Bridging Hotspot Detection and Mask Optimization via Domain-Crossing Masked Layout Modeling Article Swipe
Binwu Zhu
,
Su Zheng
,
Yuzhe Ma
,
Bei Yu
,
Martin D. F. Wong
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3728468
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3728468
With the rapid development of semiconductors, the size of transistors is continuously scaling down. The shrinking circuit size poses great challenges to optical proximity correction (OPC) and hotspot detection (HSD). Recent advancements in OPC and HSD commonly employ deep neural networks, achieving impressive performance within a limited runtime. Based on these achievements, we observe that deep-learning-based models of both HSD and OPC require knowledge of layout structure information. Furthermore, these two tasks are closely related to the lithography process during chip manufacturing. Observing such strong relationships, we propose that integrating OPC and HSD into a unified deep learning model will contribute to the performance of both tasks. To bridge the relationship between OPC and HSD, we first pre-train a layout understanding model built on the mask modeling technique, which effectively captures the layout geometric information, and then the pre-trained model can be easily fine-tuned on HSD and OPC with limited data. To fully pre-train the layout understanding model (LUM), we create a large layout dataset using layout generation techniques, solving the data-hungry issues. Experimental results show that the fine-tuned LUM model achieves remarkable performance on both OPC and HSD tasks.
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- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3728468
- OA Status
- hybrid
- Cited By
- 1
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409168397
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4409168397Canonical identifier for this work in OpenAlex
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https://doi.org/10.1145/3728468Digital Object Identifier
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Bridging Hotspot Detection and Mask Optimization via Domain-Crossing Masked Layout ModelingWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-04-04Full publication date if available
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Binwu Zhu, Su Zheng, Yuzhe Ma, Bei Yu, Martin D. F. WongList of authors in order
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https://doi.org/10.1145/3728468Publisher landing page
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hybridOpen access status per OpenAlex
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https://doi.org/10.1145/3728468Direct OA link when available
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Computer science, Hotspot (geology), Bridging (networking), Computer security, Geology, GeophysicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.closely | 73 |
| abstract_inverted_index.dataset | 164 |
| abstract_inverted_index.hotspot | 27 |
| abstract_inverted_index.issues. | 172 |
| abstract_inverted_index.limited | 46, 149 |
| abstract_inverted_index.observe | 53 |
| abstract_inverted_index.optical | 22 |
| abstract_inverted_index.process | 78 |
| abstract_inverted_index.propose | 87 |
| abstract_inverted_index.related | 74 |
| abstract_inverted_index.require | 62 |
| abstract_inverted_index.results | 174 |
| abstract_inverted_index.scaling | 12 |
| abstract_inverted_index.solving | 169 |
| abstract_inverted_index.unified | 95 |
| abstract_inverted_index.achieves | 181 |
| abstract_inverted_index.captures | 130 |
| abstract_inverted_index.commonly | 36 |
| abstract_inverted_index.learning | 97 |
| abstract_inverted_index.modeling | 126 |
| abstract_inverted_index.runtime. | 47 |
| abstract_inverted_index.Observing | 82 |
| abstract_inverted_index.achieving | 41 |
| abstract_inverted_index.detection | 28 |
| abstract_inverted_index.geometric | 133 |
| abstract_inverted_index.knowledge | 63 |
| abstract_inverted_index.networks, | 40 |
| abstract_inverted_index.pre-train | 117, 153 |
| abstract_inverted_index.proximity | 23 |
| abstract_inverted_index.shrinking | 15 |
| abstract_inverted_index.structure | 66 |
| abstract_inverted_index.challenges | 20 |
| abstract_inverted_index.contribute | 100 |
| abstract_inverted_index.correction | 24 |
| abstract_inverted_index.fine-tuned | 143, 178 |
| abstract_inverted_index.generation | 167 |
| abstract_inverted_index.impressive | 42 |
| abstract_inverted_index.remarkable | 182 |
| abstract_inverted_index.technique, | 127 |
| abstract_inverted_index.data-hungry | 171 |
| abstract_inverted_index.development | 3 |
| abstract_inverted_index.effectively | 129 |
| abstract_inverted_index.integrating | 89 |
| abstract_inverted_index.lithography | 77 |
| abstract_inverted_index.performance | 43, 103, 183 |
| abstract_inverted_index.pre-trained | 138 |
| abstract_inverted_index.techniques, | 168 |
| abstract_inverted_index.transistors | 9 |
| abstract_inverted_index.Experimental | 173 |
| abstract_inverted_index.Furthermore, | 68 |
| abstract_inverted_index.advancements | 31 |
| abstract_inverted_index.continuously | 11 |
| abstract_inverted_index.information, | 134 |
| abstract_inverted_index.information. | 67 |
| abstract_inverted_index.relationship | 110 |
| abstract_inverted_index.achievements, | 51 |
| abstract_inverted_index.understanding | 120, 156 |
| abstract_inverted_index.manufacturing. | 81 |
| abstract_inverted_index.relationships, | 85 |
| abstract_inverted_index.semiconductors, | 5 |
| abstract_inverted_index.deep-learning-based | 55 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.77821109 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |