Sub-Resolution Assist Feature Generation with Reinforcement Learning and Transfer Learning
Authors: 
Guan-Ting Liu, Wei-Chen Tai, Yi-Ting Lin, Iris Hui-Ru Jiang, James P. Shiely, Pu-Jen ChengAuthors Info & Claims
Guan-Ting Liu, Wei-Chen Tai, Yi-Ting Lin, Iris Hui-Ru Jiang, James P. Shiely, Pu-Jen ChengAuthors Info & ClaimsArticle No.: 29, Pages 1 - 9
Abstract
As modern photolithography feature sizes continue to shrink, sub-resolution assist feature (SRAF) generation has become a key resolution enhancement technique to improve the manufacturing process window. State-of-the-art works resort to machine learning to overcome the deficiencies of model-based and rule-based approaches. Nevertheless, these machine learning-based methods do not consider or implicitly consider the optical interference between SRAFs, and highly rely on post-processing to satisfy SRAF mask manufacturing rules. In this paper, we are the first to generate SRAFs using reinforcement learning to address SRAF interference and produce mask-rule-compliant results directly. In this way, our two-phase learning enables us to emulate the style of model-based SRAFs while further improving the process variation (PV) band. A state alignment and action transformation mechanism is proposed to achieve orientation equivariance while expediting the training process. We also propose a transfer learning framework, allowing SRAF generation under different light sources without retraining the model. Compared with state-of-the-art works, our method improves the solution quality in terms of PV band and edge placement error (EPE) while reducing the overall runtime.
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Index Terms
- Sub-Resolution Assist Feature Generation with Reinforcement Learning and Transfer Learning
Index terms have been assigned to the content through auto-classification.
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Published In
October 2022
1467 pages
ISBN:9781450392174
DOI:10.1145/3508352
- Conference Chair:
- Tulika Mitra,
- Program Chairs:
- Evangeline Young,
- Jinjun Xiong
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Publication History
Published: 22 December 2022
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- Research-article
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- Synopsys, Inc.
- Taiwan National Science Foundation and Technology Council
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ICCAD '22
Sponsor:
ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design
October 30 - November 3, 2022
California, San Diego
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