In this paper we propose a new model based on an analytical model for undoped symmetric double gate MOSFETs introduced by Chenming Hu et al. The proposed model targets to include the quantum confinement and the most important short channel effects. The new model results were compared with a device simulator results to validate the proposed modifications. The proposed model introduces low fitting error reaches t01 % for ultra thin and short channel double gate transistors.index terms-Compact model, double gate, short channel effects, quantum confinement

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A model for carbon nanotube field-effect transistors (CNTFETs) is developed using neural networks approach. This model accurately predicts the I-V characteristics for different structures of CNTFETs. The model is implemented inside the circuit simulator Eldo using its general user defined model (GUDM) template. To confirm the accuracy of the proposed model, the I-V characteristics are compared to device simulation results. The model is also validated using experimental data for both shottky barrier and conventional CNTFETs. The model shows excellent fitting for both the experimental and device simulation data with average percentage error doesn't exceed 1%.

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In this paper we propose an analytical modification for Hu's model which is an analytical model for undoped symmetric double gate MOSFETs. This modification targets to include the energy states quantization effect on the drain current. This leads to correct the model behavior for ultra thin double gate. Moreover, we introduce a simple method to include the velocity saturation effect in the current equation. Comparison with device simulator results is finally presented to validate the proposed modifications.