An Improved YOLOv5 Model for Lithographic Hotspot Detection.

Mu Lin, Wenjing He, Jiale Liu, Fencheng Li, Jun Luo, Yijiang Shen

Abstract read

In one paragraph

Article in Micromachines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Mu LinSchool of Automation, Guangdong University of Technology, Mega Education Center South, Guangzhou 510006, China.

Wenjing HeSchool of Automation, Guangdong University of Technology, Mega Education Center South, Guangzhou 510006, China.

Jiale LiuSchool of Automation, Guangdong University of Technology, Mega Education Center South, Guangzhou 510006, China.

Fencheng LiSchool of Automation, Guangdong University of Technology, Mega Education Center South, Guangzhou 510006, China.

Jun LuoSchool of Automation, Guangdong University of Technology, Mega Education Center South, Guangzhou 510006, China.

Yijiang ShenSchool of Automation, Guangdong University of Technology, Mega Education Center South, Guangzhou 510006, China.ORCID 0000-0002-6370-4924

Funding

National Natural Science Foundation of China 61875041Natural Science Foundation of Guangdong Province, China 2020A1515010633Natural Science Foundation of Guangdong Province, China 2021A1515012000

6 · The paper itself

Abstract

The gap between the ever-shrinking feature size of integrated circuits and lithographic manufacturing ability is causing unwanted shape deformations of printed layout patterns. The deformation region with problematic imaging, known as a hotspot (HS), should be detected and corrected before mask manufacturing. In this paper, we propose a hotspot detection method to improve the precision and recall rate of the fatal pinching and bridging error due to the poor printability of certain layout patterns by embedding a spatial attention mechanism into the YOLOv5 model. Additionally, transfer learning and pre-trained techniques are used to expedite training convergence. Simulation results outperform the depth-based or representative machine learning-based methods on the ICCAD 2012 dataset with an average recall rate of 1, a precision rate of 0.8277 and an F1-score of 0.9057.

Indexed as

lithographic hotspot detectionspatial attention mechanismYOLOv5

Identifiers

PMID40428694

DOI10.3390/mi16050568

PMCPMC12114213

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