Abstract
Lithography is fundamental to integrated circuit fabrication, necessitating large computation overhead. The advancement of machine learning (ML)-based lithography models alleviates the trade-offs between manufacturing process expense and capability. However, all previous methods regard the lithography system as an image-to-image black box mapping, utilizing network parameters to learn by rote mappings from massive mask-to-aerial or mask-to-resist image pairs, resulting in poor generalization capability. In this paper, we propose a new ML-based paradigm disassembling the rigorous lithographic model into non-parametric mask operations and learned optical kernels containing determinant source, pupil, and lithography information. By optimizing complex-valued neural fields to perform optical kernel regression from coordinates, our method can accurately restore lithography system using a small-scale training dataset with fewer parameters, demonstrating superior generalization capability as well. Experiments show that our framework can use 31% of parameters while achieving 69× smaller mean squared error with 1.3× higher throughput than the state-of-the-art.
Original language | English |
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Title of host publication | 60th ACM/IEEE Design Automation Conference - Proceedings 2023 |
Publisher | IEEE |
Pages | 1-6 |
Number of pages | 6 |
ISBN (Electronic) | 9798350323481 |
ISBN (Print) | 9798350323498 |
DOIs | |
Publication status | Published - 13 Jul 2023 |
Event | 60th ACM/IEEE Design Automation Conference, DAC 2023 - Moscone West, San Francisco, United States Duration: 9 Jul 2023 → 13 Jul 2023 https://www.dac.com/ https://60dac.conference-program.com/ https://ieeexplore.ieee.org/xpl/conhome/10247654/proceeding |
Publication series
Name | ACM/IEEE Design Automation Conference - Proceedings |
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Volume | 2023-July |
ISSN (Print) | 0738-100X |
Conference
Conference | 60th ACM/IEEE Design Automation Conference, DAC 2023 |
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Country/Territory | United States |
City | San Francisco |
Period | 9/07/23 → 13/07/23 |
Internet address |
Scopus Subject Areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Computer Science Applications
- Modelling and Simulation
User-Defined Keywords
- Adaptive optics
- Lithography
- Optical device fabrication
- Optical imaging
- Resists
- Throughput
- Training