Imbalance Aware Lithography Hotspot Detection: A Deep Learning Approach
Refereed conference paper presented and published in conference proceedings


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AbstractWith the advancement of VLSI technology nodes, light diffraction caused lithographic hotspots have become a
serious problem affecting manufacture yield. Lithography hotspot detection at the post-OPC stage is imperative
to check potential circuit failures when transferring designed patterns onto silicon wafers. Although conventional
lithography hotspot detection methods, such as machine learning, have gained satisfactory performance,
with extreme scaling of transistor feature size and more and more complicated layout patterns, conventional
methodologies may suffer from performance degradation. For example, manual or ad hoc feature extraction in
a machine learning framework may lose important information when predicting potential errors in ultra-largescale
integrated circuit masks. In this paper, we present a deep convolutional neural network (CNN) targeting
representative feature learning in lithography hotspot detection. We carefully analyze impact and effectiveness
of different CNN hyper-parameters, through which a hotspot-detection-oriented neural network model is established.
Because hotspot patterns are always minorities in VLSI mask design, the training data set is highly
imbalanced. In this situation, a neural network is no longer reliable, because a trained model with high classi-
fication accuracy may still suffer from high false negative results (missing hotspots), which is fatal in hotspot
detection problems. To address the imbalance problem, we further apply minority upsampling and random-mirror
flipping before training the network. Experimental results show that our proposed neural network model achieves
highly comparable or better performance on the ICCAD 2012 contest benchmark compared to state-of-the-art
hotspot detectors based on deep or representative machine leaning.
All Author(s) ListHaoyu Yang, Luyang Luo, Jing Su, Chenxi Lin, Bei Yu
Name of ConferenceSPIE Intl. Symp. Advanced Lithography Conference
Start Date of Conference26/02/2017
End Date of Conference02/03/2017
Place of ConferenceSan Jose
Country/Region of ConferenceUnited States of America
Proceedings TitleProceedings of SPIE Intl. Symp. Advanced Lithography Conference
Year2017
Volume Number10148
ISBN978-1-5106-0748-4
ISSN0277-786X
LanguagesEnglish-United States

Last updated on 2020-18-09 at 02:05