Abstract
Due to the lengthy design cycle, generating legal, diverse and valid layout patterns artificially to expand VLSI layout pattern libraries has become an important problem to solve in order to facilitate modern design-for-manufacturability (DFM) studies. Considering the more realistic demands and to enhance functionality, this work proposes a style-controllable and violation-fixable layout pattern generation framework based on conditional diffusion models named ControLayout, which treats pattern category and complexity as conditions to control the style of generated patterns, and leverages the idea of image masking-inpainting to fix violations adaptively. Experiments reveal its promising performance in controllability and different metrics compared with the state-of-the-art methods.
Original language | English |
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Title of host publication | GLSVLSI 2024 - Proceedings of the Great Lakes Symposium on VLSI 2024 |
Editors | Inna Partin-Vaisband, Srinivas Katkoori, Lu Peng, Boris Vaisband, Tooraj Nikoubin |
Publisher | Association for Computing Machinery (ACM) |
Pages | 511-515 |
Number of pages | 5 |
ISBN (Print) | 9798400706059 |
DOIs | |
Publication status | Published - 12 Jun 2024 |
Event | 34th Great Lakes Symposium on VLSI 2024, GLSVLSI 2024 - Clearwater, United States Duration: 12 Jun 2024 → 14 Jun 2024 https://dl.acm.org/doi/proceedings/10.1145/3649476 |
Publication series
Name | Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI |
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Conference
Conference | 34th Great Lakes Symposium on VLSI 2024, GLSVLSI 2024 |
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Country/Territory | United States |
City | Clearwater |
Period | 12/06/24 → 14/06/24 |
Internet address |
Scopus Subject Areas
- General Engineering