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.

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Metadaten
Name: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
Pagenumber:4
First Page:145
Last Page:148
Dewey Decimal Classification:620 Ingenieurwissenschaften und Maschinenbau
Access Rights:Nein
Licence (German):License Logo  Lizenzbedingungen IEEE