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Author Duo Ding ♦ Torres, A.J. ♦ Pikus, F.G. ♦ Pan, D.Z.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2011
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations
Subject Keyword Layout ♦ Kernel ♦ Artificial neural networks ♦ Support vector machines ♦ Manufacturing ♦ Predictive models ♦ Accuracy
Abstract Under real and continuously improving manufacturing conditions, lithography hotspot detection faces several key challenges. First, real hotspots become less but harder to fix at post-layout stages; second, false alarm rate must be kept low to avoid excessive and expensive post-processing hotspot removal; third, full chip physical verification and optimization require fast turn-around time. To address these issues, we propose a high performance lithographic hotspot detection flow with ultra-fast speed and high fidelity. It consists of a novel set of hotspot signature definitions and a hierarchically refined detection flow with powerful machine learning kernels, ANN (artificial neural network) and SVM (support vector machine). We have implemented our algorithm with industry-strength engine under real manufacturing conditions in 45nm process, and showed that it significantly outperforms previous state-of-the-art algorithms in hotspot detection false alarm rate (2.4X to 2300X reduction) and simulation run-time (5X to 237X reduction), meanwhile archiving similar or slightly better hotspot detection accuracies. Such high performance lithographic hotspot detection under real manufacturing conditions is especially suitable for guiding lithography friendly physical design.
Description Author affiliation: Mentor Graphics Corporation, 8005 S.W. Boeckman Road, Wilsonville, OR 97070 (Torres, A.J.; Pikus, F.G.) || ECE Dept. Univ. of Texas at Austin, Austin, TX 78712 (Duo Ding; Pan, D.Z.)
ISBN 9781424475155
ISSN 21536961
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2011-01-25
Publisher Place Japan
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781424475162
Size (in Bytes) 301.89 kB
Page Count 6
Starting Page 775
Ending Page 780


Source: IEEE Xplore Digital Library