Research on data augmentation for lithography hotspot detection using deep learning

  • 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 ths 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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Metadaten
Name:Borisov, Vadim; Scheible, Jürgen
ISBN:978-1-5106-2121-3
Erschienen in:34th European Mask and Lithography Conference : 18 - 20 June 2018, Grenoble, France
Publisher:SPIE
Place of publication:Bellingham, Washington
Editor:Uwe Behringer
Document Type:Conference Proceeding
Language:English
Year of Publication:2018
Tag:convolution neural networks; data augmentation; deep learning; lithography; litography hotspot detection; machine learning
Pagenumber:6
First Page:1
Last Page:6
Catalogue entry:Im Katalog der Hochschule Reutlingen ansehen
Dewey Decimal Classification:620 Ingenieurwissenschaften und Maschinenbau
Open Access:Nein