In 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 two dimensions, 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.

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