lowering decreases with increasing halo doping concentration but does not change monotonically with halo length. The analytical models agree well with the two-dimensional device simulator MEDICI.
Dual material gate SOI MOSFET with asymmetrical halo can suppress short channel effect and increase carriers transport efficiency. The analytical model of its subthreshold drain current is derived based on the explicit solution of two-dimensional Poisson’s equation in the depletion region. The model takes into consideration the channel length modulation effect and the contribution of the back channel current component. Its validation is verified by comparision with two dimensional device simulator MEDICI.
