Impact of feature extraction to accuracy of machine learning based hot spot detection

Machine learning based hot spot detection is an emerging area in verification of mask and layout design. In machine learning, feature extraction methods suitable for application domains are as important as learning and inference algorithm itself for detection accuracy. In this paper, several feature extraction methods were proposed and implemented, and compared using a standard bench mark dataset. Preferable characteristics for the good feature extraction will be discussed. Comparison studies indicated that combination of a good feature extraction method and a standard machine learning algorithm often gave excellent results compared with previously reported results.