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

arXiv: https://arxiv.org/abs/2507.05134 · HTML: https://mlsanity.com/paper/2507.05134/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 53 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 1 of 1)

- [Compact Model Parameter Extraction via Derivative-Free Optimization](https://mlsanity.com/paper/2406.16355/) (2024)

## Abstract (arXiv)

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× 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 (R2) = 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.