FET Modeling with Deep Neural Networks and GAN-Augmented Small Measurement Dataset

We present FET modeling using Deep Neural Networks with a small but augmented dataset. NMOS-HV measurement data from the IHP SG13G2 OpenPDK repository, including FET dimensions, V-GS, V-DS, and V-BS, were used for DC drain current prediction. To improve accuracy, we employed Generative Adversarial Networks (GANs) for data augmentation. Using only 78,972 measured data points with 80-20% training-test split, we achieved 11.16% MAPE across 27 FET sizes. Using DL Model on GAN-based augmented dataset further reduced the error to 9.84%, demonstrating that accurate FET models can be developed with significantly fewer data points than traditional ANN approaches. Our results highlight the potential of data-efficient Deep Learning and GAN augmentation for automated transistor modeling.