Rapid FinFET Modelling Using an Autoencoder

This study introduces a machine learning framework utilizing an autoencoder (AE) for efficient FinFET modeling, calibrated with a BSIM-CMG model to generate a dataset of current-voltage characteristics. The autoencoder compresses full I-V curves into a low-dimensional latent space while incorporating parameters like drain to source voltage (VDS) to enhance bias-dependent variations. This method achieves high accuracy in reconstructing I-V curves and extracting key metrics such as threshold voltage (VTH) and peak transconductance (gm), offering a valuable tool for rapid device characterization and circuit simulation with minimal training data.