Automatic layout feature extraction for lithography hotspot detection based on deep neural network

Tetsuaki Matsunawa; Shigeki Nojima; Toshiya Kotani

doi:10.1117/12.2217746

16 March 2016 Automatic layout feature extraction for lithography hotspot detection based on deep neural network

Tetsuaki Matsunawa, Shigeki Nojima, Toshiya Kotani

Proceedings Volume 9781, Design-Process-Technology Co-optimization for Manufacturability X; 97810H (2016) https://doi.org/10.1117/12.2217746
Event: SPIE Advanced Lithography, 2016, San Jose, California, United States

Abstract

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.

Citation Download Citation

Tetsuaki Matsunawa, Shigeki Nojima, and 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); https://doi.org/10.1117/12.2217746

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