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.
| Author of HS Reutlingen | 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 |
| Publication year: | 2018 |
| Tag: | convolution neural networks; data augmentation; deep learning; lithography; litography hotspot detection; machine learning |
| Page Number: | 6 |
| First Page: | 1 |
| Last Page: | 6 |
| PPN: | Im Katalog der Hochschule Reutlingen ansehen |
| DDC classes: | 620 Ingenieurwissenschaften und Maschinenbau |
| Open access?: | Nein |
