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English(EN) NN-Based Transistor Modeling for Ultra-Fast SPICE Simulation and Standard Cell Library Characterization

神经网络加速芯片设计的晶体管建模

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了基于神经网络（NN）的晶体管模型，显著加速了芯片设计的SPICE仿真。这些模型能够准确预测3纳米纳米片器件的漏极电流和电荷，证明了其作为行业标准BSIM-CMG模型的替代方案的可行性。在各种电路中，基于神经网络的方法实现了高达8倍的标准单元库表征加速，在延迟评估中的误差低于0.11%，在总功耗评估中的误差为0.76%。 AI

影响 加速芯片设计仿真，可能缩短新硬件的上市时间。

排序理由 详细介绍晶体管建模新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

神经网络加速芯片设计的晶体管建模

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详细介绍晶体管建模新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Rodion Novkin, Hussam Amrouch ·

    NN-Based Transistor Modeling for Ultra-Fast SPICE Simulation and Standard Cell Library Characterization

    arXiv:2610.31024v1 Announce Type: cross Abstract: Neural network (NN)-based transistor models have proven to be a promising solution to accelerate device modeling. Although these models demonstrated remarkable speedup in circuit simulations, they were not rigorously tested in com…

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