Investigation of machine learning for dual OPC and assist feature printing optimization

Guajardo, Marco; Chen, Yulu; Brooker, Peter; Wu, Chunwang; Hooker, Kevin; Latinwo, Folarin; Lucas, Kevin

doi:10.1117/12.2516134

Cited by 1 publication

(1 citation statement)

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“…Wang et al used machine learning to efficiently generate sub-resolution assist features (SRAF) on full-chip layout at 20nm technology node, and achieved a high imaging accuracy [12] . Guajardo et al used machine learning methods to jointly optimize the main features (MF) and SRAFs [13] . Ma et al proposed fast mask optimization algorithms based on non-parametric kernel regression, which can effectively improve the computational efficiency and mask manufacturability [14] .…”

Section: Introductionmentioning

confidence: 99%

Study of Inverse Lithography Approaches based on Deep Learning

Zhang¹,

Ma²,

Zhang³

et al. 2019

Journal of Microelectronic Manufacturing

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Computational lithography (CL) has become an indispensable technology to improve imaging resolution and fidelity of deep sub-wavelength lithography. The state-of-the-art CL approaches are capable of optimizing pixel-based mask patterns to effectively improve the degrees of optimization freedom. However, as the growth of data volume of photomask layouts, computational complexity has become a challenging problem that prohibits the applications of advanced CL algorithms. In the past, a number of innovative methods have been developed to improve the computational efficiency of CL algorithms, such as machine learning and deep learning methods. Based on the brief introduction of optical lithography, this paper reviews some recent advances of fast CL approaches based on deep learning. At the end, this paper briefly discusses some potential developments in future work.

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Section: Introductionmentioning

confidence: 99%