Abstract
Layout pattern generation via deep generative models is a promising methodology for building practical large-scale pattern libraries. However, although improving optical proximity correction (OPC) is a major target of existing pattern generation methods, they are not explicitly trained for OPC and integrated into OPC methods. In this paper, we propose EMOGen to enable the co-evolution of layout pattern generation and learning-based OPC methods. With the novel co-evolution methodology, we achieve up to 39% enhancement in OPC and 34% improvement in pattern legalization.
| Original language | English |
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| Title of host publication | Proceedings of the 61st ACM/IEEE Design Automation Conference, DAC 2024 |
| Place of Publication | New York |
| Publisher | Association for Computing Machinery (ACM) |
| Number of pages | 6 |
| ISBN (Print) | 9798400706011 |
| DOIs | |
| Publication status | Published - 7 Nov 2024 |
| Event | 61st ACM/IEEE Design Automation Conference, DAC 2024 - San Francisco, San Francisco, United States Duration: 23 Jun 2024 → 27 Jun 2024 https://dl.acm.org/doi/proceedings/10.1145/3649329 (Conference proceedings) https://www.dac.com/ |
Publication series
| Name | Proceedings of the ACM/IEEE Design Automation Conference |
|---|---|
| Publisher | Association for Computing Machinery |
Conference
| Conference | 61st ACM/IEEE Design Automation Conference, DAC 2024 |
|---|---|
| Abbreviated title | DAC 2024 |
| Country/Territory | United States |
| City | San Francisco |
| Period | 23/06/24 → 27/06/24 |
| Internet address |
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