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English(EN) Full-chip CMP modelling based on Fully Convolutional Network leveraging White Light Interferometry

深度学习模型以纳米级精度预测全芯片化学机械抛光纳米形貌

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的深度学习模型，能够以纳米级精度预测化学机械抛光（CMP）后的全芯片纳米形貌。该模型结合了白光干涉测量（WLI）和原子力显微镜（AFM）的数据，克服了现有密度阶差（DSH）建模速度慢且资源消耗大的局限性。提出的卷积神经网络（CNN）方法旨在加速集成电路（IC）行业的版图可制造性验证过程。 AI

影响 该模型可以通过提高可制造性验证的准确性和速度来加速IC设计周期。

排序理由 这是一篇详细介绍用于特定工程问题的深度学习模型的研究论文。

在 arXiv cs.LG 阅读 →

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深度学习模型以纳米级精度预测全芯片化学机械抛光纳米形貌

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Jules Exbrayat, Renan Bouis, Elie Sezestre, Viorel Balan, Arnaud Cornelis, Damien Hebras, Catherine Euvrard ·

    基于白光干涉测量法的全卷积网络全芯片CMP建模

    arXiv:2605.05062v1 Announce Type: new Abstract: As time-to-market is crucial in the Integrated Circuit (IC) industry, speeding up layout manufacturability verifi-cation is essential. Chemical-Mechanical Polishing (CMP) plays a vital role in IC fabrication but is significantly inf…

  2. arXiv cs.LG TIER_1 English(EN) · Catherine Euvrard ·

    基于白光干涉测量法的全卷积网络全芯片CMP建模

    As time-to-market is crucial in the Integrated Circuit (IC) industry, speeding up layout manufacturability verifi-cation is essential. Chemical-Mechanical Polishing (CMP) plays a vital role in IC fabrication but is significantly influenced by Layout-Dependent Effects (LDE). An ac…

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