A Deep Learning Facilitated Approach for SEM Image Denoising Towards Improved Contour Extraction for 1D and 2D Structures

Abstract

Flow from circuit design to manufacturing a mask is a complex process. A mask is used to print a particular design feature. Extraction of accurate CD contour geometries from ADI (after developed inspection) SEM images plays a pivotal role for a qualitative lithographic process as well as to verify device characterization in aggressive pitches. In our previous work, it has been shown how deep learning based de-noising is helping to improve the contour detection accuracy. We analysed and validated our result with noisy/denoised image pair for categorically different geometrical patterns, such as, L/S (linespace), T2T (tip-to-tip), pillars with different scan types etc., by using a programmable tool (Calibre®SEMSuiteTM) for contour extraction and further analysis metrics. The comparative analysis demonstrated that denoised images have significantly higher confidence analysis metrics, reduced number of missing patterns as well as false bridges against raw noisy images while keeping the same parameter settings for both data inputs. We have demonstrated that our proposed method is capable to extract contours on the body of the noisy SEM images with accuracy in close proximity with design data. By combining these advanced algorithms as options in Calibre®SEMSuiteTM, users would be able to process large amount of information for data cleaning, classification & further model calibration intelligently.