Deep Learning to Automate Parameter Extraction and Model Fitting of Two-Dimensional Transistors

• URL: http://arxiv.org/abs/2507.05134v1
• Date: Mon, 07 Jul 2025 15:46:25 GMT
• Title: Deep Learning to Automate Parameter Extraction and Model Fitting of Two-Dimensional Transistors
• 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,
• Abstract summary: We present a deep learning approach to extract physical parameters of 2D transistors from electrical measurements.<n>We train a secondary neural network to approximate a physics-based device simulator.<n>This method enables high-quality fits after training the neural network on electrical data generated from simulations of 500 devices.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 >40$\times$ 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.

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