SEM image contour extraction with deep learning method

The contour data extracted from SEM wafer images after the lithography are widely used in the critical dimension (CD), edge placement error (EPE) measurement. It is important to obtain the contours fast and accurate before the analysis of lithographic process and calibration of the lithographic models. Without the accurate contour data, the complete CDU, PVband analysis and inverse lithography technique are hard to realize. With the continuous shrink of the technology nodes, the demand for the accurate contour extraction increases. However, fast and accurate contour extraction from SEM images with defects and noises is challenging. We apply the U-Net to the semantic segmentation of SEM images. The contour extraction and evaluation can be done better after the image segmentation. Our experimental results show that satisfactory contour data of various types of lithographic patterns can be obtained with noisy SEM images.