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Journal of Micro/Nanolithography, MEMS, and MOEMS • new

Imbalance aware lithography hotspot detection: a deep learning approach
Author(s): Haoyu Yang; Luyang Luo; Jing Su; Chenxi Lin; Bei Yu
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Paper Abstract

With the advancement of very large scale integrated circuits (VLSI) technology nodes, lithographic hotspots become a serious problem that affects 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 the extreme scaling of transistor feature size and layout patterns growing in complexity, conventional methodologies may suffer from performance degradation. For example, manual or <italic<ad hoc</italic< feature extraction in a machine learning framework may lose important information when predicting potential errors in ultra-large-scale integrated circuit masks. We present a deep convolutional neural network (CNN) that targets representative feature learning in lithography hotspot detection. We carefully analyze the impact and effectiveness of different CNN hyperparameters, through which a hotspot-detection-oriented neural network model is established. Because hotspot patterns are always in the minority in VLSI mask design, the training dataset is highly imbalanced. In this situation, a neural network is no longer reliable, because a trained model with high classification accuracy may still suffer from a high number of false negative results (missing hotspots), which is fatal in hotspot detection problems. To address the imbalance problem, we further apply hotspot upsampling and random-mirror flipping before training the network. Experimental results show that our proposed neural network model achieves 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.

Paper Details

Date Published: 24 August 2017
PDF: 13 pages
J. Micro/Nanolith. 16(3) 033504 doi: 10.1117/1.JMM.16.3.033504
Published in: Journal of Micro/Nanolithography, MEMS, and MOEMS Volume 16, Issue 3
Show Author Affiliations
Haoyu Yang, The Chinese Univ. of Hong Kong (Hong Kong)
Luyang Luo, The Chinese Univ. of Hong Kong (Hong Kong)
Jing Su, ASML Brion (United States)
Chenxi Lin, ASML Brion (United States)
Bei Yu, The Chinese Univ. of Hong Kong (Hong Kong)


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