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Proceedings Paper

Research on data augmentation for lithography hotspot detection using deep learning
Author(s): Vadim Borisov; Jürgen Scheible
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Paper 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.

Paper Details

Date Published: 19 September 2018
PDF: 6 pages
Proc. SPIE 10775, 34th European Mask and Lithography Conference, 107751A (19 September 2018); doi: 10.1117/12.2326563
Show Author Affiliations
Vadim Borisov, Reutlingen Univ. (Germany)
Jürgen Scheible, Reutlingen Univ. (Germany)

Published in SPIE Proceedings Vol. 10775:
34th European Mask and Lithography Conference
Uwe F.W. Behringer; Jo Finders, Editor(s)

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