用深度学习预测芯片抛光后纳米形貌，提升制造验证效率。
Full-chip CMP modelling based on Fully Convolutional Network leveraging White Light Interferometry
- 结合白光干涉与原子力显微镜数据，训练卷积神经网络模型。
- 实现全芯片级纳米级形貌预测，精度达纳米量级。
- 适合芯片设计团队快速评估抛光不平整问题。
在集成电路（IC）行业中，缩短上市时间至关重要，加快版图可制造性验证尤为关键。化学机械抛光（CMP）在芯片制造中起核心作用，但易受版图依赖效应（LDE）影响。高精度、高效的CMP模型使设计团队能在制造前修正表面不平整，降低成本并加速设计流程。然而，现有模型多依赖密度台阶高度（DSH）建模，校准耗时且需大量硬件资源进行细粒度预测。本文提出融合白光干涉仪（WLI）与原子力显微镜（AFM）两种表面分析技术，训练深度学习模型，以实现全芯片级抛光后纳米形貌的精准预测，达到纳米级精度。所提模型基于卷积神经网络（CNN），采用两步式流程，分别在两种技术数据上独立训练，最终生成高保真的全芯片CMP模型。
原文摘要 · Abstract (English)
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 accurate and efficient CMP model enables design teams to correct surface unevenness before fabrication, reducing costs and accelerating the design phase. However, existing models often rely on Density Step Height (DSH) modeling, which is time-consuming for calibration and requires substantial hardware resources for fine-grained predictions. In this paper, we propose combining the advantages of two surface analysis techniques, White Light Interfer-ometry (WLI) and Atomic Force Microscopy (AFM), to train a deep learning model. This model aims to predict full-chip post-CMP nanotopography with nanometer-scale accuracy. Our deep learning model is based on a Convolutional Neural Network (CNN) and follows a two-step pipeline. The model is trained on each technique separately, resulting in a detailed full-chip CMP model.
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