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Deep learning automates parameter extraction for 2D transistors

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel deep learning method to automate the extraction of physical parameters from two-dimensional (2D) transistors. This approach uses a secondary neural network to approximate a physics-based device simulator, significantly reducing the amount of data needed for accurate fitting. The method has been successfully applied to WS2 transistors, achieving a high coefficient of determination and demonstrating its ability to generalize to complex fitting scenarios with numerous parameters. AI

IMPACT This research could accelerate the design and optimization of next-generation semiconductor devices by automating complex parameter extraction processes.

RANK_REASON The cluster contains an academic paper detailing a new research methodology in the field of deep learning applied to semiconductor device physics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning automates parameter extraction for 2D transistors

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The cluster contains an academic paper detailing a new research methodology in the field of deep learning applied to semiconductor device physics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Robert K. A. Bennett, Jan-Lucas Uslu, Harmon F. Gault, Asir Intisar Khan, Lauren Hoang, Tara Pe\~na, Kathryn Neilson, Young Suh Song, Zhepeng Zhang, Andrew J. Mannix, Eric Pop ·

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

    arXiv:2507.05134v2 Announce Type: replace 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…

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