Low dosage SEM image processing for metrology applications

Algorithms used for e-beam inspection and metrology need to deal with noise, blur, or other distortion sources. For metrology applications such as EUV resist patterns measurement, low electron dosage is desirable to minimize resist damage, as well as to improve turn-around time for massive metrology. However, under low dosage imaging conditions, the SEM images contain a substantial amount of noise and exhibit weak image contrast or blurry features. These factors lead to degradation of measurement precision and accuracy. Advanced image deblurring and restoration methodology becomes crucial to ensure high quality metrology performance. In this paper, we focus on a self-supervised approach to enhance SEM image quality under low dose imaging conditions. Self-supervised approach is highly desirable since it is expensive or sometimes impossible to obtain ground truth data for supervised learning. We demonstrate its capability of enhancing resolution of key features such as pattern edges while reducing the overall noise level. Comparable performance is achieved by enhancing a single frame averaged SEM image and the 4-frame averaged reference image. Performance metrics used for evaluation include CD precision, mean CD and distribution, as well as image quality metrics such as image sharpness, PSNR and SSIM.

Automated summary

This paper presents a self-supervised approach to enhance SEM image quality under low dose imaging conditions. The approach is able to improve the resolution of key features while reducing the noise level. Performance evaluation shows the feasibility and effectiveness of the method.