Robert K A Bennett, Jan-Lucas Uslu, Harmon F Gault, Asir Intisar Khan, Lauren Hoang, Tara Peña, Kathryn Neilson, Young Suh Song, Zhepeng Zhang, Andrew J Mannix, Eric Pop
The proposed image-based ensemble classifier demonstrated comparable performance to a strong signal-based model for AF detection. Although cross-format generalization posed a challenge, incorporating multiple ECG formats during training mitigated this limitation and improved model robustness.
We present a deep learning approach to extract physical parameters (e.g., mobility, Schottky contact barrier height, and defect profiles) of two-dimensional (2D) transistors from electrical measurements, enabling automated parameter extraction and technology computer-aided design (TCAD) fitting. To facilitate this task, we implement a simple data augmentation and pretraining approach by training a secondary neural network to approximate a physics-based device simulator. This method enables high-quality fits after training the neural network on electrical data generated from physics-based simulations of ~500 devices, a factor >40× fewer than other recent efforts. Consequently, fitting can be achieved by training on physically rigorous TCAD models, including complex geometry, self-consistent transport, and electrostatic effects, and is not limited to computationally inexpensive compact models. We apply our approach to reverse-engineer key parameters from experimental monolayer WS2 transistors, achieving a median coefficient of determination (R 2) = 0.99 when fitting measured electrical data. We also demonstrate that this approach generalizes and scales well by reverse-engineering electrical data from high-electron-mobility transistors while fitting 35 parameters simultaneously. To facilitate future research on deep learning approaches for the inverse design of transistors, we have published our code and sample datasets online.