Mr. Yi-Ting Lin Profile

Yi-Ting Lin

at National Taiwan Univ.

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Proceedings Article | 26 May 2022 Presentation + Paper

Novel methodology for SRAF placement over a machine learning generated probability map

Yi-Ting Lin, Sean Shang-En Tseng, Iris Hui-Ru Jiang, James Shiely

Proceedings Volume 12052, 120520X (2022) https://doi.org/10.1117/12.2613681

KEYWORDS: SRAF, Photovoltaics, Machine learning, Lithography, Source mask optimization, Optical proximity correction

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Subresolution assist feature (SRAF) insertion is an effective way to improve the printability and lithographic process window of isolated and semi-isolated features. State-of-the-art works resort to machine learning to reduce the computational cost associated with model-based methods. However, the result of SRAF probability learning, a probability map, cannot be directly treated as SRAFs. Inserting an SRAF to each layout grid predicted as 1 leads to design rule violations. Therefore, the SRAF probability learning model can only guide SRAF insertion and needs special handling to place SRAFs. For SRAF placement, we observe that placing SRAFs at probability maxima might be too greedy so that the SRAF prediction is masked to a certain extent. Therefore, we propose a novel methodology for SRAF placement over a machine learning generated probability map. First, a clustering-based method generates an initial SRAF inserted layout based on the given probability map. Then, a two-stage SRAF legalization strategy modifies the positions of SRAFs to be compliant with design rules. Experimental results show that given a probability map, our methodology can generate an SRAF inserted layout which guarantees design rule violation freeness while maintaining competitive PV band and EPE.

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