Machine learning (ML)-guided OPC using basis functions of polar Fourier transform

Suhyeong Choi; Seongbo Shim; Youngsoo Shin

doi:10.1117/12.2219073

15 March 2016 Machine learning (ML)-guided OPC using basis functions of polar Fourier transform

Suhyeong Choi, Seongbo Shim, Youngsoo Shin

Proceedings Volume 9780, Optical Microlithography XXIX; 97800H (2016) https://doi.org/10.1117/12.2219073
Event: SPIE Advanced Lithography, 2016, San Jose, California, United States

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.

Citation Download Citation

Suhyeong Choi, Seongbo Shim, and Youngsoo Shin "Machine learning (ML)-guided OPC using basis functions of polar Fourier transform", Proc. SPIE 9780, Optical Microlithography XXIX, 97800H (15 March 2016); https://doi.org/10.1117/12.2219073

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