16 March 2016 Automatic layout feature extraction for lithography hotspot detection based on deep neural network
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Lithography hotspot detection in the physical verification phase is one of the most important techniques in today's optical lithography based manufacturing process. Although lithography simulation based hotspot detection is widely used, it is also known to be time-consuming. To detect hotspots in a short runtime, several machine learning based methods have been proposed. However, it is difficult to realize highly accurate detection without an increase in false alarms because an appropriate layout feature is undefined. This paper proposes a new method to automatically extract a proper layout feature from a given layout for improvement in detection performance of machine learning based methods. Experimental results show that using a deep neural network can achieve better performance than other frameworks using manually selected layout features and detection algorithms, such as conventional logistic regression or artificial neural network.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tetsuaki Matsunawa, Tetsuaki Matsunawa, Shigeki Nojima, Shigeki Nojima, Toshiya Kotani, Toshiya Kotani, "Automatic layout feature extraction for lithography hotspot detection based on deep neural network", Proc. SPIE 9781, Design-Process-Technology Co-optimization for Manufacturability X, 97810H (16 March 2016); doi: 10.1117/12.2217746; https://doi.org/10.1117/12.2217746

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