TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Borisov, Vadim
A1  - Scheible, Jürgen
ED  - Behringer, Uwe
T1  - Research on data augmentation for lithography hotspot detection using deep learning
T2  - 34th European Mask and Lithography Conference :  18 - 20 June 2018, Grenoble, France
N2  - 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.
KW  - lithography
KW  - litography hotspot detection
KW  - deep learning
KW  - convolution neural networks
KW  - machine learning
KW  - data augmentation
Y1  - 2018
SN  - 978-1-5106-2121-3
SB  - 978-1-5106-2121-3
SP  - 1
EP  - 6
S1  - 6
PB  - SPIE
CY  - Bellingham, Washington
ER  -