TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Borisov, Vadim
A1  - Scheible, Jürgen
T1  - Lithography hotspots detection using deep learning
T2  - 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
N2  - 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.
Y1  - 2018
SN  - 978-1-5386-5153-7
SB  - 978-1-5386-5153-7
U6  - https://doi.org/10.1109/SMACD.2018.8434561
DO  - https://doi.org/10.1109/SMACD.2018.8434561
SP  - 145
EP  - 148
S1  - 4
PB  - IEEE
CY  - Piscataway, NJ
ER  -