Neural-space mapping-based large-signal modeling for MOSFET

In this article, a large-signal modeling approach based on the combination of equivalent circuit and neuro-space mapping modeling techniques is proposed for MOSFET. In order to account for the dispersion effects, two neuro-space (S) mapping based models are used to model the drain current at DC and RF conditions, respectively. Corresponding training process in our approach is also presented. Good agreement is obtained between the model and data of the DC, S parameter, and harmonic performance for a 0.13 μm channel length, 5 μm channel width per finger and 20 fingers MOSFET over a wide range of bias points, demonstrating the proposed model is valid for DC, small-signal and nonlinear operation. Comparison of DC, S-parameter, and harmonic performance between proposed model and empirical model further reveals the better accuracy of the proposed model.