English(EN) Deep Learning to Automate Parameter Extraction and Model Fitting of Two-Dimensional Transistors

深度学习自动化二维晶体管参数提取

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一种新颖的深度学习方法，用于自动化从二维（2D）晶体管中提取物理参数。该方法使用第二个神经网络来近似基于物理的器件模拟器，显著减少了精确拟合所需的数据量。该方法已成功应用于WS2晶体管，取得了较高的决定系数，并证明了其能够泛化到具有众多参数的复杂拟合场景。 AI

影响这项研究通过自动化复杂的参数提取过程，有望加速下一代半导体器件的设计和优化。

排序理由该集群包含一篇学术论文，详细介绍了深度学习应用于半导体器件物理学领域的新研究方法。[lever_c_demoted from research: ic=1 ai=1.0]

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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 · 2026-09-10 04:00

深度学习用于二维晶体管参数提取和模型拟合的自动化

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…