提出分阶段框架，精准分割光刻图像中的沟槽轮廓。
LithoSeg: A Coarse-to-Fine Framework for High-Precision Lithography Segmentation
- 先粗后细：用人工引导的SAM模型生成初始分割，再通过一维回归精修轮廓。
- 相比现有方法，分割与测量精度更高，且标注需求少。
- 适合需要高精度光刻图像分析的半导体制造场景。
精确分割与测量光刻扫描电子显微镜（SEM）图像对确保工艺控制、优化器件性能及提升半导体制造良率至关重要。光刻分割需实现像素级沟槽轮廓划分，并在不同图案几何和工艺窗口下保持一致性能。然而，现有方法常因精度与鲁棒性不足而难以实用。为此，我们提出LithoSeg，一种专为光刻分割设计的粗到精网络框架。粗阶段引入人机协同自举方案，对分割任意模型（SAM）进行最小监督下的鲁棒性增强；细阶段则通过粗掩码采样沟槽法向剖面，将二维分割转化为一维回归问题，利用轻量级MLP进行逐点精细化。LithoSeg在分割精度与量测准确性上均优于以往方法，同时所需标注更少，展现出在真实场景应用中的巨大潜力。
原文摘要 · Abstract (English)
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.
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