Research on data augmentation for lithography hotspot detection using deep learning

Vadim Borisov; Jürgen Scheible

doi:10.1117/12.2326563

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19 September 2018 Research on data augmentation for lithography hotspot detection using deep learning

Vadim Borisov; Jürgen Scheible

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Proceedings Volume 10775, 34th European Mask and Lithography Conference; 107751A (2018) https://doi.org/10.1117/12.2326563
Event: 34th European Mask and Lithography Conference, 2018, Grenoble, France

Abstract

Lithographical hotspot (LH) detection using deep learning (DL) has received much attention in the recent years. It happens mainly due to the facts the DL approach leads to a better accuracy over the traditional, state-of-the-art programming approaches. The purpose of this study is to compare existing data augmentation (DA) techniques for the integrated circuit (IC) mask data using DL methods. DA is a method which refers to the process of creating new samples similar to the training set, thereby helping to reduce the gap between classes as well as improving the performance of the DL system. Experimental results suggest that the DA methods increase overall DL models performance for the hotspot detection tasks.

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Vadim Borisov and Jürgen Scheible "Research on data augmentation for lithography hotspot detection using deep learning", Proc. SPIE 10775, 34th European Mask and Lithography Conference, 107751A (19 September 2018); https://doi.org/10.1117/12.2326563

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KEYWORDS

Lithography

Photomasks

Data modeling

Convolution

Data acquisition

Machine learning

Neural networks

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