Multi-Scale Feature Refinement Network for Lithography Hotspot Detection
Year: 2026
Type: article
Abstract: As semiconductor manufacturing advances toward smaller process nodes and more complex layouts, lithography hotspot detection faces increasing challenges. Traditional methods often struggle to capture subtle geometric variations in complex layouts, resulting in low recall and high false alarm rates. To address this problem, we propose a Multi-Scale Feature Refinement Network. The key innovation lies in a collaborative “Extraction–Refinement–Decision” framework specifically designed for Hard-To-Classify samples: the Multi-Scale Feature Extraction module comprehensively captures rich information ranging from local geometric details to global layout context; the Feature Refinement Network enhances responses to critical geometric features through a lightweight gating mechanism; and finally, ConvNeXt serves as the decision module, integrating and classifying the refined features for final hotspot prediction. Experimental results show that on the most challenging ICCAD 2019-2 benchmark, MSFRN improves recall by up to 11.3%, with the false alarm rate varying from a 0.2% increase to a 5.8% reduction, demonstrating superior detection capability.
DOI: 10.1145/3815196
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7162203547