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Proceedings Paper

Machine learning (ML)-guided OPC using basis functions of polar Fourier transform
Author(s): Suhyeong Choi; Seongbo Shim; Youngsoo Shin
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Paper Abstract

With shrinking feature size, runtime has become a limitation of model-based OPC (MB-OPC). A few machine learning-guided OPC (ML-OPC) have been studied as candidates for next-generation OPC, but they all employ too many parameters (e.g. local densities), which set their own limitations. We propose to use basis functions of polar Fourier transform (PFT) as parameters of ML-OPC. Since PFT functions are orthogonal each other and well reflect light phenomena, the number of parameters can significantly be reduced without loss of OPC accuracy. Experiments demonstrate that our new ML-OPC achieves 80% reduction in OPC time and 35% reduction in the error of predicted mask bias when compared to conventional ML-OPC.

Paper Details

Date Published: 15 March 2016
PDF: 8 pages
Proc. SPIE 9780, Optical Microlithography XXIX, 97800H (15 March 2016); doi: 10.1117/12.2219073
Show Author Affiliations
Suhyeong Choi, KAIST (Korea, Republic of)
Seongbo Shim, KAIST (Korea, Republic of)
SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Youngsoo Shin, KAIST (Korea, Republic of)


Published in SPIE Proceedings Vol. 9780:
Optical Microlithography XXIX
Andreas Erdmann; Jongwook Kye, Editor(s)

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