{"ID":2897595,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.05134","arxiv_id":"2507.05134","title":"Deep Learning to Automate Parameter Extraction and Model Fitting of Two-Dimensional Transistors","abstract":"We present a deep learning approach to extract physical parameters (e.g., mobility, Schottky contact barrier height, 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 pre-training 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 \u003e40$\\times$ 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 WS$_2$ 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 on high-electron-mobility transistors while fitting 35 parameters simultaneously. To facilitate future research on deep learning approaches for inverse transistor design, we have published our code and sample data sets online.","short_abstract":"We present a deep learning approach to extract physical parameters (e.g., mobility, Schottky contact barrier height, 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...","url_abs":"https://arxiv.org/abs/2507.05134","url_pdf":"https://arxiv.org/pdf/2507.05134v1","authors":"[\"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\"]","published":"2025-07-07T15:46:25Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cond-mat.mtrl-sci\",\"physics.app-ph\"]","methods":"[]","has_code":false}