Accurate segmentation and measurement of lithography scanning electron microscope (SEM) images are crucial for ensuring precise process control, optimizing device performance, and advancing semiconductor manufacturing yield. Lithography segmentation requires pixel-level delineation of groove contours and consistent performance across diverse pattern geometries and process window. However, existing methods often lack the necessary precision and robustness, limiting their practical applicability. To overcome this challenge, we propose LithoSeg, a coarse-to-fine network tailored for lithography segmentation. In the coarse stage, we introduce a Human-in-the-Loop Bootstrapping scheme for the Segment Anything Model (SAM) to attain robustness with minimal supervision. In the subsequent fine stage, we recast 2D segmentation as 1D regression problem by sampling groove-normal profiles using the coarse mask and performing point-wise refinement with a lightweight MLP. LithoSeg outperforms previous approaches in both segmentation accuracy and metrology precision while requiring less supervision, offering promising prospects for real-world applications.

翻译：光刻扫描电子显微镜（SEM）图像的精确分割与测量对于确保工艺控制精度、优化器件性能以及提升半导体制造良率至关重要。光刻分割需实现凹槽轮廓的像素级描绘，并在不同图案几何形状与工艺窗口下保持一致的性能。然而，现有方法往往缺乏必要的精度与鲁棒性，限制了其实际应用。为应对这一挑战，我们提出了LithoSeg，一种专为光刻分割设计的由粗到精网络。在粗分割阶段，我们为Segment Anything Model（SAM）引入了一种人机协同引导的自举方案，以在最小监督下实现鲁棒性。在随后的精分割阶段，我们通过利用粗分割掩模采样凹槽法向剖面，并使用轻量级MLP进行逐点优化，将二维分割问题转化为一维回归问题。LithoSeg在分割精度与计量学精度上均优于先前方法，同时所需监督更少，为实际应用提供了广阔前景。