Lithography hotspots detection using deep learning
- The hotspot detection has received much attention in the recent years due to a substantial mismatch between lithography wavelength and semiconductor technology feature size. This mismatch causes diffraction when transferring the layout from design onto a silicon wafer. As a result, open or short circuits (i.e. lithography hotspots) are more likely to be produced. Additionally, increasing numbers of semiconductors devices on a wafer required more time for the lithography hotspot detection analysis. In this work, we propose a fast and accurate solution based on novel artificial neural network (ANN) architecture for precise lithography hotspot detection using a convolution neural network (CNN) adopting a state of-the-art technique. The experimental results showed that the proposed model gained accuracy improvement over current state-of-theart approaches. The final code has been made publicly available.
| Author of HS Reutlingen | Borisov, Vadim; Scheible, Jürgen |
|---|---|
| DOI: | https://doi.org/10.1109/SMACD.2018.8434561 |
| ISBN: | 978-1-5386-5153-7 |
| Erschienen in: | 2018 SMACD : 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design : July 2nd-July 5th, 2018, Prague, Czech Republic |
| Publisher: | IEEE |
| Place of publication: | Piscataway, NJ |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of Publication: | 2018 |
| Page Number: | 4 |
| First Page: | 145 |
| Last Page: | 148 |
| DDC classes: | 620 Ingenieurwissenschaften und Maschinenbau |
| Open Access?: | Nein |
| Licence (German): |  Lizenzbedingungen IEEE |
