An effective method of contour extraction for SEM image based on DCNN

Abstract

SEM-image contours provide valuable information about patterning quality and capability. Geometrical properties such as critical dimension and resist sidewall angle could be extracted or estimated from SEM image contours. Those geometrical properties can be used for OPC model calibration, OPC model verification and lithography hotspot detection. This work presents a machine learning based method for contour extraction of SEM image. A designed DCNN network and self-made high quality dataset are combined for contour model training. Based on the high capability of image/feature representation and remarkable advantage of parallel computing with hardware acceleration, the model achieves high accuracy and real-time operation for contour extraction, more importantly, it provides the ability to distinguish and separate the top and bottom contours of SEM images. Additionally, the model not only removes the abundant edges but also repairs the local discontinuity caused by imperfect process and measuring technique.