Hotspot Detection via Multi-task Learning and Transformer Encoder

Binwu Zhu, Ran Chen, Xinyun Zhang, Fan Yang, Xuan Zeng, Bei Yu, Martin D. F. Wong

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

7 Citations (Scopus)


With the rapid development of semiconductors and the continuous scaling-down of circuit feature size, hotspot detection has become much more challenging and crucial as a critical step in the physical verification flow. In recent years, advanced deep learning techniques have spawned many frameworks for hotspot detection. However, most existing hotspot detectors can only detect defects arising in the central region of small clips, making the whole detection process time-consuming on large layouts. Some advanced hotspot detectors can detect multiple hotspots in a large area but need to propose potential defect regions, and a refinement step is required to locate the hotspot precisely. To simplify the procedure of multi-stage detectors, an end-to-end single-stage hotspot detector is proposed to identify hotspots on large scales without refining potential regions. Besides, multiple tasks are developed to learn various pattern topological features. Also, a feature aggregation module based on Transformer Encoder is designed to globally capture the relationship between different features, further enhancing the feature representation ability. Experimental results show that our proposed framework achieves higher accuracy over prior methods with faster inference speed.

Original languageEnglish
Title of host publicationProceedings of The 40th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2021
Number of pages8
ISBN (Electronic)9781665445078
ISBN (Print)9781665445085
Publication statusPublished - 1 Nov 2021
Event40th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2021 - Munich, Germany
Duration: 1 Nov 20214 Nov 2021 (Conference website) (Conference programme) (Conference proceedings )

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISSN (Print)1933-7760
ISSN (Electronic)1558-2434


Conference40th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2021
Internet address

Scopus Subject Areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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