Abstract

In this work, we utilize symmetric Fourier features to improve the accuracy of deep learning-augmented MOSFET current-voltage model. We first apply the Fourier analysis on the cost function and discuss the spectral bias of neural network. The benefits of augmenting the neural network with symmetric Fourier features, designed to satisfy the Gummel symmetry, are then discussed. Using TCAD-simulated FinFET as our dataset, our improved model, augmented with 8 Fourier features, achieves outstanding 3-sigma errors of 0.15% for IV characteristics, 4.14% for output conductance, and 0.33% for transconductance. This represents a cost reduction of 13 times compared to the scenario without the Fourier features, highlighting the benefits gained from their inclusion.