Lithography Hotspots Detection Using Deep Learning
Lithography Hotspots Detection Using Deep Learning
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DOI：
10.1109/smacd.2018.8434561
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
V. Borisov;J. Scheible
中科院分区：
文献类型：
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作者：
V. Borisov;J. Scheible
由于光刻波长和半导体工艺特征尺寸之间的显著失配，近年来热点检测受到了广泛关注。当将布局从设计转移到硅晶片上时，这种失配会导致衍射。因此，更有可能产生开路或短路（即光刻热点）。此外，晶片上半导体器件数量的增加需要更多的时间进行光刻热点检测分析。在这项工作中，我们提出了一种快速，准确的解决方案，基于新的人工神经网络（ANN）架构的精确光刻热点检测使用卷积神经网络（CNN），采用最先进的技术。实验结果表明，该模型获得了目前的最先进的方法的准确性提高。最后的代码已公开提供。
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-the-art approaches. The final code has been made publicly available.