LithoSeg: A Coarse-to-Fine Framework for High-Precision Lithography Segmentation

TL;DR

LithoSeg is a novel coarse-to-fine segmentation framework for SEM lithography images that enhances accuracy and robustness with minimal supervision, combining a SAM-based coarse stage and a lightweight refinement network.

Contribution

The paper introduces LithoSeg, a new coarse-to-fine approach utilizing human-in-the-loop SAM and 1D regression for improved lithography segmentation accuracy.

Findings

LithoSeg outperforms previous methods in segmentation accuracy.

It achieves higher metrology precision.

Requires less supervision than existing approaches.

Abstract

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.