a b s t r a c tIn this paper a new method for modeling semiconductor devices by use of the drift-diffusion (DD) model and neural networks is presented. Unlike the hydrodynamic (HD) model which is complicated, time consuming with high processing cost, the proposed method has lower complexity and lower simulation time. In this method the RBF neural network has been used for correcting parameters in the drift-diffusion model. Therefore solving approximate model (DD) causes to obtain accurate response. The proposed method is first applied to a silicon n-i-n diode in one dimension, and then to a silicon thin-film MOSFET in twodimensions, both for interpolation and extrapolation. The obtained results for basic variables, i.e., electron and potential distribution for different voltages, confirm the high efficiency of the proposed method.
In this paper, a two-dimensional (2D) analytical model of the surface potential variation along the channel in a fully depleted dual-gate (DG) silicon-on-insulator (SOI) metal oxide semiconductor field effect transistor (MOSFET) is proposed to investigate short-channel effects (SCEs). Our model includes the length of the two gates and the voltage difference between them. We demonstrate that the surface potential in the channel region exhibits a step function, which causes the screening of the drain potential. This results in suppressed SCEs such as the hot-carrier effect and drain-induced barrier lowering (DIBL). The obtained results of our model are compared with those of the single-gate (SG) SOI MOSFET, and the DG SOI MOSFET performance is found to be superior.
In this paper, a new approach is proposed to perform broken rotor bar fault detection in induction motors using of support vector machine (SVM) classifier. New features such as harmonic curve area, harmonic crest angle and harmonic amplitude have been extracted from power spectral density (PSD) of stator current in steady state condition using of Fast Fourier Transform (FFT). It is shown that combination of the first couple of these features had very better results compare with the harmonic amplitude feature in fault detection of motor. The proposed method was applied to a 1.5kW standard three phase induction motor using of different rotors that had various types of broken rotor bars. Experimental results confirmed the high efficiency of the proposed method for broken rotor fault detection in induction motors.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.